system

Abstract

We provide the system. [Solution] A means of obtaining the movement history of the subject, A means for analyzing the aforementioned movement history to estimate the range of the subject's wandering, A means for displaying the estimated wandering area on a map, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Early detection of elderly people who wander due to dementia or the like is an important issue for families and local communities. However, the conventional search method has a problem that it is difficult to estimate the movement range and the search takes time. In addition, since real - time update of the movement range using the usage status of public transportation and geographical information of the area has not been performed, an accurate search range could not be specified.

Means for Solving the Problems

[0005] This invention provides a system that acquires the past movement history of a wandering person, analyzes it, and identifies the estimated wandering area. Furthermore, by displaying it on a map, the search guidelines can be clearly defined. In addition, by acquiring real-time monitoring information from public transportation and updating the wandering area, the accuracy of the search can be improved. Moreover, by identifying points of interest within the estimated area, the efficiency of the search operation can be improved.

[0006] "Target individuals" refers to individuals who may go missing due to wandering, such as those with dementia or the elderly.

[0007] "Travel history" refers to data that records what places a person has visited and what routes they have taken in the past.

[0008] "Wandering range" refers to an estimated range within which a person may move without a specific destination or direction due to the effects of dementia or other factors.

[0009] "Means of displaying on a map" refers to a method or technology that visually displays the estimated wandering area on a digital map and provides it to the user.

[0010] "Public transportation monitoring information" refers to data such as surveillance camera footage and operational information for public transportation such as buses and trains.

[0011] A "point of interest" refers to a location or facility within the estimated wandering range that is considered highly likely to be visited by the wanderer. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] 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.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system for the early detection of a person wandering. This system includes a server, a terminal, and a user.

[0034] First, the server retrieves the subject's travel history from the database. This travel history includes information such as past visited locations and routes, and the public transportation used. Based on this data, the server executes an algorithm to estimate the subject's wandering range.

[0035] The estimated wandering area is processed by the server along with map data. The map data includes local geographical information, identifying important locations within the estimated area, such as bus stops, commercial facilities, and parks. This information is then listed as high-priority locations for the search.

[0036] The server sends this information to the terminal. The terminal visualizes the wandering area and points of interest received from the server on a map application and displays it to the user. This map serves as a guide for the user to conduct an effective search.

[0037] Users can check real-time updated information on the missing person's location through their device. This allows users to properly plan search operations and, if necessary, cooperate with local police and volunteers to carry out the search.

[0038] As a concrete example, suppose an elderly person went missing after being last seen at a shopping center at 3 PM. The server uses the elderly person's past data to estimate the range they could reach within approximately three hours, based on their normal walking speed and places they frequently visited. Based on this estimation, bus stop A, park B, and shopping street C are identified as key locations, and this information is sent to the user's terminal. The user can then check these key locations on the terminal and efficiently carry out search activities.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server connects to the database to retrieve the subject's travel history information. This includes information such as places visited in the past, travel routes, and public transportation used.

[0042] Step 2:

[0043] The server applies an algorithm to estimate the wandering range based on the collected movement history. The algorithm analyzes past behavior patterns and estimates the most likely movement range, taking into account movement speed and frequently visited locations.

[0044] Step 3:

[0045] The server combines the estimated wandering area with geographic information data and visualizes it on a map. Furthermore, it identifies and lists notable locations within the estimated area (such as important bus stops, commercial facilities, and parks).

[0046] Step 4:

[0047] The server prepares the generated map and information on points of interest as a data package for transmission to the terminal.

[0048] Step 5:

[0049] The device receives information sent from the server and displays it visually in a map application. It plots the estimated wandering area and points of interest on the map for the user to see in real time.

[0050] Step 6:

[0051] Users check the map display on their device and create a search plan within the estimated wandering area. If necessary, they cooperate with local residents and search volunteers to carry out search operations efficiently.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] When elderly individuals or those with dementia wander off, quickly and accurately determining their range of movement is crucial for improving search efficiency and reducing social costs. However, current systems fail to adequately predict the range of a person's wandering, and the information necessary for search operations is not acquired and updated in real time.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for acquiring movement history information, means for executing an algorithm that analyzes the movement history information to predict the range of the subject's movements, and means for integrating the predicted range of movements with map information to identify important locations. This enables the rapid prediction of the subject's movements and the identification of important locations, thereby improving the efficiency and speed of the search.

[0057] "Travel history information" refers to data about places and routes that the subject has visited in the past, as well as the means of transportation used.

[0058] "Range of activity" refers to the area in which the subject can move within a specific time period, and includes the predicted area of ​​wandering.

[0059] "Means for executing an algorithm" refers to a program or process that automatically performs analysis and calculations based on acquired data and generates prediction results.

[0060] "Map information" refers to digital data that shows the geographical features, facilities, and transportation arrangements of a region, and is used for visualization.

[0061] A "key location" refers to a specific point within the predicted range of movement that should be prioritized for search and rescue operations, such as a location or facility.

[0062] A "display device" is a screen or display device used to visually confirm information.

[0063] A "generative artificial intelligence model" is a machine learning model that generates new information or suggestions based on given prompts or data.

[0064] This invention relates to a system that uses a subject's movement history information to predict and visually display their range of activity. Specifically, it is implemented through the collaboration of a server, a terminal, and a user.

[0065] The server first retrieves the subject's travel history information from a database. This information includes past visited locations, routes, and public transportation used. The server organizes the information using a programming language such as Python and generates a dataset for analysis. For data analysis, data processing and machine learning libraries such as the pandas library and scikit-learn can be used. Using these, the server predicts the subject's range of movement.

[0066] The predicted range of activity is then integrated with map information. Here, GIS software (e.g., QGIS) is used to identify key points within the range of activity. These identified key points are plotted on a topographic map and transmitted from the server to the terminal as a visual guide.

[0067] The device receives information sent from the server and visualizes it on a map application using Google® Maps API and other tools. This allows users to receive real-time information updates and confirm the locations of important points. Based on this, users can plan search and rescue operations and movement.

[0068] Furthermore, the generative AI model can be used to generate new search strategy ideas. For example, a prompt such as "Suggest a priority route to take in this area" can be entered. This allows the generative AI model to suggest the optimal search route, and the user can then use that information to plan their search.

[0069] As a concrete example, in the search for an elderly person, this system can be used to predict their range of movement based on past travel patterns, allowing for the identification of important locations such as bus stops, commercial facilities, and parks. Based on this information, users can expect to conduct search operations more effectively.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server retrieves the subject's travel history information from the database. This information includes data on past visited locations, routes, and public transportation used. The input is raw travel history data, and the output is formatted historical data. Specifically, it extracts data using SQL queries and formats the data using the Python pandas library.

[0073] Step 2:

[0074] The server predicts the range of movement based on formatted movement history data. At this stage, it analyzes behavioral patterns based on past visit frequency and time of day, and makes predictions using a machine learning model. The input is formatted history data, and the output is the predicted range of movement. Specifically, the scikit-learn library is used to train and apply the range of movement model.

[0075] Step 3:

[0076] The server integrates the predicted range of activity with map information, thereby identifying key points within that range. The input is the predicted range of activity, and the output is integrated data including key points. Specifically, GIS software (e.g., QGIS) is used to plot the range of activity on the map data and identify key points.

[0077] Step 4:

[0078] The server sends the integrated data to the terminal. The terminal visualizes the data on a map application using the Google Maps API. The input is the integrated data from the server, and the output is the visualized map information. Specifically, the received data is reflected on the map in real time and displayed to the user.

[0079] Step 5:

[0080] The user reviews visualized information through their device and plans their search operation. If necessary, they input prompts into a generating AI model to generate a new search strategy. The input consists of visualized map information and prompts, while the output is the optimal search strategy. Specifically, prompts are sent to the generating AI model, which then develops a search plan based on the received suggestions.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] The problem of elderly people and dementia patients wandering off and going missing is a serious social issue, and there is a need for a system that supports their rapid discovery and safe return home. With current technology, it is difficult to accurately grasp the abnormal movements of the person in question in real time and efficiently identify the area where they wandered, which hinders the speed and efficiency of search operations.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes a device equipped with a function to acquire location information of a target person, a device equipped with a function to analyze the location information and estimate the target person's range of movement, and a device equipped with a function to visually represent the estimated range of movement on a map. This enables accurate estimation of the target person's range of movement and display of that information on a map, thereby allowing for the rapid and efficient planning and execution of search activities.

[0086] "Subject" refers to an individual whose specific behavior or movements are observed and tracked.

[0087] "Location information" refers to information indicating the current location of a subject, and is usually represented by geographical location data such as GPS data.

[0088] "Device" refers to a machine that includes hardware or software for performing a specific function.

[0089] "Analysis" is the process of examining data and information in detail to draw specific conclusions or inferences.

[0090] "Range of activity" refers to the geographical area in which the subject can move.

[0091] "To infer" means to predict future conditions or events based on existing data and analysis results.

[0092] "Visual representation" means displaying information or data in a format that is easy to understand visually, such as maps or graphs.

[0093] A "machine" is a collection of parts or devices configured to perform a specific task.

[0094] This invention is a system for monitoring a subject's location information in real time and quickly identifying abnormal behavior. The system mainly consists of three components: a server, a terminal, and a user.

[0095] First, the server functions as the central hub for information processing. Location information of the subject is obtained from GPS data acquired from smartphones and wearable devices. This data is first sent to the server, where a Python script is used to analyze the range of movement. The estimated range of movement is then visually represented on a map using the Google Maps API. Furthermore, the system has the capability to acquire real-time monitoring information on transportation methods and correct the analysis results accordingly.

[0096] The terminal is a device that allows users to check this information and conduct search operations effectively. Smartphones or smart glasses are primarily used, and the estimated range of movement and key locations are visually displayed on the Google Maps application. The terminal receives information from the server in real time and presents the latest data to the user.

[0097] Through this system, users can view the latest movements of a person in question on a map and select an appropriate search route. For example, it becomes possible to quickly identify important locations within a certain range from where an elderly person was last seen and efficiently visit those locations.

[0098] As a concrete example, consider a scenario where a local volunteer group uses this system to search for a missing elderly person. The system is expected to contribute to the rapid implementation of a search by indicating likely locations based on the elderly person's past behavioral patterns. Instructions can be given to the generating AI model by inputting prompts such as, "Based on the elderly person's latest GPS data and past movement history, display the reachable area on a map and suggest an efficient search route."

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server receives GPS data in real time from the subject's smartphone or wearable device. This data indicates the subject's current location. Based on this location information, the server compares it with the movement history in the database and begins data analysis to understand the latest behavioral patterns.

[0102] Step 2:

[0103] The server uses a Python analysis script to estimate the subject's range of movement based on acquired GPS data and movement history. The input data consists of real-time location information and historical movement history data, and the output identifies areas where the subject is likely to move in the future. This prepares the system for geographically visualizing the estimated range.

[0104] Step 3:

[0105] The server uses the Google Maps API to prepare data for visually displaying the estimated range of movement on a map. In this process, the estimated area is input into the API, and the corresponding geographical information is retrieved. As output, the range of movement is plotted on the map, and points of interest are identified.

[0106] Step 4:

[0107] The server acquires real-time monitoring data on means of transport (such as public transportation) and corrects and updates information on the range of movement. The input data is location information of the means of transport, and the output provides the latest range of movement. This enables more accurate search guidance.

[0108] Step 5:

[0109] The device receives information about the estimated range of movement transmitted from the server. A map application is used to display this information on the device, visually presenting it to the user. This allows the user to check the target person's range of movement in real time.

[0110] Step 6:

[0111] Users plan and execute search operations based on map information displayed on their devices. Specifically, they select routes to visit points of interest displayed on the map, enabling efficient searching. User actions contribute to a feedback loop across the entire connected system, leading to better data acquisition.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention is a system aimed at the early detection of wandering individuals, and further improves the efficiency of search activities by incorporating an emotion engine that recognizes the user's emotions. This system consists of a server, a terminal, and an emotion engine.

[0114] The server acquires the subject's movement history and real-time monitoring information on public transportation, and analyzes this data to estimate their wandering range. Based on this, the server identifies the estimated wandering range and important points of interest, and sends this information to the terminal. At this time, emotion engine data is added, and information is provided according to the user's emotional state.

[0115] The emotion engine analyzes the user's stress level and emotional state from their voice input and device operation patterns. For example, if the user shows signs of anxiety or impatience, the system will focus on providing high-priority information and advise on search procedures to help the user feel more at ease. Furthermore, if the user's stress levels are high, the system will offer support to make the search easier and reduce their burden.

[0116] As a concrete example, suppose a user begins searching for a wandering person, and the device's emotion engine detects anxiety from the user's voice. The server then narrows down the locations to high-priority ones and sends detailed search instructions to the device. The user can then efficiently search those locations without feeling stressed, increasing the likelihood of finding the wandering person in a short amount of time.

[0117] In this way, by utilizing an emotion engine, this system provides flexible information and support tailored to each user's emotional state, thereby increasing the efficiency of search operations for wandering individuals.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The server retrieves the subject's travel history data from the database. The retrieved data includes visit history, travel routes, and information on public transportation used in the past.

[0121] Step 2:

[0122] The server applies an algorithm to estimate the wandering range based on the acquired movement history data. This algorithm analyzes past behavior patterns and movement speed to estimate the most likely movement range.

[0123] Step 3:

[0124] The estimated wandering area is integrated with map information by the server. The server lists points of interest within the identified movement range (e.g., major intersections and bus stops) and prepares to send them to the terminal.

[0125] Step 4:

[0126] The device receives wandering range data and point of interest information transmitted from the server. The emotion engine installed in the device analyzes the user's emotional state from voice input and operation patterns.

[0127] Step 5:

[0128] The emotion engine adjusts how information is displayed based on the analyzed emotional state of the user. For example, if the user is showing anxiety, the device will display recommended search routes concisely and provide information in a way that provides reassurance.

[0129] Step 6:

[0130] Users begin their search based on the information presented on their device. By providing information tailored to their emotions, they can conduct their search effectively while reducing stress.

[0131] Step 7:

[0132] As the search progresses, users provide feedback to their devices with new information. This information is sent to the server, which then re-estimates the area the person is wandering and updates the information.

[0133] (Example 2)

[0134] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0135] Conventional technologies for locating wandering individuals early relied solely on movement history and surveillance data for estimation, resulting in limitations in accuracy and time efficiency. Furthermore, the lack of measures to alleviate the psychological burden on users conducting the search sometimes led to decreased search efficiency.

[0136] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0137] In this invention, the server includes means for acquiring the movement history of a target person, means for analyzing the movement history to estimate the target person's wandering range, means for displaying the estimated wandering range on a map, means for analyzing the user's voice input and operation patterns to evaluate their emotional state, and means for adjusting the content of information provided based on the evaluated emotional state. This makes it possible to quickly and accurately identify the wandering range of a person who has wandered off, and to carry out an efficient search while reducing the emotional burden on the user.

[0138] "The subject's travel history" refers to records of places the designated individual has visited or routes they have traveled in the past.

[0139] "Wandering range" refers to the area where the suspect is expected to move or the area where they are most likely to travel, and it is the location where search operations are conducted based on this range.

[0140] "User voice input" refers to voice information spoken by system users into their terminals, and is used as data to analyze their emotional state and intentions.

[0141] "Operation patterns" refer to the tendencies and methods of a user's actions when using a device, and these can serve as clues to assess their emotional state.

[0142] "Emotional state" refers to the psychological state a user exhibits under specific circumstances, and includes internal reactions such as stress and anxiety.

[0143] "Means for adjusting the content of information provided" refers to a mechanism that changes the type and priority of information provided based on the user's emotional state, enabling more appropriate support.

[0144] This invention is a system aimed at the early detection of wandering individuals, and improves the efficiency of search operations by integrating an emotion engine that analyzes the user's emotions. This system incorporates a server, a terminal, and an emotion engine, each playing a specific role.

[0145] The server first retrieves the subject's travel history from a database. This history includes past visited locations and travel routes, and Python and data processing libraries (e.g., Pandas, NumPy) are used to analyze it. Additionally, when obtaining real-time public transport information, a transportation data API (e.g., a map application API) is used. Based on this information, the server estimates the subject's wandering range and displays that range on a map.

[0146] The terminal is a user-operated device equipped with an emotion engine (e.g., emotion analysis API). When the user provides voice input, the terminal records this voice using a microphone and converts it to text using speech recognition technology (e.g., speech recognition API). The terminal sends this text to the emotion engine, which evaluates the user's emotional state. Based on the evaluation results, the information received from the server is adjusted and provided to the user.

[0147] For example, if a user shows signs of anxiety while conducting a search, the server will prioritize displaying the most important search points based on the analysis results of the emotion engine. This allows the user to continue their search with peace of mind and efficiency.

[0148] An example of a prompt message is, "Please tell me about an emotional support system that advises users on the steps they should take to efficiently search for a wandering person."

[0149] Thus, the present invention aims to reduce the psychological burden on users and improve search efficiency by making full use of emotion analysis technology.

[0150] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0151] Step 1:

[0152] The server retrieves the subject's movement history from a database. This history includes locations the subject has visited in the past and the routes they have traveled. Using Python and data processing libraries, this data is read and movement patterns are analyzed. This analysis provides the foundational data needed to estimate areas where the subject may wander.

[0153] Step 2:

[0154] The server obtains real-time public transport information through a transport data API. This input information includes data on the modes of transport the target individual is likely to use. Using the obtained information, the server performs analysis in conjunction with the travel history to identify the target individual's predicted wandering area. This allows for the correction of the wandering area based on travel history using real-time data, thereby improving accuracy.

[0155] Step 3:

[0156] The server sends the estimated wandering area along with map data to the terminal. The user checks this information through the terminal. The wandering area is visually displayed on the map, providing the user with a basis for deciding where to go next.

[0157] Step 4:

[0158] The device records the user's voice input using a microphone and converts it into text data using a speech recognition API. If the user says, "I don't know where to go," the device sends this text to a generating AI model to obtain data for sentiment analysis.

[0159] Step 5:

[0160] The device sends the acquired text data to the sentiment analysis engine, which evaluates the user's emotional state. For example, if the analysis results indicate the user is anxious, the sentiment analysis engine sends instructions to the server based on that evaluation. The output of the sentiment analysis is information about the user's psychological state.

[0161] Step 6:

[0162] The server receives the results of sentiment analysis and adjusts the priority of the information. It rearranges the search locations in the optimal order according to the user's emotional state and reconstructs the information necessary for the search. The reconstructed information is provided in a way that is tailored to the user's emotional state, allowing them to proceed with the search with greater peace of mind.

[0163] Step 7:

[0164] The device displays the reconstructed information to the user. Based on this information, the user heads to priority locations such as parks and train stations and begins an efficient search. This process promotes the early detection of the wandering person and reduces the psychological burden on the user.

[0165] (Application Example 2)

[0166] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0167] In search and rescue operations for wandering individuals, a challenge exists: increased stress and anxiety among searchers can hinder efficient searches. Especially in situations where the early discovery of elderly individuals or missing persons is crucial, providing appropriate information tailored to the searchers' psychological state is essential.

[0168] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0169] In this invention, the server includes means for acquiring the movement history of a target person, means for analyzing the movement history to estimate the target person's wandering range, means for displaying the estimated wandering range on a map, means for recognizing the user's emotions, and means for providing high-priority information based on the user's emotions. This makes it possible to appropriately judge the user's emotional state and conduct efficient search activities while reducing stress.

[0170] A "subject" refers to an individual who may be prone to wandering or disappearing, and is the subject of observation regarding their specific behaviors and movements.

[0171] "Means for acquiring movement history" refers to a system that has the function of acquiring and recording the past and present location information of a subject.

[0172] "Means for estimating the range of wandering" refers to an analytical function that identifies areas or regions where the subject may have moved, based on the acquired movement history.

[0173] "Means of displaying on a map" refers to the function of displaying the estimated wandering area on a map using a geographic information system in order to visually represent it.

[0174] A "means of recognizing user emotions" refers to a system that identifies and analyzes a user's psychological state and emotions through voice input and operation patterns.

[0175] "Means of providing high-priority information" refers to a function that selects and presents information of high importance as needed, based on the user's emotional state.

[0176] To realize this application, it is essential to use a server, a terminal, and an emotion engine. The server interacts with a location database to obtain the subject's movement history and collects real-time location information. Next, the server analyzes the obtained data to estimate the subject's wandering range. In this process, the program implements a data analysis algorithm using Python to effectively calculate the wandering range.

[0177] The estimated wandering area is displayed on a map using a Geographic Information System (GIS). This display is implemented as a user interface in a terminal application using React Native. By accessing this map information, users can visually confirm the possible locations of the person in question.

[0178] Furthermore, the device incorporates speech recognition software that recognizes emotions from the user's voice input. Here, the Google Cloud Natural Language API is used to analyze the voice data and identify the user's emotional state. Based on this information, the emotion engine extracts high-priority information, enabling the provision of information tailored to the user's emotions.

[0179] For example, if a user voice-inputs, "My mother has been going out frequently lately, and I'm worried," the emotion engine detects the anxiety, and the server automatically determines high-priority monitoring areas. As a result, "important information regarding your mother's recent activity range" is highlighted on the device. At this point, a generative AI model is used to provide appropriate advice and information to alleviate the user's psychological burden.

[0180] An example of a prompt message is, "If a user types 'I feel uneasy when I'm alone at night,' how would you provide reassuring information?"

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The server retrieves the subject's movement history from a location database. In this step, past location information is aggregated using database queries. The input is the subject's ID, and the output is a dataset of time and location associated with that ID.

[0184] Step 2:

[0185] The server analyzes the acquired movement history to estimate the subject's wandering range. It uses Python to execute a data analysis algorithm, calculating the most likely wandering range based on geographical data. The input is the location data from step 1, and the output is a list of coordinates for the estimated wandering range.

[0186] Step 3:

[0187] The server converts the estimated wandering area into a geodata format and sends it to the terminal. The input is a list of coordinates for the wandering area, and the output is map data that can be displayed on the terminal.

[0188] Step 4:

[0189] The device displays the received map data on a map using React Native. The input is geodata sent from the server, allowing the user to visually confirm their roaming area. The output is visual information displayed on the map.

[0190] Step 5:

[0191] The user inputs emotional data into the device via voice input. In this step, voice data that identifies the user's emotional state is input.

[0192] Step 6:

[0193] The device performs sentiment analysis using the Google Cloud Natural Language API. The input is the audio data from step 5, and the emotional state is output through the analysis. Specifically, it determines emotions from certain keywords or the tone of voice.

[0194] Step 7:

[0195] The emotion engine requests high-priority information from the server based on the user's emotions. The input is analyzed emotion data, and the server generates a set of information with re-evaluated importance based on that data.

[0196] Step 8:

[0197] The server selects high-priority information and sends it to the terminal. The input is a request based on the user's emotional state, and the output is a set of information corresponding to that emotion.

[0198] Step 9:

[0199] The device clearly presents high-priority information to the user. Users can receive advice and procedures to conduct search operations efficiently while reducing stress. Output consists of visual and audio notifications.

[0200] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0201] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0202] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0206] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0207] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0208] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0209] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0210] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0211] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0212] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0213] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0214] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0215] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0216] This invention is a system for the early detection of a person wandering. This system includes a server, a terminal, and a user.

[0217] First, the server retrieves the subject's travel history from the database. This travel history includes information such as past visited locations and routes, and the public transportation used. Based on this data, the server executes an algorithm to estimate the subject's wandering range.

[0218] The estimated wandering area is processed by the server along with map data. The map data includes local geographical information, identifying important locations within the estimated area, such as bus stops, commercial facilities, and parks. This information is then listed as high-priority locations for the search.

[0219] The server sends this information to the terminal. The terminal visualizes the wandering area and points of interest received from the server on a map application and displays it to the user. This map serves as a guide for the user to conduct an effective search.

[0220] Users can check real-time updated information on the missing person's location through their device. This allows users to properly plan search operations and, if necessary, cooperate with local police and volunteers to carry out the search.

[0221] As a concrete example, suppose an elderly person went missing after being last seen at a shopping center at 3 PM. The server uses the elderly person's past data to estimate the range they could reach within approximately three hours, based on their normal walking speed and places they frequently visited. Based on this estimation, bus stop A, park B, and shopping street C are identified as key locations, and this information is sent to the user's terminal. The user can then check these key locations on the terminal and efficiently carry out search activities.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The server connects to the database to retrieve the subject's travel history information. This includes information such as places visited in the past, travel routes, and public transportation used.

[0225] Step 2:

[0226] The server applies an algorithm to estimate the wandering range based on the collected movement history. The algorithm analyzes past behavior patterns and estimates the most likely movement range, taking into account movement speed and frequently visited locations.

[0227] Step 3:

[0228] The server combines the estimated wandering area with geographic information data and visualizes it on a map. Furthermore, it identifies and lists notable locations within the estimated area (such as important bus stops, commercial facilities, and parks).

[0229] Step 4:

[0230] The server prepares the generated map and information on points of interest as a data package for transmission to the terminal.

[0231] Step 5:

[0232] The device receives information sent from the server and displays it visually in a map application. It plots the estimated wandering area and points of interest on the map for the user to see in real time.

[0233] Step 6:

[0234] Users check the map display on their device and create a search plan within the estimated wandering area. If necessary, they cooperate with local residents and search volunteers to carry out search operations efficiently.

[0235] (Example 1)

[0236] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0237] When elderly individuals or those with dementia wander off, quickly and accurately determining their range of movement is crucial for improving search efficiency and reducing social costs. However, current systems fail to adequately predict the range of a person's wandering, and the information necessary for search operations is not acquired and updated in real time.

[0238] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0239] In this invention, the server includes means for acquiring movement history information, means for executing an algorithm that analyzes the movement history information to predict the range of the subject's movements, and means for integrating the predicted range of movements with map information to identify important locations. This enables the rapid prediction of the subject's movements and the identification of important locations, thereby improving the efficiency and speed of the search.

[0240] "Travel history information" refers to data about places and routes that the subject has visited in the past, as well as the means of transportation used.

[0241] "Range of activity" refers to the area in which the subject can move within a specific time period, and includes the predicted area of ​​wandering.

[0242] "Means for executing an algorithm" refers to a program or process that automatically performs analysis and calculations based on acquired data and generates prediction results.

[0243] "Map information" refers to digital data that shows the geographical features, facilities, and transportation arrangements of a region, and is used for visualization.

[0244] A "key location" refers to a specific point within the predicted range of movement that should be prioritized for search and rescue operations, such as a location or facility.

[0245] A "display device" is a screen or display device used to visually confirm information.

[0246] A "generative artificial intelligence model" is a machine learning model that generates new information or suggestions based on given prompts or data.

[0247] This invention relates to a system that uses a subject's movement history information to predict and visually display their range of activity. Specifically, it is implemented through the collaboration of a server, a terminal, and a user.

[0248] The server first retrieves the subject's travel history information from a database. This information includes past visited locations, routes, and public transportation used. The server organizes the information using a programming language such as Python and generates a dataset for analysis. For data analysis, data processing and machine learning libraries such as the pandas library and scikit-learn can be used. Using these, the server predicts the subject's range of movement.

[0249] The predicted range of activity is then integrated with map information. Here, GIS software (e.g., QGIS) is used to identify key points within the range of activity. These identified key points are plotted on a topographic map and transmitted from the server to the terminal as a visual guide.

[0250] The device receives information sent from the server and visualizes it on a map application using the Google Maps API, etc. This allows users to receive real-time information updates and confirm the locations of important points. Based on this, users can plan search operations and movements.

[0251] Furthermore, the generative AI model can be used to generate new search strategy ideas. For example, a prompt such as "Suggest a priority route to take in this area" can be entered. This allows the generative AI model to suggest the optimal search route, and the user can then use that information to plan their search.

[0252] As a concrete example, in the search for an elderly person, this system can be used to predict their range of movement based on past travel patterns, allowing for the identification of important locations such as bus stops, commercial facilities, and parks. Based on this information, users can expect to conduct search operations more effectively.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The server retrieves the subject's travel history information from the database. This information includes data on past visited locations, routes, and public transportation used. The input is raw travel history data, and the output is formatted historical data. Specifically, it extracts data using SQL queries and formats the data using the Python pandas library.

[0256] Step 2:

[0257] The server predicts the range of movement based on formatted movement history data. At this stage, it analyzes behavioral patterns based on past visit frequency and time of day, and makes predictions using a machine learning model. The input is formatted history data, and the output is the predicted range of movement. Specifically, the scikit-learn library is used to train and apply the range of movement model.

[0258] Step 3:

[0259] The server integrates the predicted range of activity with map information, thereby identifying key points within that range. The input is the predicted range of activity, and the output is integrated data including key points. Specifically, GIS software (e.g., QGIS) is used to plot the range of activity on the map data and identify key points.

[0260] Step 4:

[0261] The server sends the integrated data to the terminal. The terminal visualizes the data on a map application using the Google Maps API. The input is the integrated data from the server, and the output is the visualized map information. Specifically, the received data is reflected on the map in real time and displayed to the user.

[0262] Step 5:

[0263] The user reviews visualized information through their device and plans their search operation. If necessary, they input prompts into a generating AI model to generate a new search strategy. The input consists of visualized map information and prompts, while the output is the optimal search strategy. Specifically, prompts are sent to the generating AI model, which then develops a search plan based on the received suggestions.

[0264] (Application Example 1)

[0265] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] The problem of elderly people and dementia patients wandering off and going missing is a serious social issue, and there is a need for a system that supports their rapid discovery and safe return home. With current technology, it is difficult to accurately grasp the abnormal movements of the person in question in real time and efficiently identify the area where they wandered, which hinders the speed and efficiency of search operations.

[0267] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0268] In this invention, the server includes a device equipped with a function to acquire location information of a target person, a device equipped with a function to analyze the location information and estimate the target person's range of movement, and a device equipped with a function to visually represent the estimated range of movement on a map. This enables accurate estimation of the target person's range of movement and display of that information on a map, thereby allowing for the rapid and efficient planning and execution of search activities.

[0269] "Subject" refers to an individual whose specific behavior or movements are observed and tracked.

[0270] "Location information" refers to information indicating the current location of a subject, and is usually represented by geographical location data such as GPS data.

[0271] "Device" refers to a machine that includes hardware or software for performing a specific function.

[0272] "Analysis" is the process of examining data and information in detail to draw specific conclusions or inferences.

[0273] "Range of activity" refers to the geographical area in which the subject can move.

[0274] "To infer" means to predict future conditions or events based on existing data and analysis results.

[0275] "Visual representation" means displaying information or data in a format that is easy to understand visually, such as maps or graphs.

[0276] A "machine" is a collection of parts or devices configured to perform a specific task.

[0277] This invention is a system for monitoring a subject's location information in real time and quickly identifying abnormal behavior. The system mainly consists of three components: a server, a terminal, and a user.

[0278] First, the server functions as the central hub for information processing. Location information of the subject is obtained from GPS data acquired from smartphones and wearable devices. This data is first sent to the server, where a Python script is used to analyze the range of movement. The estimated range of movement is then visually represented on a map using the Google Maps API. Furthermore, the system has the capability to acquire real-time monitoring information on transportation methods and correct the analysis results accordingly.

[0279] The terminal is a device that allows users to check this information and conduct search operations effectively. Smartphones or smart glasses are primarily used, and the estimated range of movement and key locations are visually displayed on the Google Maps application. The terminal receives information from the server in real time and presents the latest data to the user.

[0280] Through this system, users can view the latest movements of a person in question on a map and select an appropriate search route. For example, it becomes possible to quickly identify important locations within a certain range from where an elderly person was last seen and efficiently visit those locations.

[0281] As a concrete example, consider a scenario where a local volunteer group uses this system to search for a missing elderly person. The system is expected to contribute to the rapid implementation of a search by indicating likely locations based on the elderly person's past behavioral patterns. Instructions can be given to the generating AI model by inputting prompts such as, "Based on the elderly person's latest GPS data and past movement history, display the reachable area on a map and suggest an efficient search route."

[0282] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0283] Step 1:

[0284] The server receives GPS data in real time from the target person's smartphone or wearable device. This data is information indicating the current location of the target person. Based on this location information, the server compares it with the movement history in the database and starts data analysis to grasp the latest behavior pattern.

[0285] Step 2:

[0286] The server uses a Python analysis script to infer the target person's movement range based on the acquired GPS data and movement history. The input data is real-time location information and past movement history data, and the output is to identify the area where the target person may move in the future. This prepares for geographically visualizing the inferred range.

[0287] Step 3:

[0288] The server uses the Google Maps API to prepare data for visually displaying the inferred movement range on the map. In this process, the inferred area is input into the API to obtain the corresponding geographical information. As output, the movement range is plotted on the map and the points of interest are identified.

[0289] Step 4:

[0290] The server acquires real-time monitoring data of transportation means (such as public transportation) and modifies and updates the information on the movement range. The input data is the location information of the transportation means, and the output is the latest movement range provided. This enables a more accurate search pointer.

[0291] Step 5:

[0292] The terminal receives the information on the inferred movement range sent from the server. To display the received information on the terminal, a map application is used to visually present it to the user. This allows the user to confirm the target person's movement range in real time.

[0293] Step 6:

[0294] Users plan and execute search operations based on map information displayed on their devices. Specifically, they select routes to visit points of interest displayed on the map, enabling efficient searching. User actions contribute to a feedback loop across the entire connected system, leading to better data acquisition.

[0295] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0296] This invention is a system aimed at the early detection of wandering individuals, and further improves the efficiency of search activities by incorporating an emotion engine that recognizes the user's emotions. This system consists of a server, a terminal, and an emotion engine.

[0297] The server acquires the subject's movement history and real-time monitoring information on public transportation, and analyzes this data to estimate their wandering range. Based on this, the server identifies the estimated wandering range and important points of interest, and sends this information to the terminal. At this time, emotion engine data is added, and information is provided according to the user's emotional state.

[0298] The emotion engine analyzes the user's stress level and emotional state from their voice input and device operation patterns. For example, if the user shows signs of anxiety or impatience, the system will focus on providing high-priority information and advise on search procedures to help the user feel more at ease. Furthermore, if the user's stress levels are high, the system will offer support to make the search easier and reduce their burden.

[0299] As a specific example, when a certain user starts searching for a wanderer, assume that the emotion engine of the terminal senses uneasiness from the user's voice. In response, the server narrows down the high-priority locations and sends a detailed search procedure to the terminal. The user can efficiently patrol that location without feeling stressed, increasing the likelihood of finding the wanderer in a short time.

[0300] In this way, by utilizing the emotion engine, this system provides flexible information and support according to the individual emotional states of users, enhancing the efficiency of the search activities for wanderers.

[0301] The following explains the processing flow.

[0302] Step 1:

[0303] The server obtains the movement history data of the target person from the database. The acquired data includes visit history, movement route, information on public transportation used in the past, etc.

[0304] Step 2:

[0305] Based on the acquired movement history data, the server applies an algorithm for estimating the wandering range. This algorithm analyzes past behavior patterns and movement speeds to estimate the most likely movement range.

[0306] Step 3:

[0307] The estimated wandering range is integrated with the map information by the server. The server lists up the notable points (e.g., major intersections and bus stops) within the specified movement range and prepares to send it to the terminal.

[0308] Step 4:

[0309] The terminal receives the wandering range data and notable point information sent from the server. The emotion engine installed in the terminal analyzes the user's emotional state from voice input and operation patterns.

[0310] Step 5:

[0311] The emotion engine adjusts how information is displayed based on the analyzed emotional state of the user. For example, if the user is showing anxiety, the device will display recommended search routes concisely and provide information in a way that provides reassurance.

[0312] Step 6:

[0313] Users begin their search based on the information presented on their device. By providing information tailored to their emotions, they can conduct their search effectively while reducing stress.

[0314] Step 7:

[0315] As the search progresses, users provide feedback to their devices with new information. This information is sent to the server, which then re-estimates the area the person is wandering and updates the information.

[0316] (Example 2)

[0317] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0318] Conventional technologies for locating wandering individuals early relied solely on movement history and surveillance data for estimation, resulting in limitations in accuracy and time efficiency. Furthermore, the lack of measures to alleviate the psychological burden on users conducting the search sometimes led to decreased search efficiency.

[0319] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0320] In this invention, the server includes means for acquiring the movement history of a target person, means for analyzing the movement history to estimate the target person's wandering range, means for displaying the estimated wandering range on a map, means for analyzing the user's voice input and operation patterns to evaluate their emotional state, and means for adjusting the content of information provided based on the evaluated emotional state. This makes it possible to quickly and accurately identify the wandering range of a person who has wandered off, and to carry out an efficient search while reducing the emotional burden on the user.

[0321] "The subject's travel history" refers to records of places the designated individual has visited or routes they have traveled in the past.

[0322] "Wandering range" refers to the area where the suspect is expected to move or the area where they are most likely to travel, and it is the location where search operations are conducted based on this range.

[0323] "User voice input" refers to voice information spoken by system users into their terminals, and is used as data to analyze their emotional state and intentions.

[0324] "Operation patterns" refer to the tendencies and methods of a user's actions when using a device, and these can serve as clues to assess their emotional state.

[0325] "Emotional state" refers to the psychological state a user exhibits under specific circumstances, and includes internal reactions such as stress and anxiety.

[0326] "Means for adjusting the content of information provided" refers to a mechanism that changes the type and priority of information provided based on the user's emotional state, enabling more appropriate support.

[0327] This invention is a system aimed at the early detection of wandering individuals, and improves the efficiency of search operations by integrating an emotion engine that analyzes the user's emotions. This system incorporates a server, a terminal, and an emotion engine, each playing a specific role.

[0328] The server first retrieves the subject's travel history from a database. This history includes past visited locations and travel routes, and Python and data processing libraries (e.g., Pandas, NumPy) are used to analyze it. Additionally, when obtaining real-time public transport information, a transportation data API (e.g., a map application API) is used. Based on this information, the server estimates the subject's wandering range and displays that range on a map.

[0329] The terminal is a user-operated device equipped with an emotion engine (e.g., emotion analysis API). When the user provides voice input, the terminal records this voice using a microphone and converts it to text using speech recognition technology (e.g., speech recognition API). The terminal sends this text to the emotion engine, which evaluates the user's emotional state. Based on the evaluation results, the information received from the server is adjusted and provided to the user.

[0330] For example, if a user shows signs of anxiety while conducting a search, the server will prioritize displaying the most important search points based on the analysis results of the emotion engine. This allows the user to continue their search with peace of mind and efficiency.

[0331] An example of a prompt message is, "Please tell me about an emotional support system that advises users on the steps they should take to efficiently search for a wandering person."

[0332] Thus, the present invention aims to reduce the psychological burden on users and improve search efficiency by making full use of emotion analysis technology.

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

[0334] Step 1:

[0335] The server retrieves the subject's movement history from a database. This history includes locations the subject has visited in the past and the routes they have traveled. Using Python and data processing libraries, this data is read and movement patterns are analyzed. This analysis provides the foundational data needed to estimate areas where the subject may wander.

[0336] Step 2:

[0337] The server obtains real-time public transport information through a transport data API. This input information includes data on the modes of transport the target individual is likely to use. Using the obtained information, the server performs analysis in conjunction with the travel history to identify the target individual's predicted wandering area. This allows for the correction of the wandering area based on travel history using real-time data, thereby improving accuracy.

[0338] Step 3:

[0339] The server sends the estimated wandering area along with map data to the terminal. The user checks this information through the terminal. The wandering area is visually displayed on the map, providing the user with a basis for deciding where to go next.

[0340] Step 4:

[0341] The device records the user's voice input using a microphone and converts it into text data using a speech recognition API. If the user says, "I don't know where to go," the device sends this text to a generating AI model to obtain data for sentiment analysis.

[0342] Step 5:

[0343] The device sends the acquired text data to the sentiment analysis engine, which evaluates the user's emotional state. For example, if the analysis results indicate the user is anxious, the sentiment analysis engine sends instructions to the server based on that evaluation. The output of the sentiment analysis is information about the user's psychological state.

[0344] Step 6:

[0345] The server receives the results of sentiment analysis and adjusts the priority of the information. It rearranges the search locations in the optimal order according to the user's emotional state and reconstructs the information necessary for the search. The reconstructed information is provided in a way that is tailored to the user's emotional state, allowing them to proceed with the search with greater peace of mind.

[0346] Step 7:

[0347] The device displays the reconstructed information to the user. Based on this information, the user heads to priority locations such as parks and train stations and begins an efficient search. This process promotes the early detection of the wandering person and reduces the psychological burden on the user.

[0348] (Application Example 2)

[0349] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0350] In search and rescue operations for wandering individuals, a challenge exists: increased stress and anxiety among searchers can hinder efficient searches. Especially in situations where the early discovery of elderly individuals or missing persons is crucial, providing appropriate information tailored to the searchers' psychological state is essential.

[0351] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0352] In this invention, the server includes means for acquiring the movement history of a target person, means for analyzing the movement history to estimate the target person's wandering range, means for displaying the estimated wandering range on a map, means for recognizing the user's emotions, and means for providing high-priority information based on the user's emotions. This makes it possible to appropriately judge the user's emotional state and conduct efficient search activities while reducing stress.

[0353] A "subject" refers to an individual who may be prone to wandering or disappearing, and is the subject of observation regarding their specific behaviors and movements.

[0354] "Means for acquiring movement history" refers to a system that has the function of acquiring and recording the past and present location information of a subject.

[0355] "Means for estimating the range of wandering" refers to an analytical function that identifies areas or regions where the subject may have moved, based on the acquired movement history.

[0356] "Means of displaying on a map" refers to the function of displaying the estimated wandering area on a map using a geographic information system in order to visually represent it.

[0357] A "means of recognizing user emotions" refers to a system that identifies and analyzes a user's psychological state and emotions through voice input and operation patterns.

[0358] "Means of providing high-priority information" refers to a function that selects and presents information of high importance as needed, based on the user's emotional state.

[0359] To realize this application, it is essential to use a server, a terminal, and an emotion engine. The server interacts with a location database to obtain the subject's movement history and collects real-time location information. Next, the server analyzes the obtained data to estimate the subject's wandering range. In this process, the program implements a data analysis algorithm using Python to effectively calculate the wandering range.

[0360] The estimated wandering area is displayed on a map using a Geographic Information System (GIS). This display is implemented as a user interface in a terminal application using React Native. By accessing this map information, users can visually confirm the possible locations of the person in question.

[0361] Furthermore, the device incorporates speech recognition software that recognizes emotions from the user's voice input. Here, the Google Cloud Natural Language API is used to analyze the voice data and identify the user's emotional state. Based on this information, the emotion engine extracts high-priority information, enabling the provision of information tailored to the user's emotions.

[0362] For example, if a user voice-inputs, "My mother has been going out frequently lately, and I'm worried," the emotion engine detects the anxiety, and the server automatically determines high-priority monitoring areas. As a result, "important information regarding your mother's recent activity range" is highlighted on the device. At this point, a generative AI model is used to provide appropriate advice and information to alleviate the user's psychological burden.

[0363] An example of a prompt message is, "If a user types 'I feel uneasy when I'm alone at night,' how would you provide reassuring information?"

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The server retrieves the subject's movement history from a location database. In this step, past location information is aggregated using database queries. The input is the subject's ID, and the output is a dataset of time and location associated with that ID.

[0367] Step 2:

[0368] The server analyzes the acquired movement history to estimate the subject's wandering range. It uses Python to execute a data analysis algorithm, calculating the most likely wandering range based on geographical data. The input is the location data from step 1, and the output is a list of coordinates for the estimated wandering range.

[0369] Step 3:

[0370] The server converts the estimated wandering area into a geodata format and sends it to the terminal. The input is a list of coordinates for the wandering area, and the output is map data that can be displayed on the terminal.

[0371] Step 4:

[0372] The device displays the received map data on a map using React Native. The input is geodata sent from the server, allowing the user to visually confirm their roaming area. The output is visual information displayed on the map.

[0373] Step 5:

[0374] The user inputs emotional data into the device via voice input. In this step, voice data that identifies the user's emotional state is input.

[0375] Step 6:

[0376] The device performs sentiment analysis using the Google Cloud Natural Language API. The input is the audio data from step 5, and the emotional state is output through the analysis. Specifically, it determines emotions from certain keywords or the tone of voice.

[0377] Step 7:

[0378] The emotion engine requests high-priority information from the server based on the user's emotions. The input is analyzed emotion data, and the server generates a set of information with re-evaluated importance based on that data.

[0379] Step 8:

[0380] The server selects high-priority information and sends it to the terminal. The input is a request based on the user's emotional state, and the output is a set of information corresponding to that emotion.

[0381] Step 9:

[0382] The device clearly presents high-priority information to the user. Users can receive advice and procedures to conduct search operations efficiently while reducing stress. Output consists of visual and audio notifications.

[0383] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0384] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0389] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0391] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0393] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0394] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0395] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0396] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0397] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0398] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0399] This invention is a system for the early detection of a person wandering. This system includes a server, a terminal, and a user.

[0400] First, the server retrieves the subject's travel history from the database. This travel history includes information such as past visited locations and routes, and the public transportation used. Based on this data, the server executes an algorithm to estimate the subject's wandering range.

[0401] The estimated wandering area is processed by the server along with map data. The map data includes local geographical information, identifying important locations within the estimated area, such as bus stops, commercial facilities, and parks. This information is then listed as high-priority locations for the search.

[0402] The server sends this information to the terminal. The terminal visualizes the wandering area and points of interest received from the server on a map application and displays it to the user. This map serves as a guide for the user to conduct an effective search.

[0403] Users can check real-time updated information on the missing person's location through their device. This allows users to properly plan search operations and, if necessary, cooperate with local police and volunteers to carry out the search.

[0404] As a concrete example, suppose an elderly person went missing after being last seen at a shopping center at 3 PM. The server uses the elderly person's past data to estimate the range they could reach within approximately three hours, based on their normal walking speed and places they frequently visited. Based on this estimation, bus stop A, park B, and shopping street C are identified as key locations, and this information is sent to the user's terminal. The user can then check these key locations on the terminal and efficiently carry out search activities.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The server connects to the database to retrieve the subject's travel history information. This includes information such as places visited in the past, travel routes, and public transportation used.

[0408] Step 2:

[0409] The server applies an algorithm to estimate the wandering range based on the collected movement history. The algorithm analyzes past behavior patterns and estimates the most likely movement range, taking into account movement speed and frequently visited locations.

[0410] Step 3:

[0411] The server combines the estimated wandering area with geographic information data and visualizes it on a map. Furthermore, it identifies and lists notable locations within the estimated area (such as important bus stops, commercial facilities, and parks).

[0412] Step 4:

[0413] The server prepares the generated map and information on points of interest as a data package for transmission to the terminal.

[0414] Step 5:

[0415] The device receives information sent from the server and displays it visually in a map application. It plots the estimated wandering area and points of interest on the map for the user to see in real time.

[0416] Step 6:

[0417] Users check the map display on their device and create a search plan within the estimated wandering area. If necessary, they cooperate with local residents and search volunteers to carry out search operations efficiently.

[0418] (Example 1)

[0419] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0420] When elderly individuals or those with dementia wander off, quickly and accurately determining their range of movement is crucial for improving search efficiency and reducing social costs. However, current systems fail to adequately predict the range of a person's wandering, and the information necessary for search operations is not acquired and updated in real time.

[0421] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0422] In this invention, the server includes means for acquiring movement history information, means for executing an algorithm that analyzes the movement history information to predict the range of the subject's movements, and means for integrating the predicted range of movements with map information to identify important locations. This enables the rapid prediction of the subject's movements and the identification of important locations, thereby improving the efficiency and speed of the search.

[0423] "Travel history information" refers to data about places and routes that the subject has visited in the past, as well as the means of transportation used.

[0424] "Range of activity" refers to the area in which the subject can move within a specific time period, and includes the predicted area of ​​wandering.

[0425] "Means for executing an algorithm" refers to a program or process that automatically performs analysis and calculations based on acquired data and generates prediction results.

[0426] "Map information" refers to digital data that shows the geographical features, facilities, and transportation arrangements of a region, and is used for visualization.

[0427] A "key location" refers to a specific point within the predicted range of movement that should be prioritized for search and rescue operations, such as a location or facility.

[0428] A "display device" is a screen or display device used to visually confirm information.

[0429] A "generative artificial intelligence model" is a machine learning model that generates new information or suggestions based on given prompts or data.

[0430] This invention relates to a system that uses a subject's movement history information to predict and visually display their range of activity. Specifically, it is implemented through the collaboration of a server, a terminal, and a user.

[0431] The server first retrieves the subject's travel history information from a database. This information includes past visited locations, routes, and public transportation used. The server organizes the information using a programming language such as Python and generates a dataset for analysis. For data analysis, data processing and machine learning libraries such as the pandas library and scikit-learn can be used. Using these, the server predicts the subject's range of movement.

[0432] The predicted range of activity is then integrated with map information. Here, GIS software (e.g., QGIS) is used to identify key points within the range of activity. These identified key points are plotted on a topographic map and transmitted from the server to the terminal as a visual guide.

[0433] The device receives information sent from the server and visualizes it on a map application using the Google Maps API, etc. This allows users to receive real-time information updates and confirm the locations of important points. Based on this, users can plan search operations and movements.

[0434] Furthermore, the generative AI model can be used to generate new search strategy ideas. For example, a prompt such as "Suggest a priority route to take in this area" can be entered. This allows the generative AI model to suggest the optimal search route, and the user can then use that information to plan their search.

[0435] As a concrete example, in the search for an elderly person, this system can be used to predict their range of movement based on past travel patterns, allowing for the identification of important locations such as bus stops, commercial facilities, and parks. Based on this information, users can expect to conduct search operations more effectively.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The server retrieves the subject's travel history information from the database. This information includes data on past visited locations, routes, and public transportation used. The input is raw travel history data, and the output is formatted historical data. Specifically, it extracts data using SQL queries and formats the data using the Python pandas library.

[0439] Step 2:

[0440] The server predicts the range of movement based on formatted movement history data. At this stage, it analyzes behavioral patterns based on past visit frequency and time of day, and makes predictions using a machine learning model. The input is formatted history data, and the output is the predicted range of movement. Specifically, the scikit-learn library is used to train and apply the range of movement model.

[0441] Step 3:

[0442] The server integrates the predicted range of activity with map information, thereby identifying key points within that range. The input is the predicted range of activity, and the output is integrated data including key points. Specifically, GIS software (e.g., QGIS) is used to plot the range of activity on the map data and identify key points.

[0443] Step 4:

[0444] The server sends the integrated data to the terminal. The terminal visualizes the data on a map application using the Google Maps API. The input is the integrated data from the server, and the output is the visualized map information. Specifically, the received data is reflected on the map in real time and displayed to the user.

[0445] Step 5:

[0446] The user reviews visualized information through their device and plans their search operation. If necessary, they input prompts into a generating AI model to generate a new search strategy. The input consists of visualized map information and prompts, while the output is the optimal search strategy. Specifically, prompts are sent to the generating AI model, which then develops a search plan based on the received suggestions.

[0447] (Application Example 1)

[0448] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] The problem of elderly people and dementia patients wandering off and going missing is a serious social issue, and there is a need for a system that supports their rapid discovery and safe return home. With current technology, it is difficult to accurately grasp the abnormal movements of the person in question in real time and efficiently identify the area where they wandered, which hinders the speed and efficiency of search operations.

[0450] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0451] In this invention, the server includes a device equipped with a function to acquire location information of a target person, a device equipped with a function to analyze the location information and estimate the target person's range of movement, and a device equipped with a function to visually represent the estimated range of movement on a map. This enables accurate estimation of the target person's range of movement and display of that information on a map, thereby allowing for the rapid and efficient planning and execution of search activities.

[0452] "Subject" refers to an individual whose specific behavior or movements are observed and tracked.

[0453] "Location information" refers to information indicating the current location of a subject, and is usually represented by geographical location data such as GPS data.

[0454] "Device" refers to a machine that includes hardware or software for performing a specific function.

[0455] "Analysis" is the process of examining data and information in detail to draw specific conclusions or inferences.

[0456] "Range of activity" refers to the geographical area in which the subject can move.

[0457] "To infer" means to predict future conditions or events based on existing data and analysis results.

[0458] "Visual representation" means displaying information or data in a format that is easy to understand visually, such as maps or graphs.

[0459] A "machine" is a collection of parts or devices configured to perform a specific task.

[0460] This invention is a system for monitoring a subject's location information in real time and quickly identifying abnormal behavior. The system mainly consists of three components: a server, a terminal, and a user.

[0461] First, the server functions as the central hub for information processing. Location information of the subject is obtained from GPS data acquired from smartphones and wearable devices. This data is first sent to the server, where a Python script is used to analyze the range of movement. The estimated range of movement is then visually represented on a map using the Google Maps API. Furthermore, the system has the capability to acquire real-time monitoring information on transportation methods and correct the analysis results accordingly.

[0462] The terminal is a device that allows users to check this information and conduct search operations effectively. Smartphones or smart glasses are primarily used, and the estimated range of movement and key locations are visually displayed on the Google Maps application. The terminal receives information from the server in real time and presents the latest data to the user.

[0463] Through this system, users can view the latest movements of a person in question on a map and select an appropriate search route. For example, it becomes possible to quickly identify important locations within a certain range from where an elderly person was last seen and efficiently visit those locations.

[0464] As a concrete example, consider a scenario where a local volunteer group uses this system to search for a missing elderly person. The system is expected to contribute to the rapid implementation of a search by indicating likely locations based on the elderly person's past behavioral patterns. Instructions can be given to the generating AI model by inputting prompts such as, "Based on the elderly person's latest GPS data and past movement history, display the reachable area on a map and suggest an efficient search route."

[0465] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0466] Step 1:

[0467] The server receives GPS data in real time from the subject's smartphone or wearable device. This data indicates the subject's current location. Based on this location information, the server compares it with the movement history in the database and begins data analysis to understand the latest behavioral patterns.

[0468] Step 2:

[0469] The server uses a Python analysis script to estimate the subject's range of movement based on acquired GPS data and movement history. The input data consists of real-time location information and historical movement history data, and the output identifies areas where the subject is likely to move in the future. This prepares the system for geographically visualizing the estimated range.

[0470] Step 3:

[0471] The server uses the Google Maps API to prepare data for visually displaying the estimated range of movement on a map. In this process, the estimated area is input into the API, and the corresponding geographical information is retrieved. As output, the range of movement is plotted on the map, and points of interest are identified.

[0472] Step 4:

[0473] The server acquires real-time monitoring data on means of transport (such as public transportation) and corrects and updates information on the range of movement. The input data is location information of the means of transport, and the output provides the latest range of movement. This enables more accurate search guidance.

[0474] Step 5:

[0475] The device receives information about the estimated range of movement transmitted from the server. A map application is used to display this information on the device, visually presenting it to the user. This allows the user to check the target person's range of movement in real time.

[0476] Step 6:

[0477] Users plan and execute search operations based on map information displayed on their devices. Specifically, they select routes to visit points of interest displayed on the map, enabling efficient searching. User actions contribute to a feedback loop across the entire connected system, leading to better data acquisition.

[0478] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0479] This invention is a system aimed at the early detection of wandering individuals, and further improves the efficiency of search activities by incorporating an emotion engine that recognizes the user's emotions. This system consists of a server, a terminal, and an emotion engine.

[0480] The server acquires the subject's movement history and real-time monitoring information on public transportation, and analyzes this data to estimate their wandering range. Based on this, the server identifies the estimated wandering range and important points of interest, and sends this information to the terminal. At this time, emotion engine data is added, and information is provided according to the user's emotional state.

[0481] The emotion engine analyzes the user's stress level and emotional state from their voice input and device operation patterns. For example, if the user shows signs of anxiety or impatience, the system will focus on providing high-priority information and advise on search procedures to help the user feel more at ease. Furthermore, if the user's stress levels are high, the system will offer support to make the search easier and reduce their burden.

[0482] As a concrete example, suppose a user begins searching for a wandering person, and the device's emotion engine detects anxiety from the user's voice. The server then narrows down the locations to high-priority ones and sends detailed search instructions to the device. The user can then efficiently search those locations without feeling stressed, increasing the likelihood of finding the wandering person in a short amount of time.

[0483] In this way, by utilizing an emotion engine, this system provides flexible information and support tailored to each user's emotional state, thereby increasing the efficiency of search operations for wandering individuals.

[0484] The following describes the processing flow.

[0485] Step 1:

[0486] The server retrieves the subject's travel history data from the database. The retrieved data includes visit history, travel routes, and information on public transportation used in the past.

[0487] Step 2:

[0488] The server applies an algorithm to estimate the wandering range based on the acquired movement history data. This algorithm analyzes past behavior patterns and movement speed to estimate the most likely movement range.

[0489] Step 3:

[0490] The estimated wandering area is integrated with map information by the server. The server lists points of interest within the identified movement range (e.g., major intersections and bus stops) and prepares to send them to the terminal.

[0491] Step 4:

[0492] The device receives wandering range data and point of interest information transmitted from the server. The emotion engine installed in the device analyzes the user's emotional state from voice input and operation patterns.

[0493] Step 5:

[0494] The emotion engine adjusts how information is displayed based on the analyzed emotional state of the user. For example, if the user is showing anxiety, the device will display recommended search routes concisely and provide information in a way that provides reassurance.

[0495] Step 6:

[0496] Users begin their search based on the information presented on their device. By providing information tailored to their emotions, they can conduct their search effectively while reducing stress.

[0497] Step 7:

[0498] As the search progresses, users provide feedback to their devices with new information. This information is sent to the server, which then re-estimates the area the person is wandering and updates the information.

[0499] (Example 2)

[0500] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0501] Conventional technologies for locating wandering individuals early relied solely on movement history and surveillance data for estimation, resulting in limitations in accuracy and time efficiency. Furthermore, the lack of measures to alleviate the psychological burden on users conducting the search sometimes led to decreased search efficiency.

[0502] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0503] In this invention, the server includes means for acquiring the movement history of a target person, means for analyzing the movement history to estimate the target person's wandering range, means for displaying the estimated wandering range on a map, means for analyzing the user's voice input and operation patterns to evaluate their emotional state, and means for adjusting the content of information provided based on the evaluated emotional state. This makes it possible to quickly and accurately identify the wandering range of a person who has wandered off, and to carry out an efficient search while reducing the emotional burden on the user.

[0504] "The subject's travel history" refers to records of places the designated individual has visited or routes they have traveled in the past.

[0505] "Wandering range" refers to the area where the suspect is expected to move or the area where they are most likely to travel, and it is the location where search operations are conducted based on this range.

[0506] "User voice input" refers to voice information spoken by system users into their terminals, and is used as data to analyze their emotional state and intentions.

[0507] "Operation patterns" refer to the tendencies and methods of a user's actions when using a device, and these can serve as clues to assess their emotional state.

[0508] "Emotional state" refers to the psychological state a user exhibits under specific circumstances, and includes internal reactions such as stress and anxiety.

[0509] "Means for adjusting the content of information provided" refers to a mechanism that changes the type and priority of information provided based on the user's emotional state, enabling more appropriate support.

[0510] This invention is a system aimed at the early detection of wandering individuals, and improves the efficiency of search operations by integrating an emotion engine that analyzes the user's emotions. This system incorporates a server, a terminal, and an emotion engine, each playing a specific role.

[0511] The server first retrieves the subject's travel history from a database. This history includes past visited locations and travel routes, and Python and data processing libraries (e.g., Pandas, NumPy) are used to analyze it. Additionally, when obtaining real-time public transport information, a transportation data API (e.g., a map application API) is used. Based on this information, the server estimates the subject's wandering range and displays that range on a map.

[0512] The terminal is a user-operated device equipped with an emotion engine (e.g., emotion analysis API). When the user provides voice input, the terminal records this voice using a microphone and converts it to text using speech recognition technology (e.g., speech recognition API). The terminal sends this text to the emotion engine, which evaluates the user's emotional state. Based on the evaluation results, the information received from the server is adjusted and provided to the user.

[0513] For example, if a user shows signs of anxiety while conducting a search, the server will prioritize displaying the most important search points based on the analysis results of the emotion engine. This allows the user to continue their search with peace of mind and efficiency.

[0514] An example of a prompt message is, "Please tell me about an emotional support system that advises users on the steps they should take to efficiently search for a wandering person."

[0515] Thus, the present invention aims to reduce the psychological burden on users and improve search efficiency by making full use of emotion analysis technology.

[0516] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0517] Step 1:

[0518] The server retrieves the subject's movement history from a database. This history includes locations the subject has visited in the past and the routes they have traveled. Using Python and data processing libraries, this data is read and movement patterns are analyzed. This analysis provides the foundational data needed to estimate areas where the subject may wander.

[0519] Step 2:

[0520] The server obtains real-time public transport information through a transport data API. This input information includes data on the modes of transport the target individual is likely to use. Using the obtained information, the server performs analysis in conjunction with the travel history to identify the target individual's predicted wandering area. This allows for the correction of the wandering area based on travel history using real-time data, thereby improving accuracy.

[0521] Step 3:

[0522] The server sends the estimated wandering area along with map data to the terminal. The user checks this information through the terminal. The wandering area is visually displayed on the map, providing the user with a basis for deciding where to go next.

[0523] Step 4:

[0524] The device records the user's voice input using a microphone and converts it into text data using a speech recognition API. If the user says, "I don't know where to go," the device sends this text to a generating AI model to obtain data for sentiment analysis.

[0525] Step 5:

[0526] The device sends the acquired text data to the sentiment analysis engine, which evaluates the user's emotional state. For example, if the analysis results indicate the user is anxious, the sentiment analysis engine sends instructions to the server based on that evaluation. The output of the sentiment analysis is information about the user's psychological state.

[0527] Step 6:

[0528] The server receives the results of sentiment analysis and adjusts the priority of the information. It rearranges the search locations in the optimal order according to the user's emotional state and reconstructs the information necessary for the search. The reconstructed information is provided in a way that is tailored to the user's emotional state, allowing them to proceed with the search with greater peace of mind.

[0529] Step 7:

[0530] The device displays the reconstructed information to the user. Based on this information, the user heads to priority locations such as parks and train stations and begins an efficient search. This process promotes the early detection of the wandering person and reduces the psychological burden on the user.

[0531] (Application Example 2)

[0532] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0533] In search and rescue operations for wandering individuals, a challenge exists: increased stress and anxiety among searchers can hinder efficient searches. Especially in situations where the early discovery of elderly individuals or missing persons is crucial, providing appropriate information tailored to the searchers' psychological state is essential.

[0534] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0535] In this invention, the server includes means for acquiring the movement history of a target person, means for analyzing the movement history to estimate the target person's wandering range, means for displaying the estimated wandering range on a map, means for recognizing the user's emotions, and means for providing high-priority information based on the user's emotions. This makes it possible to appropriately judge the user's emotional state and conduct efficient search activities while reducing stress.

[0536] A "subject" refers to an individual who may be prone to wandering or disappearing, and is the subject of observation regarding their specific behaviors and movements.

[0537] "Means for acquiring movement history" refers to a system that has the function of acquiring and recording the past and present location information of a subject.

[0538] "Means for estimating the range of wandering" refers to an analytical function that identifies areas or regions where the subject may have moved, based on the acquired movement history.

[0539] "Means of displaying on a map" refers to the function of displaying the estimated wandering area on a map using a geographic information system in order to visually represent it.

[0540] A "means of recognizing user emotions" refers to a system that identifies and analyzes a user's psychological state and emotions through voice input and operation patterns.

[0541] "Means of providing high-priority information" refers to a function that selects and presents information of high importance as needed, based on the user's emotional state.

[0542] To realize this application, it is essential to use a server, a terminal, and an emotion engine. The server interacts with a location database to obtain the subject's movement history and collects real-time location information. Next, the server analyzes the obtained data to estimate the subject's wandering range. In this process, the program implements a data analysis algorithm using Python to effectively calculate the wandering range.

[0543] The estimated wandering area is displayed on a map using a Geographic Information System (GIS). This display is implemented as a user interface in a terminal application using React Native. By accessing this map information, users can visually confirm the possible locations of the person in question.

[0544] Furthermore, the device incorporates speech recognition software that recognizes emotions from the user's voice input. Here, the Google Cloud Natural Language API is used to analyze the voice data and identify the user's emotional state. Based on this information, the emotion engine extracts high-priority information, enabling the provision of information tailored to the user's emotions.

[0545] For example, if a user voice-inputs, "My mother has been going out frequently lately, and I'm worried," the emotion engine detects the anxiety, and the server automatically determines high-priority monitoring areas. As a result, "important information regarding your mother's recent activity range" is highlighted on the device. At this point, a generative AI model is used to provide appropriate advice and information to alleviate the user's psychological burden.

[0546] An example of a prompt message is, "If a user types 'I feel uneasy when I'm alone at night,' how would you provide reassuring information?"

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The server retrieves the subject's movement history from a location database. In this step, past location information is aggregated using database queries. The input is the subject's ID, and the output is a dataset of time and location associated with that ID.

[0550] Step 2:

[0551] The server analyzes the acquired movement history to estimate the subject's wandering range. It uses Python to execute a data analysis algorithm, calculating the most likely wandering range based on geographical data. The input is the location data from step 1, and the output is a list of coordinates for the estimated wandering range.

[0552] Step 3:

[0553] The server converts the estimated wandering area into a geodata format and sends it to the terminal. The input is a list of coordinates for the wandering area, and the output is map data that can be displayed on the terminal.

[0554] Step 4:

[0555] The device displays the received map data on a map using React Native. The input is geodata sent from the server, allowing the user to visually confirm their roaming area. The output is visual information displayed on the map.

[0556] Step 5:

[0557] The user inputs emotional data into the device via voice input. In this step, voice data that identifies the user's emotional state is input.

[0558] Step 6:

[0559] The device performs sentiment analysis using the Google Cloud Natural Language API. The input is the audio data from step 5, and the emotional state is output through the analysis. Specifically, it determines emotions from certain keywords or the tone of voice.

[0560] Step 7:

[0561] The emotion engine requests high-priority information from the server based on the user's emotions. The input is analyzed emotion data, and the server generates a set of information with re-evaluated importance based on that data.

[0562] Step 8:

[0563] The server selects high-priority information and sends it to the terminal. The input is a request based on the user's emotional state, and the output is a set of information corresponding to that emotion.

[0564] Step 9:

[0565] The device clearly presents high-priority information to the user. Users can receive advice and procedures to conduct search operations efficiently while reducing stress. Output consists of visual and audio notifications.

[0566] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0567] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0568] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0569] [Fourth Embodiment]

[0570] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0571] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0572] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0573] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0574] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0575] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0576] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0577] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0578] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0579] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0580] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0581] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0582] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0583] This invention is a system for the early detection of a person wandering. This system includes a server, a terminal, and a user.

[0584] First, the server retrieves the subject's travel history from the database. This travel history includes information such as past visited locations and routes, and the public transportation used. Based on this data, the server executes an algorithm to estimate the subject's wandering range.

[0585] The estimated wandering area is processed by the server along with map data. The map data includes local geographical information, identifying important locations within the estimated area, such as bus stops, commercial facilities, and parks. This information is then listed as high-priority locations for the search.

[0586] The server sends this information to the terminal. The terminal visualizes the wandering area and points of interest received from the server on a map application and displays it to the user. This map serves as a guide for the user to conduct an effective search.

[0587] Users can check real-time updated information on the missing person's location through their device. This allows users to properly plan search operations and, if necessary, cooperate with local police and volunteers to carry out the search.

[0588] As a concrete example, suppose an elderly person went missing after being last seen at a shopping center at 3 PM. The server uses the elderly person's past data to estimate the range they could reach within approximately three hours, based on their normal walking speed and places they frequently visited. Based on this estimation, bus stop A, park B, and shopping street C are identified as key locations, and this information is sent to the user's terminal. The user can then check these key locations on the terminal and efficiently carry out search activities.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The server connects to the database to retrieve the subject's travel history information. This includes information such as places visited in the past, travel routes, and public transportation used.

[0592] Step 2:

[0593] The server applies an algorithm to estimate the wandering range based on the collected movement history. The algorithm analyzes past behavior patterns and estimates the most likely movement range, taking into account movement speed and frequently visited locations.

[0594] Step 3:

[0595] The server combines the estimated wandering area with geographic information data and visualizes it on a map. Furthermore, it identifies and lists notable locations within the estimated area (such as important bus stops, commercial facilities, and parks).

[0596] Step 4:

[0597] The server prepares the generated map and information on points of interest as a data package for transmission to the terminal.

[0598] Step 5:

[0599] The device receives information sent from the server and displays it visually in a map application. It plots the estimated wandering area and points of interest on the map for the user to see in real time.

[0600] Step 6:

[0601] Users check the map display on their device and create a search plan within the estimated wandering area. If necessary, they cooperate with local residents and search volunteers to carry out search operations efficiently.

[0602] (Example 1)

[0603] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0604] When elderly individuals or those with dementia wander off, quickly and accurately determining their range of movement is crucial for improving search efficiency and reducing social costs. However, current systems fail to adequately predict the range of a person's wandering, and the information necessary for search operations is not acquired and updated in real time.

[0605] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0606] In this invention, the server includes means for acquiring movement history information, means for executing an algorithm that analyzes the movement history information to predict the range of the subject's movements, and means for integrating the predicted range of movements with map information to identify important locations. This enables the rapid prediction of the subject's movements and the identification of important locations, thereby improving the efficiency and speed of the search.

[0607] "Travel history information" refers to data about places and routes that the subject has visited in the past, as well as the means of transportation used.

[0608] "Range of activity" refers to the area in which the subject can move within a specific time period, and includes the predicted area of ​​wandering.

[0609] "Means for executing an algorithm" refers to a program or process that automatically performs analysis and calculations based on acquired data and generates prediction results.

[0610] "Map information" refers to digital data that shows the geographical features, facilities, and transportation arrangements of a region, and is used for visualization.

[0611] A "key location" refers to a specific point within the predicted range of movement that should be prioritized for search and rescue operations, such as a location or facility.

[0612] A "display device" is a screen or display device used to visually confirm information.

[0613] A "generative artificial intelligence model" is a machine learning model that generates new information or suggestions based on given prompts or data.

[0614] This invention relates to a system that uses a subject's movement history information to predict and visually display their range of activity. Specifically, it is implemented through the collaboration of a server, a terminal, and a user.

[0615] The server first retrieves the subject's travel history information from a database. This information includes past visited locations, routes, and public transportation used. The server organizes the information using a programming language such as Python and generates a dataset for analysis. For data analysis, data processing and machine learning libraries such as the pandas library and scikit-learn can be used. Using these, the server predicts the subject's range of movement.

[0616] The predicted range of activity is then integrated with map information. Here, GIS software (e.g., QGIS) is used to identify key points within the range of activity. These identified key points are plotted on a topographic map and transmitted from the server to the terminal as a visual guide.

[0617] The device receives information sent from the server and visualizes it on a map application using the Google Maps API, etc. This allows users to receive real-time information updates and confirm the locations of important points. Based on this, users can plan search operations and movements.

[0618] Furthermore, the generative AI model can be used to generate new search strategy ideas. For example, a prompt such as "Suggest a priority route to take in this area" can be entered. This allows the generative AI model to suggest the optimal search route, and the user can then use that information to plan their search.

[0619] As a concrete example, in the search for an elderly person, this system can be used to predict their range of movement based on past travel patterns, allowing for the identification of important locations such as bus stops, commercial facilities, and parks. Based on this information, users can expect to conduct search operations more effectively.

[0620] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0621] Step 1:

[0622] The server retrieves the subject's travel history information from the database. This information includes data on past visited locations, routes, and public transportation used. The input is raw travel history data, and the output is formatted historical data. Specifically, it extracts data using SQL queries and formats the data using the Python pandas library.

[0623] Step 2:

[0624] The server predicts the range of movement based on formatted movement history data. At this stage, it analyzes behavioral patterns based on past visit frequency and time of day, and makes predictions using a machine learning model. The input is formatted history data, and the output is the predicted range of movement. Specifically, the scikit-learn library is used to train and apply the range of movement model.

[0625] Step 3:

[0626] The server integrates the predicted range of activity with map information, thereby identifying key points within that range. The input is the predicted range of activity, and the output is integrated data including key points. Specifically, GIS software (e.g., QGIS) is used to plot the range of activity on the map data and identify key points.

[0627] Step 4:

[0628] The server sends the integrated data to the terminal. The terminal visualizes the data on a map application using the Google Maps API. The input is the integrated data from the server, and the output is the visualized map information. Specifically, the received data is reflected on the map in real time and displayed to the user.

[0629] Step 5:

[0630] The user reviews visualized information through their device and plans their search operation. If necessary, they input prompts into a generating AI model to generate a new search strategy. The input consists of visualized map information and prompts, while the output is the optimal search strategy. Specifically, prompts are sent to the generating AI model, which then develops a search plan based on the received suggestions.

[0631] (Application Example 1)

[0632] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] The problem of elderly people and dementia patients wandering off and going missing is a serious social issue, and there is a need for a system that supports their rapid discovery and safe return home. With current technology, it is difficult to accurately grasp the abnormal movements of the person in question in real time and efficiently identify the area where they wandered, which hinders the speed and efficiency of search operations.

[0634] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0635] In this invention, the server includes a device equipped with a function to acquire location information of a target person, a device equipped with a function to analyze the location information and estimate the target person's range of movement, and a device equipped with a function to visually represent the estimated range of movement on a map. This enables accurate estimation of the target person's range of movement and display of that information on a map, thereby allowing for the rapid and efficient planning and execution of search activities.

[0636] "Subject" refers to an individual whose specific behavior or movements are observed and tracked.

[0637] "Location information" refers to information indicating the current location of a subject, and is usually represented by geographical location data such as GPS data.

[0638] "Device" refers to a machine that includes hardware or software for performing a specific function.

[0639] "Analysis" is the process of examining data and information in detail to draw specific conclusions or inferences.

[0640] "Range of activity" refers to the geographical area in which the subject can move.

[0641] "To infer" means to predict future conditions or events based on existing data and analysis results.

[0642] "Visual representation" means displaying information or data in a format that is easy to understand visually, such as maps or graphs.

[0643] A "machine" is a collection of parts or devices configured to perform a specific task.

[0644] This invention is a system for monitoring a subject's location information in real time and quickly identifying abnormal behavior. The system mainly consists of three components: a server, a terminal, and a user.

[0645] First, the server functions as the central hub for information processing. Location information of the subject is obtained from GPS data acquired from smartphones and wearable devices. This data is first sent to the server, where a Python script is used to analyze the range of movement. The estimated range of movement is then visually represented on a map using the Google Maps API. Furthermore, the system has the capability to acquire real-time monitoring information on transportation methods and correct the analysis results accordingly.

[0646] The terminal is a device that allows users to check this information and conduct search operations effectively. Smartphones or smart glasses are primarily used, and the estimated range of movement and key locations are visually displayed on the Google Maps application. The terminal receives information from the server in real time and presents the latest data to the user.

[0647] Through this system, users can view the latest movements of a person in question on a map and select an appropriate search route. For example, it becomes possible to quickly identify important locations within a certain range from where an elderly person was last seen and efficiently visit those locations.

[0648] As a concrete example, consider a scenario where a local volunteer group uses this system to search for a missing elderly person. The system is expected to contribute to the rapid implementation of a search by indicating likely locations based on the elderly person's past behavioral patterns. Instructions can be given to the generating AI model by inputting prompts such as, "Based on the elderly person's latest GPS data and past movement history, display the reachable area on a map and suggest an efficient search route."

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The server receives GPS data in real time from the subject's smartphone or wearable device. This data indicates the subject's current location. Based on this location information, the server compares it with the movement history in the database and begins data analysis to understand the latest behavioral patterns.

[0652] Step 2:

[0653] The server uses a Python analysis script to estimate the subject's range of movement based on acquired GPS data and movement history. The input data consists of real-time location information and historical movement history data, and the output identifies areas where the subject is likely to move in the future. This prepares the system for geographically visualizing the estimated range.

[0654] Step 3:

[0655] The server uses the Google Maps API to prepare data for visually displaying the estimated range of movement on a map. In this process, the estimated area is input into the API, and the corresponding geographical information is retrieved. As output, the range of movement is plotted on the map, and points of interest are identified.

[0656] Step 4:

[0657] The server acquires real-time monitoring data on means of transport (such as public transportation) and corrects and updates information on the range of movement. The input data is location information of the means of transport, and the output provides the latest range of movement. This enables more accurate search guidance.

[0658] Step 5:

[0659] The device receives information about the estimated range of movement transmitted from the server. A map application is used to display this information on the device, visually presenting it to the user. This allows the user to check the target person's range of movement in real time.

[0660] Step 6:

[0661] Users plan and execute search operations based on map information displayed on their devices. Specifically, they select routes to visit points of interest displayed on the map, enabling efficient searching. User actions contribute to a feedback loop across the entire connected system, leading to better data acquisition.

[0662] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0663] This invention is a system aimed at the early detection of wandering individuals, and further improves the efficiency of search activities by incorporating an emotion engine that recognizes the user's emotions. This system consists of a server, a terminal, and an emotion engine.

[0664] The server acquires the subject's movement history and real-time monitoring information on public transportation, and analyzes this data to estimate their wandering range. Based on this, the server identifies the estimated wandering range and important points of interest, and sends this information to the terminal. At this time, emotion engine data is added, and information is provided according to the user's emotional state.

[0665] The emotion engine analyzes the user's stress level and emotional state from their voice input and device operation patterns. For example, if the user shows signs of anxiety or impatience, the system will focus on providing high-priority information and advise on search procedures to help the user feel more at ease. Furthermore, if the user's stress levels are high, the system will offer support to make the search easier and reduce their burden.

[0666] As a concrete example, suppose a user begins searching for a wandering person, and the device's emotion engine detects anxiety from the user's voice. The server then narrows down the locations to high-priority ones and sends detailed search instructions to the device. The user can then efficiently search those locations without feeling stressed, increasing the likelihood of finding the wandering person in a short amount of time.

[0667] In this way, by utilizing an emotion engine, this system provides flexible information and support tailored to each user's emotional state, thereby increasing the efficiency of search operations for wandering individuals.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The server retrieves the subject's travel history data from the database. The retrieved data includes visit history, travel routes, and information on public transportation used in the past.

[0671] Step 2:

[0672] The server applies an algorithm to estimate the wandering range based on the acquired movement history data. This algorithm analyzes past behavior patterns and movement speed to estimate the most likely movement range.

[0673] Step 3:

[0674] The estimated wandering area is integrated with map information by the server. The server lists points of interest within the identified movement range (e.g., major intersections and bus stops) and prepares to send them to the terminal.

[0675] Step 4:

[0676] The device receives wandering range data and point of interest information transmitted from the server. The emotion engine installed in the device analyzes the user's emotional state from voice input and operation patterns.

[0677] Step 5:

[0678] The emotion engine adjusts how information is displayed based on the analyzed emotional state of the user. For example, if the user is showing anxiety, the device will display recommended search routes concisely and provide information in a way that provides reassurance.

[0679] Step 6:

[0680] Users begin their search based on the information presented on their device. By providing information tailored to their emotions, they can conduct their search effectively while reducing stress.

[0681] Step 7:

[0682] As the search progresses, users provide feedback to their devices with new information. This information is sent to the server, which then re-estimates the area the person is wandering and updates the information.

[0683] (Example 2)

[0684] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] Conventional technologies for locating wandering individuals early relied solely on movement history and surveillance data for estimation, resulting in limitations in accuracy and time efficiency. Furthermore, the lack of measures to alleviate the psychological burden on users conducting the search sometimes led to decreased search efficiency.

[0686] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0687] In this invention, the server includes means for acquiring the movement history of a target person, means for analyzing the movement history to estimate the target person's wandering range, means for displaying the estimated wandering range on a map, means for analyzing the user's voice input and operation patterns to evaluate their emotional state, and means for adjusting the content of information provided based on the evaluated emotional state. This makes it possible to quickly and accurately identify the wandering range of a person who has wandered off, and to carry out an efficient search while reducing the emotional burden on the user.

[0688] "The subject's travel history" refers to records of places the designated individual has visited or routes they have traveled in the past.

[0689] "Wandering range" refers to the area where the suspect is expected to move or the area where they are most likely to travel, and it is the location where search operations are conducted based on this range.

[0690] "User voice input" refers to voice information spoken by system users into their terminals, and is used as data to analyze their emotional state and intentions.

[0691] "Operation patterns" refer to the tendencies and methods of a user's actions when using a device, and these can serve as clues to assess their emotional state.

[0692] "Emotional state" refers to the psychological state a user exhibits under specific circumstances, and includes internal reactions such as stress and anxiety.

[0693] "Means for adjusting the content of information provided" refers to a mechanism that changes the type and priority of information provided based on the user's emotional state, enabling more appropriate support.

[0694] This invention is a system aimed at the early detection of wandering individuals, and improves the efficiency of search operations by integrating an emotion engine that analyzes the user's emotions. This system incorporates a server, a terminal, and an emotion engine, each playing a specific role.

[0695] The server first retrieves the subject's travel history from a database. This history includes past visited locations and travel routes, and Python and data processing libraries (e.g., Pandas, NumPy) are used to analyze it. Additionally, when obtaining real-time public transport information, a transportation data API (e.g., a map application API) is used. Based on this information, the server estimates the subject's wandering range and displays that range on a map.

[0696] The terminal is a user-operated device equipped with an emotion engine (e.g., emotion analysis API). When the user provides voice input, the terminal records this voice using a microphone and converts it to text using speech recognition technology (e.g., speech recognition API). The terminal sends this text to the emotion engine, which evaluates the user's emotional state. Based on the evaluation results, the information received from the server is adjusted and provided to the user.

[0697] For example, if a user shows signs of anxiety while conducting a search, the server will prioritize displaying the most important search points based on the analysis results of the emotion engine. This allows the user to continue their search with peace of mind and efficiency.

[0698] An example of a prompt message is, "Please tell me about an emotional support system that advises users on the steps they should take to efficiently search for a wandering person."

[0699] Thus, the present invention aims to reduce the psychological burden on users and improve search efficiency by making full use of emotion analysis technology.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The server retrieves the subject's movement history from a database. This history includes locations the subject has visited in the past and the routes they have traveled. Using Python and data processing libraries, this data is read and movement patterns are analyzed. This analysis provides the foundational data needed to estimate areas where the subject may wander.

[0703] Step 2:

[0704] The server obtains real-time public transport information through a transport data API. This input information includes data on the modes of transport the target individual is likely to use. Using the obtained information, the server performs analysis in conjunction with the travel history to identify the target individual's predicted wandering area. This allows for the correction of the wandering area based on travel history using real-time data, thereby improving accuracy.

[0705] Step 3:

[0706] The server sends the estimated wandering area along with map data to the terminal. The user checks this information through the terminal. The wandering area is visually displayed on the map, providing the user with a basis for deciding where to go next.

[0707] Step 4:

[0708] The device records the user's voice input using a microphone and converts it into text data using a speech recognition API. If the user says, "I don't know where to go," the device sends this text to a generating AI model to obtain data for sentiment analysis.

[0709] Step 5:

[0710] The device sends the acquired text data to the sentiment analysis engine, which evaluates the user's emotional state. For example, if the analysis results indicate the user is anxious, the sentiment analysis engine sends instructions to the server based on that evaluation. The output of the sentiment analysis is information about the user's psychological state.

[0711] Step 6:

[0712] The server receives the results of sentiment analysis and adjusts the priority of the information. It rearranges the search locations in the optimal order according to the user's emotional state and reconstructs the information necessary for the search. The reconstructed information is provided in a way that is tailored to the user's emotional state, allowing them to proceed with the search with greater peace of mind.

[0713] Step 7:

[0714] The device displays the reconstructed information to the user. Based on this information, the user heads to priority locations such as parks and train stations and begins an efficient search. This process promotes the early detection of the wandering person and reduces the psychological burden on the user.

[0715] (Application Example 2)

[0716] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0717] In search and rescue operations for wandering individuals, a challenge exists: increased stress and anxiety among searchers can hinder efficient searches. Especially in situations where the early discovery of elderly individuals or missing persons is crucial, providing appropriate information tailored to the searchers' psychological state is essential.

[0718] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0719] In this invention, the server includes means for acquiring the movement history of a target person, means for analyzing the movement history to estimate the target person's wandering range, means for displaying the estimated wandering range on a map, means for recognizing the user's emotions, and means for providing high-priority information based on the user's emotions. This makes it possible to appropriately judge the user's emotional state and conduct efficient search activities while reducing stress.

[0720] A "subject" refers to an individual who may be prone to wandering or disappearing, and is the subject of observation regarding their specific behaviors and movements.

[0721] "Means for acquiring movement history" refers to a system that has the function of acquiring and recording the past and present location information of a subject.

[0722] "Means for estimating the range of wandering" refers to an analytical function that identifies areas or regions where the subject may have moved, based on the acquired movement history.

[0723] "Means of displaying on a map" refers to the function of displaying the estimated wandering area on a map using a geographic information system in order to visually represent it.

[0724] A "means of recognizing user emotions" refers to a system that identifies and analyzes a user's psychological state and emotions through voice input and operation patterns.

[0725] "Means of providing high-priority information" refers to a function that selects and presents information of high importance as needed, based on the user's emotional state.

[0726] To realize this application, it is essential to use a server, a terminal, and an emotion engine. The server interacts with a location database to obtain the subject's movement history and collects real-time location information. Next, the server analyzes the obtained data to estimate the subject's wandering range. In this process, the program implements a data analysis algorithm using Python to effectively calculate the wandering range.

[0727] The estimated wandering area is displayed on a map using a Geographic Information System (GIS). This display is implemented as a user interface in a terminal application using React Native. By accessing this map information, users can visually confirm the possible locations of the person in question.

[0728] Furthermore, the device incorporates speech recognition software that recognizes emotions from the user's voice input. Here, the Google Cloud Natural Language API is used to analyze the voice data and identify the user's emotional state. Based on this information, the emotion engine extracts high-priority information, enabling the provision of information tailored to the user's emotions.

[0729] For example, if a user voice-inputs, "My mother has been going out frequently lately, and I'm worried," the emotion engine detects the anxiety, and the server automatically determines high-priority monitoring areas. As a result, "important information regarding your mother's recent activity range" is highlighted on the device. At this point, a generative AI model is used to provide appropriate advice and information to alleviate the user's psychological burden.

[0730] An example of a prompt message is, "If a user types 'I feel uneasy when I'm alone at night,' how would you provide reassuring information?"

[0731] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0732] Step 1:

[0733] The server retrieves the subject's movement history from a location database. In this step, past location information is aggregated using database queries. The input is the subject's ID, and the output is a dataset of time and location associated with that ID.

[0734] Step 2:

[0735] The server analyzes the acquired movement history to estimate the subject's wandering range. It uses Python to execute a data analysis algorithm, calculating the most likely wandering range based on geographical data. The input is the location data from step 1, and the output is a list of coordinates for the estimated wandering range.

[0736] Step 3:

[0737] The server converts the estimated wandering area into a geodata format and sends it to the terminal. The input is a list of coordinates for the wandering area, and the output is map data that can be displayed on the terminal.

[0738] Step 4:

[0739] The device displays the received map data on a map using React Native. The input is geodata sent from the server, allowing the user to visually confirm their roaming area. The output is visual information displayed on the map.

[0740] Step 5:

[0741] The user inputs emotional data into the device via voice input. In this step, voice data that identifies the user's emotional state is input.

[0742] Step 6:

[0743] The device performs sentiment analysis using the Google Cloud Natural Language API. The input is the audio data from step 5, and the emotional state is output through the analysis. Specifically, it determines emotions from certain keywords or the tone of voice.

[0744] Step 7:

[0745] The emotion engine requests high-priority information from the server based on the user's emotions. The input is analyzed emotion data, and the server generates a set of information with re-evaluated importance based on that data.

[0746] Step 8:

[0747] The server selects high-priority information and sends it to the terminal. The input is a request based on the user's emotional state, and the output is a set of information corresponding to that emotion.

[0748] Step 9:

[0749] The device clearly presents high-priority information to the user. Users can receive advice and procedures to conduct search operations efficiently while reducing stress. Output consists of visual and audio notifications.

[0750] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0751] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0752] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0753] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0754] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0755] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0756] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0757] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0758] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0759] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0760] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0761] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0762] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0763] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0764] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0765] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0766] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0767] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0768] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0769] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0770] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] A means of obtaining the movement history of the subject,

[0774] A means for analyzing the aforementioned movement history to estimate the range of the subject's wandering,

[0775] A means for displaying the estimated wandering area on a map,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, comprising means for acquiring real-time monitoring information of public transportation and updating the estimated wandering area.

[0779] (Claim 3)

[0780] The system according to claim 1, further comprising means for identifying points of interest within the estimated wandering range.

[0781] "Example 1"

[0782] (Claim 1)

[0783] Means for obtaining movement history information,

[0784] A means for executing an algorithm that analyzes the aforementioned movement history information to predict the range of movement of the subject,

[0785] A means for integrating the predicted range of action with map information to identify important locations,

[0786] Means for visually displaying the identified important locations on a display device,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, comprising means for checking real-time updated information and planning activities.

[0790] (Claim 3)

[0791] The system according to claim 1, further comprising means for generating input sentences for a generative artificial intelligence model to obtain new activity proposals.

[0792] "Application Example 1"

[0793] (Claim 1)

[0794] A device equipped with a function to acquire the location information of the subject,

[0795] A device equipped with a function to analyze the aforementioned location information and estimate the range of movement of the subject,

[0796] A device equipped with a function to visually represent the estimated range of movement on a map,

[0797] A machine that includes this.

[0798] (Claim 2)

[0799] The machine according to claim 1, further comprising a function to acquire real-time monitoring data of the means of transport and correct the estimated range of movement.

[0800] (Claim 3)

[0801] The machine according to claim 1, further comprising a function for identifying points of interest within the estimated range of activity.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] A means of obtaining the movement history of the subject,

[0805] A means for analyzing the aforementioned movement history to estimate the range of the subject's wandering,

[0806] A means for displaying the estimated wandering area on a map,

[0807] A means of evaluating the emotional state by analyzing the user's voice input and operation patterns,

[0808] Means for adjusting the content of information provided based on the evaluated emotional state,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, comprising means for acquiring real-time monitoring information of public transportation and updating the estimated wandering area.

[0812] (Claim 3)

[0813] The system according to claim 1, further comprising means for identifying points of interest within the estimated wandering range and setting priorities based on emotional state.

[0814] "Application example 2 of combining emotional engines"

[0815] (Claim 1)

[0816] A means of obtaining the movement history of the subject,

[0817] A means for analyzing the aforementioned movement history to estimate the range of the subject's wandering,

[0818] A means for displaying the estimated wandering area on a map,

[0819] Means of recognizing user emotions,

[0820] A means of providing high-priority information based on the user's emotions,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, comprising means for acquiring real-time monitoring information of public transportation and updating the estimated wandering area.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for identifying points of interest within the estimated wandering area, and means for advising the user on a search procedure according to their stress level. [Explanation of symbols]

[0826] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining the movement history of the subject, A means for analyzing the aforementioned movement history to estimate the range of the subject's wandering, A means for displaying the estimated wandering area on a map, A system that includes this.

2. The system according to claim 1, comprising means for acquiring real-time monitoring information of public transportation and updating the estimated wandering area.

3. The system according to claim 1, further comprising means for identifying points of interest within the estimated wandering range.

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