system

Abstract

The system according to this embodiment aims to analyze a dog's daily behavioral patterns and provide the owner with optimal care. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a notification unit. The collection unit collects the dog's daily behavioral patterns. The analysis unit analyzes the data collected by the collection unit. The notification unit notifies the owner of the prediction results obtained by the analysis unit.

Description

Technical Field

[0004]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately grasp the daily behavior patterns of dogs and provide appropriate care.

[0005] The system according to the embodiment aims to analyze the daily behavior patterns of dogs and provide optimal care for the owner.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit; the collection unit collects the daily behavior patterns of dogs; the analysis unit analyzes the data collected by the collection unit; and the notification unit notifies the owner of the prediction result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze a dog's daily behavioral patterns and provide the owner with optimal care. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The dog behavior prediction system according to an embodiment of the present invention is a system that uses the latest AI technology to predict dog behavior and proposes optimal care to the owner. This system collects and analyzes a vast amount of data, such as the daily behavior patterns, physiological rhythms, and environmental factors of individual dogs, and predicts dog behavior using advanced machine learning algorithms. For example, it precisely predicts the dog's activity level, timing of meal and water intake, elimination patterns, and times when the dog wants to play, and notifies the owner in real time. This allows the owner to proactively meet the dog's needs and provide exercise, play, toilet breaks, etc., at the optimal time. Furthermore, the AI ​​continuously updates the prediction model in response to changes in season and weather, and the dog's age and health condition, providing more accurate suggestions. Through this system, owners can provide higher quality care while being attentive to their dog's daily rhythm, which in turn leads to improved health and well-being for the dog. For example, the system collects the dog's daily behavior patterns. For instance, it records when the dog is most active, the timing of meal and water intake, and elimination patterns. This allows for a detailed understanding of the dog's behavior patterns. Next, the collected data is analyzed by AI. The AI ​​predicts the dog's behavior by considering its behavioral patterns, physiological rhythms, and environmental factors. For example, it predicts when the dog will want to play or when it will need to go to the bathroom. Furthermore, the AI ​​notifies the owner in real time based on the prediction results. For example, by predicting when the dog will want to play and notifying the owner at that time, the owner can ensure they have time to play with their dog. Similarly, by predicting when the dog will need to go to the bathroom and notifying the owner at that time, the owner can take the dog to the bathroom. The AI ​​also updates its prediction model according to seasonal and weather changes, as well as the dog's age and health status. For example, since dogs tend to drink more water in the summer, the AI ​​updates its prediction model based on this information and notifies the owner to encourage hydration at the appropriate time. In this way, owners can anticipate and meet their dog's needs, providing exercise, playtime, and bathroom breaks at the optimal time. This improves the dog's health and well-being, and deepens the bond between the dog and its owner.This means that dog behavior prediction systems can enable owners to anticipate and meet their dogs' needs, thereby improving the dogs' health and well-being.

[0029] The dog behavior prediction system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects the dog's daily behavior patterns. The data collection unit collects data such as the dog's activity level, timing of meal and water intake, elimination patterns, and times of day when the dog wants to play. The data collection unit measures the dog's activity level using a sensor, for example. The data collection unit records the timing of the dog's meal and water intake, for example. The data collection unit records the dog's elimination patterns, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit predicts the dog's behavior based on the collected data, for example. The analysis unit predicts the dog's behavior using a machine learning algorithm, for example. The analysis unit analyzes the dog's behavior patterns based on the collected data, for example. The notification unit notifies the owner of the prediction results obtained by the analysis unit. The notification unit notifies the owner of the prediction results in real time, for example. The notification unit sends a notification to the owner's smartphone, for example. The notification unit sends a notification to the owner's smartwatch, for example. This allows dog behavior prediction systems to enable owners to anticipate and meet their dogs' needs.

[0030] The data collection unit collects data on the dog's daily behavioral patterns. For example, it collects data such as the dog's activity level, timing of meals and water intake, elimination patterns, and times when the dog wants to play. Specifically, it measures the dog's activity level using sensors attached to the dog's collar or harness. These sensors have built-in accelerometers and gyroscopes, allowing for detailed recording of the dog's movements and posture. Furthermore, sensors are used to record the timing of meals and water intake, placed in food bowls and water bowls. These sensors detect the dog's movements while eating or drinking, accurately recording the timing. Sensors are also used to record elimination patterns, placed where the dog urinates, allowing for an understanding of the time and frequency of elimination. Additionally, cameras and microphones can be used to monitor the dog's behavior and record times when the dog wants to play. These devices detect the dog's movements and sounds, helping to identify playtimes. The collected data is transmitted wirelessly to a central database and updated in real time. This allows the data collection unit to gain a detailed and accurate understanding of the dog's behavioral patterns and provide this information to the analysis unit.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit predicts dog behavior based on the collected data. Specifically, it uses machine learning algorithms to predict dog behavior. Machine learning algorithms learn from past data and model dog behavior patterns. For example, if a dog tends to be active during certain times of the day, future behavior can be predicted based on those times. It also analyzes data such as the timing of a dog's eating and drinking, and its elimination patterns, to clarify how these behaviors are related. Furthermore, the analysis unit can also detect abnormal behavior patterns. For example, if a dog is less active than usual, or if the timing of its eating or drinking changes, the analysis unit can detect this as an anomaly and notify the owner. Based on this data, the analysis unit can also assess the dog's health and stress level. For example, if a dog urinates or defecates frequently, this may indicate a health problem. The analysis unit comprehensively analyzes this information and builds a model for predicting dog behavior. This allows the analysis unit to accurately predict dog behavior based on the collected data and provide this information to the owner.

[0032] The notification unit notifies the owner of the prediction results obtained by the analysis unit. For example, the notification unit notifies the owner of the prediction results in real time. Specifically, it sends notifications to the owner's smartphone. Through the smartphone application, it can provide information on the dog's behavior prediction results and abnormal behavior patterns. For example, if it is predicted that the dog will be active during a specific time period, it can send a notification during that time to alert the owner. It can also send notifications to the owner's smartwatch. The smartwatch can provide information to the owner in real time through vibration and sound notifications. Furthermore, the notification unit can also send notifications to the owner's PC or tablet. This allows the owner to check the dog's behavior prediction results no matter where they are. The notification unit can also customize the content and timing of notifications. For example, if the owner does not want to receive notifications during a specific time period, it can send notifications while avoiding that time period. It can also set the priority of notifications. For example, if an abnormal behavior pattern is detected, a high-priority notification can be sent to prompt the owner to take action quickly. This allows the notification system to help owners proactively meet their dogs' needs and maintain their health and well-being.

[0033] The data collection unit can collect data such as the dog's activity level, timing of meals and water intake, elimination patterns, and times when the dog wants to play. For example, the data collection unit may use a pedometer to measure the dog's activity level. For example, the data collection unit may use an automatic feeder to record the timing of the dog's meals and water intake. For example, the data collection unit may use a toilet sensor to record the dog's elimination patterns. This allows for a detailed understanding of the dog's behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input pedometer data into an AI to measure the dog's activity level, and the AI ​​can analyze the data to evaluate the activity level.

[0034] The analysis unit can predict dog behavior based on collected data. For example, the analysis unit predicts dog behavior using machine learning algorithms based on collected data. For example, the analysis unit analyzes dog behavior patterns based on collected data. For example, the analysis unit builds a model for predicting dog behavior based on collected data. This allows owners to proactively meet their dog's needs. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs collected data into the AI, which analyzes the data and predicts dog behavior.

[0035] The notification unit can notify the owner of the prediction results in real time. For example, the notification unit can notify the owner of the prediction results on their smartphone. For example, the notification unit can notify the owner of the prediction results on their smartwatch. For example, the notification unit can notify the owner of the prediction results on their personal computer. This allows the owner to immediately meet the dog's needs. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the prediction results into AI, which can then determine the timing and method of notification.

[0036] The analysis unit can update the prediction model in response to changes in seasons and weather, as well as the dog's age and health status. For example, the analysis unit updates the prediction model considering changes in seasons and weather. For example, the analysis unit updates the prediction model considering changes in the dog's age and health status. For example, the analysis unit updates the prediction model based on collected data. This allows for more accurate recommendations. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs seasonal and weather data into the AI, and the AI ​​updates the prediction model.

[0037] The analysis unit can improve the algorithm based on owner feedback. For example, the analysis unit collects owner feedback and improves the algorithm. For example, the analysis unit improves the accuracy of the algorithm based on owner feedback. For example, the analysis unit adjusts the algorithm parameters based on owner feedback. This improves the accuracy of the system. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs owner feedback into the AI, and the AI ​​improves the algorithm.

[0038] The data collection unit can estimate the dog's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the dog is excited, the data collection unit increases the frequency of data collection to collect detailed behavioral data. For example, if the dog is relaxed, the data collection unit decreases the frequency of data collection to collect only the minimum necessary data. For example, if the dog is stressed, the data collection unit temporarily suspends data collection and waits until the dog's stress subsides. This allows for the collection of more appropriate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's emotional data into an AI, which can then adjust the timing of data collection.

[0039] The data collection unit can analyze a dog's past behavioral history and select the optimal data collection method. For example, if the dog was active during a specific time period in the past, the data collection unit will concentrate data collection during that time period. For example, if the dog was frequently active in a specific location in the past, the data collection unit will intensify data collection in that location. For example, the data collection unit will analyze the dog's past behavioral patterns and create the most efficient data collection schedule. This will enable the selection of the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's past behavioral data into an AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter data based on the dog's current health status and environmental factors during data collection. For example, if the dog is in good health, the data collection unit will perform normal data collection. If the dog is unwell, for example, the data collection unit will temporarily suspend data collection and wait until the dog's health recovers. If environmental factors (e.g., weather or temperature) affect data collection, the data collection unit will adjust the data collection to take these factors into account. This allows for the collection of more accurate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the dog's health status and environmental factors into the AI, which can then filter the data collection.

[0041] The data collection unit can estimate the dog's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the dog is excited, the data collection unit will prioritize collecting activity level and play data. For example, if the dog is relaxed, the data collection unit will prioritize collecting eating and water intake data. For example, if the dog is stressed, the data collection unit will prioritize collecting elimination patterns and rest data. This ensures that important data is collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's emotional data into an AI, which can then determine the priority of data to collect.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the dog's geographical location during data collection. For example, if the dog is in a park, the data collection unit will prioritize the collection of play and exercise data. For example, if the dog is at home, the data collection unit will prioritize the collection of meal and rest data. For example, if the dog is at a veterinary hospital, the data collection unit will prioritize the collection of health status and treatment data. This allows for the priority collection of highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's geographical location information into the AI, which can then select highly relevant data.

[0043] The data collection unit can analyze the social media activity of dog owners and collect relevant data during data collection. For example, if an owner posts about their dog's activities on social media, the data collection unit will collect data related to those activities. For example, if an owner shares information about their dog's health on social media, the data collection unit will collect data related to that health condition. For example, if an owner posts information about their dog's food or treats on social media, the data collection unit will collect data related to that food or treats. This allows for the collection of relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the owner's social media activity into AI, which can then select relevant data.

[0044] The analysis unit can estimate the dog's emotions and adjust the behavior prediction algorithm based on the estimated emotions. For example, if the dog is excited, the analysis unit can enhance the algorithm that predicts active behavior. For example, if the dog is relaxed, the analysis unit can enhance the algorithm that predicts resting or eating behavior. For example, if the dog is stressed, the analysis unit can enhance the algorithm that predicts stress-reducing behavior. This enables more accurate behavior prediction. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotional data into the AI, which can then adjust the behavior prediction algorithm.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's behavioral patterns during the analysis. For example, if the dog's activity level is high, the analysis unit performs a detailed behavioral analysis. For example, if the dog's activity level is low, the analysis unit performs a simplified behavioral analysis. For example, if the dog's behavioral patterns are changing, the analysis unit adjusts the level of detail of the analysis accordingly. This enables efficient analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs dog behavioral data into the AI, which can then adjust the level of detail of the analysis based on the importance of the behavioral patterns.

[0046] The analysis unit can apply different analysis algorithms depending on the dog's category (e.g., breed or age) during analysis. For example, the analysis unit can apply different behavioral analysis algorithms to small dogs and large dogs. For example, the analysis unit can apply different behavioral analysis algorithms to young dogs and senior dogs. For example, the analysis unit can apply an analysis algorithm that takes into account behavioral patterns specific to a particular breed. This enables more accurate analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input dog category data into the AI, which can then select an appropriate analysis algorithm.

[0047] The analysis unit can estimate the dog's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the dog is excited, the analysis unit provides a visually stimulating display method. For example, if the dog is relaxed, the analysis unit provides a calm display method. For example, if the dog is stressed, the analysis unit provides a simple and easy-to-understand display method. This makes the display easy for the owner to understand. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotion data into the AI, and the AI ​​can adjust the display method of the analysis results.

[0048] The analysis unit can determine the priority of analysis based on when the dog's behavioral data was submitted. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. For example, the analysis unit may analyze the most recent data while referring to past behavioral data. For example, the analysis unit may prioritize the analysis of behavioral data collected during a specific period. This enables efficient analysis. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the submission dates of the behavioral data into the AI, which can then determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the dog's behavioral data during the analysis. For example, the analysis unit may prioritize analyzing data related to the dog's activity level. For example, the analysis unit may prioritize analyzing data related to the dog's food and water intake. For example, the analysis unit may prioritize analyzing data related to the dog's elimination patterns. This enables efficient analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the relevance of the behavioral data into the AI, which can then adjust the order of analysis.

[0050] The notification unit can estimate the dog's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the dog is excited, the notification unit will provide a visually stimulating notification. If the dog is relaxed, the notification unit will provide a calm notification. If the dog is stressed, the notification unit will provide a simple and easily visible notification. This makes notifications easy for the owner to understand. Some or all of the processing described above in the notification unit may be performed using AI or not. For example, the notification unit can input dog emotion data into AI, which can then adjust the way notifications are presented.

[0051] The notification unit can adjust the level of detail of the notification based on the importance of the prediction result. For example, the notification unit provides a detailed notification for important prediction results. For example, it provides a simplified notification for less important prediction results. The notification unit adjusts the level of detail of the notification in stages according to the importance of the prediction result. This ensures that important information is conveyed appropriately. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the importance of the prediction result into the AI, which can then adjust the level of detail of the notification.

[0052] The notification unit can adjust the timing of notifications according to the owner's schedule. For example, the notification unit can send notifications while avoiding busy times for the owner. For example, the notification unit can send notifications during times when the owner is free. For example, the notification unit can determine the optimal notification timing based on the owner's schedule. This ensures that the owner receives notifications at the most opportune time. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's schedule data into the AI, which can then adjust the timing of notifications.

[0053] The notification unit can estimate the dog's emotions and determine the priority of notifications based on the estimated emotions. For example, if the dog is excited, the notification unit will prioritize urgent notifications. For example, if the dog is relaxed, the notification unit will prioritize normal notifications. For example, if the dog is stressed, the notification unit will prioritize notifications related to stress reduction. This ensures that important notifications are received preferentially. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input dog emotion data into an AI, which can then determine the priority of notifications.

[0054] The notification unit can select the optimal notification method when a notification is sent, taking into account the owner's geographical location. For example, if the owner is at home, the notification unit prioritizes sending notifications to a smartphone. If the owner is out, the notification unit prioritizes sending notifications to a smartwatch. If the owner is in a specific location, the notification unit selects a notification method appropriate for that location. This ensures that the owner receives notifications in the most optimal way. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's geographical location into the AI, which can then select the optimal notification method.

[0055] The notification unit can analyze the owner's social media activity and send relevant notifications when sending notifications. For example, if the owner posts about their dog's activities on social media, the notification unit will send a notification related to that activity. For example, if the owner shares information about their dog's health on social media, the notification unit will send a notification related to that health condition. For example, if the owner posts information about their dog's food or treats on social media, the notification unit will send a notification related to that food or treats. In this way, relevant notifications can be sent. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's social media activity into AI, and the AI ​​can select relevant notifications.

[0056] The analysis unit can estimate the dog's emotions and adjust the update frequency of the prediction model based on the estimated emotions. For example, if the dog is excited, the analysis unit increases the update frequency of the prediction model to reflect the latest data. For example, if the dog is relaxed, the analysis unit decreases the update frequency of the prediction model to reflect stable data. For example, if the dog is stressed, the analysis unit temporarily suspends updating the prediction model and waits until the stress subsides. This enables predictions that reflect the latest data. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotion data into the AI, which can then adjust the update frequency of the prediction model.

[0057] The analysis unit can select the optimal update method by referring to past data when updating the prediction model. For example, the analysis unit can update the prediction model to reflect seasonal behavioral patterns based on past data. For example, the analysis unit can update the prediction model to consider the impact of specific events (e.g., illness or injury) based on past data. For example, the analysis unit can update the prediction model to reflect changes in behavioral patterns associated with the growth and aging of dogs based on past data. This enables more accurate predictions. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input past data into the AI, which can then select the optimal update method.

[0058] The analysis unit can adjust the level of detail of the update based on changes in the dog's health when updating the predictive model. For example, if the dog's health is good, the analysis unit updates the predictive model with normal detail. If the dog's health is deteriorating, the analysis unit updates the predictive model to reflect more detailed data. If the dog's health is improving, the analysis unit adjusts the level of detail of the predictive model according to the degree of improvement. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input dog health data into the AI, which can then adjust the level of detail of the update.

[0059] The analysis unit can estimate the dog's emotions and determine what to update the predictive model based on the estimated emotions. For example, if the dog is excited, the analysis unit updates the predictive model to reflect active behavior patterns. For example, if the dog is relaxed, the analysis unit updates the predictive model to reflect resting and eating behavior patterns. For example, if the dog is stressed, the analysis unit updates the predictive model to reflect stress-reducing behavior patterns. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotional data into the AI, which can then determine what to update the predictive model.

[0060] The analysis unit can select the optimal update method when updating the prediction model, taking into account the dog's geographical location information. For example, if the dog lives in an urban area, the analysis unit updates the prediction model to reflect behavioral patterns specific to urban areas. For example, if the dog lives in a suburban area, the analysis unit updates the prediction model to reflect behavioral patterns specific to suburban areas. For example, if the dog is traveling, the analysis unit updates the prediction model to reflect behavioral patterns appropriate to the environment of the travel destination. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's geographical location information into the AI, which can then select the optimal update method.

[0061] The analysis unit can adjust the update content based on feedback from dog owners when updating the predictive model. For example, the analysis unit can improve the accuracy of the predictive model based on feedback provided by owners. For example, the analysis unit can update the predictive model for specific behavioral patterns, reflecting feedback from owners. For example, the analysis unit can adjust the update frequency and level of detail of the predictive model based on feedback from owners. This enables more accurate predictions. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input feedback from owners into the AI, which can then adjust the update content.

[0062] The analysis unit can estimate the dog's emotions and determine how to improve the algorithm based on the estimated emotions. For example, if the dog is excited, the analysis unit will improve the algorithm to reflect active behavior patterns. For example, if the dog is relaxed, the analysis unit will improve the algorithm to reflect resting and eating behavior patterns. For example, if the dog is stressed, the analysis unit will improve the algorithm to reflect stress-reducing behavior patterns. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotional data into the AI, which can then determine how to improve the algorithm.

[0063] The analysis unit can select the optimal improvement method by referring to past feedback data when improving the algorithm. For example, the analysis unit improves the algorithm for a specific behavior pattern based on past feedback data. For example, the analysis unit improves the accuracy of the algorithm based on past feedback data. For example, the analysis unit adjusts the frequency and level of detail of algorithm improvements by referring to past feedback data. This enables more accurate predictions. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input past feedback data into the AI, which can then select the optimal improvement method.

[0064] The analysis unit can adjust the level of detail of algorithm improvements based on changes in the dog's behavior patterns. For example, if the dog's behavior patterns have changed significantly, the analysis unit will perform detailed algorithm improvements. For example, if the dog's behavior patterns are stable, the analysis unit will perform simplified algorithm improvements. For example, the analysis unit will adjust the level of detail of algorithm improvements in stages according to changes in the dog's behavior patterns. This enables more accurate predictions. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input dog behavior pattern data into the AI, which can then adjust the level of detail of the improvements.

[0065] The analysis unit can estimate the dog's emotions and adjust the frequency of algorithm improvements based on the estimated emotions. For example, if the dog is excited, the analysis unit increases the frequency of algorithm improvements to reflect the latest data. For example, if the dog is relaxed, the analysis unit decreases the frequency of algorithm improvements to reflect stable data. For example, if the dog is stressed, the analysis unit temporarily suspends algorithm improvements and waits until the stress subsides. This allows for more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input dog emotion data into the AI, which can then adjust the improvement frequency.

[0066] The analysis unit can select the optimal improvement method when improving the algorithm, taking into account the owner's geographical location information. For example, if the owner lives in an urban area, the analysis unit will improve the algorithm to reflect behavioral patterns specific to urban areas. For example, if the owner lives in a suburban area, the analysis unit will improve the algorithm to reflect behavioral patterns specific to suburban areas. For example, if the owner is traveling, the analysis unit will improve the algorithm to reflect behavioral patterns appropriate to the environment of the travel destination. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the owner's geographical location information into the AI, which can then select the optimal improvement method.

[0067] The analysis unit can analyze the owner's social media activity when improving algorithms and determine relevant improvements. For example, if the owner posts about their dog's activities on social media, the analysis unit will improve the algorithm related to those activities. For example, if the owner shares information about their dog's health on social media, the analysis unit will improve the algorithm related to that health status. For example, if the owner posts information about their dog's food or treats on social media, the analysis unit will improve the algorithm related to that food or treats. This allows the analysis unit to determine relevant improvements. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the owner's social media activity into the AI, which can then determine relevant improvements.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The data collection unit can analyze a dog's past behavioral history and select the optimal data collection method. For example, if the dog was active during a specific time period in the past, the data collection unit will concentrate data collection during that time period. For example, if the dog was frequently active in a specific location in the past, the data collection unit will intensify data collection in that location. For example, the data collection unit will analyze the dog's past behavioral patterns and create the most efficient data collection schedule. This will enable the selection of the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's past behavioral data into an AI, which can then select the optimal data collection method.

[0070] The data collection unit can filter data based on the dog's current health status and environmental factors during data collection. For example, if the dog is in good health, the data collection unit will perform normal data collection. If the dog is unwell, for example, the data collection unit will temporarily suspend data collection and wait until the dog's health recovers. If environmental factors (e.g., weather or temperature) affect data collection, the data collection unit will adjust the data collection to take these factors into account. This allows for the collection of more accurate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the dog's health status and environmental factors into the AI, which can then filter the data collection.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's behavioral patterns during the analysis. For example, if the dog's activity level is high, the analysis unit performs a detailed behavioral analysis. For example, if the dog's activity level is low, the analysis unit performs a simplified behavioral analysis. For example, if the dog's behavioral patterns are changing, the analysis unit adjusts the level of detail of the analysis accordingly. This enables efficient analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs dog behavioral data into the AI, which can then adjust the level of detail of the analysis based on the importance of the behavioral patterns.

[0072] The analysis unit can apply different analysis algorithms depending on the dog's category (e.g., breed or age) during analysis. For example, the analysis unit can apply different behavioral analysis algorithms to small dogs and large dogs. For example, the analysis unit can apply different behavioral analysis algorithms to young dogs and senior dogs. For example, the analysis unit can apply an analysis algorithm that takes into account behavioral patterns specific to a particular breed. This enables more accurate analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input dog category data into the AI, which can then select an appropriate analysis algorithm.

[0073] The notification unit can adjust the timing of notifications according to the owner's schedule. For example, the notification unit can send notifications while avoiding busy times for the owner. For example, the notification unit can send notifications during times when the owner is free. For example, the notification unit can determine the optimal notification timing based on the owner's schedule. This ensures that the owner receives notifications at the most opportune time. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's schedule data into the AI, which can then adjust the timing of notifications.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The data collection unit collects data on the dog's daily behavioral patterns. Specifically, it collects data such as the dog's activity level, timing of meals and water intake, elimination patterns, and times when the dog wants to play. The data collection unit uses sensors to measure the dog's activity level, records the timing of meals and water intake, and records elimination patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. Specifically, it predicts the dog's behavior based on the collected data and analyzes the dog's behavior patterns using machine learning algorithms. Step 3: The notification unit notifies the owner of the prediction results obtained by the analysis unit. Specifically, it notifies the owner of the prediction results in real time and sends a notification to the owner's smartphone or smartwatch.

[0076] (Example of form 2) The dog behavior prediction system according to an embodiment of the present invention is a system that uses the latest AI technology to predict dog behavior and proposes optimal care to the owner. This system collects and analyzes a vast amount of data, such as the daily behavior patterns, physiological rhythms, and environmental factors of individual dogs, and predicts dog behavior using advanced machine learning algorithms. For example, it precisely predicts the dog's activity level, timing of meal and water intake, elimination patterns, and times when the dog wants to play, and notifies the owner in real time. This allows the owner to proactively meet the dog's needs and provide exercise, play, toilet breaks, etc., at the optimal time. Furthermore, the AI ​​continuously updates the prediction model in response to changes in season and weather, and the dog's age and health condition, providing more accurate suggestions. Through this system, owners can provide higher quality care while being attentive to their dog's daily rhythm, which in turn leads to improved health and well-being for the dog. For example, the system collects the dog's daily behavior patterns. For instance, it records when the dog is most active, the timing of meal and water intake, and elimination patterns. This allows for a detailed understanding of the dog's behavior patterns. Next, the collected data is analyzed by AI. The AI ​​predicts the dog's behavior by considering its behavioral patterns, physiological rhythms, and environmental factors. For example, it predicts when the dog will want to play or when it will need to go to the bathroom. Furthermore, the AI ​​notifies the owner in real time based on the prediction results. For example, by predicting when the dog will want to play and notifying the owner at that time, the owner can ensure they have time to play with their dog. Similarly, by predicting when the dog will need to go to the bathroom and notifying the owner at that time, the owner can take the dog to the bathroom. The AI ​​also updates its prediction model according to seasonal and weather changes, as well as the dog's age and health status. For example, since dogs tend to drink more water in the summer, the AI ​​updates its prediction model based on this information and notifies the owner to encourage hydration at the appropriate time. In this way, owners can anticipate and meet their dog's needs, providing exercise, playtime, and bathroom breaks at the optimal time. This improves the dog's health and well-being, and deepens the bond between the dog and its owner.This means that dog behavior prediction systems can enable owners to anticipate and meet their dogs' needs, thereby improving the dogs' health and well-being.

[0077] The dog behavior prediction system according to this embodiment comprises a data collection unit, an analysis unit, and a notification unit. The data collection unit collects the dog's daily behavior patterns. The data collection unit collects data such as the dog's activity level, timing of meal and water intake, elimination patterns, and times of day when the dog wants to play. The data collection unit measures the dog's activity level using a sensor, for example. The data collection unit records the timing of the dog's meal and water intake, for example. The data collection unit records the dog's elimination patterns, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit predicts the dog's behavior based on the collected data, for example. The analysis unit predicts the dog's behavior using a machine learning algorithm, for example. The analysis unit analyzes the dog's behavior patterns based on the collected data, for example. The notification unit notifies the owner of the prediction results obtained by the analysis unit. The notification unit notifies the owner of the prediction results in real time, for example. The notification unit sends a notification to the owner's smartphone, for example. The notification unit sends a notification to the owner's smartwatch, for example. This allows dog behavior prediction systems to enable owners to anticipate and meet their dogs' needs.

[0078] The data collection unit collects data on the dog's daily behavioral patterns. For example, it collects data such as the dog's activity level, timing of meals and water intake, elimination patterns, and times when the dog wants to play. Specifically, it measures the dog's activity level using sensors attached to the dog's collar or harness. These sensors have built-in accelerometers and gyroscopes, allowing for detailed recording of the dog's movements and posture. Furthermore, sensors are used to record the timing of meals and water intake, placed in food bowls and water bowls. These sensors detect the dog's movements while eating or drinking, accurately recording the timing. Sensors are also used to record elimination patterns, placed where the dog urinates, allowing for an understanding of the time and frequency of elimination. Additionally, cameras and microphones can be used to monitor the dog's behavior and record times when the dog wants to play. These devices detect the dog's movements and sounds, helping to identify playtimes. The collected data is transmitted wirelessly to a central database and updated in real time. This allows the data collection unit to gain a detailed and accurate understanding of the dog's behavioral patterns and provide this information to the analysis unit.

[0079] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit predicts dog behavior based on the collected data. Specifically, it uses machine learning algorithms to predict dog behavior. Machine learning algorithms learn from past data and model dog behavior patterns. For example, if a dog tends to be active during certain times of the day, future behavior can be predicted based on those times. It also analyzes data such as the timing of a dog's eating and drinking, and its elimination patterns, to clarify how these behaviors are related. Furthermore, the analysis unit can also detect abnormal behavior patterns. For example, if a dog is less active than usual, or if the timing of its eating or drinking changes, the analysis unit can detect this as an anomaly and notify the owner. Based on this data, the analysis unit can also assess the dog's health and stress level. For example, if a dog urinates or defecates frequently, this may indicate a health problem. The analysis unit comprehensively analyzes this information and builds a model for predicting dog behavior. This allows the analysis unit to accurately predict dog behavior based on the collected data and provide this information to the owner.

[0080] The notification unit notifies the owner of the prediction results obtained by the analysis unit. For example, the notification unit notifies the owner of the prediction results in real time. Specifically, it sends notifications to the owner's smartphone. Through the smartphone application, it can provide information on the dog's behavior prediction results and abnormal behavior patterns. For example, if it is predicted that the dog will be active during a specific time period, it can send a notification during that time to alert the owner. It can also send notifications to the owner's smartwatch. The smartwatch can provide information to the owner in real time through vibration and sound notifications. Furthermore, the notification unit can also send notifications to the owner's PC or tablet. This allows the owner to check the dog's behavior prediction results no matter where they are. The notification unit can also customize the content and timing of notifications. For example, if the owner does not want to receive notifications during a specific time period, it can send notifications while avoiding that time period. It can also set the priority of notifications. For example, if an abnormal behavior pattern is detected, a high-priority notification can be sent to prompt the owner to take action quickly. This allows the notification system to help owners proactively meet their dogs' needs and maintain their health and well-being.

[0081] The data collection unit can collect data such as the dog's activity level, timing of meals and water intake, elimination patterns, and times when the dog wants to play. For example, the data collection unit may use a pedometer to measure the dog's activity level. For example, the data collection unit may use an automatic feeder to record the timing of the dog's meals and water intake. For example, the data collection unit may use a toilet sensor to record the dog's elimination patterns. This allows for a detailed understanding of the dog's behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input pedometer data into an AI to measure the dog's activity level, and the AI ​​can analyze the data to evaluate the activity level.

[0082] The analysis unit can predict dog behavior based on collected data. For example, the analysis unit predicts dog behavior using machine learning algorithms based on collected data. For example, the analysis unit analyzes dog behavior patterns based on collected data. For example, the analysis unit builds a model for predicting dog behavior based on collected data. This allows owners to proactively meet their dog's needs. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs collected data into the AI, which analyzes the data and predicts dog behavior.

[0083] The notification unit can notify the owner of the prediction results in real time. For example, the notification unit can notify the owner of the prediction results on their smartphone. For example, the notification unit can notify the owner of the prediction results on their smartwatch. For example, the notification unit can notify the owner of the prediction results on their personal computer. This allows the owner to immediately meet the dog's needs. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the prediction results into AI, which can then determine the timing and method of notification.

[0084] The analysis unit can update the prediction model in response to changes in seasons and weather, as well as the dog's age and health status. For example, the analysis unit updates the prediction model considering changes in seasons and weather. For example, the analysis unit updates the prediction model considering changes in the dog's age and health status. For example, the analysis unit updates the prediction model based on collected data. This allows for more accurate recommendations. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs seasonal and weather data into the AI, and the AI ​​updates the prediction model.

[0085] The analysis unit can improve the algorithm based on owner feedback. For example, the analysis unit collects owner feedback and improves the algorithm. For example, the analysis unit improves the accuracy of the algorithm based on owner feedback. For example, the analysis unit adjusts the algorithm parameters based on owner feedback. This improves the accuracy of the system. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit inputs owner feedback into the AI, and the AI ​​improves the algorithm.

[0086] The data collection unit can estimate the dog's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the dog is excited, the data collection unit increases the frequency of data collection to collect detailed behavioral data. For example, if the dog is relaxed, the data collection unit decreases the frequency of data collection to collect only the minimum necessary data. For example, if the dog is stressed, the data collection unit temporarily suspends data collection and waits until the dog's stress subsides. This allows for the collection of more appropriate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's emotional data into an AI, which can then adjust the timing of data collection.

[0087] The data collection unit can analyze a dog's past behavioral history and select the optimal data collection method. For example, if the dog was active during a specific time period in the past, the data collection unit will concentrate data collection during that time period. For example, if the dog was frequently active in a specific location in the past, the data collection unit will intensify data collection in that location. For example, the data collection unit will analyze the dog's past behavioral patterns and create the most efficient data collection schedule. This will enable the selection of the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's past behavioral data into an AI, which can then select the optimal data collection method.

[0088] The data collection unit can filter data based on the dog's current health status and environmental factors during data collection. For example, if the dog is in good health, the data collection unit will perform normal data collection. If the dog is unwell, for example, the data collection unit will temporarily suspend data collection and wait until the dog's health recovers. If environmental factors (e.g., weather or temperature) affect data collection, the data collection unit will adjust the data collection to take these factors into account. This allows for the collection of more accurate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the dog's health status and environmental factors into the AI, which can then filter the data collection.

[0089] The data collection unit can estimate the dog's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the dog is excited, the data collection unit will prioritize collecting activity level and play data. For example, if the dog is relaxed, the data collection unit will prioritize collecting eating and water intake data. For example, if the dog is stressed, the data collection unit will prioritize collecting elimination patterns and rest data. This ensures that important data is collected preferentially. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's emotional data into an AI, which can then determine the priority of data to collect.

[0090] The data collection unit can prioritize the collection of highly relevant data by considering the dog's geographical location during data collection. For example, if the dog is in a park, the data collection unit will prioritize the collection of play and exercise data. For example, if the dog is at home, the data collection unit will prioritize the collection of meal and rest data. For example, if the dog is at a veterinary hospital, the data collection unit will prioritize the collection of health status and treatment data. This allows for the priority collection of highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's geographical location information into the AI, which can then select highly relevant data.

[0091] The data collection unit can analyze the social media activity of dog owners and collect relevant data during data collection. For example, if an owner posts about their dog's activities on social media, the data collection unit will collect data related to those activities. For example, if an owner shares information about their dog's health on social media, the data collection unit will collect data related to that health condition. For example, if an owner posts information about their dog's food or treats on social media, the data collection unit will collect data related to that food or treats. This allows for the collection of relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the owner's social media activity into AI, which can then select relevant data.

[0092] The analysis unit can estimate the dog's emotions and adjust the behavior prediction algorithm based on the estimated emotions. For example, if the dog is excited, the analysis unit can enhance the algorithm that predicts active behavior. For example, if the dog is relaxed, the analysis unit can enhance the algorithm that predicts resting or eating behavior. For example, if the dog is stressed, the analysis unit can enhance the algorithm that predicts stress-reducing behavior. This enables more accurate behavior prediction. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotional data into the AI, which can then adjust the behavior prediction algorithm.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's behavioral patterns during the analysis. For example, if the dog's activity level is high, the analysis unit performs a detailed behavioral analysis. For example, if the dog's activity level is low, the analysis unit performs a simplified behavioral analysis. For example, if the dog's behavioral patterns are changing, the analysis unit adjusts the level of detail of the analysis accordingly. This enables efficient analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs dog behavioral data into the AI, which can then adjust the level of detail of the analysis based on the importance of the behavioral patterns.

[0094] The analysis unit can apply different analysis algorithms depending on the dog's category (e.g., breed or age) during analysis. For example, the analysis unit can apply different behavioral analysis algorithms to small dogs and large dogs. For example, the analysis unit can apply different behavioral analysis algorithms to young dogs and senior dogs. For example, the analysis unit can apply an analysis algorithm that takes into account behavioral patterns specific to a particular breed. This enables more accurate analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input dog category data into the AI, which can then select an appropriate analysis algorithm.

[0095] The analysis unit can estimate the dog's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the dog is excited, the analysis unit provides a visually stimulating display method. For example, if the dog is relaxed, the analysis unit provides a calm display method. For example, if the dog is stressed, the analysis unit provides a simple and easy-to-understand display method. This makes the display easy for the owner to understand. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotion data into the AI, and the AI ​​can adjust the display method of the analysis results.

[0096] The analysis unit can determine the priority of analysis based on when the dog's behavioral data was submitted. For example, the analysis unit may prioritize the analysis of the most recent behavioral data. For example, the analysis unit may analyze the most recent data while referring to past behavioral data. For example, the analysis unit may prioritize the analysis of behavioral data collected during a specific period. This enables efficient analysis. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input the submission dates of the behavioral data into the AI, which can then determine the priority of analysis.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the dog's behavioral data during the analysis. For example, the analysis unit may prioritize analyzing data related to the dog's activity level. For example, the analysis unit may prioritize analyzing data related to the dog's food and water intake. For example, the analysis unit may prioritize analyzing data related to the dog's elimination patterns. This enables efficient analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the relevance of the behavioral data into the AI, which can then adjust the order of analysis.

[0098] The notification unit can estimate the dog's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the dog is excited, the notification unit will provide a visually stimulating notification. If the dog is relaxed, the notification unit will provide a calm notification. If the dog is stressed, the notification unit will provide a simple and easily visible notification. This makes notifications easy for the owner to understand. Some or all of the processing described above in the notification unit may be performed using AI or not. For example, the notification unit can input dog emotion data into AI, which can then adjust the way notifications are presented.

[0099] The notification unit can adjust the level of detail of the notification based on the importance of the prediction result. For example, the notification unit provides a detailed notification for important prediction results. For example, it provides a simplified notification for less important prediction results. The notification unit adjusts the level of detail of the notification in stages according to the importance of the prediction result. This ensures that important information is conveyed appropriately. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the importance of the prediction result into the AI, which can then adjust the level of detail of the notification.

[0100] The notification unit can adjust the timing of notifications according to the owner's schedule. For example, the notification unit can send notifications while avoiding busy times for the owner. For example, the notification unit can send notifications during times when the owner is free. For example, the notification unit can determine the optimal notification timing based on the owner's schedule. This ensures that the owner receives notifications at the most opportune time. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's schedule data into the AI, which can then adjust the timing of notifications.

[0101] The notification unit can estimate the dog's emotions and determine the priority of notifications based on the estimated emotions. For example, if the dog is excited, the notification unit will prioritize urgent notifications. For example, if the dog is relaxed, the notification unit will prioritize normal notifications. For example, if the dog is stressed, the notification unit will prioritize notifications related to stress reduction. This ensures that important notifications are received preferentially. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input dog emotion data into an AI, which can then determine the priority of notifications.

[0102] The notification unit can select the optimal notification method when a notification is sent, taking into account the owner's geographical location. For example, if the owner is at home, the notification unit prioritizes sending notifications to a smartphone. If the owner is out, the notification unit prioritizes sending notifications to a smartwatch. If the owner is in a specific location, the notification unit selects a notification method appropriate for that location. This ensures that the owner receives notifications in the most optimal way. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's geographical location into the AI, which can then select the optimal notification method.

[0103] The notification unit can analyze the owner's social media activity and send relevant notifications when sending notifications. For example, if the owner posts about their dog's activities on social media, the notification unit will send a notification related to that activity. For example, if the owner shares information about their dog's health on social media, the notification unit will send a notification related to that health condition. For example, if the owner posts information about their dog's food or treats on social media, the notification unit will send a notification related to that food or treats. In this way, relevant notifications can be sent. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's social media activity into AI, and the AI ​​can select relevant notifications.

[0104] The analysis unit can estimate the dog's emotions and adjust the update frequency of the prediction model based on the estimated emotions. For example, if the dog is excited, the analysis unit increases the update frequency of the prediction model to reflect the latest data. For example, if the dog is relaxed, the analysis unit decreases the update frequency of the prediction model to reflect stable data. For example, if the dog is stressed, the analysis unit temporarily suspends updating the prediction model and waits until the stress subsides. This enables predictions that reflect the latest data. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotion data into the AI, which can then adjust the update frequency of the prediction model.

[0105] The analysis unit can select the optimal update method by referring to past data when updating the prediction model. For example, the analysis unit can update the prediction model to reflect seasonal behavioral patterns based on past data. For example, the analysis unit can update the prediction model to consider the impact of specific events (e.g., illness or injury) based on past data. For example, the analysis unit can update the prediction model to reflect changes in behavioral patterns associated with the growth and aging of dogs based on past data. This enables more accurate predictions. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input past data into the AI, which can then select the optimal update method.

[0106] The analysis unit can adjust the level of detail of the update based on changes in the dog's health when updating the predictive model. For example, if the dog's health is good, the analysis unit updates the predictive model with normal detail. If the dog's health is deteriorating, the analysis unit updates the predictive model to reflect more detailed data. If the dog's health is improving, the analysis unit adjusts the level of detail of the predictive model according to the degree of improvement. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input dog health data into the AI, which can then adjust the level of detail of the update.

[0107] The analysis unit can estimate the dog's emotions and determine what to update the predictive model based on the estimated emotions. For example, if the dog is excited, the analysis unit updates the predictive model to reflect active behavior patterns. For example, if the dog is relaxed, the analysis unit updates the predictive model to reflect resting and eating behavior patterns. For example, if the dog is stressed, the analysis unit updates the predictive model to reflect stress-reducing behavior patterns. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotional data into the AI, which can then determine what to update the predictive model.

[0108] The analysis unit can select the optimal update method when updating the prediction model, taking into account the dog's geographical location information. For example, if the dog lives in an urban area, the analysis unit updates the prediction model to reflect behavioral patterns specific to urban areas. For example, if the dog lives in a suburban area, the analysis unit updates the prediction model to reflect behavioral patterns specific to suburban areas. For example, if the dog is traveling, the analysis unit updates the prediction model to reflect behavioral patterns appropriate to the environment of the travel destination. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's geographical location information into the AI, which can then select the optimal update method.

[0109] The analysis unit can adjust the update content based on feedback from dog owners when updating the predictive model. For example, the analysis unit can improve the accuracy of the predictive model based on feedback provided by owners. For example, the analysis unit can update the predictive model for specific behavioral patterns, reflecting feedback from owners. For example, the analysis unit can adjust the update frequency and level of detail of the predictive model based on feedback from owners. This enables more accurate predictions. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input feedback from owners into the AI, which can then adjust the update content.

[0110] The analysis unit can estimate the dog's emotions and determine how to improve the algorithm based on the estimated emotions. For example, if the dog is excited, the analysis unit will improve the algorithm to reflect active behavior patterns. For example, if the dog is relaxed, the analysis unit will improve the algorithm to reflect resting and eating behavior patterns. For example, if the dog is stressed, the analysis unit will improve the algorithm to reflect stress-reducing behavior patterns. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotional data into the AI, which can then determine how to improve the algorithm.

[0111] The analysis unit can select the optimal improvement method by referring to past feedback data when improving the algorithm. For example, the analysis unit improves the algorithm for a specific behavior pattern based on past feedback data. For example, the analysis unit improves the accuracy of the algorithm based on past feedback data. For example, the analysis unit adjusts the frequency and level of detail of algorithm improvements by referring to past feedback data. This enables more accurate predictions. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input past feedback data into the AI, which can then select the optimal improvement method.

[0112] The analysis unit can adjust the level of detail of algorithm improvements based on changes in the dog's behavior patterns. For example, if the dog's behavior patterns have changed significantly, the analysis unit will perform detailed algorithm improvements. For example, if the dog's behavior patterns are stable, the analysis unit will perform simplified algorithm improvements. For example, the analysis unit will adjust the level of detail of algorithm improvements in stages according to changes in the dog's behavior patterns. This enables more accurate predictions. Some or all of the above processes in the analysis unit are performed using AI. For example, the analysis unit can input dog behavior pattern data into the AI, which can then adjust the level of detail of the improvements.

[0113] The analysis unit can estimate the dog's emotions and adjust the frequency of algorithm improvements based on the estimated emotions. For example, if the dog is excited, the analysis unit increases the frequency of algorithm improvements to reflect the latest data. For example, if the dog is relaxed, the analysis unit decreases the frequency of algorithm improvements to reflect stable data. For example, if the dog is stressed, the analysis unit temporarily suspends algorithm improvements and waits until the stress subsides. This allows for more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input dog emotion data into the AI, which can then adjust the improvement frequency.

[0114] The analysis unit can select the optimal improvement method when improving the algorithm, taking into account the owner's geographical location information. For example, if the owner lives in an urban area, the analysis unit will improve the algorithm to reflect behavioral patterns specific to urban areas. For example, if the owner lives in a suburban area, the analysis unit will improve the algorithm to reflect behavioral patterns specific to suburban areas. For example, if the owner is traveling, the analysis unit will improve the algorithm to reflect behavioral patterns appropriate to the environment of the travel destination. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the owner's geographical location information into the AI, which can then select the optimal improvement method.

[0115] The analysis unit can analyze the owner's social media activity when improving algorithms and determine relevant improvements. For example, if the owner posts about their dog's activities on social media, the analysis unit will improve the algorithm related to those activities. For example, if the owner shares information about their dog's health on social media, the analysis unit will improve the algorithm related to that health status. For example, if the owner posts information about their dog's food or treats on social media, the analysis unit will improve the algorithm related to that food or treats. This allows the analysis unit to determine relevant improvements. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the owner's social media activity into the AI, which can then determine relevant improvements.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The analysis unit can estimate the dog's emotions and adjust the behavior prediction algorithm based on the estimated emotions. For example, if the dog is excited, the analysis unit can enhance the algorithm that predicts active behavior. For example, if the dog is relaxed, the analysis unit can enhance the algorithm that predicts resting or eating behavior. For example, if the dog is stressed, the analysis unit can enhance the algorithm that predicts stress-reducing behavior. This enables more accurate behavior prediction. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotional data into the AI, which can then adjust the behavior prediction algorithm.

[0118] The notification unit can estimate the dog's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the dog is excited, the notification unit will provide a visually stimulating notification. If the dog is relaxed, the notification unit will provide a calm notification. If the dog is stressed, the notification unit will provide a simple and easily visible notification. This makes notifications easy for the owner to understand. Some or all of the processing described above in the notification unit may be performed using AI or not. For example, the notification unit can input dog emotion data into AI, which can then adjust the way notifications are presented.

[0119] The data collection unit can estimate the dog's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the dog is excited, the data collection unit increases the frequency of data collection to collect detailed behavioral data. For example, if the dog is relaxed, the data collection unit decreases the frequency of data collection to collect only the minimum necessary data. For example, if the dog is stressed, the data collection unit temporarily suspends data collection and waits until the dog's stress subsides. This allows for the collection of more appropriate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's emotional data into an AI, which can then adjust the timing of data collection.

[0120] The analysis unit can estimate the dog's emotions and adjust the update frequency of the prediction model based on the estimated emotions. For example, if the dog is excited, the analysis unit increases the update frequency of the prediction model to reflect the latest data. For example, if the dog is relaxed, the analysis unit decreases the update frequency of the prediction model to reflect stable data. For example, if the dog is stressed, the analysis unit temporarily suspends updating the prediction model and waits until the stress subsides. This enables predictions that reflect the latest data. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotion data into the AI, which can then adjust the update frequency of the prediction model.

[0121] The analysis unit can estimate the dog's emotions and determine how to improve the algorithm based on the estimated emotions. For example, if the dog is excited, the analysis unit will improve the algorithm to reflect active behavior patterns. For example, if the dog is relaxed, the analysis unit will improve the algorithm to reflect resting and eating behavior patterns. For example, if the dog is stressed, the analysis unit will improve the algorithm to reflect stress-reducing behavior patterns. This enables more accurate predictions. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input the dog's emotional data into the AI, which can then determine how to improve the algorithm.

[0122] The data collection unit can analyze a dog's past behavioral history and select the optimal data collection method. For example, if the dog was active during a specific time period in the past, the data collection unit will concentrate data collection during that time period. For example, if the dog was frequently active in a specific location in the past, the data collection unit will intensify data collection in that location. For example, the data collection unit will analyze the dog's past behavioral patterns and create the most efficient data collection schedule. This will enable the selection of the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the dog's past behavioral data into an AI, which can then select the optimal data collection method.

[0123] The data collection unit can filter data based on the dog's current health status and environmental factors during data collection. For example, if the dog is in good health, the data collection unit will perform normal data collection. If the dog is unwell, for example, the data collection unit will temporarily suspend data collection and wait until the dog's health recovers. If environmental factors (e.g., weather or temperature) affect data collection, the data collection unit will adjust the data collection to take these factors into account. This allows for the collection of more accurate data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the dog's health status and environmental factors into the AI, which can then filter the data collection.

[0124] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's behavioral patterns during the analysis. For example, if the dog's activity level is high, the analysis unit performs a detailed behavioral analysis. For example, if the dog's activity level is low, the analysis unit performs a simplified behavioral analysis. For example, if the dog's behavioral patterns are changing, the analysis unit adjusts the level of detail of the analysis accordingly. This enables efficient analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit inputs dog behavioral data into the AI, which can then adjust the level of detail of the analysis based on the importance of the behavioral patterns.

[0125] The analysis unit can apply different analysis algorithms depending on the dog's category (e.g., breed or age) during analysis. For example, the analysis unit can apply different behavioral analysis algorithms to small dogs and large dogs. For example, the analysis unit can apply different behavioral analysis algorithms to young dogs and senior dogs. For example, the analysis unit can apply an analysis algorithm that takes into account behavioral patterns specific to a particular breed. This enables more accurate analysis. Some or all of the above processing in the analysis unit is performed using AI. For example, the analysis unit can input dog category data into the AI, which can then select an appropriate analysis algorithm.

[0126] The notification unit can adjust the timing of notifications according to the owner's schedule. For example, the notification unit can send notifications while avoiding busy times for the owner. For example, the notification unit can send notifications during times when the owner is free. For example, the notification unit can determine the optimal notification timing based on the owner's schedule. This ensures that the owner receives notifications at the most opportune time. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input the owner's schedule data into the AI, which can then adjust the timing of notifications.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects data on the dog's daily behavioral patterns. Specifically, it collects data such as the dog's activity level, timing of meals and water intake, elimination patterns, and times when the dog wants to play. The data collection unit uses sensors to measure the dog's activity level, records the timing of meals and water intake, and records elimination patterns. Step 2: The analysis unit analyzes the data collected by the collection unit. Specifically, it predicts the dog's behavior based on the collected data and analyzes the dog's behavior patterns using machine learning algorithms. Step 3: The notification unit notifies the owner of the prediction results obtained by the analysis unit. Specifically, it notifies the owner of the prediction results in real time and sends a notification to the owner's smartphone or smartwatch.

[0129] 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.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit measures the dog's activity level using the sensors of the smart device 14 and records the timing of meals and water intake, as well as the pattern of excretion. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and predicts the dog's behavior using a machine learning algorithm based on the collected data. The notification unit is implemented in the control unit 46A of the smart device 14 and notifies the owner of the prediction results in real time to their smartphone or smartwatch. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] 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.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit measures the dog's activity level using the sensors in the smart glasses 214 and records the timing of meals and water intake, as well as the pattern of excretion. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and predicts the dog's behavior using a machine learning algorithm based on the collected data. The notification unit is implemented in the control unit 46A of the smart glasses 214 and notifies the owner of the prediction results in real time on their smartphone or smartwatch. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] 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.

[0156] 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.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit measures the dog's activity level using the sensors in the headset terminal 314 and records the timing of meals and water intake, as well as the pattern of excretion. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and predicts the dog's behavior using a machine learning algorithm based on the collected data. The notification unit is implemented in the control unit 46A of the headset terminal 314 and notifies the owner of the prediction results in real time to their smartphone or smartwatch. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] 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.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] 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.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] 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.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the data collection unit, analysis unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit measures the dog's activity level using the robot 414's sensors and records the timing of meals and water intake, as well as the pattern of excretion. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and predicts the dog's behavior using a machine learning algorithm based on the collected data. The notification unit is implemented, for example, by the control unit 46A of the robot 414, and notifies the owner of the prediction results in real time to their smartphone or smartwatch. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0182] 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.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] 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.

[0185] 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.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] 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."

[0188] 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.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] 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.

[0200] (Note 1) A collection department that collects the daily behavioral patterns of dogs, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a notification unit that notifies the owner of the prediction results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on the dog's activity level, timing of meals and water intake, elimination patterns, and times of day when it wants to play. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Predicting dog behavior based on collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, The prediction results are notified to the pet owner in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The predictive model is updated according to seasonal and weather changes, as well as the dog's age and health status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We improve the algorithm based on owner feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the dog's emotions and adjusts the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the dog's past behavioral history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the dog's current health status and environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the dog's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data, taking into account the dog's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze the social media activity of dog owners and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the dog's emotions and adjusts the behavior prediction algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the dog's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the dog's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the dog's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on when the dog behavior data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the dog's behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, It estimates the dog's emotions and adjusts the way notifications are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending a notification, adjust the level of detail based on the importance of the prediction result. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When sending a notification, the timing of the notification will be adjusted according to the owner's schedule. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, It estimates the dog's emotions and prioritizes notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending a notification, the system will select the most suitable notification method, taking into account the owner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, the system analyzes the owner's social media activity and sends relevant notifications. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, The system estimates the dog's emotions and adjusts the update frequency of the predictive model based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, When updating the predictive model, historical data is referenced to select the optimal update method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, When updating the predictive model, adjust the level of detail of the update based on changes in the dog's health. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, The system estimates the dog's emotions and determines how to update the predictive model based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, When updating the prediction model, the optimal update method is selected by considering the dog's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, When updating the predictive model, we adjust the update based on feedback from dog owners. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, The system estimates the dog's emotions and determines how to improve the algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit, When improving an algorithm, past feedback data is referenced to select the optimal improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit, When improving the algorithm, adjust the level of detail of the improvements based on changes in the dog's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit, The algorithm estimates the dog's emotions and adjusts the frequency of improvement based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit, When improving the algorithm, the optimal improvement method is selected by considering the owner's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit, When improving the algorithm, we analyze the owner's social media activity to determine relevant improvements. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects the daily behavioral patterns of dogs, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a notification unit that notifies the owner of the prediction results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on the dog's activity level, timing of meals and water intake, elimination patterns, and times of day when it wants to play. The system according to feature 1.

3. The aforementioned analysis unit, Predicting dog behavior based on collected data. The system according to feature 1.

4. The aforementioned notification unit, The prediction results are notified to the pet owner in real time. The system according to feature 1.

5. The aforementioned analysis unit, The predictive model is updated according to seasonal and weather changes, as well as the dog's age and health status. The system according to feature 1.

6. The aforementioned analysis unit, We improve the algorithm based on owner feedback. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the dog's emotions and adjusts the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the dog's past behavioral history and select the optimal data collection method. The system according to feature 1.

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