System

Abstract

To provide a system capable of supporting rapid rescue by predicting the destinations and stops of lost elderly people and children.SOLUTION: A system in an embodiment includes a collection unit, a prediction unit, and a providing unit. The collection unit collects personal characteristics data of a subject. The prediction unit predicts the destinations and stops of the subject based on the data collected by the collection unit. The providing unit provides information representing the predicted location.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to quickly and accurately predict the destination or stopovers of lost elderly or young children, resulting in low efficiency of rescue operations.

[0005] The system according to the embodiment aims to predict the destination or stopovers of lost elderly people or small children and to assist in their rapid rescue. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a prediction unit, and a provision unit. The collection unit collects personal characteristic data of a subject. The prediction unit predicts a destination and a stopover of the subject based on the data collected by the collection unit. The provision unit provides information indicating the predicted location. Effect of the Invention

[0007] The system according to the embodiment can predict the destination or stopover points of lost elderly people or small children, and assist in their rapid rescue. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

[0019] The smart device 14 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A rescue support system according to an embodiment of the present invention is a system that accumulates personal characteristic data of lost persons such as elderly people and infants, predicts the destination and stopovers based on the data, and assists in rescue. This rescue support system collects personal characteristic data of the subject, and an AI or generation AI predicts the destination and stopovers of the subject based on the collected data, and provides information indicating the predicted location. For example, the rescue support system collects personal characteristic data of the subject. For example, data on the subject's past behavior history and health condition is collected. This makes it possible to grasp the subject's behavior pattern and health condition. Next, based on the collected data, the AI ​​or generation AI predicts the subject's destination and stopovers. For example, from the past behavior history, the subject's frequent visits and stopovers are identified. In addition, based on the health condition data, it is predicted what kind of place the subject is likely to go to. Finally, information indicating the predicted location is provided. For example, if the subject gets lost, rescue is assisted by providing information indicating the predicted destination and stopovers. This makes it possible to ensure the safety of the subject. This system makes it possible to predict the destination and stopovers of lost people such as elderly people and small children, and to quickly rescue them. For example, if an elderly person gets lost, the places they often visit and stopovers can be predicted based on data on their past behavioral history and health condition, and this information can be provided, allowing them to be quickly rescued. Similarly, if a small child gets lost, the destination and stopovers can be predicted based on data on their past behavioral history and health condition, and this information can be provided, allowing them to be quickly rescued. In this way, the rescue support system can predict the destination and stopovers of lost people such as elderly people and small children, and to quickly rescue them.

[0029] The rescue support system according to the embodiment includes a collection unit, a prediction unit, and a provision unit. The collection unit collects personal characteristic data of the subject. The personal characteristic data of the subject includes, for example, age, sex, hobbies, behavior patterns, etc., but is not limited to such examples. The collection unit collects, for example, data on the subject's past behavior history and health condition. The behavior history log includes, for example, movement history, visited places, time periods, etc. The health condition data includes, for example, heart rate, blood pressure, body temperature, etc. The collection unit can collect these data using a sensor or device. For example, the collection unit collects the subject's health condition data in real time using a wearable device. The collection unit can also collect the subject's movement history using a GPS device. The prediction unit predicts the destination and stopovers of the subject based on the data collected by the collection unit. The prediction unit analyzes the collected data using, for example, AI or generation AI, and predicts the destination and stopovers. The analysis by AI includes, for example, machine learning algorithms and data preprocessing methods. The generation AI can predict the destination and the stopovers using, for example, a text generation AI (for example, LLM) or a multimodal generation AI. For example, the prediction unit identifies the places that the subject frequently visits and the stopovers from the past behavior history. In addition, the prediction unit predicts what places the subject is likely to go to based on the health condition data. The provision unit provides information indicating the place predicted by the prediction unit. The provision unit provides information indicating the predicted place, for example, taking into consideration the notification method and the information format. For example, the provision unit notifies the subject of the destination and the stopovers through a smartphone app. In addition, the provision unit can also provide information using email or SMS. As a result, the rescue support system according to the embodiment can predict the destination and the stopovers based on the subject's personal characteristic data and support a quick rescue.

[0030] The collecting unit collects the individual characteristic data of the subject. The individual characteristic data of the subject includes, for example, age, sex, hobbies, behavioral patterns, etc., but is not limited to such examples. The collecting unit collects, for example, the past behavioral history and health condition data of the subject. The behavioral history log includes, for example, movement history, visited places, time periods, etc. The health condition data includes, for example, heart rate, blood pressure, body temperature, etc. The collecting unit can collect these data using a sensor or device. For example, the collecting unit collects the health condition data of the subject in real time using a wearable device. The collecting unit can also collect the movement history of the subject using a GPS device. Specifically, the wearable device is worn on the subject's wrist or chest, and continuously monitors vital signs such as heart rate, blood pressure, and body temperature. These data are transmitted to the collecting unit via Bluetooth (registered trademark) or Wi-Fi and stored in a central database. Furthermore, the GPS device tracks the subject's location information in real time and records the movement history and visited places. This allows the collecting unit to grasp the subject's health condition and behavioral patterns in detail. The collection unit can also collect data from the subject's smartphone or tablet. For example, information such as the subject's schedule, notes, and contact information can be obtained through a smartphone app, and the information can be used to analyze behavioral patterns. This allows the collection unit to collect a wide range of data from various devices and grasp the subject's personal characteristics in detail. Furthermore, the collection unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the prediction unit and provision unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The prediction unit predicts the destination and stopovers of the subject based on the data collected by the collection unit. The prediction unit, for example, uses AI or generation AI to analyze the collected data and predict the destination and stopovers. Analysis by AI includes, for example, machine learning algorithms and data preprocessing methods. The generation AI can predict the destination and stopovers using, for example, text generation AI (for example, LLM) or multimodal generation AI. For example, the prediction unit identifies places and stopovers frequently visited by the subject from past behavioral history. In addition, the prediction unit predicts what places the subject is likely to go to based on health condition data. Specifically, a machine learning algorithm is used to analyze the subject's past behavioral patterns and predict places visited on a specific day of the week or time period. For example, if the subject goes to a specific gym every Friday evening, it can be predicted that the subject is likely to go to the same gym on the following Friday evening based on the behavioral pattern. Furthermore, the generation AI can be used to make predictions taking into account the subject's health condition and current situation. For example, if the subject's heart rate or blood pressure is higher than normal, it can predict that the subject is likely to go to a place where he or she can relax or to a medical institution. This allows the prediction unit to comprehensively analyze the subject's behavioral patterns and health condition, and predict the destination and stopovers with high accuracy. Furthermore, the prediction unit can continuously correct the prediction results based on data updated in real time to respond to the latest situation. For example, if the subject's movement history or health condition changes suddenly, the prediction unit immediately imports new data and updates the prediction results. In addition, the prediction unit can make more accurate predictions by taking into account the characteristics of each region and past behavioral history. This allows the prediction unit to always provide highly accurate predictions based on the latest information, and support rapid and appropriate responses.

[0032] The providing unit provides information indicating the location predicted by the prediction unit. The providing unit provides information indicating the predicted location, for example, taking into consideration the notification method and the format of the information. For example, the providing unit notifies the target person of the destination and the stopovers through a smartphone app. The providing unit can also provide information using email or SMS. Specifically, the smartphone app displays the target person's current location and the predicted destination on a map and provides a navigation function. This allows the target person to quickly and accurately reach the predicted destination. The providing unit can also notify the target person of the destination and the stopovers using email or SMS. For example, the target person can be notified of the expected time of arrival at a specific location and urge the target person to make necessary preparations. Furthermore, the providing unit can also provide information to the target person by voice using a voice assistant or smart speaker. This makes it possible to provide appropriate information to the target person even in situations where it is difficult to provide visual information. The providing unit can quickly and reliably convey information to the target person by combining these information providing means. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the information provision. For example, the information providing unit can evaluate whether the provided information was useful and, if necessary, review the method and content of providing the information. This allows the information providing unit to provide information to the user quickly and reliably, maximizing the effectiveness of the rescue support system.

[0033] The collection unit can collect the log of the behavior history of the subject and the data of the health condition as personal characteristic data. For example, the collection unit collects the log of the behavior history of the subject. The log of the behavior history includes, for example, a movement history, a visited place, a time period, and the like. The collection unit can collect the movement history of the subject using a GPS device. The collection unit also collects health condition data. The health condition data includes, for example, a heart rate, a blood pressure, a body temperature, and the like. The collection unit can collect the health condition data of the subject in real time using a wearable device. This allows for more accurate prediction by collecting the behavior history and health condition data of the subject. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the log of the behavior history of the subject to AI and cause the AI ​​to perform an analysis of the behavior pattern.

[0034] The prediction unit can analyze the collected data using AI and predict the destination and the stopovers. The prediction unit can, for example, analyze the collected data using AI. The analysis by AI includes, for example, a machine learning algorithm and a data preprocessing method. The prediction unit can analyze the collected data using AI and predict the destination and the stopovers. For example, the prediction unit identifies places that the subject frequently visits and the stopovers from the past behavior history. In addition, the prediction unit predicts what places the subject is likely to go to based on the health condition data. This improves the prediction accuracy of the destination and the stopovers by the data analysis by AI. Part or all of the above-mentioned processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the collected data to AI and cause AI to execute the prediction of the destination and the stopovers.

[0035] The collection unit can analyze the subject's past behavior history and select an appropriate data collection method. The collection unit, for example, analyzes the subject's past behavior history. The past behavior history includes, for example, visit frequency, behavior pattern, time period, etc. The collection unit can determine the timing and location of data collection based on the past behavior history. For example, the collection unit determines the timing and location of data collection based on places that the subject has frequently visited in the past. In addition, the collection unit can analyze the subject's past behavior pattern and collect data in a specific time period. Furthermore, the collection unit can select a data collection method based on the transportation means used by the subject in the past. This enables efficient data collection by selecting a data collection method based on the past behavior history. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the subject's past behavior history into AI and cause AI to select the optimal data collection method.

[0036] The collection unit can perform filtering based on the subject's current health condition and living situation when collecting data. The collection unit, for example, considers the subject's current health condition when collecting data. The health condition includes, for example, heart rate, blood pressure, body temperature, etc. The collection unit can collect detailed data when the health condition is good. In addition, the collection unit can temporarily stop data collection when the subject is in poor health and resume it when the health condition recovers. Furthermore, the collection unit considers the living situation of the subject. The living situation includes, for example, at work, on vacation, etc. The collection unit can adjust the timing of data collection based on the living situation. This allows more accurate data to be obtained by collecting data according to the health condition and living situation. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the subject's health condition data to AI and have AI perform filtering of data collection.

[0037] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the subject when collecting data. The collection unit, for example, considers the geographical location information of the subject when collecting data. The geographical location information includes, for example, GPS data, location information services, and the like. The collection unit can preferentially collect highly relevant data based on the geographical location information. For example, when the subject is in a specific area, the collection unit preferentially collects data related to the area. In addition, when the subject is moving, the collection unit can preferentially collect data related to the moving route. Furthermore, when the subject is in a specific facility, the collection unit can preferentially collect data related to the facility. As a result, highly relevant data can be obtained by data collection based on the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the subject to AI and cause AI to execute collection of highly relevant data.

[0038] The collection unit can analyze the social media activity of the subject at the time of data collection and collect related data. The collection unit, for example, analyzes the social media activity of the subject at the time of data collection. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like. The collection unit can collect related data based on the social media activity. For example, the collection unit determines the timing and location of data collection based on the location information shared by the subject on social media. In addition, the collection unit can analyze the content of the subject's social media activity and collect data related to the behavioral pattern. Furthermore, the collection unit can collect data related to the places and events that the subject follows on social media. In this way, data related to the behavioral pattern can be obtained by data collection based on the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of the subject to AI and cause the AI ​​to collect related data.

[0039] The prediction unit can improve the accuracy of prediction based on the behavior pattern of the subject at the time of prediction. The prediction unit, for example, considers the behavior pattern of the subject at the time of prediction. The behavior pattern includes, for example, daily routines, the frequency of specific behaviors, and the like. The prediction unit can improve the accuracy of prediction based on the behavior pattern. For example, the prediction unit analyzes the past behavior pattern of the subject and improves the prediction accuracy of the destination. In addition, the prediction unit can make a prediction taking into account the behavior pattern of a specific time period. Furthermore, the prediction unit can improve the prediction accuracy of the stopover destination based on the behavior pattern. This improves the prediction accuracy in prediction based on the behavior pattern. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the behavior pattern data of the subject to AI and cause the AI ​​to execute the improvement of the prediction accuracy.

[0040] The prediction unit can make a prediction based on the health condition data of the subject at the time of prediction. The prediction unit, for example, takes into account the health condition data of the subject at the time of prediction. The health condition data includes, for example, heart rate, blood pressure, and body temperature. The prediction unit can make a prediction based on the health condition data. For example, the prediction unit makes a prediction taking into consideration that the range of movement will be wider when the subject is in good health. In addition, when the subject is in poor health, the prediction unit can predict a nearby destination with priority. Furthermore, the prediction unit can predict a destination to a specific facility (e.g., hospital, pharmacy) based on the health condition data. This makes it possible to predict a more appropriate destination or stopover point based on the health condition data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the health condition data of the subject to AI and cause the AI ​​to execute the prediction.

[0041] The prediction unit can perform prediction based on the geographic distribution of the subject at the time of prediction. The prediction unit, for example, considers the geographic distribution of the subject at the time of prediction. The geographic distribution includes, for example, the place of residence, the range of movement, and the frequency of visits. The prediction unit can perform prediction based on the geographic distribution. For example, when the subject is in a specific area, the prediction unit predicts a destination related to the area with priority. In addition, when the subject is moving, the prediction unit can predict a destination related to the movement route. Furthermore, when the subject is in a specific facility, the prediction unit can predict a destination related to the facility. This allows more accurate destinations and stopovers to be predicted based on the geographic distribution. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the geographic distribution data of the subject to AI and cause the AI ​​to execute the prediction.

[0042] The prediction unit can improve the accuracy of the prediction based on the literature related to the subject at the time of prediction. For example, the prediction unit refers to literature related to the subject at the time of prediction. The related literature includes, for example, academic papers, technical reports, patent documents, etc. The prediction unit can improve the accuracy of the prediction based on the related literature. For example, the prediction unit refers to literature related to the subject's past behavior and improves the accuracy of the prediction. In addition, the prediction unit can refer to literature related to the subject's health condition and improve the prediction accuracy of the destination. Furthermore, the prediction unit can refer to literature related to the subject's behavior pattern and improve the prediction accuracy of the stopover destination. This improves the prediction accuracy in the prediction based on the related literature. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the literature data related to the subject to AI and cause the AI ​​to execute the improvement of the prediction accuracy.

[0043] The providing unit can select an appropriate information providing method based on the target person's past behavior history when providing information. For example, the providing unit refers to the target person's past behavior history when providing information. The past behavior history includes, for example, visit frequency, behavior pattern, time period, and the like. The providing unit can select an information providing method based on the past behavior history. For example, the providing unit selects an optimal method based on the information providing method used by the target person in the past. In addition, the providing unit can select an optimal information providing method for a specific time period from the target person's past behavior history. Furthermore, the providing unit can analyze the target person's past behavior pattern and select an optimal information providing method. This enables efficient information provision by selecting an information providing method based on the past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the target person's past behavior history data into AI and cause AI to select an information providing method.

[0044] The providing unit can customize the information providing means based on the current health condition of the subject when providing information. For example, the providing unit considers the current health condition of the subject when providing information. The health condition includes, for example, heart rate, blood pressure, and body temperature. The providing unit can provide detailed information when the health condition is good. In addition, the providing unit can provide concise and to the point information when the subject is in poor health. Furthermore, the providing unit can adjust the frequency of information provision based on the health condition. This makes it possible to provide more appropriate information by customizing the information providing means based on the health condition. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the health condition data of the subject to AI and cause the AI ​​to customize the information providing means.

[0045] The providing unit can select an appropriate information providing method based on the geographical location information of the subject when providing information. For example, the providing unit considers the geographical location information of the subject when providing information. The geographical location information includes, for example, GPS data, location information services, and the like. The providing unit can select an information providing method based on the geographical location information. For example, when the subject is in a specific area, the providing unit can provide information related to the area with priority. In addition, when the subject is moving, the providing unit can provide information related to the moving route with priority. Furthermore, when the subject is in a specific facility, the providing unit can provide information related to the facility with priority. Thereby, by selecting an information providing method based on the geographical location information, highly relevant information can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the subject to AI and cause the AI ​​to select an information providing method.

[0046] The providing unit can analyze the social media activity of the subject and suggest a means of providing information when providing information. The providing unit, for example, analyzes the social media activity of the subject when providing information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like. The providing unit can suggest a means of providing information based on the social media activity. For example, the providing unit determines the timing and location of information provision based on the location information shared by the subject on social media. In addition, the providing unit can analyze the content of the subject's social media activity and provide information related to the behavioral pattern. Furthermore, the providing unit can provide information related to places and events that the subject follows on social media. This makes it possible to provide more appropriate information by suggesting a means of providing information based on social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the subject into AI and suggest a means of providing information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The collection unit can collect environmental data around the subject in addition to the subject's personal characteristic data. For example, the collection unit can collect environmental data such as the temperature, humidity, and noise level around the subject using a sensor. This allows the subject to understand what kind of environment he or she is in and can be used to predict his or her behavioral pattern. Furthermore, the collection unit can collect the movements and behavior of people around the subject using a camera or microphone. This allows the subject to understand how the subject interacts with other people and can be used to predict his or her behavioral pattern. For example, if the subject frequently talks with other people at a specific location, the location is more likely to be predicted as a destination or a stopover. The collection unit can also collect traffic conditions around the subject. This allows the subject to understand what kind of transportation the subject uses and can be used to predict his or her behavioral pattern. For example, if the subject uses a bus during a specific time period, the bus stop is more likely to be predicted as a destination or a stopover during that time period.

[0049] The prediction unit can cluster the behavioral patterns of the subject based on the collected data. For example, the prediction unit can classify the behavioral patterns of the subject into multiple clusters and apply different prediction algorithms to each cluster. This enables more accurate prediction of destinations and stopovers. For example, if the subject has different behavioral patterns on weekdays and weekends, clusters for weekdays and weekends can be created and a prediction algorithm suitable for each can be applied. The prediction unit can also cluster the behavioral patterns of the subject by time period. For example, if the subject has different behavioral patterns in the morning and at night, clusters for the morning and at night can be created and a prediction algorithm suitable for each can be applied. Furthermore, the prediction unit can cluster the behavioral patterns of the subject by season. For example, if the subject has different behavioral patterns in summer and winter, clusters for summer and winter can be created and a prediction algorithm suitable for each can be applied.

[0050] The providing unit can customize the format of the information based on the subject's preferences when providing information about the predicted destination or stopover. For example, if the subject prefers visual information, the providing unit can use a map or image to indicate the destination or stopover. Also, if the subject prefers auditory information, the providing unit can indicate the destination or stopover using voice guidance. Furthermore, if the subject prefers text information, the providing unit can indicate the destination or stopover using a detailed text description. This makes it possible to provide information according to the subject's preferences, and more effective rescue support can be realized. For example, voice guidance can be provided to a subject with a visual impairment, and text descriptions can be provided to a subject with a hearing impairment. Also, the providing unit can customize the format of the information according to the subject's age and cultural background. For example, simple and easy-to-understand information can be provided to the elderly, and detailed and diverse information can be provided to the younger generation.

[0051] The providing unit can select an appropriate information providing method based on the geographical location information of the subject when providing information. For example, when the subject is in a specific area, the providing unit can provide information related to the area with priority. Furthermore, when the subject is moving, the providing unit can provide information related to the moving route with priority. Furthermore, when the subject is in a specific facility, the providing unit can provide information related to the facility with priority. This allows highly relevant information to be provided by selecting an information providing method based on the geographical location information. For example, when the subject is in a tourist spot, information on tourist spots and restaurants related to the tourist spot can be provided. Furthermore, when the subject is moving, traffic information and route guidance can be provided. Furthermore, when the subject is in a hospital, information on medical departments and waiting times can be provided.

[0052] When providing information, the providing unit can analyze the target's social media activity and suggest a means of providing the information. For example, the providing unit can determine the timing and location of providing the information based on the location information shared by the target on social media. The providing unit can also analyze the target's social media activity content and provide information related to the behavioral pattern. Furthermore, the providing unit can provide information related to places and events that the target follows on social media. This enables more appropriate information provision by suggesting an information provision means based on social media activity. For example, if the target is participating in a specific event, information related to the event can be provided. Furthermore, if the target is following a specific restaurant, information on the restaurant's menu and business hours can be provided. Furthermore, if the target is visiting a specific tourist spot, information on tourist spots and activities related to the tourist spot can be provided.

[0053] The providing unit can customize the means of providing information based on the current health condition of the subject when providing information. For example, when the subject's health condition is good, the providing unit can provide detailed information. Also, when the subject's health condition is poor, the providing unit can provide concise, to-the-point information. Furthermore, the providing unit can adjust the frequency of information provision based on the health condition. This enables more appropriate information provision by customizing the information provision means based on the health condition. For example, when the subject's health condition is good, information can be provided using detailed maps and images to deepen understanding. Also, when the subject's health condition is poor, information can be provided using concise text and icons to increase visibility. Furthermore, the frequency of information provision can be adjusted according to the subject's health condition to avoid excessive information provision.

[0054] The process flow of the first embodiment will be briefly described below.

[0055] Step 1: The collection unit collects personal characteristic data of the subject. The personal characteristic data of the subject includes age, sex, hobbies, behavioral patterns, past behavioral history, and health condition data (heart rate, blood pressure, body temperature, etc.). The collection unit collects this data using sensors and devices (wearable devices, GPS devices, etc.). Step 2: The prediction unit predicts the target's destination and stopovers based on the data collected by the collection unit. The prediction unit uses AI or generative AI (text generation AI, multimodal generation AI, etc.) to analyze the collected data and predict the target's destination and stopovers. For example, based on past behavioral history and health condition data, it identifies places the target frequently visits or is likely to visit. Step 3: The providing unit provides information indicating the location predicted by the prediction unit. Taking into consideration the notification method and the format of the information, the providing unit notifies the target person of the destination and stopover points via a smartphone app, email, SMS, etc.

[0056] (Example 2) A rescue support system according to an embodiment of the present invention is a system that accumulates personal characteristic data of lost persons such as elderly people and infants, predicts the destination and stopovers based on the data, and assists in rescue. This rescue support system collects personal characteristic data of the subject, and an AI or generation AI predicts the destination and stopovers of the subject based on the collected data, and provides information indicating the predicted location. For example, the rescue support system collects personal characteristic data of the subject. For example, data on the subject's past behavior history and health condition is collected. This makes it possible to grasp the subject's behavior pattern and health condition. Next, based on the collected data, the AI ​​or generation AI predicts the subject's destination and stopovers. For example, from the past behavior history, the subject's frequent visits and stopovers are identified. In addition, based on the health condition data, it is predicted what kind of place the subject is likely to go to. Finally, information indicating the predicted location is provided. For example, if the subject gets lost, rescue is assisted by providing information indicating the predicted destination and stopovers. This makes it possible to ensure the safety of the subject. This system makes it possible to predict the destination and stopovers of lost people such as elderly people and small children, and to quickly rescue them. For example, if an elderly person gets lost, the places they often visit and stopovers can be predicted based on data on their past behavioral history and health condition, and this information can be provided, allowing them to be quickly rescued. Similarly, if a small child gets lost, the destination and stopovers can be predicted based on data on their past behavioral history and health condition, and this information can be provided, allowing them to be quickly rescued. In this way, the rescue support system can predict the destination and stopovers of lost people such as elderly people and small children, and to quickly rescue them.

[0057] The rescue support system according to the embodiment includes a collection unit, a prediction unit, and a provision unit. The collection unit collects personal characteristic data of the subject. The personal characteristic data of the subject includes, for example, age, sex, hobbies, behavior patterns, etc., but is not limited to such examples. The collection unit collects, for example, data on the subject's past behavior history and health condition. The behavior history log includes, for example, movement history, visited places, time periods, etc. The health condition data includes, for example, heart rate, blood pressure, body temperature, etc. The collection unit can collect these data using a sensor or device. For example, the collection unit collects the subject's health condition data in real time using a wearable device. The collection unit can also collect the subject's movement history using a GPS device. The prediction unit predicts the destination and stopovers of the subject based on the data collected by the collection unit. The prediction unit analyzes the collected data using, for example, AI or generation AI, and predicts the destination and stopovers. The analysis by AI includes, for example, machine learning algorithms and data preprocessing methods. The generation AI can predict the destination and the stopovers using, for example, a text generation AI (for example, LLM) or a multimodal generation AI. For example, the prediction unit identifies the places that the subject frequently visits and the stopovers from the past behavior history. In addition, the prediction unit predicts what places the subject is likely to go to based on the health condition data. The provision unit provides information indicating the place predicted by the prediction unit. The provision unit provides information indicating the predicted place, for example, taking into consideration the notification method and the information format. For example, the provision unit notifies the subject of the destination and the stopovers through a smartphone app. In addition, the provision unit can also provide information using email or SMS. As a result, the rescue support system according to the embodiment can predict the destination and the stopovers based on the subject's personal characteristic data and support a quick rescue.

[0058] The collecting unit collects the individual characteristic data of the subject. The individual characteristic data of the subject includes, for example, age, sex, hobbies, behavioral patterns, etc., but is not limited to such examples. The collecting unit collects, for example, the subject's past behavioral history and health condition data. The behavioral history log includes, for example, movement history, visited places, time periods, etc. The health condition data includes, for example, heart rate, blood pressure, body temperature, etc. The collecting unit can collect these data using a sensor or device. For example, the collecting unit collects the subject's health condition data in real time using a wearable device. The collecting unit can also collect the subject's movement history using a GPS device. Specifically, the wearable device is worn on the subject's wrist or chest, and continuously monitors vital signs such as heart rate, blood pressure, and body temperature. These data are transmitted to the collecting unit via Bluetooth or Wi-Fi and stored in a central database. Furthermore, the GPS device tracks the subject's location information in real time and records the movement history and visited places. This allows the collecting unit to grasp the subject's health condition and behavioral patterns in detail. The collection unit can also collect data from the subject's smartphone or tablet. For example, information such as the subject's schedule, notes, and contact information can be obtained through a smartphone app, and the information can be used to analyze behavioral patterns. This allows the collection unit to collect a wide range of data from various devices and grasp the subject's personal characteristics in detail. Furthermore, the collection unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the prediction unit and provision unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0059] The prediction unit predicts the destination and stopovers of the subject based on the data collected by the collection unit. The prediction unit, for example, uses AI or generation AI to analyze the collected data and predict the destination and stopovers. Analysis by AI includes, for example, machine learning algorithms and data preprocessing methods. The generation AI can predict the destination and stopovers using, for example, text generation AI (for example, LLM) or multimodal generation AI. For example, the prediction unit identifies places and stopovers frequently visited by the subject from past behavioral history. In addition, the prediction unit predicts what places the subject is likely to go to based on health condition data. Specifically, a machine learning algorithm is used to analyze the subject's past behavioral patterns and predict places visited on a specific day of the week or time period. For example, if the subject goes to a specific gym every Friday evening, it can be predicted that the subject is likely to go to the same gym on the following Friday evening based on the behavioral pattern. Furthermore, the generation AI can be used to make predictions taking into account the subject's health condition and current situation. For example, if the subject's heart rate or blood pressure is higher than normal, it can predict that the subject is likely to go to a place where he or she can relax or to a medical institution. This allows the prediction unit to comprehensively analyze the subject's behavioral patterns and health condition, and predict the destination and stopovers with high accuracy. Furthermore, the prediction unit can continuously correct the prediction results based on data updated in real time to respond to the latest situation. For example, if the subject's movement history or health condition changes suddenly, the prediction unit immediately imports new data and updates the prediction results. In addition, the prediction unit can make more accurate predictions by taking into account the characteristics of each region and past behavioral history. This allows the prediction unit to always provide highly accurate predictions based on the latest information, and support rapid and appropriate responses.

[0060] The providing unit provides information indicating the location predicted by the prediction unit. The providing unit provides information indicating the predicted location, for example, taking into consideration the notification method and the format of the information. For example, the providing unit notifies the target person of the destination and the stopovers through a smartphone app. The providing unit can also provide information using email or SMS. Specifically, the smartphone app displays the target person's current location and the predicted destination on a map and provides a navigation function. This allows the target person to quickly and accurately reach the predicted destination. The providing unit can also notify the target person of the destination and the stopovers using email or SMS. For example, the target person can be notified of the expected time of arrival at a specific location and urge the target person to make necessary preparations. Furthermore, the providing unit can also provide information to the target person by voice using a voice assistant or smart speaker. This makes it possible to provide appropriate information to the target person even in situations where it is difficult to provide visual information. The providing unit can quickly and reliably convey information to the target person by combining these information providing means. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the information provision. For example, the information providing unit can evaluate whether the provided information was useful and, if necessary, review the method and content of providing the information. This allows the information providing unit to provide information to the user quickly and reliably, maximizing the effectiveness of the rescue support system.

[0061] The collection unit can collect the log of the behavior history of the subject and the data of the health condition as personal characteristic data. For example, the collection unit collects the log of the behavior history of the subject. The log of the behavior history includes, for example, a movement history, a visited place, a time period, and the like. The collection unit can collect the movement history of the subject using a GPS device. The collection unit also collects health condition data. The health condition data includes, for example, a heart rate, a blood pressure, a body temperature, and the like. The collection unit can collect the health condition data of the subject in real time using a wearable device. This allows for more accurate prediction by collecting the behavior history and health condition data of the subject. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the log of the behavior history of the subject to AI and cause the AI ​​to perform an analysis of the behavior pattern.

[0062] The prediction unit can analyze the collected data using AI and predict the destination and the stopovers. The prediction unit can, for example, analyze the collected data using AI. The analysis by AI includes, for example, a machine learning algorithm and a data preprocessing method. The prediction unit can analyze the collected data using AI and predict the destination and the stopovers. For example, the prediction unit identifies places that the subject frequently visits and the stopovers from the past behavior history. In addition, the prediction unit predicts what places the subject is likely to go to based on the health condition data. This improves the prediction accuracy of the destination and the stopovers by the data analysis by AI. Part or all of the above-mentioned processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the collected data to AI and cause AI to execute the prediction of the destination and the stopovers.

[0063] The collection unit can estimate the emotion of the subject and adjust the timing of data collection based on the estimated emotion. The collection unit, for example, estimates the emotion of the subject. For example, techniques such as facial expression recognition, voice analysis, and text analysis are used to estimate the emotion. The collection unit can estimate the emotion of the subject using an emotion estimation algorithm. For example, when the subject is stressed, the collection unit reduces the frequency of data collection and collects data when the subject is relaxed. In addition, when the subject is relaxed, the collection unit can collect detailed data and grasp the behavior pattern more accurately. Furthermore, when the subject is excited, the collection unit can temporarily stop data collection and resume it when the emotion stabilizes. This enables more appropriate data collection by adjusting the timing of data collection according to the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the subject's emotional data into an AI and have the AI ​​adjust the timing of data collection.

[0064] The collection unit can analyze the subject's past behavior history and select an appropriate data collection method. The collection unit, for example, analyzes the subject's past behavior history. The past behavior history includes, for example, visit frequency, behavior pattern, time period, etc. The collection unit can determine the timing and location of data collection based on the past behavior history. For example, the collection unit determines the timing and location of data collection based on places that the subject has frequently visited in the past. In addition, the collection unit can analyze the subject's past behavior pattern and collect data in a specific time period. Furthermore, the collection unit can select a data collection method based on the transportation means used by the subject in the past. This enables efficient data collection by selecting a data collection method based on the past behavior history. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the subject's past behavior history into AI and cause AI to select the optimal data collection method.

[0065] The collection unit can perform filtering based on the subject's current health condition and living situation when collecting data. The collection unit, for example, considers the subject's current health condition when collecting data. The health condition includes, for example, heart rate, blood pressure, body temperature, etc. The collection unit can collect detailed data when the health condition is good. In addition, the collection unit can temporarily stop data collection when the subject is in poor health and resume it when the health condition recovers. Furthermore, the collection unit considers the living situation of the subject. The living situation includes, for example, at work, on vacation, etc. The collection unit can adjust the timing of data collection based on the living situation. This allows more accurate data to be obtained by collecting data according to the health condition and living situation. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the subject's health condition data to AI and have AI perform filtering of data collection.

[0066] The collection unit can estimate the emotion of the subject and determine the priority of data to be collected based on the estimated emotion. The collection unit, for example, estimates the emotion of the subject. For example, techniques such as facial expression recognition, voice analysis, and text analysis are used to estimate the emotion. The collection unit can estimate the emotion of the subject using an emotion estimation algorithm. For example, when the subject is stressed, the collection unit can preferentially collect data related to stress reduction. Also, when the subject is relaxed, the collection unit can preferentially collect data related to behavioral patterns. Furthermore, when the subject is excited, the collection unit can preferentially collect data related to emotional stabilization. This allows important data to be preferentially collected by determining the priority of data based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the subject's emotional data into an AI and have the AI ​​prioritize the data.

[0067] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the subject when collecting data. The collection unit, for example, considers the geographical location information of the subject when collecting data. The geographical location information includes, for example, GPS data, location information services, and the like. The collection unit can preferentially collect highly relevant data based on the geographical location information. For example, when the subject is in a specific area, the collection unit preferentially collects data related to the area. In addition, when the subject is moving, the collection unit can preferentially collect data related to the moving route. Furthermore, when the subject is in a specific facility, the collection unit can preferentially collect data related to the facility. As a result, highly relevant data can be obtained by data collection based on the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the subject to AI and cause AI to execute collection of highly relevant data.

[0068] The collection unit can analyze the social media activity of the subject at the time of data collection and collect related data. The collection unit, for example, analyzes the social media activity of the subject at the time of data collection. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like. The collection unit can collect related data based on the social media activity. For example, the collection unit determines the timing and location of data collection based on the location information shared by the subject on social media. In addition, the collection unit can analyze the content of the subject's social media activity and collect data related to the behavioral pattern. Furthermore, the collection unit can collect data related to the places and events that the subject follows on social media. In this way, data related to the behavioral pattern can be obtained by data collection based on the social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of the subject to AI and cause the AI ​​to collect related data.

[0069] The prediction unit can estimate the emotion of the subject and adjust the prediction algorithm based on the estimated emotion. The prediction unit, for example, estimates the emotion of the subject. For example, techniques such as facial expression recognition, voice analysis, and text analysis are used to estimate the emotion. The prediction unit can estimate the emotion of the subject using an emotion estimation algorithm. For example, when the subject is stressed, the prediction unit predicts a destination related to stress reduction with priority. In addition, when the subject is relaxed, the prediction unit can adjust the prediction algorithm based on the behavior pattern. Furthermore, when the subject is excited, the prediction unit can predict a destination related to emotional stability with priority. This enables more accurate prediction by adjusting the prediction algorithm based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the subject's emotional data into the AI ​​and have the AI ​​adjust the prediction algorithm.

[0070] The prediction unit can improve the accuracy of prediction based on the behavior pattern of the subject at the time of prediction. The prediction unit, for example, considers the behavior pattern of the subject at the time of prediction. The behavior pattern includes, for example, daily routines, the frequency of specific behaviors, and the like. The prediction unit can improve the accuracy of prediction based on the behavior pattern. For example, the prediction unit analyzes the past behavior pattern of the subject and improves the prediction accuracy of the destination. In addition, the prediction unit can make a prediction taking into account the behavior pattern of a specific time period. Furthermore, the prediction unit can improve the prediction accuracy of the stopover destination based on the behavior pattern. This improves the prediction accuracy in prediction based on the behavior pattern. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the behavior pattern data of the subject to AI and cause the AI ​​to execute the improvement of the prediction accuracy.

[0071] The prediction unit can make a prediction based on the health condition data of the subject at the time of prediction. The prediction unit, for example, takes into account the health condition data of the subject at the time of prediction. The health condition data includes, for example, heart rate, blood pressure, and body temperature. The prediction unit can make a prediction based on the health condition data. For example, the prediction unit makes a prediction taking into consideration that the range of movement will be wider when the subject is in good health. In addition, when the subject is in poor health, the prediction unit can predict a nearby destination with priority. Furthermore, the prediction unit can predict a destination to a specific facility (e.g., hospital, pharmacy) based on the health condition data. This makes it possible to predict a more appropriate destination or stopover point based on the health condition data. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the health condition data of the subject to AI and cause the AI ​​to execute the prediction.

[0072] The prediction unit can estimate the emotion of the subject and adjust the display method of the prediction result based on the estimated emotion. The prediction unit, for example, estimates the emotion of the subject. For example, techniques such as facial expression recognition, voice analysis, and text analysis are used to estimate the emotion. The prediction unit can estimate the emotion of the subject using an emotion estimation algorithm. For example, the prediction unit provides a simple and highly visible display method when the subject is nervous. Furthermore, the prediction unit can provide a display method including detailed information when the subject is relaxed. Furthermore, the prediction unit can provide a display method that focuses on the main points when the subject is in a hurry. This makes it possible to provide information with high visibility by adjusting the display method based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the subject's emotional data into the AI ​​and have the AI ​​adjust the way in which the prediction results are displayed.

[0073] The prediction unit can perform prediction based on the geographic distribution of the subject at the time of prediction. The prediction unit, for example, considers the geographic distribution of the subject at the time of prediction. The geographic distribution includes, for example, the place of residence, the range of movement, and the frequency of visits. The prediction unit can perform prediction based on the geographic distribution. For example, when the subject is in a specific area, the prediction unit predicts a destination related to the area with priority. In addition, when the subject is moving, the prediction unit can predict a destination related to the movement route. Furthermore, when the subject is in a specific facility, the prediction unit can predict a destination related to the facility. This allows more accurate destinations and stopovers to be predicted based on the geographic distribution. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the geographic distribution data of the subject to AI and cause the AI ​​to execute the prediction.

[0074] The prediction unit can improve the accuracy of the prediction based on the literature related to the subject at the time of prediction. For example, the prediction unit refers to literature related to the subject at the time of prediction. The related literature includes, for example, academic papers, technical reports, patent documents, etc. The prediction unit can improve the accuracy of the prediction based on the related literature. For example, the prediction unit refers to literature related to the subject's past behavior and improves the accuracy of the prediction. In addition, the prediction unit can refer to literature related to the subject's health condition and improve the prediction accuracy of the destination. Furthermore, the prediction unit can refer to literature related to the subject's behavior pattern and improve the prediction accuracy of the stopover destination. This improves the prediction accuracy in the prediction based on the related literature. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the literature data related to the subject to AI and cause the AI ​​to execute the improvement of the prediction accuracy.

[0075] The providing unit can estimate the emotion of the subject and adjust the method of providing information based on the estimated emotion. The providing unit, for example, estimates the emotion of the subject. For example, techniques such as facial expression recognition, voice analysis, and text analysis are used to estimate the emotion. The providing unit can estimate the emotion of the subject using an emotion estimation algorithm. For example, when the subject is nervous, the providing unit provides a display method with simple and high visibility. Furthermore, when the subject is relaxed, the providing unit can provide a display method including detailed information. Furthermore, when the subject is in a hurry, the providing unit can provide a display method that focuses on the main points. This makes it possible to provide information with high visibility by adjusting the information provision method based on the emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the subject's emotional data into the AI ​​and have the AI ​​adjust the way information is provided.

[0076] The providing unit can select an appropriate information providing method based on the target person's past behavior history when providing information. For example, the providing unit refers to the target person's past behavior history when providing information. The past behavior history includes, for example, visit frequency, behavior pattern, time period, and the like. The providing unit can select an information providing method based on the past behavior history. For example, the providing unit selects an optimal method based on the information providing method used by the target person in the past. In addition, the providing unit can select an optimal information providing method for a specific time period from the target person's past behavior history. Furthermore, the providing unit can analyze the target person's past behavior pattern and select an optimal information providing method. This enables efficient information provision by selecting an information providing method based on the past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the target person's past behavior history data into AI and cause AI to select an information providing method.

[0077] The providing unit can customize the information providing means based on the current health condition of the subject when providing information. For example, the providing unit considers the current health condition of the subject when providing information. The health condition includes, for example, heart rate, blood pressure, and body temperature. The providing unit can provide detailed information when the health condition is good. In addition, the providing unit can provide concise and to the point information when the subject is in poor health. Furthermore, the providing unit can adjust the frequency of information provision based on the health condition. This makes it possible to provide more appropriate information by customizing the information providing means based on the health condition. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the health condition data of the subject to AI and cause the AI ​​to customize the information providing means.

[0078] The providing unit can estimate the emotion of the subject and determine the priority of information provision based on the estimated emotion. The providing unit, for example, estimates the emotion of the subject. For example, a technology such as facial expression recognition, voice analysis, and text analysis is used for estimating the emotion. The providing unit can estimate the emotion of the subject using an emotion estimation algorithm. For example, when the subject is stressed, the providing unit can provide information related to stress reduction preferentially. Furthermore, when the subject is relaxed, the providing unit can provide information related to behavioral patterns preferentially. Furthermore, when the subject is excited, the providing unit can provide information related to emotional stability preferentially. This allows important information to be provided preferentially in the priority determination of information provision based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the provision unit can input the subject's emotional data into the AI ​​and have the AI ​​decide the priority of information provision.

[0079] The providing unit can select an appropriate information providing method based on the geographical location information of the subject when providing information. For example, the providing unit considers the geographical location information of the subject when providing information. The geographical location information includes, for example, GPS data, location information services, and the like. The providing unit can select an information providing method based on the geographical location information. For example, when the subject is in a specific area, the providing unit can provide information related to the area with priority. In addition, when the subject is moving, the providing unit can provide information related to the moving route with priority. Furthermore, when the subject is in a specific facility, the providing unit can provide information related to the facility with priority. Thereby, by selecting an information providing method based on the geographical location information, highly relevant information can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the subject to AI and cause the AI ​​to select an information providing method.

[0080] The providing unit can analyze the social media activity of the subject and suggest a means of providing information when providing information. The providing unit, for example, analyzes the social media activity of the subject when providing information. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like. The providing unit can suggest a means of providing information based on the social media activity. For example, the providing unit determines the timing and location of information provision based on the location information shared by the subject on social media. In addition, the providing unit can analyze the content of the subject's social media activity and provide information related to the behavioral pattern. Furthermore, the providing unit can provide information related to places and events that the subject follows on social media. This makes it possible to provide more appropriate information by suggesting a means of providing information based on social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the subject into AI and suggest a means of providing information.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The collection unit can collect environmental data around the subject in addition to the subject's personal characteristic data. For example, the collection unit can collect environmental data such as the temperature, humidity, and noise level around the subject using a sensor. This allows the subject to understand what kind of environment he or she is in and can be used to predict his or her behavioral pattern. Furthermore, the collection unit can collect the movements and behavior of people around the subject using a camera or microphone. This allows the subject to understand how the subject interacts with other people and can be used to predict his or her behavioral pattern. For example, if the subject frequently talks with other people at a specific location, the location is more likely to be predicted as a destination or a stopover. The collection unit can also collect traffic conditions around the subject. This allows the subject to understand what kind of transportation the subject uses and can be used to predict his or her behavioral pattern. For example, if the subject uses a bus during a specific time period, the bus stop is more likely to be predicted as a destination or a stopover during that time period.

[0083] The prediction unit can cluster the behavioral patterns of the subject based on the collected data. For example, the prediction unit can classify the behavioral patterns of the subject into multiple clusters and apply different prediction algorithms to each cluster. This enables more accurate prediction of destinations and stopovers. For example, if the subject has different behavioral patterns on weekdays and weekends, clusters for weekdays and weekends can be created and a prediction algorithm suitable for each can be applied. The prediction unit can also cluster the behavioral patterns of the subject by time period. For example, if the subject has different behavioral patterns in the morning and at night, clusters for the morning and at night can be created and a prediction algorithm suitable for each can be applied. Furthermore, the prediction unit can cluster the behavioral patterns of the subject by season. For example, if the subject has different behavioral patterns in summer and winter, clusters for summer and winter can be created and a prediction algorithm suitable for each can be applied.

[0084] The providing unit can customize the format of the information based on the subject's preferences when providing information about the predicted destination or stopover. For example, if the subject prefers visual information, the providing unit can use a map or image to indicate the destination or stopover. Also, if the subject prefers auditory information, the providing unit can indicate the destination or stopover using voice guidance. Furthermore, if the subject prefers text information, the providing unit can indicate the destination or stopover using a detailed text description. This makes it possible to provide information according to the subject's preferences, and more effective rescue support can be realized. For example, voice guidance can be provided to a subject with a visual impairment, and text descriptions can be provided to a subject with a hearing impairment. Also, the providing unit can customize the format of the information according to the subject's age and cultural background. For example, simple and easy-to-understand information can be provided to the elderly, and detailed and diverse information can be provided to the younger generation.

[0085] The collection unit can estimate the subject's emotions and adjust the frequency of data collection based on the estimated emotions. For example, the collection unit can reduce the frequency of data collection when the subject is stressed and collect data when the subject is relaxed. In addition, the collection unit can temporarily stop data collection when the subject is excited and resume data collection when the subject's emotions are stable. In addition, the collection unit can collect detailed data and grasp behavioral patterns more accurately when the subject is relaxed. This allows more appropriate data collection by adjusting the frequency of data collection according to emotions. For example, when the subject is stressed, data on heart rate and blood pressure can be collected and measures can be taken to reduce stress. In addition, when the subject is relaxed, data on movement history and visited places can be collected and used to predict behavioral patterns. In addition, when the subject is excited, data collection can be temporarily stopped and resumed when the subject's emotions are stable, thereby maintaining the accuracy of the data.

[0086] The prediction unit can estimate the emotion of the subject and adjust the prediction algorithm based on the estimated emotion. For example, when the subject is stressed, the prediction unit can predict destinations related to stress reduction with priority. Furthermore, when the subject is relaxed, the prediction unit can adjust the prediction algorithm based on the behavior pattern. Furthermore, when the subject is excited, the prediction unit can predict destinations related to emotional stability with priority. This allows for more accurate prediction by adjusting the prediction algorithm based on emotion. For example, when the subject is stressed, quiet places and places where one can relax can be predicted with priority. Furthermore, when the subject is relaxed, prediction can be made based on the usual behavior pattern. Furthermore, when the subject is excited, prediction can be temporarily stopped until the emotion stabilizes, and resumed when the emotion stabilizes.

[0087] The providing unit can estimate the emotion of the subject and adjust the method of providing information based on the estimated emotion. For example, when the subject is nervous, the providing unit can provide a display method that is simple and highly visible. Furthermore, when the subject is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, when the subject is in a hurry, the providing unit can provide a display method that focuses on the main points. This makes it possible to provide information with high visibility by adjusting the information providing method based on emotion. For example, when the subject is nervous, information can be provided using simple text or icons to increase visibility. Furthermore, when the subject is relaxed, information can be provided using detailed maps or images to deepen understanding. Furthermore, when the subject is in a hurry, important information can be displayed preferentially to encourage a quick response.

[0088] The collection unit can estimate the emotion of the subject and determine the priority of data to be collected based on the estimated emotion. For example, when the subject is stressed, the collection unit can preferentially collect data related to stress reduction. Furthermore, when the subject is relaxed, the collection unit can preferentially collect data related to behavioral patterns. Furthermore, when the subject is excited, the collection unit can preferentially collect data related to emotional stability. This allows important data to be preferentially collected by determining the priority of data based on emotion. For example, when the subject is stressed, data on heart rate and blood pressure can be preferentially collected, and measures can be taken to reduce stress. Furthermore, when the subject is relaxed, data on movement history and visited places can be preferentially collected, and this can be used to predict behavioral patterns. Furthermore, when the subject is excited, data related to emotional stability can be preferentially collected, and data collection can be temporarily stopped until the emotion stabilizes.

[0089] The providing unit can select an appropriate information providing method based on the geographical location information of the subject when providing information. For example, when the subject is in a specific area, the providing unit can provide information related to the area with priority. Furthermore, when the subject is moving, the providing unit can provide information related to the moving route with priority. Furthermore, when the subject is in a specific facility, the providing unit can provide information related to the facility with priority. This allows highly relevant information to be provided by selecting an information providing method based on the geographical location information. For example, when the subject is in a tourist spot, information on tourist spots and restaurants related to the tourist spot can be provided. Furthermore, when the subject is moving, traffic information and route guidance can be provided. Furthermore, when the subject is in a hospital, information on medical departments and waiting times can be provided.

[0090] When providing information, the providing unit can analyze the target's social media activity and suggest a means of providing the information. For example, the providing unit can determine the timing and location of providing the information based on the location information shared by the target on social media. The providing unit can also analyze the target's social media activity content and provide information related to the behavioral pattern. Furthermore, the providing unit can provide information related to places and events that the target follows on social media. This enables more appropriate information provision by suggesting an information provision means based on social media activity. For example, if the target is participating in a specific event, information related to the event can be provided. Furthermore, if the target is following a specific restaurant, information on the restaurant's menu and business hours can be provided. Furthermore, if the target is visiting a specific tourist spot, information on tourist spots and activities related to the tourist spot can be provided.

[0091] The providing unit can customize the means of providing information based on the current health condition of the subject when providing information. For example, when the subject's health condition is good, the providing unit can provide detailed information. Also, when the subject's health condition is poor, the providing unit can provide concise, to-the-point information. Furthermore, the providing unit can adjust the frequency of information provision based on the health condition. This enables more appropriate information provision by customizing the information provision means based on the health condition. For example, when the subject's health condition is good, information can be provided using detailed maps and images to deepen understanding. Also, when the subject's health condition is poor, information can be provided using concise text and icons to increase visibility. Furthermore, the frequency of information provision can be adjusted according to the subject's health condition to avoid excessive information provision.

[0092] The process flow of the second embodiment will be briefly described below.

[0093] Step 1: The collection unit collects personal characteristic data of the subject. The personal characteristic data of the subject includes age, sex, hobbies, behavioral patterns, past behavioral history, and health condition data (heart rate, blood pressure, body temperature, etc.). The collection unit collects this data using sensors and devices (wearable devices, GPS devices, etc.). Step 2: The prediction unit predicts the target's destination and stopovers based on the data collected by the collection unit. The prediction unit uses AI or generative AI (text generation AI, multimodal generation AI, etc.) to analyze the collected data and predict the target's destination and stopovers. For example, based on past behavioral history and health condition data, it identifies places the target frequently visits or is likely to visit. Step 3: The providing unit provides information indicating the location predicted by the prediction unit. Taking into consideration the notification method and the format of the information, the providing unit notifies the target person of the destination and stopover points via a smartphone app, email, SMS, etc.

[0094] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0096] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0097] For example, the collection unit is realized by either the data processing device 12 or the smart device 14. For example, the specific processing unit 290 of the data processing device 12 can perform a process of collecting personal characteristic data of the subject. For example, the control unit 46A of the smart device 14 can collect health condition data and movement history of the subject using a wearable device or a GPS device. The prediction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the destination and stopovers by analyzing the collected data. The provision unit is realized by, for example, the control unit 46A of the smart device 14, and can provide information indicating the predicted location through a smartphone app or email. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0105] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0108] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0112] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] For example, the collection unit is realized by either the data processing device 12 or the smart glasses 214. For example, the specific processing unit 290 of the data processing device 12 can perform processing to collect personal characteristic data of the subject. For example, the control unit 46A of the smart glasses 214 can collect health condition data and movement history of the subject using a wearable device or a GPS device. The prediction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the destination and stopovers by analyzing the collected data. The provision unit is realized by, for example, the control unit 46A of the smart glasses 214, and can provide information indicating the predicted location through a smartphone app or email. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the collection unit is realized by either the data processing device 12 or the headset type terminal 314. For example, the specific processing unit 290 of the data processing device 12 can perform processing to collect personal characteristic data of the subject. For example, the control unit 46A of the headset type terminal 314 can collect health condition data and movement history of the subject using a wearable device or a GPS device. The prediction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the destination and stopovers by analyzing the collected data. The provision unit is realized by, for example, the control unit 46A of the headset type terminal 314, and can provide information indicating the predicted location through a smartphone app or email. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] For example, the collection unit is realized by either the data processing device 12 or the robot 414. For example, the specific processing unit 290 of the data processing device 12 can perform processing to collect personal characteristic data of the subject. For example, the control unit 46A of the robot 414 can collect health condition data and movement history of the subject using a wearable device or a GPS device. The prediction unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and predicts the destination and stopovers by analyzing the collected data. The provision unit is realized by, for example, the control unit 46A of the robot 414, and can provide information indicating the predicted location through a smartphone app or email. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0148] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0152] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] In addition, 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 upon request from the data processing device 12.

[0157] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0159] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0163] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0165] (Appendix 1) A collection unit that collects personal characteristic data of a subject; A prediction unit that predicts the destination and stopovers of the subject based on the data collected by the collection unit; A providing unit that provides information indicating the predicted location. A system characterized by: (Appendix 2) The collecting unit includes: Collecting the subject's behavioral history log and health condition data as personal characteristic data 2. The system of claim 1, (Appendix 3) The prediction unit is AI analyzes collected data and predicts destinations and stops 2. The system of claim 1, (Appendix 4) The collecting unit includes: Inferring a subject's emotions and adjusting the timing of data collection based on the inferred emotions A system characterized by: (Appendix 5) The collecting unit includes: Analyze the subjects' past behavioral history and select the appropriate data collection method 2. The system of claim 1, (Appendix 6) The collecting unit includes: During data collection, filtering is done based on subjects' current health and living conditions 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring the emotions of subjects and prioritizing data collection based on the inferred emotions A system characterized by: (Appendix 8) The collecting unit includes: When collecting data, prioritize collection of relevant data based on the subjects’ geographic location 2. The system of claim 1, (Appendix 9) The collecting unit includes: During data collection, analyze the social media activity of subjects and collect relevant data A system characterized by: (Appendix 10) The prediction unit is Inferring a person's emotions and adjusting predictive algorithms based on the inferred emotions A system characterized by: (Appendix 11) The prediction unit is When making predictions, improve the accuracy of the prediction based on the target person's behavioral patterns A system characterized by: (Appendix 12) The prediction unit is At the time of prediction, predictions are made based on the subject's health condition data. 2. The system of claim 1, (Appendix 13) The prediction unit is Infer the target person's emotions and adjust the way prediction results are displayed based on the inferred emotions A system characterized by: (Appendix 14) The prediction unit is At the time of forecasting, the prediction is made based on the geographical distribution of the subjects. 2. The system of claim 1, (Appendix 15) The prediction unit is When making predictions, improve the accuracy of the prediction based on the subject's related literature 2. The system of claim 1, (Appendix 16) The providing unit is Estimate the target person's emotions and adjust the way information is provided based on the estimated emotions A system characterized by: (Appendix 17) The providing unit is When providing information, select the appropriate method of providing information based on the target person's past behavioral history. A system characterized by: (Appendix 18) The providing unit is When providing information, customize the means of providing information based on the subject's current health status A system characterized by: (Appendix 19) The providing unit is Estimate the target person's emotions and determine the priority of providing information based on the estimated emotions A system characterized by: (Appendix 20) The providing unit is When providing information, select the appropriate information provision method based on the target person's geographic location information. A system characterized by: (Appendix 21) The providing unit is When providing information, analyze the target person's social media activity to suggest a means of providing the information A system characterized by: [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects personal characteristic data of a subject; A prediction unit that predicts the destination and stopovers of the subject based on the data collected by the collection unit; A providing unit that provides information indicating the location predicted by the prediction unit. A system characterized by:

2. The collecting unit includes: Collecting the subject's behavioral history log and health condition data as personal characteristic data 2. The system of claim 1 .

3. The collecting unit includes: Inferring the subject's emotion and adjusting the timing of data collection based on the inferred emotion.

2. The system of claim 1 .

4. The collecting unit includes: Analyze the subject's past behavioral history and select an appropriate data collection method 2. The system of claim 1 .

5. The collecting unit includes: During data collection, filtering is performed based on the subject's current health and living conditions.

2. The system of claim 1 .

6. The collecting unit includes: Estimating the subject's emotions and prioritizing data to be collected based on the estimated emotions.

2. The system of claim 1 .

7. The collecting unit includes: When collecting data, prioritize collection of data that is more relevant based on the subject's geographic location information.

2. The system of claim 1 .

8. The collecting unit includes: At the time of data collection, analyze the social media activity of said subjects and collect relevant data 2. The system of claim 1 .

9. The prediction unit is Inferring a sentiment of the subject and adjusting a predictive algorithm based on the inferred sentiment.

2. The system of claim 1 .

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