system

Abstract

Provide a system. 【Solution means】 Means for collecting position information data obtained from a position information data provider, Means for collecting data from an external information provider that provides event information and weather information, Means for integrating the collected position information data, event information, and weather information to generate one data set, Means for creating a high-precision crowd flow prediction model based on the generated data set, Means for generating real-time crowd flow prediction information using the high-precision crowd flow prediction model, Means for distributing the generated crowd flow prediction information to user terminals, Means for visually displaying the distributed crowd flow prediction information on a user terminal A system including.

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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional crowd information providing systems, only regionally or temporally limited information is provided, and it is difficult to easily obtain highly accurate real-time crowd prediction. For this reason, there is a problem that it is insufficient as a judgment material for effectively avoiding congestion during large-scale events or peak hours. Furthermore, there is also a problem that it is difficult to customize according to specific regions or time zones that users are interested in.

Means for Solving the Problems

[0005] This invention provides a system that integrates external information such as location data providers, event information, and weather information to create a highly accurate pedestrian flow prediction model. By integrating the collected data, it enables detailed pedestrian flow predictions down to the centimeter and minute, and delivers them to user terminals in real time. Furthermore, by providing means to customize the prediction information according to the user's interests or needs and to visually display highly accurate pedestrian flow information for specific areas and time periods, it provides information that enables users to efficiently avoid congestion and plan optimal actions.

[0006] "Location data" refers to coordinate information and associated data that indicates a physical location, and is information used to identify the geographical location of a user or object.

[0007] "Event information" refers to data about events or activities that take place at a specific location and time, including details such as the date, time, location, and scale of the event.

[0008] "Weather information" refers to data about current and predicted weather conditions, including information such as temperature, precipitation, wind speed, and weather conditions.

[0009] A "human flow prediction model" is a mathematical model used to predict future human flow and congestion levels based on past and present human flow data.

[0010] A "dataset" is a collection of data that has been gathered and integrated for a specific purpose, and is a set of information that is used for analysis and model generation.

[0011] "Distribution" refers to the act of sending data or information to a specific recipient, specifically delivering data through a communication network.

[0012] "Visual display" means depicting information on a screen in a way that is easy to understand intuitively, and includes display in a graphical format.

[0013] "Customization" refers to adjusting products or services to meet the specific requests and preferences of a particular user, and involves making changes to suit user needs. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that provides highly accurate pedestrian flow prediction by integrating location information, event information, weather information, etc., from location information data providers. This system includes three entities: a server, a terminal, and a user.

[0036] The server first collects the necessary data from location data providers and external information providers. The server integrates this data to generate a single dataset. Based on this dataset, an AI algorithm is used to create a pedestrian flow prediction model and perform real-time congestion predictions. The prediction model has been trained and validated using the extensive dataset collected, resulting in high accuracy.

[0037] The terminal receives highly accurate pedestrian flow prediction data distributed from the server. This information is then used to visually display the pedestrian flow situation on the terminal. Users can manipulate this visual information to view details based on their areas of interest and time of day. This allows users to avoid congestion and choose the optimal time and route.

[0038] As a concrete example, if a user uses this system before attending a large event in an urban area, they can check the predicted crowd flow around the event venue in real time. The server analyzes event information and data from similar past events to predict the congestion on the day. This predicted information is presented visually to the user via a terminal, allowing the user to use it to plan an efficient journey.

[0039] Furthermore, when users visit a shopping mall, they can obtain information predicting the level of congestion and peak times within the mall, allowing them to create a more comfortable shopping plan. In this way, the server is responsible for integrating and analyzing information, while terminals intuitively display it, enabling users to directly utilize the information and act efficiently. This system achieves real-time and highly accurate pedestrian flow prediction.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server obtains real-time location information for users and objects from location data providers. In addition, it connects to external data providers that provide event information and weather information, and collects this data as well.

[0043] Step 2:

[0044] The server preprocesses the various data it collects, standardizes the format, and stores it in a database. Preprocessing includes denoising data, supplementing incomplete data, and synchronizing timestamps of different types of information.

[0045] Step 3:

[0046] Based on the integrated data from the servers, an AI algorithm is used to build a pedestrian flow prediction model. This model learns from past data to predict future pedestrian flow patterns.

[0047] Step 4:

[0048] The server generates pedestrian flow prediction results and prepares the necessary prediction information according to each user's customization options. The server then prepares this information for distribution.

[0049] Step 5:

[0050] The device connects to the server in response to the user's request and receives the provided forecast information. Based on the received data, the device displays pedestrian flow information in a format optimized for the user.

[0051] Step 6:

[0052] Users check the visual display provided on their devices and plan their actions based on congestion levels and predicted fluctuations. They adjust their arrival time and travel route to achieve efficient travel.

[0053] (Example 1)

[0054] 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."

[0055] In modern society, accurately understanding the movement of people in specific areas and at specific times is crucial for transportation planning and commercial activities. However, conventional methods often involve analyzing location and external information individually, requiring significant time and effort for data integration and analysis, and lacking real-time capabilities. There is a need to address this challenge and provide highly accurate, real-time pedestrian flow predictions to support efficient travel and congestion avoidance.

[0056] 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.

[0057] In this invention, the server includes means for acquiring location information data, means for collecting data from data sources that provide external information, and means for integrating the collected location information and external information to generate a single dataset. This enables the generation of highly accurate and real-time movement prediction information.

[0058] "Location data" refers to information that indicates the geographical location of individual moving objects or users, and is usually expressed as latitude and longitude.

[0059] "External information" refers to data other than location data, such as weather information and event information, which are information about the environment and circumstances that may affect movement or stay.

[0060] "Data sources" refer to information providers and services that provide location data and external information, including location information providers and weather information services.

[0061] A "high-precision movement prediction model" is an algorithm or system that incorporates mathematical and statistical methods to accurately predict people's movement patterns and congestion levels using multiple datasets.

[0062] An "information terminal" is a device that receives data distributed from a server and presents that information to the user in a visual or interactive format, and this includes mobile phones and tablets.

[0063] This invention is a system that provides highly accurate, real-time movement prediction information, and is realized through the cooperation of a server, terminals, and users.

[0064] The server first acquires data from location data providers and external information providers. Specifically, the software uses the HTTP protocol to send API requests and receive location information, weather, event information, etc. At this time, a dedicated data acquisition module is used to enable unified processing of various data formats. Subsequently, the data is preprocessed using data analysis libraries in Python or R to remove outliers and impute missing values.

[0065] The server uses a pre-trained AI algorithm to build a movement prediction model based on an integrated dataset. Specifically, it uses machine learning frameworks such as TENSORFLOW® and PyTorch to perform time-series predictions using historical movement data, enabling real-time congestion predictions. These prediction results are delivered to terminals using compression technology for efficient data transfer.

[0066] The terminal provides a user interface for visually displaying the forecast data received from the server. Specifically, an application will be developed to run on smartphones and tablets, displaying color-coded congestion information through a map-based interface. This will allow users to intuitively understand the information and adjust their travel plans accordingly.

[0067] Users optimize their actions based on travel prediction information displayed on their devices. For example, if a user is attending a large event, they can check the predicted congestion in the surrounding area around the event's start time and choose the optimal travel route.

[0068] For example, a user can find out the optimal time to visit a shopping mall by entering a prompt message into the system such as, "Please tell me the congestion level of the shopping malls in the city at 3 PM."

[0069] By using such a system, users can take advantage of real-time movement prediction information and act more efficiently.

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

[0071] Step 1:

[0072] The server retrieves data from location data providers and external information providers. API keys and request parameters are provided as input, and HTTP requests are sent based on this information. The output is a data response containing location information, weather information, and event information. This data is stored on the server in JSON format for subsequent processing.

[0073] Step 2:

[0074] The server integrates the acquired data to generate a single dataset. Its input consists of multiple previously acquired datasets. Data processing involves merging the data using the Python Pandas library and organizing it based on time and location. Anomalies are detected and imputed to produce a clean dataset. This integrated dataset is then used to train an AI model.

[0075] Step 3:

[0076] The server builds a movement prediction model based on the integrated dataset. The dataset generated in the previous step is used as input. For data computation, a machine learning algorithm using TensorFlow is employed to learn past trends. As output, a highly accurate movement prediction model is generated, and its accuracy is evaluated through internal validation.

[0077] Step 4:

[0078] The server generates real-time traffic prediction information using a pre-trained traffic prediction model. It takes the current time and real-time external data as input and processes them through the model. As a result, it outputs a traffic congestion prediction for a specific area in the future. This prediction information is compressed and sent to the terminal.

[0079] Step 5:

[0080] The device visually displays movement prediction information received from the server. It receives compressed data from the server as input. The device decompresses the data and displays it clearly on a map within the mobile application interface. Users can view the visualized congestion information and adjust their actions accordingly.

[0081] Step 6:

[0082] Users develop action plans using predictive information displayed on their devices. Based on the input information, users can, for example, decide on shopping times to avoid crowds or optimize their travel routes. The output is a more efficient travel schedule or visit plan.

[0083] (Application Example 1)

[0084] 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."

[0085] Currently, in physical spaces visited by many people, there are limited means of obtaining information about congestion in advance, causing inconvenience to users. Furthermore, the lack of guidance on optimal routes and times for visits hinders a pleasant experience. Therefore, there is a need for a means to provide users with accurate, real-time information on congestion levels and suggest optimal visit plans.

[0086] 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.

[0087] In this invention, the server includes means for collecting location data obtained from a location data source, means for collecting event information and weather information from external information sources, and means for integrating the collected information to generate a single dataset. This allows users to understand the degree of physical space occupancy in real time and propose the optimal visit time, enabling them to plan a comfortable visit.

[0088] A "location data source" is a provider of information about geographical location.

[0089] "External information sources" refer to sources that provide information other than location information, specifically events and weather information.

[0090] A "single dataset" is a collection of data that integrates and combines multiple pieces of information into one.

[0091] A "high-precision moving object prediction model" is a model based on an algorithm built to predict the movement of a moving object, and it has high accuracy in real time.

[0092] A "user terminal" is a communication device that a user can carry with them, and is a device for receiving and displaying information.

[0093] "Density of expansion" is an indicator that shows the density of people and objects in a physical space.

[0094] "Optimal visiting time" refers to the time of day when visitors can enjoy a less crowded and more comfortable experience.

[0095] An "optimal travel route" is the route that enables the user to travel in the most efficient and comfortable way.

[0096] The system based on this invention collects information from location data sources and external information sources, integrates them to perform highly accurate movement predictions, and provides users with the optimal visit time and travel route.

[0097] The server collects location data from location data sources and also gathers event and weather information from external sources. This information is integrated on the server and processed as a single dataset. Using this dataset, the server uses AI frameworks such as TensorFlow and PyTorch to build a highly accurate moving object prediction model. This model generates real-time prediction information for moving objects.

[0098] The terminal receives movement prediction information distributed from the server and presents it visually to the user. The application on the terminal, such as a smartphone or tablet, is developed using Flutter® or React Native, allowing users to intuitively view the information. The terminal has a function to notify the user of the optimal time to visit, guiding them to avoid congestion and have a comfortable visit. It also guides the user to the optimal route within the physical space, improving the visit experience.

[0099] For example, if a user plans to visit an area that is crowded on weekends, the system will suggest the least crowded times and provide appropriate routes to support efficient planning.

[0100] Examples of prompts for a generative AI model:

[0101] "Please predict the crowd levels at nearby stores next Friday evening. In particular, please provide detailed information about the crowd levels at major retailers. Also, please recommend the best route to take."

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The server collects location data from location data sources. It receives geographically location-related data as input and stores it in its database. The data is retrieved in real time via an API. The server periodically collects this information to maintain its up-to-date state.

[0105] Step 2:

[0106] The server collects event and weather information from external sources. Current event schedules and weather conditions are taken as input and added to the dataset. Techniques such as web scraping and RSS feeds are used for this collection. The integrated information forms a more comprehensive dataset.

[0107] Step 3:

[0108] The server integrates collected location data, event information, and weather information to generate a single dataset. Multiple data sources collected in previous stages are used as input, and the data is formatted, cleaned, and integrated. This process includes data redundancy removal and time-series data organization. The output is a consistent dataset suitable for analysis.

[0109] Step 4:

[0110] The server builds a highly accurate moving object prediction model using an integrated dataset. The well-structured dataset is input to the AI ​​model for training and evaluation. TensorFlow and PyTorch are used to train a model suitable for moving object prediction. This process yields a highly accurate model.

[0111] Step 5:

[0112] The server generates real-time movement prediction information using the constructed movement prediction model. The model takes the latest integrated dataset as input to predict congestion levels and movement patterns. Real-time prediction data is output, which is then sent to the user.

[0113] Step 6:

[0114] The device receives mobile object prediction information distributed from the server. It receives prediction data sent from the server as input and prepares it for presentation to the user. The data is formatted for display on the mobile app.

[0115] Step 7:

[0116] The terminal uses received motion prediction information to present the user with the optimal visit time and travel route. It receives prediction data as input and generates an optimal schedule and route based on the user's current location and destination. The output is visually displayed to the user, supporting real-time travel planning.

[0117] 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.

[0118] This invention combines an emotion engine with a system that integrates external information such as location data, event information, and weather information to provide highly accurate pedestrian flow predictions. This system is primarily composed of a server, terminals, and users, and aims to recognize the user's emotional state and provide the user with appropriate information based on the results.

[0119] First, the server collects location data from location data providers, external event information, and weather information in real time. This data is integrated and stored in a database, and then combined into a pedestrian flow prediction model using an AI algorithm. This model utilizes past and present data to predict future pedestrian flow and congestion levels.

[0120] Furthermore, the server uses an emotion engine to analyze the user's emotions. The emotion engine estimates the user's current emotional state based on voice and biometric data acquired from smartphones and wearable devices. This emotional information is used in combination with pedestrian flow prediction information to customize the information provided to the user.

[0121] The terminal receives customized pedestrian flow prediction information from the server, taking into account the user's emotional state. This information is tailored to the user's emotional state and includes suggestions for calmer routes to reduce stress, as well as congestion information for entertainment areas. The terminal displays this information visually, allowing the user to plan an efficient and comfortable journey based on it.

[0122] As a concrete example, when a user visits a tourist destination on a holiday, the system suggests routes and appropriate visiting times that will alleviate the user's anxiety. In this process, the server analyzes pedestrian flow prediction data along with the user's desire to relax, and delivers the optimal plan to the terminal, enabling the user to enjoy their trip more.

[0123] The system of the present invention allows users not only to avoid physical congestion but also to optimize their behavior according to their individual emotional needs.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server obtains location information in real time from a location data provider. It also accesses external information providers that offer event and weather information to collect necessary data. This data is stored in a centrally managed database.

[0127] Step 2:

[0128] The server integrates various pieces of information to generate a single dataset. The data integration process includes data cleaning, time synchronization, and format standardization, followed by the creation of a human flow prediction model via an AI algorithm.

[0129] Step 3:

[0130] The server uses an emotion engine to process and analyze the user's emotional data. This emotional data is determined from the user's voice tone, heart rate, facial recognition results, etc., to identify the user's current emotional state.

[0131] Step 4:

[0132] The server combines information from the pedestrian flow prediction model with analysis results from the emotion engine to generate user-optimized pedestrian flow prediction information. This information includes stress-reducing routes based on emotional state and recommended visit times based on interests.

[0133] Step 5:

[0134] The device receives customized pedestrian flow prediction information delivered from the server. The device then displays this information visually, relating it to the user's emotional state.

[0135] Step 6:

[0136] Users check the display on their device and plan their actions based on the provided predictive information. This allows them to choose efficient routes and times to travel while avoiding stress.

[0137] This series of processes allows users to create plans that address not only their physical needs but also their emotional needs, in addition to providing information to avoid physical congestion.

[0138] (Example 2)

[0139] 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".

[0140] In modern society, many people experience stress from being in crowded environments. Knowing about congestion in advance, especially during travel and daily commutes, would allow for more comfortable and efficient planning. However, conventional systems only consider physical pedestrian flow information, making it difficult to provide optimal transportation options based on individual emotional states.

[0141] 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.

[0142] In this invention, the server includes means for collecting location information from a location information providing device, means for collecting event information and weather information from an external information providing device, means for creating highly accurate pedestrian flow predictions using an AI model, means for identifying the user's emotional state from voice data and biometric measurement data, and means for generating customized information by combining the user's emotional state and pedestrian flow prediction information. This makes it possible for the user to obtain an optimal travel plan according to their own emotional state.

[0143] A "location information provider" is a device that provides geographical data related to the user's location.

[0144] An "external information provider" is a device that provides additional data different from location information, such as event information and weather information.

[0145] An "artificial intelligence model" is an algorithm that analyzes large amounts of data and makes predictions and inferences based on that data.

[0146] "Voice data" refers to recorded information about the user's voice, and is used for sentiment analysis and other purposes.

[0147] "Biometric data" refers to data related to a person's physiological state, such as heart rate and skin current.

[0148] "Customized information" refers to information that has been tailored to the user's specific needs and emotional state.

[0149] A "user device" is a device that a user can directly operate and use for displaying or inputting information.

[0150] "Feedback" refers to information such as opinions and evaluations based on the user's experience.

[0151] The system of the present invention integrates the user's location information, emotional state, and pedestrian flow prediction information to provide individually customized movement information.

[0152] The server first collects location information in real time from location information providers. Simultaneously, it also acquires event information and weather information from external information providers. This information is collected in a database and compiled into an integrated dataset. This process standardizes the data format and establishes associations to facilitate subsequent processing.

[0153] Next, the server uses an artificial intelligence model to predict pedestrian flow. This model utilizes machine learning frameworks such as TensorFlow and PyTorch to predict future pedestrian flow trends based on historical data. The prediction results are constantly updated according to current real-time data.

[0154] Furthermore, sentiment analysis is performed. The server uses voice data and biometric data transmitted from the user's smartphone or wearable device to process and identify the user's emotional state. This analysis utilizes natural language processing and machine learning techniques.

[0155] Based on this emotional state and pedestrian flow prediction information, the server generates customized information optimized for the user. This includes suggesting quieter routes for users who want to avoid congestion and providing safe travel plans that take weather conditions into consideration.

[0156] The device receives this information and displays it in a visually easy-to-understand format for the user. The user can then use the provided information to create an efficient and comfortable travel plan.

[0157] For example, when a user uses the system to visit a tourist destination on a holiday, the server can consider the user's emotional state of wanting to relax and suggest the optimal route and visit time. To achieve this, an example of a prompt message could be, "Please begin providing information combining crowd flow prediction and sentiment analysis to suggest a calm route in the tourist destination."

[0158] The key feature of this system is that it not only avoids physical congestion but also enables the rapid and effective provision of information that addresses users' emotional needs.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server acquires location information in real time from a location information provider. As input, it collects latitude and longitude data using the API endpoint provided by the location information provider. This location data is integrated and stored in a database. The output is a dataset of each user's current location.

[0162] Step 2:

[0163] The server collects event and weather information from external information providers. As input, it makes requests to external APIs to obtain event location and weather data. This information is integrated with location data and stored in a database as a single dataset. The output is a comprehensive dataset containing location, event, and weather conditions.

[0164] Step 3:

[0165] The server uses a generative AI model to predict pedestrian flow based on an integrated dataset. Historical data is supplied to the AI ​​model as input, and predictions are made based on current data. Specifically, TensorFlow or PyTorch is launched in a Python environment, and the prediction algorithm is executed. The output is a prediction model showing future pedestrian flow trends.

[0166] Step 4:

[0167] The server acquires voice and biometric data from smartphones and wearable devices to analyze the user's emotional state. It collects data from devices via Bluetooth or Wi-Fi as input. Using natural language processing and machine learning algorithms, it identifies emotions from the user's voice tone and heart rate. The output is data indicating the user's emotional state.

[0168] Step 5:

[0169] The server combines the user's emotional state with a pedestrian flow prediction model to generate customized information. It utilizes emotional analysis results and pedestrian flow prediction data as input. Based on this, it calculates the optimal travel route and visit plan for the user. The output is customized travel information.

[0170] Step 6:

[0171] The terminal receives customized information provided by the server and displays it to the user. It receives information packets from the server as input and launches an application to visualize the data. This information is displayed on the user's screen as maps and text. The output is visually organized user-friendly information.

[0172] Step 7:

[0173] Users provide feedback through their devices. A user feedback form is used as input, where they fill in information about their user experience. This feedback is sent to the server and used to improve the AI ​​model. The output is a new dataset that helps in model tuning.

[0174] (Application Example 2)

[0175] 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 device 14 will be referred to as the "terminal."

[0176] Modern users lead busy lives and are exposed to a vast amount of information, making it difficult for them to choose the most suitable travel routes and living environments that match their emotions and stress levels. Furthermore, the optimization of living environments within the home according to emotional states is insufficient, and new methods are needed to improve quality of life.

[0177] 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.

[0178] In this invention, the server includes data collection means for acquiring location information data, data collection means for providing external information, and data integration means for integrating the collected data to generate a data set. This enables the optimization of the living environment and comfortable travel planning in accordance with the user's emotional state.

[0179] "Location data" refers to data that indicates geographical information about a specific point in time, and is used to pinpoint the exact location of a user or object.

[0180] "Data collection means" refers to a mechanism or method for obtaining necessary data from various sources, thereby providing a foundation for integrating information.

[0181] "Data integration means" refers to the process of combining different types of data to generate a single, continuous, and unified data set.

[0182] A "human flow prediction model" refers to a computational algorithm or statistical method used to estimate future human movement patterns and congestion levels based on location information and related data.

[0183] A "predictive tool" is a mechanism that provides functions and processes for predicting future events or patterns based on a specific model.

[0184] "Information distribution means" refers to communication technology or methods for transmitting generated information to a user terminal or other receiving terminal.

[0185] "Display means" refers to technologies or tools that provide the function of visually representing information on a terminal.

[0186] "Emotion recognition means" refers to technologies and algorithms that use a user's voice and biometric data to identify and analyze their emotional state.

[0187] "Information adaptation means" refers to a process or function that personalizes and appropriately provides predictive information according to the user's specific needs and circumstances.

[0188] "Environmental optimization means" refers to technologies and methods for adjusting living and working environments based on users' emotions and lifestyle needs to achieve an optimal state.

[0189] This invention is a system that optimizes the living environment based on the user's emotional state to achieve comfortable travel. The server collects location data from a location data provider and also obtains event information and weather information from an external information provider. By integrating this data, a highly accurate human flow prediction model is created that enables real-time prediction of human flow.

[0190] The server also uses voice and biometric data collected from smartphones and wearable devices to analyze the user's emotional state using an emotion engine. For emotion recognition, IBM Watson®'s Natural Language Understanding API and machine learning libraries such as TensorFlow and PyTorch can be used.

[0191] The terminal receives pedestrian flow prediction information distributed from the server. Furthermore, it visually displays and provides information that takes into account the user's emotional state. This allows users to adjust their travel plans based on suggestions for calmer routes and optimal visit times that suit their emotional state. For example, when visiting a tourist destination on a holiday, a calmer route to alleviate stress is suggested, allowing the user to enjoy their sightseeing more.

[0192] As a concrete example of a prompt, it can be phrased as, "Generate prompts to create a relaxing living environment for the family. Please also tell me about lighting and time settings." By utilizing a generation AI model, information can be obtained to make the user's life more comfortable.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server obtains location information from a location data provider and also collects event and weather information from external information providers. After receiving this data, it performs data integration to generate a data set. This data set serves as the basis for creating subsequent human flow prediction models.

[0196] Step 2:

[0197] The server uses an integrated data set to generate highly accurate pedestrian flow prediction models. AI algorithms process location information, event information, and weather information to build models that predict future pedestrian flow and congestion. This prediction information is used to provide users with travel plans.

[0198] Step 3:

[0199] The server collects voice and biometric data from the user's smartphone or wearable device and analyzes the user's emotional state using an emotion engine. In this process, the user's emotional state is generated as status data, which forms the basis for providing personalized information to each user.

[0200] Step 4:

[0201] The server customizes predictive information based on the sentiment analysis results and delivers it to the user's terminal. Here, tailored information is generated, such as suggestions for relaxing routes and optimal visit times, providing information that matches the user's emotional state.

[0202] Step 5:

[0203] The terminal receives information delivered from the server and displays it visually to the user. The user can then use this information to plan their trip. For example, a less crowded sightseeing route might be suggested, allowing the user to visit tourist destinations more comfortably.

[0204] Step 6:

[0205] When users utilize the AI ​​model based on the information they provide, they use prompts. These prompts allow for the generation of even more personalized information. For example, additional information can be obtained in the form of, "Generate prompts to create a relaxing living environment for my family."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention is a system that provides highly accurate pedestrian flow prediction by integrating location information, event information, weather information, etc., from location information data providers. This system includes three entities: a server, a terminal, and a user.

[0223] The server first collects the necessary data from location data providers and external information providers. The server integrates this data to generate a single dataset. Based on this dataset, an AI algorithm is used to create a pedestrian flow prediction model and perform real-time congestion predictions. The prediction model has been trained and validated using the extensive dataset collected, resulting in high accuracy.

[0224] The terminal receives highly accurate pedestrian flow prediction data distributed from the server. This information is then used to visually display the pedestrian flow situation on the terminal. Users can manipulate this visual information to view details based on their areas of interest and time of day. This allows users to avoid congestion and choose the optimal time and route.

[0225] As a concrete example, if a user uses this system before attending a large event in an urban area, they can check the predicted crowd flow around the event venue in real time. The server analyzes event information and data from similar past events to predict the congestion on the day. This predicted information is presented visually to the user via a terminal, allowing the user to use it to plan an efficient journey.

[0226] Furthermore, when users visit a shopping mall, they can obtain information predicting the level of congestion and peak times within the mall, allowing them to create a more comfortable shopping plan. In this way, the server is responsible for integrating and analyzing information, while terminals intuitively display it, enabling users to directly utilize the information and act efficiently. This system achieves real-time and highly accurate pedestrian flow prediction.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server obtains real-time location information for users and objects from location data providers. In addition, it connects to external data providers that provide event information and weather information, and collects this data as well.

[0230] Step 2:

[0231] The server preprocesses the various data it collects, standardizes the format, and stores it in a database. Preprocessing includes denoising data, supplementing incomplete data, and synchronizing timestamps of different types of information.

[0232] Step 3:

[0233] Based on the integrated data from the servers, an AI algorithm is used to build a pedestrian flow prediction model. This model learns from past data to predict future pedestrian flow patterns.

[0234] Step 4:

[0235] The server generates pedestrian flow prediction results and prepares the necessary prediction information according to each user's customization options. The server then prepares this information for distribution.

[0236] Step 5:

[0237] The device connects to the server in response to the user's request and receives the provided forecast information. Based on the received data, the device displays pedestrian flow information in a format optimized for the user.

[0238] Step 6:

[0239] Users check the visual display provided on their devices and plan their actions based on congestion levels and predicted fluctuations. They adjust their arrival time and travel route to achieve efficient travel.

[0240] (Example 1)

[0241] 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."

[0242] In modern society, accurately understanding the movement of people in specific areas and at specific times is crucial for transportation planning and commercial activities. However, conventional methods often involve analyzing location and external information individually, requiring significant time and effort for data integration and analysis, and lacking real-time capabilities. There is a need to address this challenge and provide highly accurate, real-time pedestrian flow predictions to support efficient travel and congestion avoidance.

[0243] 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.

[0244] In this invention, the server includes means for acquiring location information data, means for collecting data from data sources that provide external information, and means for integrating the collected location information and external information to generate a single dataset. This enables the generation of highly accurate and real-time movement prediction information.

[0245] "Location data" refers to information that indicates the geographical location of individual moving objects or users, and is usually expressed as latitude and longitude.

[0246] "External information" refers to data other than location data, such as weather information and event information, which are information about the environment and circumstances that may affect movement or stay.

[0247] "Data sources" refer to information providers and services that provide location data and external information, including location information providers and weather information services.

[0248] A "high-precision movement prediction model" is an algorithm or system that incorporates mathematical and statistical methods to accurately predict people's movement patterns and congestion levels using multiple datasets.

[0249] An "information terminal" is a device that receives data distributed from a server and presents that information to the user in a visual or interactive format, and this includes mobile phones and tablets.

[0250] This invention is a system that provides highly accurate, real-time movement prediction information, and is realized through the cooperation of a server, terminals, and users.

[0251] The server first acquires data from location data providers and external information providers. Specifically, the software uses the HTTP protocol to send API requests and receive location information, weather, event information, etc. At this time, a dedicated data acquisition module is used to enable unified processing of various data formats. Subsequently, the data is preprocessed using data analysis libraries in Python or R to remove outliers and impute missing values.

[0252] The server uses a pre-trained AI algorithm to build a movement prediction model based on an integrated dataset. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to perform time-series predictions using historical movement data, enabling real-time congestion predictions. These prediction results are delivered to terminals using compression techniques for efficient data transfer.

[0253] The terminal provides a user interface for visually displaying the forecast data received from the server. Specifically, an application will be developed to run on smartphones and tablets, displaying color-coded congestion information through a map-based interface. This will allow users to intuitively understand the information and adjust their travel plans accordingly.

[0254] Users optimize their actions based on travel prediction information displayed on their devices. For example, if a user is attending a large event, they can check the predicted congestion in the surrounding area around the event's start time and choose the optimal travel route.

[0255] For example, a user can find out the optimal time to visit a shopping mall by entering a prompt message into the system such as, "Please tell me the congestion level of the shopping malls in the city at 3 PM."

[0256] By using such a system, users can take advantage of real-time movement prediction information and act more efficiently.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server retrieves data from location data providers and external information providers. API keys and request parameters are provided as input, and HTTP requests are sent based on this information. The output is a data response containing location information, weather information, and event information. This data is stored on the server in JSON format for subsequent processing.

[0260] Step 2:

[0261] The server integrates the acquired data to generate a single dataset. Its input consists of multiple previously acquired datasets. Data processing involves merging the data using the Python Pandas library and organizing it based on time and location. Anomalies are detected and imputed to produce a clean dataset. This integrated dataset is then used to train an AI model.

[0262] Step 3:

[0263] The server builds a movement prediction model based on the integrated dataset. The dataset generated in the previous step is used as input. For data computation, a machine learning algorithm using TensorFlow is employed to learn past trends. As output, a highly accurate movement prediction model is generated, and its accuracy is evaluated through internal validation.

[0264] Step 4:

[0265] The server generates real-time traffic prediction information using a pre-trained traffic prediction model. It takes the current time and real-time external data as input and processes them through the model. As a result, it outputs a traffic congestion prediction for a specific area in the future. This prediction information is compressed and sent to the terminal.

[0266] Step 5:

[0267] The device visually displays movement prediction information received from the server. It receives compressed data from the server as input. The device decompresses the data and displays it clearly on a map within the mobile application interface. Users can view the visualized congestion information and adjust their actions accordingly.

[0268] Step 6:

[0269] Users develop action plans using predictive information displayed on their devices. Based on the input information, users can, for example, decide on shopping times to avoid crowds or optimize their travel routes. The output is a more efficient travel schedule or visit plan.

[0270] (Application Example 1)

[0271] 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."

[0272] Currently, in physical spaces visited by many people, there are limited means of obtaining information about congestion in advance, causing inconvenience to users. Furthermore, the lack of guidance on optimal routes and times for visits hinders a pleasant experience. Therefore, there is a need for a means to provide users with accurate, real-time information on congestion levels and suggest optimal visit plans.

[0273] 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.

[0274] In this invention, the server includes means for collecting location data obtained from a location data source, means for collecting event information and weather information from external information sources, and means for integrating the collected information to generate a single dataset. This allows users to understand the degree of physical space occupancy in real time and propose the optimal visit time, enabling them to plan a comfortable visit.

[0275] A "location data source" is a provider of information about geographical location.

[0276] "External information sources" refer to sources that provide information other than location information, specifically events and weather information.

[0277] A "single dataset" is a collection of data that integrates and combines multiple pieces of information into one.

[0278] A "high-precision moving object prediction model" is a model based on an algorithm built to predict the movement of a moving object, and it has high accuracy in real time.

[0279] A "user terminal" is a communication device that a user can carry with them, and is a device for receiving and displaying information.

[0280] "Density of expansion" is an indicator that shows the density of people and objects in a physical space.

[0281] The "optimal visiting time" refers to the time of visit that allows the user to have a comfortable experience with less congestion.

[0282] The "optimal travel route" is a route that enables the most efficient and comfortable travel for the user.

[0283] The system based on this invention collects information from location information data sources and external information sources, integrates them, and performs highly accurate moving object prediction to provide the user with the optimal visiting time and travel route.

[0284] The server collects location information data obtained from location information data sources, and further collects event information and weather information from external information sources. These pieces of information are integrated on the server and processed as a single dataset. Using this dataset, the server constructs a highly accurate moving object prediction model using an AI framework such as TensorFlow or PyTorch. This model generates real-time prediction information about the moving object.

[0285] The terminal receives the moving object prediction information distributed from the server and visually presents it to the user. Applications on terminals such as smartphones and tablets are developed using Flutter or React Native, and users can intuitively check the information. The terminal has a function to notify the user of the optimal visiting time, guiding the user to avoid congestion and have a comfortable visit. It also guides the optimal travel route within the physical space to improve the visiting experience.

[0286] For example, when a user intends to visit an area where many people gather on weekends, the system proposes the least crowded time slot and shows an appropriate route to support an efficient plan.

[0287] Examples of prompt sentences for the generative AI model:

[0288] "Please predict the crowd levels at nearby stores next Friday evening. In particular, please provide detailed information about the crowd levels at major retailers. Also, please recommend the best route to take."

[0289] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0290] Step 1:

[0291] The server collects location data from location data sources. It receives geographically location-related data as input and stores it in its database. The data is retrieved in real time via an API. The server periodically collects this information to maintain its up-to-date state.

[0292] Step 2:

[0293] The server collects event and weather information from external sources. Current event schedules and weather conditions are taken as input and added to the dataset. Techniques such as web scraping and RSS feeds are used for this collection. The integrated information forms a more comprehensive dataset.

[0294] Step 3:

[0295] The server integrates collected location data, event information, and weather information to generate a single dataset. Multiple data sources collected in previous stages are used as input, and the data is formatted, cleaned, and integrated. This process includes data redundancy removal and time-series data organization. The output is a consistent dataset suitable for analysis.

[0296] Step 4:

[0297] The server builds a highly accurate moving object prediction model using an integrated dataset. The well-structured dataset is input to the AI ​​model for training and evaluation. TensorFlow and PyTorch are used to train a model suitable for moving object prediction. This process yields a highly accurate model.

[0298] Step 5:

[0299] The server generates real-time movement prediction information using the constructed movement prediction model. The model takes the latest integrated dataset as input to predict congestion levels and movement patterns. Real-time prediction data is output, which is then sent to the user.

[0300] Step 6:

[0301] The device receives mobile object prediction information distributed from the server. It receives prediction data sent from the server as input and prepares it for presentation to the user. The data is formatted for display on the mobile app.

[0302] Step 7:

[0303] The terminal uses received motion prediction information to present the user with the optimal visit time and travel route. It receives prediction data as input and generates an optimal schedule and route based on the user's current location and destination. The output is visually displayed to the user, supporting real-time travel planning.

[0304] 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.

[0305] The present invention combines an emotion engine with a system that integrates external information such as location information data, event information, and weather information to provide highly accurate pedestrian flow prediction. This system is mainly composed of a server, terminals, and users, and aims to recognize the emotional state of users and provide appropriate information to users based on the results.

[0306] First, the server collects location information from a location information data provider, external event information, and weather information in real time. These data are integrated and stored in a database, and then summarized as a pedestrian flow prediction model using an AI algorithm. This model utilizes past and current data to predict future human flow and congestion situations.

[0307] Furthermore, the server analyzes the emotions of users using an emotion engine. The emotion engine estimates the current emotional state of users based on voice and biometric data obtained from smartphones or wearable devices. This emotional information is used to customize the information provided to users in combination with the pedestrian flow prediction information.

[0308] The terminal receives customized pedestrian flow prediction information considering the emotional state from the server. This information is adjusted according to the emotional state of the user and includes suggestions for a gentle route to reduce stress and congestion information in entertainment areas. The terminal visually displays this information, and the user can make an efficient and comfortable travel plan based on this information.

[0309] As a specific example, when a user visits a tourist destination on a holiday, the system proposes routes to relieve the user's tension and appropriate visiting times. At this time, the server analyzes the pedestrian flow prediction data and the user's emotion of wanting to relax together, and distributes the optimal plan to the terminal, enabling the user to enjoy the tour more.

[0310] The system of the present invention allows users not only to avoid physical congestion but also to optimize their behavior according to their individual emotional needs.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The server obtains location information in real time from a location data provider. It also accesses external information providers that offer event and weather information to collect necessary data. This data is stored in a centrally managed database.

[0314] Step 2:

[0315] The server integrates various pieces of information to generate a single dataset. The data integration process includes data cleaning, time synchronization, and format standardization, followed by the creation of a human flow prediction model via an AI algorithm.

[0316] Step 3:

[0317] The server uses an emotion engine to process and analyze the user's emotional data. This emotional data is determined from the user's voice tone, heart rate, facial recognition results, etc., to identify the user's current emotional state.

[0318] Step 4:

[0319] The server combines information from the pedestrian flow prediction model with analysis results from the emotion engine to generate user-optimized pedestrian flow prediction information. This information includes stress-reducing routes based on emotional state and recommended visit times based on interests.

[0320] Step 5:

[0321] The device receives customized pedestrian flow prediction information delivered from the server. The device then displays this information visually, relating it to the user's emotional state.

[0322] Step 6:

[0323] Users check the display on their device and plan their actions based on the provided predictive information. This allows them to choose efficient routes and times to travel while avoiding stress.

[0324] This series of processes allows users to create plans that address not only their physical needs but also their emotional needs, in addition to providing information to avoid physical congestion.

[0325] (Example 2)

[0326] 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".

[0327] In modern society, many people experience stress from being in crowded environments. Knowing about congestion in advance, especially during travel and daily commutes, would allow for more comfortable and efficient planning. However, conventional systems only consider physical pedestrian flow information, making it difficult to provide optimal transportation options based on individual emotional states.

[0328] 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.

[0329] In this invention, the server includes means for collecting location information from a location information providing device, means for collecting event information and weather information from an external information providing device, means for creating highly accurate pedestrian flow predictions using an AI model, means for identifying the user's emotional state from voice data and biometric measurement data, and means for generating customized information by combining the user's emotional state and pedestrian flow prediction information. This makes it possible for the user to obtain an optimal travel plan according to their own emotional state.

[0330] A "location information provider" is a device that provides geographical data related to the user's location.

[0331] An "external information provider" is a device that provides additional data different from location information, such as event information and weather information.

[0332] An "artificial intelligence model" is an algorithm that analyzes large amounts of data and makes predictions and inferences based on that data.

[0333] "Voice data" refers to recorded information about the user's voice, and is used for sentiment analysis and other purposes.

[0334] "Biometric data" refers to data related to a person's physiological state, such as heart rate and skin current.

[0335] "Customized information" refers to information that has been tailored to the user's specific needs and emotional state.

[0336] A "user device" is a device that a user can directly operate and use for displaying or inputting information.

[0337] "Feedback" refers to information such as opinions and evaluations based on the user's experience.

[0338] The system of the present invention integrates the user's location information, emotional state, and pedestrian flow prediction information to provide individually customized movement information.

[0339] The server first collects location information in real time from location information providers. Simultaneously, it also acquires event information and weather information from external information providers. This information is collected in a database and compiled into an integrated dataset. This process standardizes the data format and establishes associations to facilitate subsequent processing.

[0340] Next, the server uses an artificial intelligence model to predict pedestrian flow. This model utilizes machine learning frameworks such as TensorFlow and PyTorch to predict future pedestrian flow trends based on historical data. The prediction results are constantly updated according to current real-time data.

[0341] Furthermore, sentiment analysis is performed. The server uses voice data and biometric data transmitted from the user's smartphone or wearable device to process and identify the user's emotional state. This analysis utilizes natural language processing and machine learning techniques.

[0342] Based on this emotional state and pedestrian flow prediction information, the server generates customized information optimized for the user. This includes suggesting quieter routes for users who want to avoid congestion and providing safe travel plans that take weather conditions into consideration.

[0343] The device receives this information and displays it in a visually easy-to-understand format for the user. The user can then use the provided information to create an efficient and comfortable travel plan.

[0344] For example, when a user uses the system to visit a tourist destination on a holiday, the server can consider the user's emotional state of wanting to relax and suggest the optimal route and visit time. To achieve this, an example of a prompt message could be, "Please begin providing information combining crowd flow prediction and sentiment analysis to suggest a calm route in the tourist destination."

[0345] The key feature of this system is that it not only avoids physical congestion but also enables the rapid and effective provision of information that addresses users' emotional needs.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The server acquires location information in real time from a location information provider. As input, it collects latitude and longitude data using the API endpoint provided by the location information provider. This location data is integrated and stored in a database. The output is a dataset of each user's current location.

[0349] Step 2:

[0350] The server collects event and weather information from external information providers. As input, it makes requests to external APIs to obtain event location and weather data. This information is integrated with location data and stored in a database as a single dataset. The output is a comprehensive dataset containing location, event, and weather conditions.

[0351] Step 3:

[0352] The server uses a generative AI model to predict pedestrian flow based on an integrated dataset. Historical data is supplied to the AI ​​model as input, and predictions are made based on current data. Specifically, TensorFlow or PyTorch is launched in a Python environment, and the prediction algorithm is executed. The output is a prediction model showing future pedestrian flow trends.

[0353] Step 4:

[0354] The server acquires voice and biometric data from smartphones and wearable devices to analyze the user's emotional state. It collects data from devices via Bluetooth or Wi-Fi as input. Using natural language processing and machine learning algorithms, it identifies emotions from the user's voice tone and heart rate. The output is data indicating the user's emotional state.

[0355] Step 5:

[0356] The server combines the user's emotional state with a pedestrian flow prediction model to generate customized information. It utilizes emotional analysis results and pedestrian flow prediction data as input. Based on this, it calculates the optimal travel route and visit plan for the user. The output is customized travel information.

[0357] Step 6:

[0358] The terminal receives customized information provided by the server and displays it to the user. It receives information packets from the server as input and launches an application to visualize the data. This information is displayed on the user's screen as maps and text. The output is visually organized user-friendly information.

[0359] Step 7:

[0360] Users provide feedback through their devices. A user feedback form is used as input, where they fill in information about their user experience. This feedback is sent to the server and used to improve the AI ​​model. The output is a new dataset that helps in model tuning.

[0361] (Application Example 2)

[0362] 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."

[0363] Modern users lead busy lives and are exposed to a vast amount of information, making it difficult for them to choose the most suitable travel routes and living environments that match their emotions and stress levels. Furthermore, the optimization of living environments within the home according to emotional states is insufficient, and new methods are needed to improve quality of life.

[0364] 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.

[0365] In this invention, the server includes data collection means for acquiring location information data, data collection means for providing external information, and data integration means for integrating the collected data to generate a data set. This enables the optimization of the living environment and comfortable travel planning in accordance with the user's emotional state.

[0366] "Location data" refers to data that indicates geographical information about a specific point in time, and is used to pinpoint the exact location of a user or object.

[0367] "Data collection means" refers to a mechanism or method for obtaining necessary data from various sources, thereby providing a foundation for integrating information.

[0368] "Data integration means" refers to the process of combining different types of data to generate a single, continuous, and unified data set.

[0369] A "human flow prediction model" refers to a computational algorithm or statistical method used to estimate future human movement patterns and congestion levels based on location information and related data.

[0370] A "predictive tool" is a mechanism that provides functions and processes for predicting future events or patterns based on a specific model.

[0371] "Information distribution means" refers to communication technology or methods for transmitting generated information to a user terminal or other receiving terminal.

[0372] "Display means" refers to technologies or tools that provide the function of visually representing information on a terminal.

[0373] "Emotion recognition means" refers to technologies and algorithms that use a user's voice and biometric data to identify and analyze their emotional state.

[0374] "Information adaptation means" refers to a process or function that personalizes and appropriately provides predictive information according to the user's specific needs and circumstances.

[0375] "Environmental optimization means" refers to technologies and methods for adjusting living and working environments based on users' emotions and lifestyle needs to achieve an optimal state.

[0376] This invention is a system that optimizes the living environment based on the user's emotional state to achieve comfortable travel. The server collects location data from a location data provider and also obtains event information and weather information from an external information provider. By integrating this data, a highly accurate human flow prediction model is created that enables real-time prediction of human flow.

[0377] The server also uses voice and biometric data collected from smartphones and wearable devices to analyze the user's emotional state using an emotion engine. Emotion recognition can utilize IBM Watson's Natural Language Understanding API or machine learning libraries such as TensorFlow and PyTorch.

[0378] The terminal receives pedestrian flow prediction information distributed from the server. Furthermore, it visually displays and provides information that takes into account the user's emotional state. This allows users to adjust their travel plans based on suggestions for calmer routes and optimal visit times that suit their emotional state. For example, when visiting a tourist destination on a holiday, a calmer route to alleviate stress is suggested, allowing the user to enjoy their sightseeing more.

[0379] As a concrete example of a prompt, it can be phrased as, "Generate prompts to create a relaxing living environment for the family. Please also tell me about lighting and time settings." By utilizing a generation AI model, information can be obtained to make the user's life more comfortable.

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server obtains location information from a location data provider and also collects event and weather information from external information providers. After receiving this data, it performs data integration to generate a data set. This data set serves as the basis for creating subsequent human flow prediction models.

[0383] Step 2:

[0384] The server uses an integrated data set to generate highly accurate pedestrian flow prediction models. AI algorithms process location information, event information, and weather information to build models that predict future pedestrian flow and congestion. This prediction information is used to provide users with travel plans.

[0385] Step 3:

[0386] The server collects voice and biometric data from the user's smartphone or wearable device and analyzes the user's emotional state using an emotion engine. In this process, the user's emotional state is generated as status data, which forms the basis for providing personalized information to each user.

[0387] Step 4:

[0388] The server customizes predictive information based on the sentiment analysis results and delivers it to the user's terminal. Here, tailored information is generated, such as suggestions for relaxing routes and optimal visit times, providing information that matches the user's emotional state.

[0389] Step 5:

[0390] The terminal receives information delivered from the server and displays it visually to the user. The user can then use this information to plan their trip. For example, a less crowded sightseeing route might be suggested, allowing the user to visit tourist destinations more comfortably.

[0391] Step 6:

[0392] When users utilize the AI ​​model based on the information they provide, they use prompts. These prompts allow for the generation of even more personalized information. For example, additional information can be obtained in the form of, "Generate prompts to create a relaxing living environment for my family."

[0393] 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.

[0394] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is a system that provides highly accurate pedestrian flow prediction by integrating location information, event information, weather information, etc., from location information data providers. This system includes three entities: a server, a terminal, and a user.

[0410] The server first collects the necessary data from location data providers and external information providers. The server integrates this data to generate a single dataset. Based on this dataset, an AI algorithm is used to create a pedestrian flow prediction model and perform real-time congestion predictions. The prediction model has been trained and validated using the extensive dataset collected, resulting in high accuracy.

[0411] The terminal receives highly accurate pedestrian flow prediction data distributed from the server. This information is then used to visually display the pedestrian flow situation on the terminal. Users can manipulate this visual information to view details based on their areas of interest and time of day. This allows users to avoid congestion and choose the optimal time and route.

[0412] As a concrete example, if a user uses this system before attending a large event in an urban area, they can check the predicted crowd flow around the event venue in real time. The server analyzes event information and data from similar past events to predict the congestion on the day. This predicted information is presented visually to the user via a terminal, allowing the user to use it to plan an efficient journey.

[0413] Furthermore, when users visit a shopping mall, they can obtain information predicting the level of congestion and peak times within the mall, allowing them to create a more comfortable shopping plan. In this way, the server is responsible for integrating and analyzing information, while terminals intuitively display it, enabling users to directly utilize the information and act efficiently. This system achieves real-time and highly accurate pedestrian flow prediction.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The server obtains real-time location information for users and objects from location data providers. In addition, it connects to external data providers that provide event information and weather information, and collects this data as well.

[0417] Step 2:

[0418] The server preprocesses the various data it collects, standardizes the format, and stores it in a database. Preprocessing includes denoising data, supplementing incomplete data, and synchronizing timestamps of different types of information.

[0419] Step 3:

[0420] Based on the integrated data from the servers, an AI algorithm is used to build a pedestrian flow prediction model. This model learns from past data to predict future pedestrian flow patterns.

[0421] Step 4:

[0422] The server generates pedestrian flow prediction results and prepares the necessary prediction information according to each user's customization options. The server then prepares this information for distribution.

[0423] Step 5:

[0424] The device connects to the server in response to the user's request and receives the provided forecast information. Based on the received data, the device displays pedestrian flow information in a format optimized for the user.

[0425] Step 6:

[0426] Users check the visual display provided on their devices and plan their actions based on congestion levels and predicted fluctuations. They adjust their arrival time and travel route to achieve efficient travel.

[0427] (Example 1)

[0428] 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."

[0429] In modern society, accurately understanding the movement of people in specific areas and at specific times is crucial for transportation planning and commercial activities. However, conventional methods often involve analyzing location and external information individually, requiring significant time and effort for data integration and analysis, and lacking real-time capabilities. There is a need to address this challenge and provide highly accurate, real-time pedestrian flow predictions to support efficient travel and congestion avoidance.

[0430] 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.

[0431] In this invention, the server includes means for acquiring location information data, means for collecting data from data sources that provide external information, and means for integrating the collected location information and external information to generate a single dataset. This enables the generation of highly accurate and real-time movement prediction information.

[0432] "Location data" refers to information that indicates the geographical location of individual moving objects or users, and is usually expressed as latitude and longitude.

[0433] "External information" refers to data other than location data, such as weather information and event information, which are information about the environment and circumstances that may affect movement or stay.

[0434] "Data sources" refer to information providers and services that provide location data and external information, including location information providers and weather information services.

[0435] A "high-precision movement prediction model" is an algorithm or system that incorporates mathematical and statistical methods to accurately predict people's movement patterns and congestion levels using multiple datasets.

[0436] An "information terminal" is a device that receives data distributed from a server and presents that information to the user in a visual or interactive format, and this includes mobile phones and tablets.

[0437] This invention is a system that provides highly accurate, real-time movement prediction information, and is realized through the cooperation of a server, terminals, and users.

[0438] The server first acquires data from location data providers and external information providers. Specifically, the software uses the HTTP protocol to send API requests and receive location information, weather, event information, etc. At this time, a dedicated data acquisition module is used to enable unified processing of various data formats. Subsequently, the data is preprocessed using data analysis libraries in Python or R to remove outliers and impute missing values.

[0439] The server uses a pre-trained AI algorithm to build a movement prediction model based on an integrated dataset. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to perform time-series predictions using historical movement data, enabling real-time congestion predictions. These prediction results are delivered to terminals using compression techniques for efficient data transfer.

[0440] The terminal provides a user interface for visually displaying the forecast data received from the server. Specifically, an application will be developed to run on smartphones and tablets, displaying color-coded congestion information through a map-based interface. This will allow users to intuitively understand the information and adjust their travel plans accordingly.

[0441] Users optimize their actions based on travel prediction information displayed on their devices. For example, if a user is attending a large event, they can check the predicted congestion in the surrounding area around the event's start time and choose the optimal travel route.

[0442] For example, a user can find out the optimal time to visit a shopping mall by entering a prompt message into the system such as, "Please tell me the congestion level of the shopping malls in the city at 3 PM."

[0443] By using such a system, users can take advantage of real-time movement prediction information and act more efficiently.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The server retrieves data from location data providers and external information providers. API keys and request parameters are provided as input, and HTTP requests are sent based on this information. The output is a data response containing location information, weather information, and event information. This data is stored on the server in JSON format for subsequent processing.

[0447] Step 2:

[0448] The server integrates the acquired data to generate a single dataset. Its input consists of multiple previously acquired datasets. Data processing involves merging the data using the Python Pandas library and organizing it based on time and location. Anomalies are detected and imputed to produce a clean dataset. This integrated dataset is then used to train an AI model.

[0449] Step 3:

[0450] The server builds a movement prediction model based on the integrated dataset. The dataset generated in the previous step is used as input. For data computation, a machine learning algorithm using TensorFlow is employed to learn past trends. As output, a highly accurate movement prediction model is generated, and its accuracy is evaluated through internal validation.

[0451] Step 4:

[0452] The server generates real-time traffic prediction information using a pre-trained traffic prediction model. It takes the current time and real-time external data as input and processes them through the model. As a result, it outputs a traffic congestion prediction for a specific area in the future. This prediction information is compressed and sent to the terminal.

[0453] Step 5:

[0454] The device visually displays movement prediction information received from the server. It receives compressed data from the server as input. The device decompresses the data and displays it clearly on a map within the mobile application interface. Users can view the visualized congestion information and adjust their actions accordingly.

[0455] Step 6:

[0456] Users develop action plans using predictive information displayed on their devices. Based on the input information, users can, for example, decide on shopping times to avoid crowds or optimize their travel routes. The output is a more efficient travel schedule or visit plan.

[0457] (Application Example 1)

[0458] 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."

[0459] Currently, in physical spaces visited by many people, there are limited means of obtaining information about congestion in advance, causing inconvenience to users. Furthermore, the lack of guidance on optimal routes and times for visits hinders a pleasant experience. Therefore, there is a need for a means to provide users with accurate, real-time information on congestion levels and suggest optimal visit plans.

[0460] 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.

[0461] In this invention, the server includes means for collecting location data obtained from a location data source, means for collecting event information and weather information from external information sources, and means for integrating the collected information to generate a single dataset. This allows users to understand the degree of physical space occupancy in real time and propose the optimal visit time, enabling them to plan a comfortable visit.

[0462] A "location data source" is a provider of information about geographical location.

[0463] "External information sources" refer to sources that provide information other than location information, specifically events and weather information.

[0464] A "single dataset" is a collection of data that integrates and combines multiple pieces of information into one.

[0465] A "high-precision moving object prediction model" is a model based on an algorithm built to predict the movement of a moving object, and it has high accuracy in real time.

[0466] A "user terminal" is a communication device that a user can carry with them, and is a device for receiving and displaying information.

[0467] "Density of expansion" is an indicator that shows the density of people and objects in a physical space.

[0468] "Optimal visiting time" refers to the time of day when visitors can enjoy a less crowded and more comfortable experience.

[0469] An "optimal travel route" is the route that enables the user to travel in the most efficient and comfortable way.

[0470] The system based on this invention collects information from location data sources and external information sources, integrates them to perform highly accurate movement predictions, and provides users with the optimal visit time and travel route.

[0471] The server collects location data from location data sources and also gathers event and weather information from external sources. This information is integrated on the server and processed as a single dataset. Using this dataset, the server uses AI frameworks such as TensorFlow and PyTorch to build a highly accurate moving object prediction model. This model generates real-time prediction information for moving objects.

[0472] The device receives movement prediction information distributed from the server and presents it visually to the user. The application on the device, such as a smartphone or tablet, is developed using Flutter or React Native, allowing users to intuitively view the information. The device has a function to notify the user of the optimal time to visit, guiding them to avoid congestion and have a comfortable visit. It also guides the user to the optimal route within the physical space, improving the visit experience.

[0473] For example, if a user plans to visit an area that is crowded on weekends, the system will suggest the least crowded times and provide appropriate routes to support efficient planning.

[0474] Examples of prompts for a generative AI model:

[0475] "Please predict the crowd levels at nearby stores next Friday evening. In particular, please provide detailed information about the crowd levels at major retailers. Also, please recommend the best route to take."

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The server collects location data from location data sources. It receives geographically location-related data as input and stores it in its database. The data is retrieved in real time via an API. The server periodically collects this information to maintain its up-to-date state.

[0479] Step 2:

[0480] The server collects event and weather information from external sources. Current event schedules and weather conditions are taken as input and added to the dataset. Techniques such as web scraping and RSS feeds are used for this collection. The integrated information forms a more comprehensive dataset.

[0481] Step 3:

[0482] The server integrates collected location data, event information, and weather information to generate a single dataset. Multiple data sources collected in previous stages are used as input, and the data is formatted, cleaned, and integrated. This process includes data redundancy removal and time-series data organization. The output is a consistent dataset suitable for analysis.

[0483] Step 4:

[0484] The server builds a highly accurate moving object prediction model using an integrated dataset. The well-structured dataset is input to the AI ​​model for training and evaluation. TensorFlow and PyTorch are used to train a model suitable for moving object prediction. This process yields a highly accurate model.

[0485] Step 5:

[0486] The server generates real-time movement prediction information using the constructed movement prediction model. The model takes the latest integrated dataset as input to predict congestion levels and movement patterns. Real-time prediction data is output, which is then sent to the user.

[0487] Step 6:

[0488] The device receives mobile object prediction information distributed from the server. It receives prediction data sent from the server as input and prepares it for presentation to the user. The data is formatted for display on the mobile app.

[0489] Step 7:

[0490] The terminal uses received motion prediction information to present the user with the optimal visit time and travel route. It receives prediction data as input and generates an optimal schedule and route based on the user's current location and destination. The output is visually displayed to the user, supporting real-time travel planning.

[0491] 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.

[0492] This invention combines an emotion engine with a system that integrates external information such as location data, event information, and weather information to provide highly accurate pedestrian flow predictions. This system is primarily composed of a server, terminals, and users, and aims to recognize the user's emotional state and provide the user with appropriate information based on the results.

[0493] First, the server collects location data from location data providers, external event information, and weather information in real time. This data is integrated and stored in a database, and then combined into a pedestrian flow prediction model using an AI algorithm. This model utilizes past and present data to predict future pedestrian flow and congestion levels.

[0494] Furthermore, the server uses an emotion engine to analyze the user's emotions. The emotion engine estimates the user's current emotional state based on voice and biometric data acquired from smartphones and wearable devices. This emotional information is used in combination with pedestrian flow prediction information to customize the information provided to the user.

[0495] The terminal receives customized pedestrian flow prediction information from the server, taking into account the user's emotional state. This information is tailored to the user's emotional state and includes suggestions for calmer routes to reduce stress, as well as congestion information for entertainment areas. The terminal displays this information visually, allowing the user to plan an efficient and comfortable journey based on it.

[0496] As a concrete example, when a user visits a tourist destination on a holiday, the system suggests routes and appropriate visiting times that will alleviate the user's anxiety. In this process, the server analyzes pedestrian flow prediction data along with the user's desire to relax, and delivers the optimal plan to the terminal, enabling the user to enjoy their trip more.

[0497] The system of the present invention allows users not only to avoid physical congestion but also to optimize their behavior according to their individual emotional needs.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The server obtains location information in real time from a location data provider. It also accesses external information providers that offer event and weather information to collect necessary data. This data is stored in a centrally managed database.

[0501] Step 2:

[0502] The server integrates various pieces of information to generate a single dataset. The data integration process includes data cleaning, time synchronization, and format standardization, followed by the creation of a human flow prediction model via an AI algorithm.

[0503] Step 3:

[0504] The server uses an emotion engine to process and analyze the user's emotional data. This emotional data is determined from the user's voice tone, heart rate, facial recognition results, etc., to identify the user's current emotional state.

[0505] Step 4:

[0506] The server combines information from the pedestrian flow prediction model with analysis results from the emotion engine to generate user-optimized pedestrian flow prediction information. This information includes stress-reducing routes based on emotional state and recommended visit times based on interests.

[0507] Step 5:

[0508] The device receives customized pedestrian flow prediction information delivered from the server. The device then displays this information visually, relating it to the user's emotional state.

[0509] Step 6:

[0510] Users check the display on their device and plan their actions based on the provided predictive information. This allows them to choose efficient routes and times to travel while avoiding stress.

[0511] This series of processes allows users to create plans that address not only their physical needs but also their emotional needs, in addition to providing information to avoid physical congestion.

[0512] (Example 2)

[0513] 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."

[0514] In modern society, many people experience stress from being in crowded environments. Knowing about congestion in advance, especially during travel and daily commutes, would allow for more comfortable and efficient planning. However, conventional systems only consider physical pedestrian flow information, making it difficult to provide optimal transportation options based on individual emotional states.

[0515] 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.

[0516] In this invention, the server includes means for collecting location information from a location information providing device, means for collecting event information and weather information from an external information providing device, means for creating highly accurate pedestrian flow predictions using an AI model, means for identifying the user's emotional state from voice data and biometric measurement data, and means for generating customized information by combining the user's emotional state and pedestrian flow prediction information. This makes it possible for the user to obtain an optimal travel plan according to their own emotional state.

[0517] A "location information provider" is a device that provides geographical data related to the user's location.

[0518] An "external information provider" is a device that provides additional data different from location information, such as event information and weather information.

[0519] An "artificial intelligence model" is an algorithm that analyzes large amounts of data and makes predictions and inferences based on that data.

[0520] "Voice data" refers to recorded information about the user's voice, and is used for sentiment analysis and other purposes.

[0521] "Biometric data" refers to data related to a person's physiological state, such as heart rate and skin current.

[0522] "Customized information" refers to information that has been tailored to the user's specific needs and emotional state.

[0523] A "user device" is a device that a user can directly operate and use for displaying or inputting information.

[0524] "Feedback" refers to information such as opinions and evaluations based on the user's experience.

[0525] The system of the present invention integrates the user's location information, emotional state, and pedestrian flow prediction information to provide individually customized movement information.

[0526] The server first collects location information in real time from location information providers. Simultaneously, it also acquires event information and weather information from external information providers. This information is collected in a database and compiled into an integrated dataset. This process standardizes the data format and establishes associations to facilitate subsequent processing.

[0527] Next, the server uses an artificial intelligence model to predict pedestrian flow. This model utilizes machine learning frameworks such as TensorFlow and PyTorch to predict future pedestrian flow trends based on historical data. The prediction results are constantly updated according to current real-time data.

[0528] Furthermore, sentiment analysis is performed. The server uses voice data and biometric data transmitted from the user's smartphone or wearable device to process and identify the user's emotional state. This analysis utilizes natural language processing and machine learning techniques.

[0529] Based on this emotional state and pedestrian flow prediction information, the server generates customized information optimized for the user. This includes suggesting quieter routes for users who want to avoid congestion and providing safe travel plans that take weather conditions into consideration.

[0530] The device receives this information and displays it in a visually easy-to-understand format for the user. The user can then use the provided information to create an efficient and comfortable travel plan.

[0531] For example, when a user uses the system to visit a tourist destination on a holiday, the server can consider the user's emotional state of wanting to relax and suggest the optimal route and visit time. To achieve this, an example of a prompt message could be, "Please begin providing information combining crowd flow prediction and sentiment analysis to suggest a calm route in the tourist destination."

[0532] The key feature of this system is that it not only avoids physical congestion but also enables the rapid and effective provision of information that addresses users' emotional needs.

[0533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0534] Step 1:

[0535] The server acquires location information in real time from a location information provider. As input, it collects latitude and longitude data using the API endpoint provided by the location information provider. This location data is integrated and stored in a database. The output is a dataset of each user's current location.

[0536] Step 2:

[0537] The server collects event and weather information from external information providers. As input, it makes requests to external APIs to obtain event location and weather data. This information is integrated with location data and stored in a database as a single dataset. The output is a comprehensive dataset containing location, event, and weather conditions.

[0538] Step 3:

[0539] The server uses a generative AI model to predict pedestrian flow based on an integrated dataset. Historical data is supplied to the AI ​​model as input, and predictions are made based on current data. Specifically, TensorFlow or PyTorch is launched in a Python environment, and the prediction algorithm is executed. The output is a prediction model showing future pedestrian flow trends.

[0540] Step 4:

[0541] The server acquires voice and biometric data from smartphones and wearable devices to analyze the user's emotional state. It collects data from devices via Bluetooth or Wi-Fi as input. Using natural language processing and machine learning algorithms, it identifies emotions from the user's voice tone and heart rate. The output is data indicating the user's emotional state.

[0542] Step 5:

[0543] The server combines the user's emotional state with a pedestrian flow prediction model to generate customized information. It utilizes emotional analysis results and pedestrian flow prediction data as input. Based on this, it calculates the optimal travel route and visit plan for the user. The output is customized travel information.

[0544] Step 6:

[0545] The terminal receives customized information provided by the server and displays it to the user. It receives information packets from the server as input and launches an application to visualize the data. This information is displayed on the user's screen as maps and text. The output is visually organized user-friendly information.

[0546] Step 7:

[0547] Users provide feedback through their devices. A user feedback form is used as input, where they fill in information about their user experience. This feedback is sent to the server and used to improve the AI ​​model. The output is a new dataset that helps in model tuning.

[0548] (Application Example 2)

[0549] 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."

[0550] Modern users lead busy lives and are exposed to a vast amount of information, making it difficult for them to choose the most suitable travel routes and living environments that match their emotions and stress levels. Furthermore, the optimization of living environments within the home according to emotional states is insufficient, and new methods are needed to improve quality of life.

[0551] 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.

[0552] In this invention, the server includes data collection means for acquiring location information data, data collection means for providing external information, and data integration means for integrating the collected data to generate a data set. This enables the optimization of the living environment and comfortable travel planning in accordance with the user's emotional state.

[0553] "Location data" refers to data that indicates geographical information about a specific point in time, and is used to pinpoint the exact location of a user or object.

[0554] "Data collection means" refers to a mechanism or method for obtaining necessary data from various sources, thereby providing a foundation for integrating information.

[0555] "Data integration means" refers to the process of combining different types of data to generate a single, continuous, and unified data set.

[0556] A "human flow prediction model" refers to a computational algorithm or statistical method used to estimate future human movement patterns and congestion levels based on location information and related data.

[0557] A "predictive tool" is a mechanism that provides functions and processes for predicting future events or patterns based on a specific model.

[0558] "Information distribution means" refers to communication technology or methods for transmitting generated information to a user terminal or other receiving terminal.

[0559] "Display means" refers to technologies or tools that provide the function of visually representing information on a terminal.

[0560] "Emotion recognition means" refers to technologies and algorithms that use a user's voice and biometric data to identify and analyze their emotional state.

[0561] "Information adaptation means" refers to a process or function that personalizes and appropriately provides predictive information according to the user's specific needs and circumstances.

[0562] "Environmental optimization means" refers to technologies and methods for adjusting living and working environments based on users' emotions and lifestyle needs to achieve an optimal state.

[0563] This invention is a system that optimizes the living environment based on the user's emotional state to achieve comfortable travel. The server collects location data from a location data provider and also obtains event information and weather information from an external information provider. By integrating this data, a highly accurate human flow prediction model is created that enables real-time prediction of human flow.

[0564] The server also uses voice and biometric data collected from smartphones and wearable devices to analyze the user's emotional state using an emotion engine. Emotion recognition can utilize IBM Watson's Natural Language Understanding API or machine learning libraries such as TensorFlow and PyTorch.

[0565] The terminal receives pedestrian flow prediction information distributed from the server. Furthermore, it visually displays and provides information that takes into account the user's emotional state. This allows users to adjust their travel plans based on suggestions for calmer routes and optimal visit times that suit their emotional state. For example, when visiting a tourist destination on a holiday, a calmer route to alleviate stress is suggested, allowing the user to enjoy their sightseeing more.

[0566] As a concrete example of a prompt, it can be phrased as, "Generate prompts to create a relaxing living environment for the family. Please also tell me about lighting and time settings." By utilizing a generation AI model, information can be obtained to make the user's life more comfortable.

[0567] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0568] Step 1:

[0569] The server obtains location information from a location data provider and also collects event and weather information from external information providers. After receiving this data, it performs data integration to generate a data set. This data set serves as the basis for creating subsequent human flow prediction models.

[0570] Step 2:

[0571] The server uses an integrated data set to generate highly accurate pedestrian flow prediction models. AI algorithms process location information, event information, and weather information to build models that predict future pedestrian flow and congestion. This prediction information is used to provide users with travel plans.

[0572] Step 3:

[0573] The server collects voice and biometric data from the user's smartphone or wearable device and analyzes the user's emotional state using an emotion engine. In this process, the user's emotional state is generated as status data, which forms the basis for providing personalized information to each user.

[0574] Step 4:

[0575] The server customizes predictive information based on the sentiment analysis results and delivers it to the user's terminal. Here, tailored information is generated, such as suggestions for relaxing routes and optimal visit times, providing information that matches the user's emotional state.

[0576] Step 5:

[0577] The terminal receives information delivered from the server and displays it visually to the user. The user can then use this information to plan their trip. For example, a less crowded sightseeing route might be suggested, allowing the user to visit tourist destinations more comfortably.

[0578] Step 6:

[0579] When users utilize the AI ​​model based on the information they provide, they use prompts. These prompts allow for the generation of even more personalized information. For example, additional information can be obtained in the form of, "Generate prompts to create a relaxing living environment for my family."

[0580] 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.

[0581] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0582] 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.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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".

[0597] This invention is a system that provides highly accurate pedestrian flow prediction by integrating location information, event information, weather information, etc., from location information data providers. This system includes three entities: a server, a terminal, and a user.

[0598] The server first collects the necessary data from location data providers and external information providers. The server integrates this data to generate a single dataset. Based on this dataset, an AI algorithm is used to create a pedestrian flow prediction model and perform real-time congestion predictions. The prediction model has been trained and validated using the extensive dataset collected, resulting in high accuracy.

[0599] The terminal receives highly accurate pedestrian flow prediction data distributed from the server. This information is then used to visually display the pedestrian flow situation on the terminal. Users can manipulate this visual information to view details based on their areas of interest and time of day. This allows users to avoid congestion and choose the optimal time and route.

[0600] As a concrete example, if a user uses this system before attending a large event in an urban area, they can check the predicted crowd flow around the event venue in real time. The server analyzes event information and data from similar past events to predict the congestion on the day. This predicted information is presented visually to the user via a terminal, allowing the user to use it to plan an efficient journey.

[0601] Furthermore, when users visit a shopping mall, they can obtain information predicting the level of congestion and peak times within the mall, allowing them to create a more comfortable shopping plan. In this way, the server is responsible for integrating and analyzing information, while terminals intuitively display it, enabling users to directly utilize the information and act efficiently. This system achieves real-time and highly accurate pedestrian flow prediction.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] The server obtains real-time location information for users and objects from location data providers. In addition, it connects to external data providers that provide event information and weather information, and collects this data as well.

[0605] Step 2:

[0606] The server preprocesses the various data it collects, standardizes the format, and stores it in a database. Preprocessing includes denoising data, supplementing incomplete data, and synchronizing timestamps of different types of information.

[0607] Step 3:

[0608] Based on the integrated data from the servers, an AI algorithm is used to build a pedestrian flow prediction model. This model learns from past data to predict future pedestrian flow patterns.

[0609] Step 4:

[0610] The server generates pedestrian flow prediction results and prepares the necessary prediction information according to each user's customization options. The server then prepares this information for distribution.

[0611] Step 5:

[0612] The device connects to the server in response to the user's request and receives the provided forecast information. Based on the received data, the device displays pedestrian flow information in a format optimized for the user.

[0613] Step 6:

[0614] Users check the visual display provided on their devices and plan their actions based on congestion levels and predicted fluctuations. They adjust their arrival time and travel route to achieve efficient travel.

[0615] (Example 1)

[0616] 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".

[0617] In modern society, accurately understanding the movement of people in specific areas and at specific times is crucial for transportation planning and commercial activities. However, conventional methods often involve analyzing location and external information individually, requiring significant time and effort for data integration and analysis, and lacking real-time capabilities. There is a need to address this challenge and provide highly accurate, real-time pedestrian flow predictions to support efficient travel and congestion avoidance.

[0618] 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.

[0619] In this invention, the server includes means for acquiring location information data, means for collecting data from data sources that provide external information, and means for integrating the collected location information and external information to generate a single dataset. This enables the generation of highly accurate and real-time movement prediction information.

[0620] "Location data" refers to information that indicates the geographical location of individual moving objects or users, and is usually expressed as latitude and longitude.

[0621] "External information" refers to data other than location data, such as weather information and event information, which are information about the environment and circumstances that may affect movement or stay.

[0622] "Data sources" refer to information providers and services that provide location data and external information, including location information providers and weather information services.

[0623] A "high-precision movement prediction model" is an algorithm or system that incorporates mathematical and statistical methods to accurately predict people's movement patterns and congestion levels using multiple datasets.

[0624] An "information terminal" is a device that receives data distributed from a server and presents that information to the user in a visual or interactive format, and this includes mobile phones and tablets.

[0625] This invention is a system that provides highly accurate, real-time movement prediction information, and is realized through the cooperation of a server, terminals, and users.

[0626] The server first acquires data from location data providers and external information providers. Specifically, the software uses the HTTP protocol to send API requests and receive location information, weather, event information, etc. At this time, a dedicated data acquisition module is used to enable unified processing of various data formats. Subsequently, the data is preprocessed using data analysis libraries in Python or R to remove outliers and impute missing values.

[0627] The server uses a pre-trained AI algorithm to build a movement prediction model based on an integrated dataset. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to perform time-series predictions using historical movement data, enabling real-time congestion predictions. These prediction results are delivered to terminals using compression techniques for efficient data transfer.

[0628] The terminal provides a user interface for visually displaying the forecast data received from the server. Specifically, an application will be developed to run on smartphones and tablets, displaying color-coded congestion information through a map-based interface. This will allow users to intuitively understand the information and adjust their travel plans accordingly.

[0629] Users optimize their actions based on travel prediction information displayed on their devices. For example, if a user is attending a large event, they can check the predicted congestion in the surrounding area around the event's start time and choose the optimal travel route.

[0630] For example, a user can find out the optimal time to visit a shopping mall by entering a prompt message into the system such as, "Please tell me the congestion level of the shopping malls in the city at 3 PM."

[0631] By using such a system, users can take advantage of real-time movement prediction information and act more efficiently.

[0632] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0633] Step 1:

[0634] The server retrieves data from location data providers and external information providers. API keys and request parameters are provided as input, and HTTP requests are sent based on this information. The output is a data response containing location information, weather information, and event information. This data is stored on the server in JSON format for subsequent processing.

[0635] Step 2:

[0636] The server integrates the acquired data to generate a single dataset. Its input consists of multiple previously acquired datasets. Data processing involves merging the data using the Python Pandas library and organizing it based on time and location. Anomalies are detected and imputed to produce a clean dataset. This integrated dataset is then used to train an AI model.

[0637] Step 3:

[0638] The server builds a movement prediction model based on the integrated dataset. The dataset generated in the previous step is used as input. For data computation, a machine learning algorithm using TensorFlow is employed to learn past trends. As output, a highly accurate movement prediction model is generated, and its accuracy is evaluated through internal validation.

[0639] Step 4:

[0640] The server generates real-time traffic prediction information using a pre-trained traffic prediction model. It takes the current time and real-time external data as input and processes them through the model. As a result, it outputs a traffic congestion prediction for a specific area in the future. This prediction information is compressed and sent to the terminal.

[0641] Step 5:

[0642] The device visually displays movement prediction information received from the server. It receives compressed data from the server as input. The device decompresses the data and displays it clearly on a map within the mobile application interface. Users can view the visualized congestion information and adjust their actions accordingly.

[0643] Step 6:

[0644] Users develop action plans using predictive information displayed on their devices. Based on the input information, users can, for example, decide on shopping times to avoid crowds or optimize their travel routes. The output is a more efficient travel schedule or visit plan.

[0645] (Application Example 1)

[0646] 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".

[0647] Currently, in physical spaces visited by many people, there are limited means of obtaining information about congestion in advance, causing inconvenience to users. Furthermore, the lack of guidance on optimal routes and times for visits hinders a pleasant experience. Therefore, there is a need for a means to provide users with accurate, real-time information on congestion levels and suggest optimal visit plans.

[0648] 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.

[0649] In this invention, the server includes means for collecting location data obtained from a location data source, means for collecting event information and weather information from external information sources, and means for integrating the collected information to generate a single dataset. This allows users to understand the degree of physical space occupancy in real time and propose the optimal visit time, enabling them to plan a comfortable visit.

[0650] A "location data source" is a provider of information about geographical location.

[0651] "External information sources" refer to sources that provide information other than location information, specifically events and weather information.

[0652] A "single dataset" is a collection of data that integrates and combines multiple pieces of information into one.

[0653] A "high-precision moving object prediction model" is a model based on an algorithm built to predict the movement of a moving object, and it has high accuracy in real time.

[0654] A "user terminal" is a communication device that a user can carry with them, and is a device for receiving and displaying information.

[0655] "Density of expansion" is an indicator that shows the density of people and objects in a physical space.

[0656] "Optimal visiting time" refers to the time of day when visitors can enjoy a less crowded and more comfortable experience.

[0657] An "optimal travel route" is the route that enables the user to travel in the most efficient and comfortable way.

[0658] The system based on this invention collects information from location data sources and external information sources, integrates them to perform highly accurate movement predictions, and provides users with the optimal visit time and travel route.

[0659] The server collects location data from location data sources and also gathers event and weather information from external sources. This information is integrated on the server and processed as a single dataset. Using this dataset, the server uses AI frameworks such as TensorFlow and PyTorch to build a highly accurate moving object prediction model. This model generates real-time prediction information for moving objects.

[0660] The device receives movement prediction information distributed from the server and presents it visually to the user. The application on the device, such as a smartphone or tablet, is developed using Flutter or React Native, allowing users to intuitively view the information. The device has a function to notify the user of the optimal time to visit, guiding them to avoid congestion and have a comfortable visit. It also guides the user to the optimal route within the physical space, improving the visit experience.

[0661] For example, if a user plans to visit an area that is crowded on weekends, the system will suggest the least crowded times and provide appropriate routes to support efficient planning.

[0662] Examples of prompts for a generative AI model:

[0663] "Please predict the crowd levels at nearby stores next Friday evening. In particular, please provide detailed information about the crowd levels at major retailers. Also, please recommend the best route to take."

[0664] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0665] Step 1:

[0666] The server collects location data from location data sources. It receives geographically location-related data as input and stores it in its database. The data is retrieved in real time via an API. The server periodically collects this information to maintain its up-to-date state.

[0667] Step 2:

[0668] The server collects event and weather information from external sources. Current event schedules and weather conditions are taken as input and added to the dataset. Techniques such as web scraping and RSS feeds are used for this collection. The integrated information forms a more comprehensive dataset.

[0669] Step 3:

[0670] The server integrates collected location data, event information, and weather information to generate a single dataset. Multiple data sources collected in previous stages are used as input, and the data is formatted, cleaned, and integrated. This process includes data redundancy removal and time-series data organization. The output is a consistent dataset suitable for analysis.

[0671] Step 4:

[0672] The server builds a highly accurate moving object prediction model using an integrated dataset. The well-structured dataset is input to the AI ​​model for training and evaluation. TensorFlow and PyTorch are used to train a model suitable for moving object prediction. This process yields a highly accurate model.

[0673] Step 5:

[0674] The server generates real-time movement prediction information using the constructed movement prediction model. The model takes the latest integrated dataset as input to predict congestion levels and movement patterns. Real-time prediction data is output, which is then sent to the user.

[0675] Step 6:

[0676] The device receives mobile object prediction information distributed from the server. It receives prediction data sent from the server as input and prepares it for presentation to the user. The data is formatted for display on the mobile app.

[0677] Step 7:

[0678] The terminal uses received motion prediction information to present the user with the optimal visit time and travel route. It receives prediction data as input and generates an optimal schedule and route based on the user's current location and destination. The output is visually displayed to the user, supporting real-time travel planning.

[0679] 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.

[0680] This invention combines an emotion engine with a system that integrates external information such as location data, event information, and weather information to provide highly accurate pedestrian flow predictions. This system is primarily composed of a server, terminals, and users, and aims to recognize the user's emotional state and provide the user with appropriate information based on the results.

[0681] First, the server collects location data from location data providers, external event information, and weather information in real time. This data is integrated and stored in a database, and then combined into a pedestrian flow prediction model using an AI algorithm. This model utilizes past and present data to predict future pedestrian flow and congestion levels.

[0682] Furthermore, the server uses an emotion engine to analyze the user's emotions. The emotion engine estimates the user's current emotional state based on voice and biometric data acquired from smartphones and wearable devices. This emotional information is used in combination with pedestrian flow prediction information to customize the information provided to the user.

[0683] The terminal receives customized pedestrian flow prediction information from the server, taking into account the user's emotional state. This information is tailored to the user's emotional state and includes suggestions for calmer routes to reduce stress, as well as congestion information for entertainment areas. The terminal displays this information visually, allowing the user to plan an efficient and comfortable journey based on it.

[0684] As a concrete example, when a user visits a tourist destination on a holiday, the system suggests routes and appropriate visiting times that will alleviate the user's anxiety. In this process, the server analyzes pedestrian flow prediction data along with the user's desire to relax, and delivers the optimal plan to the terminal, enabling the user to enjoy their trip more.

[0685] The system of the present invention allows users not only to avoid physical congestion but also to optimize their behavior according to their individual emotional needs.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The server obtains location information in real time from a location data provider. It also accesses external information providers that offer event and weather information to collect necessary data. This data is stored in a centrally managed database.

[0689] Step 2:

[0690] The server integrates various pieces of information to generate a single dataset. The data integration process includes data cleaning, time synchronization, and format standardization, followed by the creation of a human flow prediction model via an AI algorithm.

[0691] Step 3:

[0692] The server uses an emotion engine to process and analyze the user's emotional data. This emotional data is determined from the user's voice tone, heart rate, facial recognition results, etc., to identify the user's current emotional state.

[0693] Step 4:

[0694] The server combines information from the pedestrian flow prediction model with analysis results from the emotion engine to generate user-optimized pedestrian flow prediction information. This information includes stress-reducing routes based on emotional state and recommended visit times based on interests.

[0695] Step 5:

[0696] The device receives customized pedestrian flow prediction information delivered from the server. The device then displays this information visually, relating it to the user's emotional state.

[0697] Step 6:

[0698] Users check the display on their device and plan their actions based on the provided predictive information. This allows them to choose efficient routes and times to travel while avoiding stress.

[0699] This series of processes allows users to create plans that address not only their physical needs but also their emotional needs, in addition to providing information to avoid physical congestion.

[0700] (Example 2)

[0701] 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".

[0702] In modern society, many people experience stress from being in crowded environments. Knowing about congestion in advance, especially during travel and daily commutes, would allow for more comfortable and efficient planning. However, conventional systems only consider physical pedestrian flow information, making it difficult to provide optimal transportation options based on individual emotional states.

[0703] 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.

[0704] In this invention, the server includes means for collecting location information from a location information providing device, means for collecting event information and weather information from an external information providing device, means for creating highly accurate pedestrian flow predictions using an AI model, means for identifying the user's emotional state from voice data and biometric measurement data, and means for generating customized information by combining the user's emotional state and pedestrian flow prediction information. This makes it possible for the user to obtain an optimal travel plan according to their own emotional state.

[0705] A "location information provider" is a device that provides geographical data related to the user's location.

[0706] An "external information provider" is a device that provides additional data different from location information, such as event information and weather information.

[0707] An "artificial intelligence model" is an algorithm that analyzes large amounts of data and makes predictions and inferences based on that data.

[0708] "Voice data" refers to recorded information about the user's voice, and is used for sentiment analysis and other purposes.

[0709] "Biometric data" refers to data related to a person's physiological state, such as heart rate and skin current.

[0710] "Customized information" refers to information that has been tailored to the user's specific needs and emotional state.

[0711] A "user device" is a device that a user can directly operate and use for displaying or inputting information.

[0712] "Feedback" refers to information such as opinions and evaluations based on the user's experience.

[0713] The system of the present invention integrates the user's location information, emotional state, and pedestrian flow prediction information to provide individually customized movement information.

[0714] The server first collects location information in real time from location information providers. Simultaneously, it also acquires event information and weather information from external information providers. This information is collected in a database and compiled into an integrated dataset. This process standardizes the data format and establishes associations to facilitate subsequent processing.

[0715] Next, the server uses an artificial intelligence model to predict pedestrian flow. This model utilizes machine learning frameworks such as TensorFlow and PyTorch to predict future pedestrian flow trends based on historical data. The prediction results are constantly updated according to current real-time data.

[0716] Furthermore, sentiment analysis is performed. The server uses voice data and biometric data transmitted from the user's smartphone or wearable device to process and identify the user's emotional state. This analysis utilizes natural language processing and machine learning techniques.

[0717] Based on this emotional state and pedestrian flow prediction information, the server generates customized information optimized for the user. This includes suggesting quieter routes for users who want to avoid congestion and providing safe travel plans that take weather conditions into consideration.

[0718] The device receives this information and displays it in a visually easy-to-understand format for the user. The user can then use the provided information to create an efficient and comfortable travel plan.

[0719] For example, when a user uses the system to visit a tourist destination on a holiday, the server can consider the user's emotional state of wanting to relax and suggest the optimal route and visit time. To achieve this, an example of a prompt message could be, "Please begin providing information combining crowd flow prediction and sentiment analysis to suggest a calm route in the tourist destination."

[0720] The key feature of this system is that it not only avoids physical congestion but also enables the rapid and effective provision of information that addresses users' emotional needs.

[0721] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0722] Step 1:

[0723] The server acquires location information in real time from a location information provider. As input, it collects latitude and longitude data using the API endpoint provided by the location information provider. This location data is integrated and stored in a database. The output is a dataset of each user's current location.

[0724] Step 2:

[0725] The server collects event and weather information from external information providers. As input, it makes requests to external APIs to obtain event location and weather data. This information is integrated with location data and stored in a database as a single dataset. The output is a comprehensive dataset containing location, event, and weather conditions.

[0726] Step 3:

[0727] The server uses a generative AI model to predict pedestrian flow based on an integrated dataset. Historical data is supplied to the AI ​​model as input, and predictions are made based on current data. Specifically, TensorFlow or PyTorch is launched in a Python environment, and the prediction algorithm is executed. The output is a prediction model showing future pedestrian flow trends.

[0728] Step 4:

[0729] The server acquires voice and biometric data from smartphones and wearable devices to analyze the user's emotional state. It collects data from devices via Bluetooth or Wi-Fi as input. Using natural language processing and machine learning algorithms, it identifies emotions from the user's voice tone and heart rate. The output is data indicating the user's emotional state.

[0730] Step 5:

[0731] The server combines the user's emotional state with a pedestrian flow prediction model to generate customized information. It utilizes emotional analysis results and pedestrian flow prediction data as input. Based on this, it calculates the optimal travel route and visit plan for the user. The output is customized travel information.

[0732] Step 6:

[0733] The terminal receives customized information provided by the server and displays it to the user. It receives information packets from the server as input and launches an application to visualize the data. This information is displayed on the user's screen as maps and text. The output is visually organized user-friendly information.

[0734] Step 7:

[0735] Users provide feedback through their devices. A user feedback form is used as input, where they fill in information about their user experience. This feedback is sent to the server and used to improve the AI ​​model. The output is a new dataset that helps in model tuning.

[0736] (Application Example 2)

[0737] 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".

[0738] Modern users lead busy lives and are exposed to a vast amount of information, making it difficult for them to choose the most suitable travel routes and living environments that match their emotions and stress levels. Furthermore, the optimization of living environments within the home according to emotional states is insufficient, and new methods are needed to improve quality of life.

[0739] 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.

[0740] In this invention, the server includes data collection means for acquiring location information data, data collection means for providing external information, and data integration means for integrating the collected data to generate a data set. This enables the optimization of the living environment and comfortable travel planning in accordance with the user's emotional state.

[0741] "Location data" refers to data that indicates geographical information about a specific point in time, and is used to pinpoint the exact location of a user or object.

[0742] "Data collection means" refers to a mechanism or method for obtaining necessary data from various sources, thereby providing a foundation for integrating information.

[0743] "Data integration means" refers to the process of combining different types of data to generate a single, continuous, and unified data set.

[0744] A "human flow prediction model" refers to a computational algorithm or statistical method used to estimate future human movement patterns and congestion levels based on location information and related data.

[0745] A "predictive tool" is a mechanism that provides functions and processes for predicting future events or patterns based on a specific model.

[0746] "Information distribution means" refers to communication technology or methods for transmitting generated information to a user terminal or other receiving terminal.

[0747] "Display means" refers to technologies or tools that provide the function of visually representing information on a terminal.

[0748] "Emotion recognition means" refers to technologies and algorithms that use a user's voice and biometric data to identify and analyze their emotional state.

[0749] "Information adaptation means" refers to a process or function that personalizes and appropriately provides predictive information according to the user's specific needs and circumstances.

[0750] "Environmental optimization means" refers to technologies and methods for adjusting living and working environments based on users' emotions and lifestyle needs to achieve an optimal state.

[0751] This invention is a system that optimizes the living environment based on the user's emotional state to achieve comfortable travel. The server collects location data from a location data provider and also obtains event information and weather information from an external information provider. By integrating this data, a highly accurate human flow prediction model is created that enables real-time prediction of human flow.

[0752] The server also uses voice and biometric data collected from smartphones and wearable devices to analyze the user's emotional state using an emotion engine. Emotion recognition can utilize IBM Watson's Natural Language Understanding API or machine learning libraries such as TensorFlow and PyTorch.

[0753] The terminal receives pedestrian flow prediction information distributed from the server. Furthermore, it visually displays and provides information that takes into account the user's emotional state. This allows users to adjust their travel plans based on suggestions for calmer routes and optimal visit times that suit their emotional state. For example, when visiting a tourist destination on a holiday, a calmer route to alleviate stress is suggested, allowing the user to enjoy their sightseeing more.

[0754] As a concrete example of a prompt, it can be phrased as, "Generate prompts to create a relaxing living environment for the family. Please also tell me about lighting and time settings." By utilizing a generation AI model, information can be obtained to make the user's life more comfortable.

[0755] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0756] Step 1:

[0757] The server obtains location information from a location data provider and also collects event and weather information from external information providers. After receiving this data, it performs data integration to generate a data set. This data set serves as the basis for creating subsequent human flow prediction models.

[0758] Step 2:

[0759] The server uses an integrated data set to generate highly accurate pedestrian flow prediction models. AI algorithms process location information, event information, and weather information to build models that predict future pedestrian flow and congestion. This prediction information is used to provide users with travel plans.

[0760] Step 3:

[0761] The server collects voice and biometric data from the user's smartphone or wearable device and analyzes the user's emotional state using an emotion engine. In this process, the user's emotional state is generated as status data, which forms the basis for providing personalized information to each user.

[0762] Step 4:

[0763] The server customizes predictive information based on the sentiment analysis results and delivers it to the user's terminal. Here, tailored information is generated, such as suggestions for relaxing routes and optimal visit times, providing information that matches the user's emotional state.

[0764] Step 5:

[0765] The terminal receives information delivered from the server and displays it visually to the user. The user can then use this information to plan their trip. For example, a less crowded sightseeing route might be suggested, allowing the user to visit tourist destinations more comfortably.

[0766] Step 6:

[0767] When users utilize the AI ​​model based on the information they provide, they use prompts. These prompts allow for the generation of even more personalized information. For example, additional information can be obtained in the form of, "Generate prompts to create a relaxing living environment for my family."

[0768] 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.

[0769] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] The following is further disclosed regarding the embodiments described above.

[0790] (Claim 1)

[0791] A means of collecting location data obtained from a location data provider,

[0792] A means of collecting data from external information providers that provide event information and weather information,

[0793] A means for integrating collected location data, event information, and weather information to generate a single dataset,

[0794] A means for creating a highly accurate human flow prediction model based on the generated dataset,

[0795] A means for generating real-time pedestrian flow prediction information using the aforementioned high-precision pedestrian flow prediction model,

[0796] A means for distributing the generated pedestrian flow prediction information to user terminals,

[0797] A means for visually displaying the distributed pedestrian flow prediction information on the user terminal.

[0798] Includes system.

[0799] (Claim 2)

[0800] The system according to claim 1, further comprising data processing means for detecting fluctuations in pedestrian flow based on acquired location data.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising information selection means for customizing predictive information based on the user's interests or needs.

[0803] "Example 1"

[0804] (Claim 1)

[0805] Means for acquiring location data,

[0806] Means for collecting data from data sources that provide external information,

[0807] A means of integrating collected location information and external information to generate a single dataset,

[0808] A means for generating a highly accurate movement prediction model based on the generated dataset,

[0809] A means for generating movement prediction information in real time using the aforementioned movement prediction model,

[0810] A means for distributing the generated movement prediction information to an information terminal,

[0811] A means for visually displaying distributed movement prediction information on an information terminal.

[0812] Includes system.

[0813] (Claim 2)

[0814] The system according to claim 1, further comprising data processing means for detecting changes in movement based on acquired location information.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising information selection means for customizing predictive information based on the user's interests or needs.

[0817] "Application Example 1"

[0818] (Claim 1)

[0819] A means for collecting location data obtained from a location data source,

[0820] Means of collecting event information and weather information from external sources,

[0821] A means for integrating collected location data, event information, and weather information to generate a single dataset,

[0822] A means for creating a highly accurate moving object prediction model based on the generated dataset,

[0823] A means for generating real-time mobile object prediction information using the aforementioned high-precision mobile object prediction model,

[0824] A means for distributing the generated mobile object prediction information to the user's terminal,

[0825] A means for visually displaying the distributed mobile object prediction information on the user's terminal,

[0826] A method for calculating the degree of occupancy of the physical space visited by a user and suggesting the optimal visit time,

[0827] A means of guiding the optimal movement path within physical space,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising data processing means for detecting changes in a moving object based on acquired location information data.

[0831] (Claim 3)

[0832] The system according to claim 1, comprising information selection means for customizing predictive information based on the user's interests or needs.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] A means for collecting location information obtained from a location information providing device,

[0836] A means of collecting data from external information providers that provide event information and weather information,

[0837] A means for integrating collected location information, event information, and weather information to generate a single dataset,

[0838] A means for creating highly accurate pedestrian flow predictions using an artificial intelligence model based on the generated dataset,

[0839] A means for generating real-time pedestrian flow forecast information using high-precision pedestrian flow forecasting,

[0840] A means of identifying a user's emotional state using voice data and biometric data,

[0841] A means of generating customized information by combining the user's emotional state and pedestrian flow prediction information,

[0842] A means for distributing the generated customization information to the user's device,

[0843] A means for visually displaying the delivered customization information on the user's device,

[0844] A means of collecting user feedback to improve generated AI models.

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, further comprising data processing means for detecting fluctuations in pedestrian flow based on acquired location information.

[0848] (Claim 3)

[0849] The system according to claim 1, further comprising information selection means for customizing predictive information based on the user's emotional state or as needed.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] A data collection method for obtaining location information data,

[0853] Data collection means for providing external information,

[0854] A data integration means for integrating collected data to generate a data set,

[0855] A model generation means for creating a human flow prediction model based on the generated data set,

[0856] A prediction means for generating real-time pedestrian flow prediction information using the aforementioned pedestrian flow prediction model,

[0857] An information distribution means for delivering pedestrian flow prediction information to user terminals,

[0858] A display means for visually displaying information delivered on a user terminal,

[0859] A means of recognizing emotions for analyzing the emotional state of a user,

[0860] Information adaptation means for customizing predictive information based on emotional state,

[0861] Adjustment means to optimize the environment

[0862] Includes system.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising means for optimizing the environment to adjust environmental settings in consideration of emotional state.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising a means for proposing a living environment based on the emotional state of members of a family or living group. [Explanation of Symbols]

[0867] 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 collecting location data obtained from a location data provider, A means of collecting data from external information providers that provide event information and weather information, A means for integrating collected location data, event information, and weather information to generate a single dataset, A means for creating a highly accurate human flow prediction model based on the generated dataset, A means for generating real-time pedestrian flow prediction information using the aforementioned high-precision pedestrian flow prediction model, A means for distributing the generated pedestrian flow prediction information to user terminals, A means for visually displaying the distributed pedestrian flow prediction information on the user terminal. Includes system.

2. The system according to claim 1, further comprising data processing means for detecting fluctuations in pedestrian flow based on acquired location data.

3. The system according to claim 1, further comprising information selection means for customizing predictive information based on the user's interests or needs.

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