system

Abstract

We provide the system. [Solution] Means for acquiring and integrating movement data, A means for simulating transportation routes using the integrated data and evaluating their profitability, A means of considering the introduction of autonomous driving technology based on evaluation results, A system that includes this.

Description

Technical Field

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

Background Art

[0006] "Mobility data" refers to data that represents people's location information and movement patterns, and is used to analyze transportation demand.

[0007] "Integration" is the process of organizing information obtained from multiple data sources and combining it into a consistent dataset.

[0008] "Simulation" is a method of virtually recreating real-world situations and predicting behavior under specific conditions within that simulation.

[0009] "Profitability" refers to a business or project's ability to generate profits, and is an indicator used to evaluate its continuity and growth.

[0010] "Autonomous driving technology" refers to technologies that enable vehicles to drive autonomously without a human driver, and includes sensors, AI algorithms, communication systems, and other such technologies.

[0011] A "transportation route" refers to a designated path or line used by public transport for travel.

[0012] A "large-scale language model" is an AI model designed for natural language processing, and it is trained on a large amount of text data.

[0013] A "people movement database" is a database that stores information on people's movements obtained from various sources, and is used for analysis and prediction. [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 the data processing device and 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] The following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit, or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit, or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0019] In the following embodiments, the 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 provides a system for designing efficient and profitable transportation routes, offering a series of processes from analyzing mobility data and evaluating profitability to considering the introduction of autonomous driving technology. The following describes a specific implementation of this system.

[0036] The core of this system is the function of collecting and integrating mobility data. The server accesses a nationwide human flow database and collects mobility pattern data for specific regions. The collected data is standardized in format and integrated into a dataset. This dataset is used as basic information for forecasting demand for transportation routes.

[0037] Next, the terminal runs a traffic demand simulation using the integrated dataset. This process utilizes a large-scale language model to evaluate multiple proposed traffic routes in detail, considering their profitability, environmental impact, and user convenience. Specifically, it becomes possible to propose new routes based on passenger count predictions in urban areas, and estimate the operating costs and expected revenue of those routes.

[0038] Furthermore, users select the most efficient transportation plan based on the evaluation results. If the introduction of autonomous driving technology is deemed effective from the perspectives of profitability, safety, and environmental impact, the server will conduct additional simulations. The purpose of these simulations is to quantitatively demonstrate cost reductions and improvements in passenger satisfaction associated with the operation of autonomous vehicles.

[0039] Through these steps, this system optimizes transportation networks and provides sustainable transportation solutions in communities facing declining birth rates and an aging population. Ultimately, users will be able to operate transportation rationally and efficiently by utilizing this system.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server accesses a human movement database to retrieve movement data for the target area. This data includes location information and movement patterns for a specific period.

[0043] Step 2:

[0044] The server normalizes the movement data it acquires and imputes missing values. Furthermore, it converts it to a unified format and creates an integrated dataset based on this.

[0045] Step 3:

[0046] The terminal inputs an integrated dataset and begins simulating traffic demand using a large-scale language model. This simulation evaluates the profitability, convenience, and environmental impact of each proposed traffic route.

[0047] Step 4:

[0048] The server evaluates the profitability of transportation routes based on the simulation results, formats the evaluation results, and provides them to the user as a visual report.

[0049] Step 5:

[0050] Users can review the provided reports via their devices and analyze the evaluation results. This allows them to select the most suitable transportation plan.

[0051] Step 6:

[0052] The system evaluates the feasibility of introducing autonomous driving technology to the transportation plan selected by the user. During this process, the server performs additional simulations to estimate the reduction in operating costs and the improvement in safety associated with introducing autonomous driving technology, and provides the results to the user.

[0053] Step 7:

[0054] The user makes the final decision on the transportation plan based on all the information and then begins the actual implementation process.

[0055] (Example 1)

[0056] 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."

[0057] Modern urban transportation systems must be efficient and sustainable, while also addressing demographic diversification and increasing environmental impact. Traditional transportation planning methods struggle to perform sufficient data analysis, making it difficult to accurately predict new transportation demands or design optimal routes. Furthermore, there is a lack of appropriate evaluation methods to realize improvements in transportation efficiency and safety through the introduction of autonomous driving technology.

[0058] 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.

[0059] In this invention, the server includes means for collecting and integrating mobility information, means for simulating traffic routes using the integrated information and evaluating profitability and environmental impact, and means for selecting and implementing traffic plans based on the evaluation results. This enables accurate forecasting of traffic demand and the creation of efficient traffic plans.

[0060] "Movement information" refers to data about the routes taken by individual moving entities (people or objects) within a specific time frame.

[0061] "Means of integration" refers to the process of transforming data collected from multiple sources into a consistent format and combining it into a single, comprehensive dataset.

[0062] A "transportation route" refers to the path that a means of transportation takes to move a user from one point to another.

[0063] "Simulation" refers to a technology that reproduces real-world traffic conditions using mathematical models and virtually analyzes their movement and changes.

[0064] "Profitability" is an indicator that shows how much profit a particular transportation plan or route has the potential to generate.

[0065] "Environmental impact" refers to the potential effects and changes that transportation plans and their implementation may have on the natural environment and local communities.

[0066] "Transportation planning" refers to specific policies and strategies for optimizing the operation and allocation of transportation methods in a particular region or on a particular route.

[0067] "Autonomous driving technology" is a general term for technologies used to enable vehicles to move on their own without human intervention.

[0068] "Information sources" refer to places or services from which data and information can be obtained. In the case of human flow information, this includes sensor networks and digital platforms.

[0069] This invention is a system for designing efficient and sustainable transportation routes, providing a series of processes from collecting mobility information to evaluating profitability and considering the introduction of autonomous driving technology.

[0070] The server first collects movement information from nationwide human flow sources. This process uses database systems (e.g., MySQL® or PostgreSQL) to retrieve data. The collected data is then formatted using the Python Pandas library and managed as a unified dataset. This dataset forms the basis for subsequent traffic route simulations.

[0071] Next, the device performs a traffic route simulation using the integrated dataset. This process utilizes a generative AI model, such as a large-scale language model like GPT-4®. The device inputs the prompt "Based on the given travel data, propose an efficient traffic route and evaluate its profitability and environmental impact" into the generative AI, which then evaluates multiple traffic route options, their profitability, environmental impact, and convenience.

[0072] Based on these simulation results, users can select new transportation plans, including the introduction of autonomous driving technology. For example, they can design new bus routes in busy areas and calculate their operating costs and expected revenues. The server then analyzes the benefits of introducing autonomous driving technology and conducts additional simulations to evaluate the resulting cost reductions and improvements in passenger satisfaction.

[0073] By using this system, users can design optimal transportation plans that meet local travel demand, enabling efficient and sustainable transportation operations.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects movement information from nationwide human flow sources and stores it in a database. Specifically, it uses SQL queries to extract relevant datasets and obtain movement patterns by time of day and region. The input is query conditions from the database, and the output is a formatted collection of movement information. For example, it collects data based on conditions such as "obtain the number of people who traveled from city A to city B within a specific date range."

[0077] Step 2:

[0078] The server integrates the collected movement information using the Python Pandas library and formats it into a consistent dataset. The input is the movement information output from step 1, and the output is an integrated dataset with each data field unified. Specific operations include conversion between different data formats and cleaning up unnecessary data.

[0079] Step 3:

[0080] The terminal performs traffic route simulations using an integrated dataset. This utilizes a generative AI model, specifically a large-scale language model (e.g., GPT-4). The input is the integrated dataset, and the output consists of proposed traffic routes generated by the simulation and evaluation reports for each route. Specifically, the terminal sends the prompt message "Based on the given travel data, propose efficient traffic routes and evaluate their profitability and environmental impact" to the large-scale model via an API.

[0081] Step 4:

[0082] The user determines and selects the most effective transportation plan based on evaluation reports obtained from the terminal. The input is the evaluation report, and the output is the selected transportation plan. A concrete example is the process of deciding whether to prioritize a particular route based on passenger count forecasts.

[0083] Step 5:

[0084] The server analyzes the effects of introducing autonomous driving technology to the selected traffic plan through additional simulations. The input is the selected traffic plan, and the output is a report on the expected cost reductions and improvements in passenger satisfaction resulting from the introduction of autonomous driving technology. The specific actions include simulating the operation of autonomous vehicles and analyzing the results.

[0085] (Application Example 1)

[0086] 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."

[0087] With the spread of autonomous driving technology, there is a need to build efficient transportation networks. However, there is a lack of systems that provide optimal routes tailored to current traffic conditions. Furthermore, creating operational plans that take future demand forecasts into account is difficult, resulting in a failure to provide convenient transportation options for users.

[0088] 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.

[0089] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic routes using the integrated information and evaluating profitability, means for considering the introduction of autonomous driving technology based on the evaluation results, and means for providing users with optimal route information that takes into account current traffic conditions and future demand forecasts. This makes it possible to provide efficient autonomous driving routes and to formulate operation plans that respond to future traffic demand.

[0090] "Mobility information" refers to data on traffic flow and people's movements, and is used to forecast demand for transportation routes.

[0091] "Integration" is the process of combining multiple datasets into one and converting them into a format necessary for analysis and evaluation.

[0092] "Transportation route simulation" is a method of virtually testing various transportation routes based on collected data and analyzing the results.

[0093] "Profitability assessment" is the process of estimating how much profit a proposed transportation route will generate.

[0094] "The introduction of autonomous driving technology" refers to the application of technology that enables vehicles to operate safely and efficiently without driver intervention.

[0095] "Current traffic conditions" refers to data related to traffic conditions, such as the degree of congestion on roads and public transport, and accident information, as of the present time.

[0096] "Future demand forecasting" is the process of estimating transportation demand for a specific period in the future, based on past travel patterns.

[0097] "Optimal route information" refers to data on the most desirable travel route, presented after considering factors such as efficiency, cost, and user convenience.

[0098] The system for implementing this invention efficiently manages traffic information and enables the suggestion of automated driving routes. The server first collects travel information from a nationwide database and integrates it to create basic data for predicting traffic route demand. At this stage, data processing tools such as Python and SQL are used to format and clean the data.

[0099] Next, the terminal utilizes the integrated data to perform traffic route simulations. Here, a large-scale language model using TENSORFLOW® is employed to evaluate the profitability, environmental impact, and user convenience of multiple proposed traffic routes. Furthermore, through devices such as smartphones and smart glasses, the system provides users with real-time optimal route information that takes into account current traffic conditions and future demand forecasts.

[0100] As a concrete example, imagine a scenario where a user uses their smartphone during their morning commute to inquire about the optimal autonomous driving route from home to the office. In this case, the server analyzes real-time traffic data and presents the user with a route that avoids congestion while minimizing environmental impact. This allows the user to reach their destination comfortably and efficiently.

[0101] Using a generative AI model, an example of a prompt would be, "Please suggest the optimal autonomous driving route from Shinjuku to Shibuya at 8 AM." Based on this prompt, the system calculates the optimal route and provides it to the user.

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

[0103] Step 1:

[0104] The server retrieves mobility data from a nationwide mobility information infrastructure. This involves querying the database and extracting information about traffic flow and people's movements. The input is nationwide mobility data, and the output is mobility pattern data for a specified region. This data is standardized through integration and other processes.

[0105] Step 2:

[0106] The server integrates the acquired data and transforms it into a usable dataset. Data processing tools are used to clean the data into a consistent format and eliminate redundant information. The input is the movement data obtained in step 1, and the output is the integrated movement dataset.

[0107] Step 3:

[0108] The terminal performs transportation route simulations using an integrated dataset. A large-scale language model is used to evaluate multiple route options. The input is the dataset obtained in step 2, and the output is the evaluation results from the simulation. The evaluation includes profitability, environmental impact, and user convenience.

[0109] Step 4:

[0110] The terminal will consider introducing autonomous driving technology based on the evaluation results. This includes a profitability analysis of the evaluated route options. The input is the evaluation results from step 3, and the output is whether or not to introduce autonomous driving technology, or a proposal for its implementation.

[0111] Step 5:

[0112] Users request optimal route information via their smartphones or smart glasses. The device calculates and presents the optimal route considering current traffic conditions and future demand forecasts. Inputs are the user's request and real-time traffic data, and output is the optimal route information for the current time.

[0113] Step 6:

[0114] The server uses a generated AI model and prompt statements to forecast future demand. In this process, the AI ​​model estimates future demand using historical data patterns and current information. The input is traffic data from the past to the present, and the output is the predicted future traffic trends.

[0115] Through these steps, the system can efficiently and quickly provide users with the optimal route for autonomous driving.

[0116] 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.

[0117] This invention provides a system that optimizes transportation routes using mobility data and further takes user emotions into account to provide personalized transportation plans. This system includes the acquisition and integration of mobility data, simulation using pedestrian flow data, profitability evaluation, consideration of the introduction of autonomous driving technology, and the process of recognizing and responding to user emotions using an emotion engine.

[0118] The core of this system is the server's function of collecting and analyzing various types of data. First, the server retrieves data representing people's movement patterns from an online database. This data is then normalized into a unified format for subsequent processes.

[0119] Next, the terminal uses this integrated data to perform a traffic demand simulation. Here, a large-scale language model is used to comprehensively evaluate various transportation route options and their profitability and convenience. At this stage, the most efficient transportation plan is identified, and the optimal option is selected from a profitability perspective.

[0120] If the introduction of autonomous driving technology is proposed based on the simulation results, further analysis will be conducted. The server will evaluate the operational effectiveness of the autonomous driving system and quantitatively show its impact on traffic infrastructure.

[0121] A key feature of this system is its built-in emotion engine for recognizing user emotions. It collects emotional data from the user's facial expressions and voice, and the terminal analyzes this data in real time. The analysis results are used to adjust the proposed transportation plan, enabling the system to provide suggestions that are best suited to the user's current emotional state.

[0122] For example, if a user is tired, the emotion engine recognizes this state and suggests the shortest and most comfortable route. Conversely, if the user is relaxed, a route that allows them to enjoy the scenery is recommended.

[0123] Through this process, the present invention provides users with the most beneficial and efficient transportation solution, offering more nuanced service compared to conventional systems. This system allows users to enjoy a selection of transportation options tailored to their individual needs.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The server retrieves regional movement data from various databases. This data includes location information and route history, and is used to prepare for the analysis of people's movement patterns.

[0127] Step 2:

[0128] The server normalizes the movement data it acquires. By unifying the data format and imputing missing data, an integrated dataset is created to improve the accuracy of the analysis.

[0129] Step 3:

[0130] The terminal inputs the integrated dataset into a large-scale language model and performs a simulation of traffic demand. The simulation generates multiple proposed transportation routes and evaluates their profitability, convenience, and environmental impact.

[0131] Step 4:

[0132] The terminal identifies the most efficient transportation route based on the simulation results and calculates the expected revenue for that route. This allows for the selection of the most profitable transportation plan.

[0133] Step 5:

[0134] Users will review the details of the selected transportation plan via their devices and evaluate its feasibility. This evaluation will also take into account additional costs and operational benefits if autonomous driving technology is included.

[0135] Step 6:

[0136] The device uses an emotion engine to acquire emotional data through facial recognition and voice analysis of the user. This data is processed in real time to evaluate the user's emotional state.

[0137] Step 7:

[0138] The server analyzes the emotional data it has acquired and adjusts the suggested transportation plan to best suit the user's current state. This suggestion is customized according to the user's desired situation.

[0139] Step 8:

[0140] The user reviews the proposed customized transportation plan, makes a final approval or adjustment, and then it is implemented in actual operation.

[0141] (Example 2)

[0142] 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".

[0143] Current transportation systems do not adequately utilize mobility information, resulting in ineffective optimization of transportation plans. Furthermore, there is a lack of transportation plan proposals that take into account user emotions and individual needs. Therefore, there is a need for advanced systems that can simultaneously improve both transportation efficiency and user satisfaction.

[0144] 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.

[0145] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic planning using the integrated information and evaluating profitability, means for considering the introduction of autonomous driving technology based on the evaluation results, and means for recognizing the user's emotional state and adjusting the traffic plan based on that state. This makes it possible to provide efficient and personalized traffic plans for each user.

[0146] "Mobility information" refers to data that represents people's movement patterns, and includes a wide range of information such as location information and transportation usage history.

[0147] "Means of integration" refers to the process of converting travel information in different formats into a single, unified format to create a consistent dataset.

[0148] "Transportation planning simulation" is a method of virtually estimating and comparing the effectiveness and efficiency of various transportation routes and schedules based on travel information.

[0149] "Methods for evaluating profitability" refer to the process of quantitatively measuring the economic benefits that a transportation plan will bring and determining its financial feasibility.

[0150] "Means of considering the introduction of autonomous driving technology" refers to a method of analyzing and evaluating the technical and economic impacts of incorporating autonomous driving systems into traffic planning, based on the results of simulations.

[0151] "Means for recognizing emotional states" refers to technologies that analyze a user's emotions in real time from their facial expressions, voice, etc., to determine their current mental state.

[0152] "Means of adjusting transportation plans" refers to a process of flexibly changing and optimizing transportation plans based on the recognized emotional state of the user.

[0153] This invention is a system for highly optimizing transportation systems and improving the user experience. Specifically, the server utilizes hardware and software to acquire movement information from various sources and integrate it into a unified format. The specific software used here includes a database management system and data normalization tools. The server uses these to efficiently acquire and process data, ensuring that subsequent processing proceeds smoothly.

[0154] Next, the device performs a traffic planning simulation based on the integrated data. A large-scale language model (generative AI model) with high predictive capabilities is used here. For example, it utilizes a generative AI model such as GPT-4 to generate different traffic route scenarios and evaluate the profitability and convenience of each. Through this process, the device can identify the optimal traffic plan.

[0155] Furthermore, to recognize the user's emotional state in real time, the device is equipped with a high-precision emotion analysis engine. This engine analyzes the user's emotions from their voice and facial expressions, and uses the results to adjust traffic plans. The specific hardware for this purpose includes a camera and a microphone.

[0156] This process provides users with personalized transportation plans. For example, when a user uses public transport, the device detects the user's fatigue level and provides the fastest and least stressful route. Alternatively, if it detects that the user is relaxed, it suggests a route with scenic views.

[0157] An example of a prompt to input into a generative AI model is, "Suggest the best route based on the user's current emotions and traffic conditions." This prompt will cause the system to generate the best possible transportation options tailored to the user's situation.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server acquires movement information from the internet and various sensors. This movement information includes data on transportation usage and GPS location data. Once the data is acquired, the server converts it into a unified format. Data formatting tools are used to integrate data acquired from different sources. Finally, the integrated data is prepared as input data for traffic planning simulations.

[0161] Step 2:

[0162] The terminal performs traffic planning simulations based on integrated mobility information. Using the integrated data as input, the terminal utilizes a large-scale language model (e.g., GPT-4). This model generates diverse traffic scenarios and evaluates the profitability and convenience of each. During this evaluation process, the terminal quantifies the profitability of the traffic plan and outputs comparative indices to evaluate the convenience of each scenario. The goal is to identify the most profitable and convenient traffic plan.

[0163] Step 3:

[0164] Upon receiving the simulation results, the server considers introducing autonomous driving technology based on the evaluation. The server analyzes the safety, economics, and operational effectiveness of adding an autonomous driving system to the selected traffic plan. It simulates the impact of autonomous driving technology using a numerical model, and the results are reflected in the final evaluation of the plan.

[0165] Step 4:

[0166] The device performs processing to recognize the user's emotional state in real time. A facial recognition system and a voice analysis system analyze the user's emotions and acquire the results as emotional data. This data represents the user's current emotional state and serves as input for the next processing step.

[0167] Step 5:

[0168] The terminal adjusts the transportation plan to take the user's emotional state into account. Using emotional data as input, the terminal dynamically modifies the transportation plan. If the user is tired, it suggests a more comfortable and faster route; if they are relaxed, it selects a route that allows them to enjoy the scenery. The terminal then outputs a personalized and optimal transportation plan for the user.

[0169] Throughout this entire process, the system can provide optimal and customized transportation solutions for each user.

[0170] (Application Example 2)

[0171] 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."

[0172] In recent years, with the advancement of autonomous driving technology, there has been a growing demand for efficient and safe transportation plans. However, conventional systems struggle to provide transportation plans that take into account the individual emotions and psychological states of users, limiting the potential for improving the user experience. Furthermore, while dynamic demand forecasting is essential for optimizing transportation routes, there is a challenge in the lack of adequate systems to handle this.

[0173] 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.

[0174] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic routes using the integrated information and evaluating profitability, means for considering the introduction of an autonomous driving system based on the evaluation results, means for recognizing and analyzing emotional states, and means for adjusting traffic plans based on the emotional analysis. This makes it possible to provide more personalized traffic plans that incorporate the user's emotions and movement information in real time.

[0175] "Movement information" refers to data about the spatial position and movement of individuals and objects, and is used for optimizing and simulating transportation routes.

[0176] "Integration" is the process of bringing together information from different forms and sources into a single system and making it usable.

[0177] "Simulation" is a method of virtually reproducing real-world phenomena or processes using mathematical models and analyzing the results.

[0178] "Profitability assessment" is an analytical method used to measure the degree to which a particular business or plan generates economic benefits.

[0179] An "autonomous driving system" is a technology that uses technologies such as machine learning and artificial intelligence to control a vehicle while minimizing human intervention.

[0180] "Considering implementation" refers to the process of evaluating whether to apply or adopt a new technology or system.

[0181] "Emotional state" refers to the sensory reactions and psychological conditions exhibited by the user or object.

[0182] "Emotional analysis" is a technology that recognizes a person's emotions and psychological state at any given time from data such as voice and facial expressions.

[0183] "Adjusting transportation plans" is the process of making modifications and changes to optimize transportation methods and routes according to the environment and circumstances.

[0184] This invention consists of a system that provides personalized transportation plans by utilizing travel information and the user's emotional state. The system functions as follows:

[0185] The server acquires and integrates movement information from online databases and sensors. Specifically, it collects GPS data and public transport operation data, and normalizes them into a unified format. Furthermore, it uses this integrated information to simulate transportation routes and evaluate their profitability and convenience from multiple perspectives. During this simulation process, a large-scale language model is used to predict user transportation demand. Based on this prediction, the optimal transportation plan is identified.

[0186] The user's device collects and analyzes their emotional state in real time via its camera and microphone. Cloud-based services, such as Google® Cloud Emotion API, are used for this analysis. The analyzed emotional data is sent to a server and used to adjust the suggested travel plan. If the user's emotions indicate stress, a relaxing route is suggested; if their emotions are stable, an efficient route is suggested.

[0187] For example, during a family road trip, the device might detect that the children are bored. In this case, the device retrieves information from the server about scenic routes and restroom break points along the way, and sends instructions to the vehicle's navigation system.

[0188] An example of a prompt message from a generative AI model would be, "How are you feeling today? If you're feeling stressed, I'll suggest a relaxing route. Let's choose the best path to your destination while enjoying the scenery," which the model would then evaluate and provide the user with the most suitable transportation plan.

[0189] In this way, the present invention provides a personalized transportation experience based on the user's individual needs and emotional state.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server retrieves movement information from an online database. Input data includes GPS data and public transport operation data, and the output generates movement information normalized into a unified format. The data absorbs format differences and is organized into a consistent dataset.

[0193] Step 2:

[0194] The server uses travel information normalized into a unified format to simulate transportation routes. The input data is the travel information in the unified format obtained in the previous step, and the output generates the results of a profitability evaluation. In the simulation, a large-scale language model is used to forecast transportation demand, and the optimal plan is determined through a multifaceted evaluation.

[0195] Step 3:

[0196] The server evaluates the feasibility of implementing an autonomous driving system based on the results of profitability assessments. The input is simulation results, and the output generates quantitative data showing the benefits and impacts of implementation. It determines whether autonomous driving technology is applicable and analyzes its efficiency and effectiveness.

[0197] Step 4:

[0198] The device uses a camera and microphone to collect the user's emotional state in real time. The input data consists of the user's facial expressions and voice, and the output generates emotion analysis results. Emotion analysis utilizes the Google Cloud Emotion API and other tools to analyze different emotional states and intensities in detail.

[0199] Step 5:

[0200] The terminal sends a request to the server for traffic plan adjustment based on the generated emotion analysis results. The input is the emotion analysis results, and the output is a traffic plan suggestion that is optimal for the user. Through this adjustment, routes and plans are suggested that are tailored to the user's current emotions and psychological state.

[0201] Step 6:

[0202] Users review and select suggested transportation plans through their terminal. The input is a plan suggestion from the server, and the selected transportation plan is applied to the vehicle's navigation system as output. The user's selection allows for a personalized experience tailored to their individual needs.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention provides a system for designing efficient and profitable transportation routes, offering a series of processes from analyzing mobility data and evaluating profitability to considering the introduction of autonomous driving technology. The following describes a specific implementation of this system.

[0220] The core of this system is the function of collecting and integrating mobility data. The server accesses a nationwide human flow database and collects mobility pattern data for specific regions. The collected data is standardized in format and integrated into a dataset. This dataset is used as basic information for forecasting demand for transportation routes.

[0221] Next, the terminal runs a traffic demand simulation using the integrated dataset. This process utilizes a large-scale language model to evaluate multiple proposed traffic routes in detail, considering their profitability, environmental impact, and user convenience. Specifically, it becomes possible to propose new routes based on passenger count predictions in urban areas, and estimate the operating costs and expected revenue of those routes.

[0222] Furthermore, users select the most efficient transportation plan based on the evaluation results. If the introduction of autonomous driving technology is deemed effective from the perspectives of profitability, safety, and environmental impact, the server will conduct additional simulations. The purpose of these simulations is to quantitatively demonstrate cost reductions and improvements in passenger satisfaction associated with the operation of autonomous vehicles.

[0223] Through these steps, this system optimizes transportation networks and provides sustainable transportation solutions in communities facing declining birth rates and an aging population. Ultimately, users will be able to operate transportation rationally and efficiently by utilizing this system.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The server accesses a human movement database to retrieve movement data for the target area. This data includes location information and movement patterns for a specific period.

[0227] Step 2:

[0228] The server normalizes the movement data it acquires and imputes missing values. Furthermore, it converts it to a unified format and creates an integrated dataset based on this.

[0229] Step 3:

[0230] The terminal inputs an integrated dataset and begins simulating traffic demand using a large-scale language model. This simulation evaluates the profitability, convenience, and environmental impact of each proposed traffic route.

[0231] Step 4:

[0232] The server evaluates the profitability of transportation routes based on the simulation results, formats the evaluation results, and provides them to the user as a visual report.

[0233] Step 5:

[0234] Users can review the provided reports via their devices and analyze the evaluation results. This allows them to select the most suitable transportation plan.

[0235] Step 6:

[0236] The system evaluates the feasibility of introducing autonomous driving technology to the transportation plan selected by the user. During this process, the server performs additional simulations to estimate the reduction in operating costs and the improvement in safety associated with introducing autonomous driving technology, and provides the results to the user.

[0237] Step 7:

[0238] The user makes the final decision on the transportation plan based on all the information and then begins the actual implementation process.

[0239] (Example 1)

[0240] 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."

[0241] Modern urban transportation systems must be efficient and sustainable, while also addressing demographic diversification and increasing environmental impact. Traditional transportation planning methods struggle to perform sufficient data analysis, making it difficult to accurately predict new transportation demands or design optimal routes. Furthermore, there is a lack of appropriate evaluation methods to realize improvements in transportation efficiency and safety through the introduction of autonomous driving technology.

[0242] 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.

[0243] In this invention, the server includes means for collecting and integrating mobility information, means for simulating traffic routes using the integrated information and evaluating profitability and environmental impact, and means for selecting and implementing traffic plans based on the evaluation results. This enables accurate forecasting of traffic demand and the creation of efficient traffic plans.

[0244] "Movement information" refers to data about the routes taken by individual moving entities (people or objects) within a specific time frame.

[0245] "Means of integration" refers to the process of transforming data collected from multiple sources into a consistent format and combining it into a single, comprehensive dataset.

[0246] A "transportation route" refers to the path that a means of transportation takes to move a user from one point to another.

[0247] "Simulation" refers to a technology that reproduces real-world traffic conditions using mathematical models and virtually analyzes their movement and changes.

[0248] "Profitability" is an indicator that shows how much profit a particular transportation plan or route has the potential to generate.

[0249] "Environmental impact" refers to the potential effects and changes that transportation plans and their implementation may have on the natural environment and local communities.

[0250] "Transportation planning" refers to specific policies and strategies for optimizing the operation and allocation of transportation methods in a particular region or on a particular route.

[0251] "Autonomous driving technology" is a general term for technologies used to enable vehicles to move on their own without human intervention.

[0252] "Information sources" refer to places or services from which data and information can be obtained. In the case of human flow information, this includes sensor networks and digital platforms.

[0253] This invention is a system for designing efficient and sustainable transportation routes, providing a series of processes from collecting mobility information to evaluating profitability and considering the introduction of autonomous driving technology.

[0254] The server first collects movement information from nationwide human flow sources. This process uses database systems (e.g., MySQL or PostgreSQL) to retrieve data. The collected data is then formatted using the Python Pandas library and managed as a unified dataset. This dataset forms the basis for subsequent traffic route simulations.

[0255] Next, the device performs a traffic route simulation using the integrated dataset. This process utilizes a generative AI model, such as a large-scale language model like GPT-4. The device inputs the prompt "Based on the given travel data, propose an efficient traffic route and evaluate its profitability and environmental impact" into the generative AI, which then generates multiple traffic route options and evaluates their profitability, environmental impact, and convenience.

[0256] Based on these simulation results, users can select new transportation plans, including the introduction of autonomous driving technology. For example, they can design new bus routes in busy areas and calculate their operating costs and expected revenues. The server then analyzes the benefits of introducing autonomous driving technology and conducts additional simulations to evaluate the resulting cost reductions and improvements in passenger satisfaction.

[0257] By using this system, users can design optimal transportation plans that meet local travel demand, enabling efficient and sustainable transportation operations.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server collects movement information from nationwide human flow sources and stores it in a database. Specifically, it uses SQL queries to extract relevant datasets and obtain movement patterns by time of day and region. The input is query conditions from the database, and the output is a formatted collection of movement information. For example, it collects data based on conditions such as "obtain the number of people who traveled from city A to city B within a specific date range."

[0261] Step 2:

[0262] The server integrates the collected movement information using the Python Pandas library and formats it into a consistent dataset. The input is the movement information output from step 1, and the output is an integrated dataset with each data field unified. Specific operations include conversion between different data formats and cleaning up unnecessary data.

[0263] Step 3:

[0264] The terminal performs traffic route simulations using an integrated dataset. This utilizes a generative AI model, specifically a large-scale language model (e.g., GPT-4). The input is the integrated dataset, and the output consists of proposed traffic routes generated by the simulation and evaluation reports for each route. Specifically, the terminal sends the prompt message "Based on the given travel data, propose efficient traffic routes and evaluate their profitability and environmental impact" to the large-scale model via an API.

[0265] Step 4:

[0266] The user determines and selects the most effective transportation plan based on evaluation reports obtained from the terminal. The input is the evaluation report, and the output is the selected transportation plan. A concrete example is the process of deciding whether to prioritize a particular route based on passenger count forecasts.

[0267] Step 5:

[0268] The server analyzes the effects of introducing autonomous driving technology to the selected traffic plan through additional simulations. The input is the selected traffic plan, and the output is a report on the expected cost reductions and improvements in passenger satisfaction resulting from the introduction of autonomous driving technology. The specific actions include simulating the operation of autonomous vehicles and analyzing the results.

[0269] (Application Example 1)

[0270] 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."

[0271] With the spread of autonomous driving technology, there is a need to build efficient transportation networks. However, there is a lack of systems that provide optimal routes tailored to current traffic conditions. Furthermore, creating operational plans that take future demand forecasts into account is difficult, resulting in a failure to provide convenient transportation options for users.

[0272] 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.

[0273] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic routes using the integrated information and evaluating profitability, means for considering the introduction of autonomous driving technology based on the evaluation results, and means for providing users with optimal route information that takes into account current traffic conditions and future demand forecasts. This makes it possible to provide efficient autonomous driving routes and to formulate operation plans that respond to future traffic demand.

[0274] "Mobility information" refers to data on traffic flow and people's movements, and is used to forecast demand for transportation routes.

[0275] "Integration" is the process of combining multiple datasets into one and converting them into a format necessary for analysis and evaluation.

[0276] "Transportation route simulation" is a method of virtually testing various transportation routes based on collected data and analyzing the results.

[0277] "Profitability assessment" is the process of estimating how much profit a proposed transportation route will generate.

[0278] "The introduction of autonomous driving technology" refers to the application of technology that enables vehicles to operate safely and efficiently without driver intervention.

[0279] "Current traffic conditions" refers to data related to traffic conditions, such as the degree of congestion on roads and public transport, and accident information, as of the present time.

[0280] "Future demand forecasting" is the process of estimating transportation demand for a specific period in the future, based on past travel patterns.

[0281] "Optimal route information" refers to data regarding the most desirable travel route presented after considering efficiency, cost, user convenience, etc.

[0282] The system for implementing this invention efficiently manages traffic information and enables the proposal of an autonomous driving route. First, the server collects movement information from a national database and creates basic data for predicting the demand of traffic routes by integrating it. At this stage, data shaping and cleaning are performed using data processing tools such as Python and SQL.

[0283] Next, the terminal utilizes the integrated data to perform a simulation of traffic routes. Here, a large language model using TensorFlow is employed to evaluate the profitability, environmental impact, and user convenience of multiple traffic route plans. Also, through devices such as smartphones and smart glasses, optimal route information considering the current traffic situation and future demand prediction is provided to the user in real time.

[0284] As a specific example, assume a scenario where a user queries the optimal autonomous driving route from home to the office using a smartphone during morning commuting. In this case, the server analyzes real-time traffic data and presents a route to the user that avoids traffic jams while minimizing the environmental impact. This enables the user to reach the destination comfortably and efficiently.

[0285] Using a generative AI model, an example of a prompt sentence is "Please propose the optimal autonomous driving route from Shinjuku to Shibuya at 8 am." Based on this prompt, the system calculates the optimal route and provides it to the user.

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The server retrieves mobility data from a nationwide mobility information infrastructure. This involves querying the database and extracting information about traffic flow and people's movements. The input is nationwide mobility data, and the output is mobility pattern data for a specified region. This data is standardized through integration and other processes.

[0289] Step 2:

[0290] The server integrates the acquired data and transforms it into a usable dataset. Data processing tools are used to clean the data into a consistent format and eliminate redundant information. The input is the movement data obtained in step 1, and the output is the integrated movement dataset.

[0291] Step 3:

[0292] The terminal performs transportation route simulations using an integrated dataset. A large-scale language model is used to evaluate multiple route options. The input is the dataset obtained in step 2, and the output is the evaluation results from the simulation. The evaluation includes profitability, environmental impact, and user convenience.

[0293] Step 4:

[0294] The terminal will consider introducing autonomous driving technology based on the evaluation results. This includes a profitability analysis of the evaluated route options. The input is the evaluation results from step 3, and the output is whether or not to introduce autonomous driving technology, or a proposal for its implementation.

[0295] Step 5:

[0296] Users request optimal route information via their smartphones or smart glasses. The device calculates and presents the optimal route considering current traffic conditions and future demand forecasts. Inputs are the user's request and real-time traffic data, and output is the optimal route information for the current time.

[0297] Step 6:

[0298] The server uses a generated AI model and prompt statements to forecast future demand. In this process, the AI ​​model estimates future demand using historical data patterns and current information. The input is traffic data from the past to the present, and the output is the predicted future traffic trends.

[0299] Through these steps, the system can efficiently and quickly provide users with the optimal route for autonomous driving.

[0300] 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.

[0301] This invention provides a system that optimizes transportation routes using mobility data and further takes user emotions into account to provide personalized transportation plans. This system includes the acquisition and integration of mobility data, simulation using pedestrian flow data, profitability evaluation, consideration of the introduction of autonomous driving technology, and the process of recognizing and responding to user emotions using an emotion engine.

[0302] The core of this system is the server's function of collecting and analyzing various types of data. First, the server retrieves data representing people's movement patterns from an online database. This data is then normalized into a unified format for subsequent processes.

[0303] Next, the terminal uses this integrated data to perform a traffic demand simulation. Here, a large-scale language model is used to comprehensively evaluate various transportation route options and their profitability and convenience. At this stage, the most efficient transportation plan is identified, and the optimal option is selected from a profitability perspective.

[0304] When the introduction of autonomous driving technology is proposed based on the simulation results, further analysis is carried out. The server evaluates the operation effect of the autonomous driving system and quantitatively shows the impact it has on the traffic infrastructure.

[0305] Here, as an important feature, this system is equipped with an emotion engine for user emotion recognition. It collects emotion data from the user's expressions and voices, and the terminal analyzes this in real time. The analysis results are used to adjust the proposed traffic plan, making it possible to provide a proposal that is most suitable for the user's current emotional state.

[0306] As a specific example, when the user is tired, the emotion engine recognizes this state and proposes the shortest and most comfortable route. Also, when the user is relaxed, a route that allows them to enjoy the scenery is recommended.

[0307] Through such a process, the present invention provides the most beneficial and efficient traffic solution for the user and enables more detailed responses compared to conventional systems. The user can enjoy the selection of transportation means according to their individual needs with this system.

[0308] The following describes the processing flow.

[0309] Step 1:

[0310] The server obtains regional movement data from various databases. This data includes location information, route history, etc., and based on this, preparations are made to analyze people's movement patterns.

[0311] Step 2:

[0312] The server normalizes the obtained movement data. By unifying the data format and complementing missing data, an integrated dataset for enhancing the accuracy of analysis is created.

[0313] Step 3:

[0314] The terminal inputs the integrated dataset into a large-scale language model and performs a simulation of traffic demand. The simulation generates multiple proposed transportation routes and evaluates their profitability, convenience, and environmental impact.

[0315] Step 4:

[0316] The terminal identifies the most efficient transportation route based on the simulation results and calculates the expected revenue for that route. This allows for the selection of the most profitable transportation plan.

[0317] Step 5:

[0318] Users will review the details of the selected transportation plan via their devices and evaluate its feasibility. This evaluation will also take into account additional costs and operational benefits if autonomous driving technology is included.

[0319] Step 6:

[0320] The device uses an emotion engine to acquire emotional data through facial recognition and voice analysis of the user. This data is processed in real time to evaluate the user's emotional state.

[0321] Step 7:

[0322] The server analyzes the emotional data it has acquired and adjusts the suggested transportation plan to best suit the user's current state. This suggestion is customized according to the user's desired situation.

[0323] Step 8:

[0324] The user reviews the proposed customized transportation plan, makes a final approval or adjustment, and then it is implemented in actual operation.

[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] Current transportation systems do not adequately utilize mobility information, resulting in ineffective optimization of transportation plans. Furthermore, there is a lack of transportation plan proposals that take into account user emotions and individual needs. Therefore, there is a need for advanced systems that can simultaneously improve both transportation efficiency and user satisfaction.

[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 acquiring and integrating movement information, means for simulating traffic planning using the integrated information and evaluating profitability, means for considering the introduction of autonomous driving technology based on the evaluation results, and means for recognizing the user's emotional state and adjusting the traffic plan based on that state. This makes it possible to provide efficient and personalized traffic plans for each user.

[0330] "Mobility information" refers to data that represents people's movement patterns, and includes a wide range of information such as location information and transportation usage history.

[0331] "Means of integration" refers to the process of converting travel information in different formats into a single, unified format to create a consistent dataset.

[0332] "Transportation planning simulation" is a method of virtually estimating and comparing the effectiveness and efficiency of various transportation routes and schedules based on travel information.

[0333] "Methods for evaluating profitability" refer to the process of quantitatively measuring the economic benefits that a transportation plan will bring and determining its financial feasibility.

[0334] "Means of considering the introduction of autonomous driving technology" refers to a method of analyzing and evaluating the technical and economic impacts of incorporating autonomous driving systems into traffic planning, based on the results of simulations.

[0335] "Means for recognizing emotional states" refers to technologies that analyze a user's emotions in real time from their facial expressions, voice, etc., to determine their current mental state.

[0336] "Means of adjusting transportation plans" refers to a process of flexibly changing and optimizing transportation plans based on the recognized emotional state of the user.

[0337] This invention is a system for highly optimizing transportation systems and improving the user experience. Specifically, the server utilizes hardware and software to acquire movement information from various sources and integrate it into a unified format. The specific software used here includes a database management system and data normalization tools. The server uses these to efficiently acquire and process data, ensuring that subsequent processing proceeds smoothly.

[0338] Next, the device performs a traffic planning simulation based on the integrated data. A large-scale language model (generative AI model) with high predictive capabilities is used here. For example, it utilizes a generative AI model such as GPT-4 to generate different traffic route scenarios and evaluate the profitability and convenience of each. Through this process, the device can identify the optimal traffic plan.

[0339] Furthermore, to recognize the user's emotional state in real time, the device is equipped with a high-precision emotion analysis engine. This engine analyzes the user's emotions from their voice and facial expressions, and uses the results to adjust traffic plans. The specific hardware for this purpose includes a camera and a microphone.

[0340] This process provides users with personalized transportation plans. For example, when a user uses public transport, the device detects the user's fatigue level and provides the fastest and least stressful route. Alternatively, if it detects that the user is relaxed, it suggests a route with scenic views.

[0341] An example of a prompt to input into a generative AI model is, "Suggest the best route based on the user's current emotions and traffic conditions." This prompt will cause the system to generate the best possible transportation options tailored to the user's situation.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] The server acquires movement information from the internet and various sensors. This movement information includes data on transportation usage and GPS location data. Once the data is acquired, the server converts it into a unified format. Data formatting tools are used to integrate data acquired from different sources. Finally, the integrated data is prepared as input data for traffic planning simulations.

[0345] Step 2:

[0346] The terminal performs traffic planning simulations based on integrated mobility information. Using the integrated data as input, the terminal utilizes a large-scale language model (e.g., GPT-4). This model generates diverse traffic scenarios and evaluates the profitability and convenience of each. During this evaluation process, the terminal quantifies the profitability of the traffic plan and outputs comparative indices to evaluate the convenience of each scenario. The goal is to identify the most profitable and convenient traffic plan.

[0347] Step 3:

[0348] Upon receiving the simulation results, the server considers introducing autonomous driving technology based on the evaluation. The server analyzes the safety, economics, and operational effectiveness of adding an autonomous driving system to the selected traffic plan. It simulates the impact of autonomous driving technology using a numerical model, and the results are reflected in the final evaluation of the plan.

[0349] Step 4:

[0350] The device performs processing to recognize the user's emotional state in real time. A facial recognition system and a voice analysis system analyze the user's emotions and acquire the results as emotional data. This data represents the user's current emotional state and serves as input for the next processing step.

[0351] Step 5:

[0352] The terminal adjusts the transportation plan to take the user's emotional state into account. Using emotional data as input, the terminal dynamically modifies the transportation plan. If the user is tired, it suggests a more comfortable and faster route; if they are relaxed, it selects a route that allows them to enjoy the scenery. The terminal then outputs a personalized and optimal transportation plan for the user.

[0353] Throughout this entire process, the system can provide optimal and customized transportation solutions for each user.

[0354] (Application Example 2)

[0355] 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."

[0356] In recent years, with the advancement of autonomous driving technology, there has been a growing demand for efficient and safe transportation plans. However, conventional systems struggle to provide transportation plans that take into account the individual emotions and psychological states of users, limiting the potential for improving the user experience. Furthermore, while dynamic demand forecasting is essential for optimizing transportation routes, there is a challenge in the lack of adequate systems to handle this.

[0357] 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.

[0358] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic routes using the integrated information and evaluating profitability, means for considering the introduction of an autonomous driving system based on the evaluation results, means for recognizing and analyzing emotional states, and means for adjusting traffic plans based on the emotional analysis. This makes it possible to provide more personalized traffic plans that incorporate the user's emotions and movement information in real time.

[0359] "Movement information" refers to data about the spatial position and movement of individuals and objects, and is used for optimizing and simulating transportation routes.

[0360] "Integration" is the process of bringing together information from different forms and sources into a single system and making it usable.

[0361] "Simulation" is a method of virtually reproducing real-world phenomena or processes using mathematical models and analyzing the results.

[0362] "Profitability assessment" is an analytical method used to measure the degree to which a particular business or plan generates economic benefits.

[0363] An "autonomous driving system" is a technology that uses technologies such as machine learning and artificial intelligence to control a vehicle while minimizing human intervention.

[0364] "Considering implementation" refers to the process of evaluating whether to apply or adopt a new technology or system.

[0365] "Emotional state" refers to the sensory reactions and psychological conditions exhibited by the user or object.

[0366] "Emotional analysis" is a technology that recognizes a person's emotions and psychological state at any given time from data such as voice and facial expressions.

[0367] "Adjusting transportation plans" is the process of making modifications and changes to optimize transportation methods and routes according to the environment and circumstances.

[0368] This invention consists of a system that provides personalized transportation plans by utilizing travel information and the user's emotional state. The system functions as follows:

[0369] The server acquires and integrates movement information from online databases and sensors. Specifically, it collects GPS data and public transport operation data, and normalizes them into a unified format. Furthermore, it uses this integrated information to simulate transportation routes and evaluate their profitability and convenience from multiple perspectives. During this simulation process, a large-scale language model is used to predict user transportation demand. Based on this prediction, the optimal transportation plan is identified.

[0370] The user's device collects and analyzes their emotional state in real time via its camera and microphone. Cloud-based services such as the Google Cloud Emotion API are used for this analysis. The analyzed emotional data is sent to a server and used to adjust the suggested travel plan. If the user's emotions indicate stress, a relaxing route is suggested; if their emotions are stable, an efficient route is suggested.

[0371] For example, during a family road trip, the device might detect that the children are bored. In this case, the device retrieves information from the server about scenic routes and restroom break points along the way, and sends instructions to the vehicle's navigation system.

[0372] An example of a prompt message from a generative AI model would be, "How are you feeling today? If you're feeling stressed, I'll suggest a relaxing route. Let's choose the best path to your destination while enjoying the scenery," which the model would then evaluate and provide the user with the most suitable transportation plan.

[0373] In this way, the present invention provides a personalized transportation experience based on the user's individual needs and emotional state.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server retrieves movement information from an online database. Input data includes GPS data and public transport operation data, and the server generates movement information normalized into a unified format as output. The data absorbs format differences and is organized into a consistent dataset.

[0377] Step 2:

[0378] The server uses travel information normalized into a unified format to simulate transportation routes. The input data is the travel information in the unified format obtained in the previous step, and the output generates the results of a profitability evaluation. In the simulation, a large-scale language model is used to forecast transportation demand, and the optimal plan is determined through a multifaceted evaluation.

[0379] Step 3:

[0380] The server evaluates the feasibility of implementing an autonomous driving system based on the results of profitability assessments. The input is simulation results, and the output generates quantitative data showing the benefits and impacts of implementation. It determines whether autonomous driving technology is applicable and analyzes its efficiency and effectiveness.

[0381] Step 4:

[0382] The device uses a camera and microphone to collect the user's emotional state in real time. The input data consists of the user's facial expressions and voice, and the output generates emotion analysis results. Emotion analysis utilizes the Google Cloud Emotion API and other tools to analyze different emotional states and intensities in detail.

[0383] Step 5:

[0384] The terminal sends a request to the server for traffic plan adjustment based on the generated emotion analysis results. The input is the emotion analysis results, and the output is a traffic plan suggestion that is optimal for the user. Through this adjustment, routes and plans are suggested that are tailored to the user's current emotions and psychological state.

[0385] Step 6:

[0386] Users review and select suggested transportation plans through their terminal. The input is a plan suggestion from the server, and the selected transportation plan is applied to the vehicle's navigation system as output. The user's selection allows for a personalized experience tailored to their individual needs.

[0387] 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.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention provides a system for designing efficient and profitable transportation routes, offering a series of processes from analyzing mobility data and evaluating profitability to considering the introduction of autonomous driving technology. The following describes a specific implementation of this system.

[0404] The core of this system is the function of collecting and integrating mobility data. The server accesses a nationwide human flow database and collects mobility pattern data for specific regions. The collected data is standardized in format and integrated into a dataset. This dataset is used as basic information for forecasting demand for transportation routes.

[0405] Next, the terminal runs a traffic demand simulation using the integrated dataset. This process utilizes a large-scale language model to evaluate multiple proposed traffic routes in detail, considering their profitability, environmental impact, and user convenience. Specifically, it becomes possible to propose new routes based on passenger count predictions in urban areas, and estimate the operating costs and expected revenue of those routes.

[0406] Furthermore, users select the most efficient transportation plan based on the evaluation results. If the introduction of autonomous driving technology is deemed effective from the perspectives of profitability, safety, and environmental impact, the server will conduct additional simulations. The purpose of these simulations is to quantitatively demonstrate cost reductions and improvements in passenger satisfaction associated with the operation of autonomous vehicles.

[0407] Through these steps, this system optimizes transportation networks and provides sustainable transportation solutions in communities facing declining birth rates and an aging population. Ultimately, users will be able to operate transportation rationally and efficiently by utilizing this system.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The server accesses a human movement database to retrieve movement data for the target area. This data includes location information and movement patterns for a specific period.

[0411] Step 2:

[0412] The server normalizes the movement data it acquires and imputes missing values. Furthermore, it converts it to a unified format and creates an integrated dataset based on this.

[0413] Step 3:

[0414] The terminal inputs an integrated dataset and begins simulating traffic demand using a large-scale language model. This simulation evaluates the profitability, convenience, and environmental impact of each proposed traffic route.

[0415] Step 4:

[0416] The server evaluates the profitability of transportation routes based on the simulation results, formats the evaluation results, and provides them to the user as a visual report.

[0417] Step 5:

[0418] Users can review the provided reports via their devices and analyze the evaluation results. This allows them to select the most suitable transportation plan.

[0419] Step 6:

[0420] The system evaluates the feasibility of introducing autonomous driving technology to the transportation plan selected by the user. During this process, the server performs additional simulations to estimate the reduction in operating costs and the improvement in safety associated with introducing autonomous driving technology, and provides the results to the user.

[0421] Step 7:

[0422] The user makes the final decision on the transportation plan based on all the information and then begins the actual implementation process.

[0423] (Example 1)

[0424] 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."

[0425] Modern urban transportation systems must be efficient and sustainable, while also addressing demographic diversification and increasing environmental impact. Traditional transportation planning methods struggle to perform sufficient data analysis, making it difficult to accurately predict new transportation demands or design optimal routes. Furthermore, there is a lack of appropriate evaluation methods to realize improvements in transportation efficiency and safety through the introduction of autonomous driving technology.

[0426] 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.

[0427] In this invention, the server includes means for collecting and integrating mobility information, means for simulating traffic routes using the integrated information and evaluating profitability and environmental impact, and means for selecting and implementing traffic plans based on the evaluation results. This enables accurate forecasting of traffic demand and the creation of efficient traffic plans.

[0428] "Movement information" refers to data about the routes taken by individual moving entities (people or objects) within a specific time frame.

[0429] "Means of integration" refers to the process of transforming data collected from multiple sources into a consistent format and combining it into a single, comprehensive dataset.

[0430] A "transportation route" refers to the path that a means of transportation takes to move a user from one point to another.

[0431] "Simulation" refers to a technology that reproduces real-world traffic conditions using mathematical models and virtually analyzes their movement and changes.

[0432] "Profitability" is an indicator that shows how much profit a particular transportation plan or route has the potential to generate.

[0433] "Environmental impact" refers to the potential effects and changes that transportation plans and their implementation may have on the natural environment and local communities.

[0434] "Transportation planning" refers to specific policies and strategies for optimizing the operation and allocation of transportation methods in a particular region or on a particular route.

[0435] "Autonomous driving technology" is a general term for technologies used to enable vehicles to move on their own without human intervention.

[0436] "Information sources" refer to places or services from which data and information can be obtained. In the case of human flow information, this includes sensor networks and digital platforms.

[0437] This invention is a system for designing efficient and sustainable transportation routes, providing a series of processes from collecting mobility information to evaluating profitability and considering the introduction of autonomous driving technology.

[0438] The server first collects movement information from nationwide human flow sources. This process uses database systems (e.g., MySQL or PostgreSQL) to retrieve data. The collected data is then formatted using the Python Pandas library and managed as a unified dataset. This dataset forms the basis for subsequent traffic route simulations.

[0439] Next, the device performs a traffic route simulation using the integrated dataset. This process utilizes a generative AI model, such as a large-scale language model like GPT-4. The device inputs the prompt "Based on the given travel data, propose an efficient traffic route and evaluate its profitability and environmental impact" into the generative AI, which then generates multiple traffic route options and evaluates their profitability, environmental impact, and convenience.

[0440] Based on these simulation results, users can select new transportation plans, including the introduction of autonomous driving technology. For example, they can design new bus routes in busy areas and calculate their operating costs and expected revenues. The server then analyzes the benefits of introducing autonomous driving technology and conducts additional simulations to evaluate the resulting cost reductions and improvements in passenger satisfaction.

[0441] By using this system, users can design optimal transportation plans that meet local travel demand, enabling efficient and sustainable transportation operations.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The server collects movement information from nationwide human flow sources and stores it in a database. Specifically, it uses SQL queries to extract relevant datasets and obtain movement patterns by time of day and region. The input is query conditions from the database, and the output is a formatted collection of movement information. For example, it collects data based on conditions such as "obtain the number of people who traveled from city A to city B within a specific date range."

[0445] Step 2:

[0446] The server integrates the collected movement information using the Python Pandas library and formats it into a consistent dataset. The input is the movement information output from step 1, and the output is an integrated dataset with each data field unified. Specific operations include conversion between different data formats and cleaning up unnecessary data.

[0447] Step 3:

[0448] The terminal performs traffic route simulations using an integrated dataset. This utilizes a generative AI model, specifically a large-scale language model (e.g., GPT-4). The input is the integrated dataset, and the output consists of proposed traffic routes generated by the simulation and evaluation reports for each route. Specifically, the terminal sends the prompt message "Based on the given travel data, propose efficient traffic routes and evaluate their profitability and environmental impact" to the large-scale model via an API.

[0449] Step 4:

[0450] The user determines and selects the most effective transportation plan based on evaluation reports obtained from the terminal. The input is the evaluation report, and the output is the selected transportation plan. A concrete example is the process of deciding whether to prioritize a particular route based on passenger count forecasts.

[0451] Step 5:

[0452] The server analyzes the effects of introducing autonomous driving technology to the selected traffic plan through additional simulations. The input is the selected traffic plan, and the output is a report on the expected cost reductions and improvements in passenger satisfaction resulting from the introduction of autonomous driving technology. The specific actions include simulating the operation of autonomous vehicles and analyzing the results.

[0453] (Application Example 1)

[0454] 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."

[0455] With the spread of autonomous driving technology, there is a need to build efficient transportation networks. However, there is a lack of systems that provide optimal routes tailored to current traffic conditions. Furthermore, creating operational plans that take future demand forecasts into account is difficult, resulting in a failure to provide convenient transportation options for users.

[0456] 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.

[0457] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic routes using the integrated information and evaluating profitability, means for considering the introduction of autonomous driving technology based on the evaluation results, and means for providing users with optimal route information that takes into account current traffic conditions and future demand forecasts. This makes it possible to provide efficient autonomous driving routes and to formulate operation plans that respond to future traffic demand.

[0458] "Mobility information" refers to data on traffic flow and people's movements, and is used to forecast demand for transportation routes.

[0459] "Integration" is the process of combining multiple datasets into one and converting them into a format necessary for analysis and evaluation.

[0460] "Transportation route simulation" is a method of virtually testing various transportation routes based on collected data and analyzing the results.

[0461] "Profitability assessment" is the process of estimating how much profit a proposed transportation route will generate.

[0462] "The introduction of autonomous driving technology" refers to the application of technology that enables vehicles to operate safely and efficiently without driver intervention.

[0463] "Current traffic conditions" refers to data related to traffic conditions, such as the degree of congestion on roads and public transport, and accident information, as of the present time.

[0464] "Future demand forecasting" is the process of estimating transportation demand for a specific period in the future, based on past travel patterns.

[0465] "Optimal route information" refers to data on the most desirable travel route, presented after considering factors such as efficiency, cost, and user convenience.

[0466] The system for implementing this invention efficiently manages traffic information and enables the suggestion of automated driving routes. The server first collects travel information from a nationwide database and integrates it to create basic data for predicting traffic route demand. At this stage, data processing tools such as Python and SQL are used to format and clean the data.

[0467] Next, the terminal utilizes the integrated data to perform traffic route simulations. Here, a large-scale language model using TensorFlow is employed to evaluate the profitability, environmental impact, and user convenience of multiple proposed traffic routes. Furthermore, through devices such as smartphones and smart glasses, the system provides users with real-time optimal route information that takes into account current traffic conditions and future demand forecasts.

[0468] As a concrete example, imagine a scenario where a user uses their smartphone during their morning commute to inquire about the optimal autonomous driving route from home to the office. In this case, the server analyzes real-time traffic data and presents the user with a route that avoids congestion while minimizing environmental impact. This allows the user to reach their destination comfortably and efficiently.

[0469] Using a generative AI model, an example of a prompt would be, "Please suggest the optimal autonomous driving route from Shinjuku to Shibuya at 8 AM." Based on this prompt, the system calculates the optimal route and provides it to the user.

[0470] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0471] Step 1:

[0472] The server retrieves mobility data from a nationwide mobility information infrastructure. This involves querying the database and extracting information about traffic flow and people's movements. The input is nationwide mobility data, and the output is mobility pattern data for a specified region. This data is standardized through integration and other processes.

[0473] Step 2:

[0474] The server integrates the acquired data and transforms it into a usable dataset. Data processing tools are used to clean the data into a consistent format and eliminate redundant information. The input is the movement data obtained in step 1, and the output is the integrated movement dataset.

[0475] Step 3:

[0476] The terminal performs transportation route simulations using an integrated dataset. A large-scale language model is used to evaluate multiple route options. The input is the dataset obtained in step 2, and the output is the evaluation results from the simulation. The evaluation includes profitability, environmental impact, and user convenience.

[0477] Step 4:

[0478] The terminal will consider introducing autonomous driving technology based on the evaluation results. This includes a profitability analysis of the evaluated route options. The input is the evaluation results from step 3, and the output is whether or not to introduce autonomous driving technology, or a proposal for its implementation.

[0479] Step 5:

[0480] Users request optimal route information via their smartphones or smart glasses. The device calculates and presents the optimal route considering current traffic conditions and future demand forecasts. Inputs are the user's request and real-time traffic data, and output is the optimal route information for the current time.

[0481] Step 6:

[0482] The server uses a generated AI model and prompt statements to forecast future demand. In this process, the AI ​​model estimates future demand using historical data patterns and current information. The input is traffic data from the past to the present, and the output is the predicted future traffic trends.

[0483] Through these steps, the system can efficiently and quickly provide users with the optimal route for autonomous driving.

[0484] 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.

[0485] This invention provides a system that optimizes transportation routes using mobility data and further takes user emotions into account to provide personalized transportation plans. This system includes the acquisition and integration of mobility data, simulation using pedestrian flow data, profitability evaluation, consideration of the introduction of autonomous driving technology, and the process of recognizing and responding to user emotions using an emotion engine.

[0486] The core of this system is the server's function of collecting and analyzing various types of data. First, the server retrieves data representing people's movement patterns from an online database. This data is then normalized into a unified format for subsequent processes.

[0487] Next, the terminal uses this integrated data to perform a traffic demand simulation. Here, a large-scale language model is used to comprehensively evaluate various transportation route options and their profitability and convenience. At this stage, the most efficient transportation plan is identified, and the optimal option is selected from a profitability perspective.

[0488] If the introduction of autonomous driving technology is proposed based on the simulation results, further analysis will be conducted. The server will evaluate the operational effectiveness of the autonomous driving system and quantitatively show its impact on traffic infrastructure.

[0489] A key feature of this system is its built-in emotion engine for recognizing user emotions. It collects emotional data from the user's facial expressions and voice, and the terminal analyzes this data in real time. The analysis results are used to adjust the proposed transportation plan, enabling the system to provide suggestions that are best suited to the user's current emotional state.

[0490] For example, if a user is tired, the emotion engine recognizes this state and suggests the shortest and most comfortable route. Conversely, if the user is relaxed, a route that allows them to enjoy the scenery is recommended.

[0491] Through this process, the present invention provides users with the most beneficial and efficient transportation solution, offering more nuanced service compared to conventional systems. This system allows users to enjoy a selection of transportation options tailored to their individual needs.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The server retrieves regional movement data from various databases. This data includes location information and route history, and is used to prepare for the analysis of people's movement patterns.

[0495] Step 2:

[0496] The server normalizes the movement data it acquires. By unifying the data format and imputing missing data, an integrated dataset is created to improve the accuracy of the analysis.

[0497] Step 3:

[0498] The terminal inputs the integrated dataset into a large-scale language model and performs a simulation of traffic demand. The simulation generates multiple proposed transportation routes and evaluates their profitability, convenience, and environmental impact.

[0499] Step 4:

[0500] The terminal identifies the most efficient transportation route based on the simulation results and calculates the expected revenue for that route. This allows for the selection of the most profitable transportation plan.

[0501] Step 5:

[0502] Users will review the details of the selected transportation plan via their devices and evaluate its feasibility. This evaluation will also take into account additional costs and operational benefits if autonomous driving technology is included.

[0503] Step 6:

[0504] The device uses an emotion engine to acquire emotional data through facial recognition and voice analysis of the user. This data is processed in real time to evaluate the user's emotional state.

[0505] Step 7:

[0506] The server analyzes the emotional data it has acquired and adjusts the suggested transportation plan to best suit the user's current state. This suggestion is customized according to the user's desired situation.

[0507] Step 8:

[0508] The user reviews the proposed customized transportation plan, makes a final approval or adjustment, and then it is implemented in actual operation.

[0509] (Example 2)

[0510] 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."

[0511] Current transportation systems do not adequately utilize mobility information, resulting in ineffective optimization of transportation plans. Furthermore, there is a lack of transportation plan proposals that take into account user emotions and individual needs. Therefore, there is a need for advanced systems that can simultaneously improve both transportation efficiency and user satisfaction.

[0512] 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.

[0513] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic planning using the integrated information and evaluating profitability, means for considering the introduction of autonomous driving technology based on the evaluation results, and means for recognizing the user's emotional state and adjusting the traffic plan based on that state. This makes it possible to provide efficient and personalized traffic plans for each user.

[0514] "Mobility information" refers to data that represents people's movement patterns, and includes a wide range of information such as location information and transportation usage history.

[0515] "Means of integration" refers to the process of converting travel information in different formats into a single, unified format to create a consistent dataset.

[0516] "Transportation planning simulation" is a method of virtually estimating and comparing the effectiveness and efficiency of various transportation routes and schedules based on travel information.

[0517] "Methods for evaluating profitability" refer to the process of quantitatively measuring the economic benefits that a transportation plan will bring and determining its financial feasibility.

[0518] "Means of considering the introduction of autonomous driving technology" refers to a method of analyzing and evaluating the technical and economic impacts of incorporating autonomous driving systems into traffic planning, based on the results of simulations.

[0519] "Means for recognizing emotional states" refers to technologies that analyze a user's emotions in real time from their facial expressions, voice, etc., to determine their current mental state.

[0520] "Means of adjusting transportation plans" refers to a process of flexibly changing and optimizing transportation plans based on the recognized emotional state of the user.

[0521] This invention is a system for highly optimizing transportation systems and improving the user experience. Specifically, the server utilizes hardware and software to acquire movement information from various sources and integrate it into a unified format. The specific software used here includes a database management system and data normalization tools. The server uses these to efficiently acquire and process data, ensuring that subsequent processing proceeds smoothly.

[0522] Next, the device performs a traffic planning simulation based on the integrated data. A large-scale language model (generative AI model) with high predictive capabilities is used here. For example, it utilizes a generative AI model such as GPT-4 to generate different traffic route scenarios and evaluate the profitability and convenience of each. Through this process, the device can identify the optimal traffic plan.

[0523] Furthermore, to recognize the user's emotional state in real time, the device is equipped with a high-precision emotion analysis engine. This engine analyzes the user's emotions from their voice and facial expressions, and uses the results to adjust traffic plans. The specific hardware for this purpose includes a camera and a microphone.

[0524] This process provides users with personalized transportation plans. For example, when a user uses public transport, the device detects the user's fatigue level and provides the fastest and least stressful route. Alternatively, if it detects that the user is relaxed, it suggests a route with scenic views.

[0525] An example of a prompt to input into a generative AI model is, "Suggest the best route based on the user's current emotions and traffic conditions." This prompt will cause the system to generate the best possible transportation options tailored to the user's situation.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] The server acquires movement information from the internet and various sensors. This movement information includes data on transportation usage and GPS location data. Once the data is acquired, the server converts it into a unified format. Data formatting tools are used to integrate data acquired from different sources. Finally, the integrated data is prepared as input data for traffic planning simulations.

[0529] Step 2:

[0530] The terminal performs traffic planning simulations based on integrated mobility information. Using the integrated data as input, the terminal utilizes a large-scale language model (e.g., GPT-4). This model generates diverse traffic scenarios and evaluates the profitability and convenience of each. During this evaluation process, the terminal quantifies the profitability of the traffic plan and outputs comparative indices to evaluate the convenience of each scenario. The goal is to identify the most profitable and convenient traffic plan.

[0531] Step 3:

[0532] Upon receiving the simulation results, the server considers introducing autonomous driving technology based on the evaluation. The server analyzes the safety, economics, and operational effectiveness of adding an autonomous driving system to the selected traffic plan. It simulates the impact of autonomous driving technology using a numerical model, and the results are reflected in the final evaluation of the plan.

[0533] Step 4:

[0534] The device performs processing to recognize the user's emotional state in real time. A facial recognition system and a voice analysis system analyze the user's emotions and acquire the results as emotional data. This data represents the user's current emotional state and serves as input for the next processing step.

[0535] Step 5:

[0536] The terminal adjusts the transportation plan to take the user's emotional state into account. Using emotional data as input, the terminal dynamically modifies the transportation plan. If the user is tired, it suggests a more comfortable and faster route; if they are relaxed, it selects a route that allows them to enjoy the scenery. The terminal then outputs a personalized and optimal transportation plan for the user.

[0537] Throughout this entire process, the system can provide optimal and customized transportation solutions for each user.

[0538] (Application Example 2)

[0539] 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."

[0540] In recent years, with the advancement of autonomous driving technology, there has been a growing demand for efficient and safe transportation plans. However, conventional systems struggle to provide transportation plans that take into account the individual emotions and psychological states of users, limiting the potential for improving the user experience. Furthermore, while dynamic demand forecasting is essential for optimizing transportation routes, there is a challenge in the lack of adequate systems to handle this.

[0541] 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.

[0542] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic routes using the integrated information and evaluating profitability, means for considering the introduction of an autonomous driving system based on the evaluation results, means for recognizing and analyzing emotional states, and means for adjusting traffic plans based on the emotional analysis. This makes it possible to provide more personalized traffic plans that incorporate the user's emotions and movement information in real time.

[0543] "Movement information" refers to data about the spatial position and movement of individuals and objects, and is used for optimizing and simulating transportation routes.

[0544] "Integration" is the process of bringing together information from different forms and sources into a single system and making it usable.

[0545] "Simulation" is a method of virtually reproducing real-world phenomena or processes using mathematical models and analyzing the results.

[0546] "Profitability assessment" is an analytical method used to measure the degree to which a particular business or plan generates economic benefits.

[0547] An "autonomous driving system" is a technology that uses technologies such as machine learning and artificial intelligence to control a vehicle while minimizing human intervention.

[0548] "Considering implementation" refers to the process of evaluating whether to apply or adopt a new technology or system.

[0549] "Emotional state" refers to the sensory reactions and psychological conditions exhibited by the user or object.

[0550] "Emotional analysis" is a technology that recognizes a person's emotions and psychological state at any given time from data such as voice and facial expressions.

[0551] "Adjusting transportation plans" is the process of making modifications and changes to optimize transportation methods and routes according to the environment and circumstances.

[0552] This invention consists of a system that provides personalized transportation plans by utilizing travel information and the user's emotional state. The system functions as follows:

[0553] The server acquires and integrates movement information from online databases and sensors. Specifically, it collects GPS data and public transport operation data, and normalizes them into a unified format. Furthermore, it uses this integrated information to simulate transportation routes and evaluate their profitability and convenience from multiple perspectives. During this simulation process, a large-scale language model is used to predict user transportation demand. Based on this prediction, the optimal transportation plan is identified.

[0554] The user's device collects and analyzes their emotional state in real time via its camera and microphone. Cloud-based services such as the Google Cloud Emotion API are used for this analysis. The analyzed emotional data is sent to a server and used to adjust the suggested travel plan. If the user's emotions indicate stress, a relaxing route is suggested; if their emotions are stable, an efficient route is suggested.

[0555] For example, during a family road trip, the device might detect that the children are bored. In this case, the device retrieves information from the server about scenic routes and restroom break points along the way, and sends instructions to the vehicle's navigation system.

[0556] An example of a prompt message from a generative AI model would be, "How are you feeling today? If you're feeling stressed, I'll suggest a relaxing route. Let's choose the best path to your destination while enjoying the scenery," which the model would then evaluate and provide the user with the most suitable transportation plan.

[0557] In this way, the present invention provides a personalized transportation experience based on the user's individual needs and emotional state.

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The server retrieves movement information from an online database. Input data includes GPS data and public transport operation data, and the server generates movement information normalized into a unified format as output. The data absorbs format differences and is organized into a consistent dataset.

[0561] Step 2:

[0562] The server uses travel information normalized into a unified format to simulate transportation routes. The input data is the travel information in the unified format obtained in the previous step, and the output generates the results of a profitability evaluation. In the simulation, a large-scale language model is used to forecast transportation demand, and the optimal plan is determined through a multifaceted evaluation.

[0563] Step 3:

[0564] The server evaluates the feasibility of implementing an autonomous driving system based on the results of profitability assessments. The input is simulation results, and the output generates quantitative data showing the benefits and impacts of implementation. It determines whether autonomous driving technology is applicable and analyzes its efficiency and effectiveness.

[0565] Step 4:

[0566] The device uses a camera and microphone to collect the user's emotional state in real time. The input data consists of the user's facial expressions and voice, and the output generates emotion analysis results. Emotion analysis utilizes the Google Cloud Emotion API and other tools to analyze different emotional states and intensities in detail.

[0567] Step 5:

[0568] The terminal sends a request to the server for traffic plan adjustment based on the generated emotion analysis results. The input is the emotion analysis results, and the output is a traffic plan suggestion that is optimal for the user. Through this adjustment, routes and plans are suggested that are tailored to the user's current emotions and psychological state.

[0569] Step 6:

[0570] Users review and select suggested transportation plans through their terminal. The input is a plan suggestion from the server, and the selected transportation plan is applied to the vehicle's navigation system as output. The user's selection allows for a personalized experience tailored to their individual needs.

[0571] 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.

[0572] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] This invention provides a system for designing efficient and profitable transportation routes, offering a series of processes from analyzing mobility data and evaluating profitability to considering the introduction of autonomous driving technology. The following describes a specific implementation of this system.

[0589] The core of this system is the function of collecting and integrating mobility data. The server accesses a nationwide human flow database and collects mobility pattern data for specific regions. The collected data is standardized in format and integrated into a dataset. This dataset is used as basic information for forecasting demand for transportation routes.

[0590] Next, the terminal runs a traffic demand simulation using the integrated dataset. This process utilizes a large-scale language model to evaluate multiple proposed traffic routes in detail, considering their profitability, environmental impact, and user convenience. Specifically, it becomes possible to propose new routes based on passenger count predictions in urban areas, and estimate the operating costs and expected revenue of those routes.

[0591] Furthermore, users select the most efficient transportation plan based on the evaluation results. If the introduction of autonomous driving technology is deemed effective from the perspectives of profitability, safety, and environmental impact, the server will conduct additional simulations. The purpose of these simulations is to quantitatively demonstrate cost reductions and improvements in passenger satisfaction associated with the operation of autonomous vehicles.

[0592] Through these steps, this system optimizes transportation networks and provides sustainable transportation solutions in communities facing declining birth rates and an aging population. Ultimately, users will be able to operate transportation rationally and efficiently by utilizing this system.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] The server accesses a human movement database to retrieve movement data for the target area. This data includes location information and movement patterns for a specific period.

[0596] Step 2:

[0597] The server normalizes the movement data it acquires and imputes missing values. Furthermore, it converts it to a unified format and creates an integrated dataset based on this.

[0598] Step 3:

[0599] The terminal inputs an integrated dataset and begins simulating traffic demand using a large-scale language model. This simulation evaluates the profitability, convenience, and environmental impact of each proposed traffic route.

[0600] Step 4:

[0601] The server evaluates the profitability of transportation routes based on the simulation results, formats the evaluation results, and provides them to the user as a visual report.

[0602] Step 5:

[0603] Users can review the provided reports via their devices and analyze the evaluation results. This allows them to select the most suitable transportation plan.

[0604] Step 6:

[0605] The system evaluates the feasibility of introducing autonomous driving technology to the transportation plan selected by the user. During this process, the server performs additional simulations to estimate the reduction in operating costs and the improvement in safety associated with introducing autonomous driving technology, and provides the results to the user.

[0606] Step 7:

[0607] The user makes the final decision on the transportation plan based on all the information and then begins the actual implementation process.

[0608] (Example 1)

[0609] 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".

[0610] Modern urban transportation systems must be efficient and sustainable, while also addressing demographic diversification and increasing environmental impact. Traditional transportation planning methods struggle to perform sufficient data analysis, making it difficult to accurately predict new transportation demands or design optimal routes. Furthermore, there is a lack of appropriate evaluation methods to realize improvements in transportation efficiency and safety through the introduction of autonomous driving technology.

[0611] 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.

[0612] In this invention, the server includes means for collecting and integrating mobility information, means for simulating traffic routes using the integrated information and evaluating profitability and environmental impact, and means for selecting and implementing traffic plans based on the evaluation results. This enables accurate forecasting of traffic demand and the creation of efficient traffic plans.

[0613] "Movement information" refers to data about the routes taken by individual moving entities (people or objects) within a specific time frame.

[0614] "Means of integration" refers to the process of transforming data collected from multiple sources into a consistent format and combining it into a single, comprehensive dataset.

[0615] A "transportation route" refers to the path that a means of transportation takes to move a user from one point to another.

[0616] "Simulation" refers to a technology that reproduces real-world traffic conditions using mathematical models and virtually analyzes their movement and changes.

[0617] "Profitability" is an indicator that shows how much profit a particular transportation plan or route has the potential to generate.

[0618] "Environmental impact" refers to the potential effects and changes that transportation plans and their implementation may have on the natural environment and local communities.

[0619] "Transportation planning" refers to specific policies and strategies for optimizing the operation and allocation of transportation methods in a particular region or on a particular route.

[0620] "Autonomous driving technology" is a general term for technologies used to enable vehicles to move on their own without human intervention.

[0621] "Information sources" refer to places or services from which data and information can be obtained. In the case of human flow information, this includes sensor networks and digital platforms.

[0622] This invention is a system for designing efficient and sustainable transportation routes, providing a series of processes from collecting mobility information to evaluating profitability and considering the introduction of autonomous driving technology.

[0623] The server first collects movement information from nationwide human flow sources. This process uses database systems (e.g., MySQL or PostgreSQL) to retrieve data. The collected data is then formatted using the Python Pandas library and managed as a unified dataset. This dataset forms the basis for subsequent traffic route simulations.

[0624] Next, the device performs a traffic route simulation using the integrated dataset. This process utilizes a generative AI model, such as a large-scale language model like GPT-4. The device inputs the prompt "Based on the given travel data, propose an efficient traffic route and evaluate its profitability and environmental impact" into the generative AI, which then generates multiple traffic route options and evaluates their profitability, environmental impact, and convenience.

[0625] Based on these simulation results, users can select new transportation plans, including the introduction of autonomous driving technology. For example, they can design new bus routes in busy areas and calculate their operating costs and expected revenues. The server then analyzes the benefits of introducing autonomous driving technology and conducts additional simulations to evaluate the resulting cost reductions and improvements in passenger satisfaction.

[0626] By using this system, users can design optimal transportation plans that meet local travel demand, enabling efficient and sustainable transportation operations.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] The server collects movement information from nationwide human flow sources and stores it in a database. Specifically, it uses SQL queries to extract relevant datasets and obtain movement patterns by time of day and region. The input is query conditions from the database, and the output is a formatted collection of movement information. For example, it collects data based on conditions such as "obtain the number of people who traveled from city A to city B within a specific date range."

[0630] Step 2:

[0631] The server integrates the collected movement information using the Python Pandas library and formats it into a consistent dataset. The input is the movement information output from step 1, and the output is an integrated dataset with each data field unified. Specific operations include conversion between different data formats and cleaning up unnecessary data.

[0632] Step 3:

[0633] The terminal performs traffic route simulations using an integrated dataset. This utilizes a generative AI model, specifically a large-scale language model (e.g., GPT-4). The input is the integrated dataset, and the output consists of proposed traffic routes generated by the simulation and evaluation reports for each route. Specifically, the terminal sends the prompt message "Based on the given travel data, propose efficient traffic routes and evaluate their profitability and environmental impact" to the large-scale model via an API.

[0634] Step 4:

[0635] The user determines and selects the most effective transportation plan based on evaluation reports obtained from the terminal. The input is the evaluation report, and the output is the selected transportation plan. A concrete example is the process of deciding whether to prioritize a particular route based on passenger count forecasts.

[0636] Step 5:

[0637] The server analyzes the effects of introducing autonomous driving technology to the selected traffic plan through additional simulations. The input is the selected traffic plan, and the output is a report on the expected cost reductions and improvements in passenger satisfaction resulting from the introduction of autonomous driving technology. The specific actions include simulating the operation of autonomous vehicles and analyzing the results.

[0638] (Application Example 1)

[0639] 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".

[0640] With the spread of autonomous driving technology, there is a need to build efficient transportation networks. However, there is a lack of systems that provide optimal routes tailored to current traffic conditions. Furthermore, creating operational plans that take future demand forecasts into account is difficult, resulting in a failure to provide convenient transportation options for users.

[0641] 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.

[0642] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic routes using the integrated information and evaluating profitability, means for considering the introduction of autonomous driving technology based on the evaluation results, and means for providing users with optimal route information that takes into account current traffic conditions and future demand forecasts. This makes it possible to provide efficient autonomous driving routes and to formulate operation plans that respond to future traffic demand.

[0643] "Mobility information" refers to data on traffic flow and people's movements, and is used to forecast demand for transportation routes.

[0644] "Integration" is the process of combining multiple datasets into one and converting them into a format necessary for analysis and evaluation.

[0645] "Transportation route simulation" is a method of virtually testing various transportation routes based on collected data and analyzing the results.

[0646] "Profitability assessment" is the process of estimating how much profit a proposed transportation route will generate.

[0647] "The introduction of autonomous driving technology" refers to the application of technology that enables vehicles to operate safely and efficiently without driver intervention.

[0648] "Current traffic conditions" refers to data related to traffic conditions, such as the degree of congestion on roads and public transport, and accident information, as of the present time.

[0649] "Future demand forecasting" is the process of estimating transportation demand for a specific period in the future, based on past travel patterns.

[0650] "Optimal route information" refers to data on the most desirable travel route, presented after considering factors such as efficiency, cost, and user convenience.

[0651] The system for implementing this invention efficiently manages traffic information and enables the suggestion of automated driving routes. The server first collects travel information from a nationwide database and integrates it to create basic data for predicting traffic route demand. At this stage, data processing tools such as Python and SQL are used to format and clean the data.

[0652] Next, the terminal utilizes the integrated data to perform traffic route simulations. Here, a large-scale language model using TensorFlow is employed to evaluate the profitability, environmental impact, and user convenience of multiple proposed traffic routes. Furthermore, through devices such as smartphones and smart glasses, the system provides users with real-time optimal route information that takes into account current traffic conditions and future demand forecasts.

[0653] As a concrete example, imagine a scenario where a user uses their smartphone during their morning commute to inquire about the optimal autonomous driving route from home to the office. In this case, the server analyzes real-time traffic data and presents the user with a route that avoids congestion while minimizing environmental impact. This allows the user to reach their destination comfortably and efficiently.

[0654] Using a generative AI model, an example of a prompt would be, "Please suggest the optimal autonomous driving route from Shinjuku to Shibuya at 8 AM." Based on this prompt, the system calculates the optimal route and provides it to the user.

[0655] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0656] Step 1:

[0657] The server retrieves mobility data from a nationwide mobility information infrastructure. This involves querying the database and extracting information about traffic flow and people's movements. The input is nationwide mobility data, and the output is mobility pattern data for a specified region. This data is standardized through integration and other processes.

[0658] Step 2:

[0659] The server integrates the acquired data and transforms it into a usable dataset. Data processing tools are used to clean the data into a consistent format and eliminate redundant information. The input is the movement data obtained in step 1, and the output is the integrated movement dataset.

[0660] Step 3:

[0661] The terminal performs transportation route simulations using an integrated dataset. A large-scale language model is used to evaluate multiple route options. The input is the dataset obtained in step 2, and the output is the evaluation results from the simulation. The evaluation includes profitability, environmental impact, and user convenience.

[0662] Step 4:

[0663] The terminal will consider introducing autonomous driving technology based on the evaluation results. This includes a profitability analysis of the evaluated route options. The input is the evaluation results from step 3, and the output is whether or not to introduce autonomous driving technology, or a proposal for its implementation.

[0664] Step 5:

[0665] Users request optimal route information via their smartphones or smart glasses. The device calculates and presents the optimal route considering current traffic conditions and future demand forecasts. Inputs are the user's request and real-time traffic data, and output is the optimal route information for the current time.

[0666] Step 6:

[0667] The server uses a generated AI model and prompt statements to forecast future demand. In this process, the AI ​​model estimates future demand using historical data patterns and current information. The input is traffic data from the past to the present, and the output is the predicted future traffic trends.

[0668] Through these steps, the system can efficiently and quickly provide users with the optimal route for autonomous driving.

[0669] 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.

[0670] This invention provides a system that optimizes transportation routes using mobility data and further takes user emotions into account to provide personalized transportation plans. This system includes the acquisition and integration of mobility data, simulation using pedestrian flow data, profitability evaluation, consideration of the introduction of autonomous driving technology, and the process of recognizing and responding to user emotions using an emotion engine.

[0671] The core of this system is the server's function of collecting and analyzing various types of data. First, the server retrieves data representing people's movement patterns from an online database. This data is then normalized into a unified format for subsequent processes.

[0672] Next, the terminal uses this integrated data to perform a traffic demand simulation. Here, a large-scale language model is used to comprehensively evaluate various transportation route options and their profitability and convenience. At this stage, the most efficient transportation plan is identified, and the optimal option is selected from a profitability perspective.

[0673] If the introduction of autonomous driving technology is proposed based on the simulation results, further analysis will be conducted. The server will evaluate the operational effectiveness of the autonomous driving system and quantitatively show its impact on traffic infrastructure.

[0674] A key feature of this system is its built-in emotion engine for recognizing user emotions. It collects emotional data from the user's facial expressions and voice, and the terminal analyzes this data in real time. The analysis results are used to adjust the proposed transportation plan, enabling the system to provide suggestions that are best suited to the user's current emotional state.

[0675] For example, if a user is tired, the emotion engine recognizes this state and suggests the shortest and most comfortable route. Conversely, if the user is relaxed, a route that allows them to enjoy the scenery is recommended.

[0676] Through this process, the present invention provides users with the most beneficial and efficient transportation solution, offering more nuanced service compared to conventional systems. This system allows users to enjoy a selection of transportation options tailored to their individual needs.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The server retrieves regional movement data from various databases. This data includes location information and route history, and is used to prepare for the analysis of people's movement patterns.

[0680] Step 2:

[0681] The server normalizes the movement data it acquires. By unifying the data format and imputing missing data, an integrated dataset is created to improve the accuracy of the analysis.

[0682] Step 3:

[0683] The terminal inputs the integrated dataset into a large-scale language model and performs a simulation of traffic demand. The simulation generates multiple proposed transportation routes and evaluates their profitability, convenience, and environmental impact.

[0684] Step 4:

[0685] The terminal identifies the most efficient transportation route based on the simulation results and calculates the expected revenue for that route. This allows for the selection of the most profitable transportation plan.

[0686] Step 5:

[0687] Users will review the details of the selected transportation plan via their devices and evaluate its feasibility. This evaluation will also take into account additional costs and operational benefits if autonomous driving technology is included.

[0688] Step 6:

[0689] The device uses an emotion engine to acquire emotional data through facial recognition and voice analysis of the user. This data is processed in real time to evaluate the user's emotional state.

[0690] Step 7:

[0691] The server analyzes the emotional data it has acquired and adjusts the suggested transportation plan to best suit the user's current state. This suggestion is customized according to the user's desired situation.

[0692] Step 8:

[0693] The user reviews the proposed customized transportation plan, makes a final approval or adjustment, and then it is implemented in actual operation.

[0694] (Example 2)

[0695] 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".

[0696] Current transportation systems do not adequately utilize mobility information, resulting in ineffective optimization of transportation plans. Furthermore, there is a lack of transportation plan proposals that take into account user emotions and individual needs. Therefore, there is a need for advanced systems that can simultaneously improve both transportation efficiency and user satisfaction.

[0697] 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.

[0698] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic planning using the integrated information and evaluating profitability, means for considering the introduction of autonomous driving technology based on the evaluation results, and means for recognizing the user's emotional state and adjusting the traffic plan based on that state. This makes it possible to provide efficient and personalized traffic plans for each user.

[0699] "Mobility information" refers to data that represents people's movement patterns, and includes a wide range of information such as location information and transportation usage history.

[0700] "Means of integration" refers to the process of converting travel information in different formats into a single, unified format to create a consistent dataset.

[0701] "Transportation planning simulation" is a method of virtually estimating and comparing the effectiveness and efficiency of various transportation routes and schedules based on travel information.

[0702] "Methods for evaluating profitability" refer to the process of quantitatively measuring the economic benefits that a transportation plan will bring and determining its financial feasibility.

[0703] "Means of considering the introduction of autonomous driving technology" refers to a method of analyzing and evaluating the technical and economic impacts of incorporating autonomous driving systems into traffic planning, based on the results of simulations.

[0704] "Means for recognizing emotional states" refers to technologies that analyze a user's emotions in real time from their facial expressions, voice, etc., to determine their current mental state.

[0705] "Means of adjusting transportation plans" refers to a process of flexibly changing and optimizing transportation plans based on the recognized emotional state of the user.

[0706] This invention is a system for highly optimizing transportation systems and improving the user experience. Specifically, the server utilizes hardware and software to acquire movement information from various sources and integrate it into a unified format. The specific software used here includes a database management system and data normalization tools. The server uses these to efficiently acquire and process data, ensuring that subsequent processing proceeds smoothly.

[0707] Next, the device performs a traffic planning simulation based on the integrated data. A large-scale language model (generative AI model) with high predictive capabilities is used here. For example, it utilizes a generative AI model such as GPT-4 to generate different traffic route scenarios and evaluate the profitability and convenience of each. Through this process, the device can identify the optimal traffic plan.

[0708] Furthermore, to recognize the user's emotional state in real time, the device is equipped with a high-precision emotion analysis engine. This engine analyzes the user's emotions from their voice and facial expressions, and uses the results to adjust traffic plans. The specific hardware for this purpose includes a camera and a microphone.

[0709] This process provides users with personalized transportation plans. For example, when a user uses public transport, the device detects the user's fatigue level and provides the fastest and least stressful route. Alternatively, if it detects that the user is relaxed, it suggests a route with scenic views.

[0710] An example of a prompt to input into a generative AI model is, "Suggest the best route based on the user's current emotions and traffic conditions." This prompt will cause the system to generate the best possible transportation options tailored to the user's situation.

[0711] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0712] Step 1:

[0713] The server acquires movement information from the internet and various sensors. This movement information includes data on transportation usage and GPS location data. Once the data is acquired, the server converts it into a unified format. Data formatting tools are used to integrate data acquired from different sources. Finally, the integrated data is prepared as input data for traffic planning simulations.

[0714] Step 2:

[0715] The terminal performs traffic planning simulations based on integrated mobility information. Using the integrated data as input, the terminal utilizes a large-scale language model (e.g., GPT-4). This model generates diverse traffic scenarios and evaluates the profitability and convenience of each. During this evaluation process, the terminal quantifies the profitability of the traffic plan and outputs comparative indices to evaluate the convenience of each scenario. The goal is to identify the most profitable and convenient traffic plan.

[0716] Step 3:

[0717] Upon receiving the simulation results, the server considers introducing autonomous driving technology based on the evaluation. The server analyzes the safety, economics, and operational effectiveness of adding an autonomous driving system to the selected traffic plan. It simulates the impact of autonomous driving technology using a numerical model, and the results are reflected in the final evaluation of the plan.

[0718] Step 4:

[0719] The device performs processing to recognize the user's emotional state in real time. A facial recognition system and a voice analysis system analyze the user's emotions and acquire the results as emotional data. This data represents the user's current emotional state and serves as input for the next processing step.

[0720] Step 5:

[0721] The terminal adjusts the transportation plan to take the user's emotional state into account. Using emotional data as input, the terminal dynamically modifies the transportation plan. If the user is tired, it suggests a more comfortable and faster route; if they are relaxed, it selects a route that allows them to enjoy the scenery. The terminal then outputs a personalized and optimal transportation plan for the user.

[0722] Throughout this entire process, the system can provide optimal and customized transportation solutions for each user.

[0723] (Application Example 2)

[0724] 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".

[0725] In recent years, with the advancement of autonomous driving technology, there has been a growing demand for efficient and safe transportation plans. However, conventional systems struggle to provide transportation plans that take into account the individual emotions and psychological states of users, limiting the potential for improving the user experience. Furthermore, while dynamic demand forecasting is essential for optimizing transportation routes, there is a challenge in the lack of adequate systems to handle this.

[0726] 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.

[0727] In this invention, the server includes means for acquiring and integrating movement information, means for simulating traffic routes using the integrated information and evaluating profitability, means for considering the introduction of an autonomous driving system based on the evaluation results, means for recognizing and analyzing emotional states, and means for adjusting traffic plans based on the emotional analysis. This makes it possible to provide more personalized traffic plans that incorporate the user's emotions and movement information in real time.

[0728] "Movement information" refers to data about the spatial position and movement of individuals and objects, and is used for optimizing and simulating transportation routes.

[0729] "Integration" is the process of bringing together information from different forms and sources into a single system and making it usable.

[0730] "Simulation" is a method of virtually reproducing real-world phenomena or processes using mathematical models and analyzing the results.

[0731] "Profitability assessment" is an analytical method used to measure the degree to which a particular business or plan generates economic benefits.

[0732] An "autonomous driving system" is a technology that uses technologies such as machine learning and artificial intelligence to control a vehicle while minimizing human intervention.

[0733] "Considering implementation" refers to the process of evaluating whether to apply or adopt a new technology or system.

[0734] "Emotional state" refers to the sensory reactions and psychological conditions exhibited by the user or object.

[0735] "Emotional analysis" is a technology that recognizes a person's emotions and psychological state at any given time from data such as voice and facial expressions.

[0736] "Adjusting transportation plans" is the process of making modifications and changes to optimize transportation methods and routes according to the environment and circumstances.

[0737] This invention consists of a system that provides personalized transportation plans by utilizing travel information and the user's emotional state. The system functions as follows:

[0738] The server acquires and integrates movement information from online databases and sensors. Specifically, it collects GPS data and public transport operation data, and normalizes them into a unified format. Furthermore, it uses this integrated information to simulate transportation routes and evaluate their profitability and convenience from multiple perspectives. During this simulation process, a large-scale language model is used to predict user transportation demand. Based on this prediction, the optimal transportation plan is identified.

[0739] The user's device collects and analyzes their emotional state in real time via its camera and microphone. Cloud-based services such as the Google Cloud Emotion API are used for this analysis. The analyzed emotional data is sent to a server and used to adjust the suggested travel plan. If the user's emotions indicate stress, a relaxing route is suggested; if their emotions are stable, an efficient route is suggested.

[0740] For example, during a family road trip, the device might detect that the children are bored. In this case, the device retrieves information from the server about scenic routes and restroom break points along the way, and sends instructions to the vehicle's navigation system.

[0741] An example of a prompt message from a generative AI model would be, "How are you feeling today? If you're feeling stressed, I'll suggest a relaxing route. Let's choose the best path to your destination while enjoying the scenery," which the model would then evaluate and provide the user with the most suitable transportation plan.

[0742] In this way, the present invention provides a personalized transportation experience based on the user's individual needs and emotional state.

[0743] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0744] Step 1:

[0745] The server retrieves movement information from an online database. Input data includes GPS data and public transport operation data, and the server generates movement information normalized into a unified format as output. The data absorbs format differences and is organized into a consistent dataset.

[0746] Step 2:

[0747] The server uses travel information normalized into a unified format to simulate transportation routes. The input data is the travel information in the unified format obtained in the previous step, and the output generates the results of a profitability evaluation. In the simulation, a large-scale language model is used to forecast transportation demand, and the optimal plan is determined through a multifaceted evaluation.

[0748] Step 3:

[0749] The server evaluates the feasibility of implementing an autonomous driving system based on the results of profitability assessments. The input is simulation results, and the output generates quantitative data showing the benefits and impacts of implementation. It determines whether autonomous driving technology is applicable and analyzes its efficiency and effectiveness.

[0750] Step 4:

[0751] The device uses a camera and microphone to collect the user's emotional state in real time. The input data consists of the user's facial expressions and voice, and the output generates emotion analysis results. Emotion analysis utilizes the Google Cloud Emotion API and other tools to analyze different emotional states and intensities in detail.

[0752] Step 5:

[0753] The terminal sends a request to the server for traffic plan adjustment based on the generated emotion analysis results. The input is the emotion analysis results, and the output is a traffic plan suggestion that is optimal for the user. Through this adjustment, routes and plans are suggested that are tailored to the user's current emotions and psychological state.

[0754] Step 6:

[0755] Users review and select suggested transportation plans through their terminal. The input is a plan suggestion from the server, and the selected transportation plan is applied to the vehicle's navigation system as output. The user's selection allows for a personalized experience tailored to their individual needs.

[0756] 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.

[0757] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] Means for acquiring and integrating movement data,

[0780] A means for simulating transportation routes using the integrated data and evaluating their profitability,

[0781] A means of considering the introduction of autonomous driving technology based on evaluation results,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, wherein the simulation means comprises a method for predicting traffic demand using a large-scale language model.

[0785] (Claim 3)

[0786] The system according to claim 1, wherein the movement data is obtained from a human flow database.

[0787] "Example 1"

[0788] (Claim 1)

[0789] Means for collecting and integrating movement information,

[0790] A means for simulating traffic routes using the integrated information and evaluating profitability and environmental impact,

[0791] The means of selecting and implementing a transportation plan based on the evaluation results,

[0792] A means for analyzing the applicability of autonomous driving technology in the said transportation plan,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, comprising means for performing traffic demand forecasting and evaluation based on prompt sentences using a large-scale language model.

[0796] (Claim 3)

[0797] The system according to claim 1, wherein movement information is obtained from a human flow information source.

[0798] "Application Example 1"

[0799] (Claim 1)

[0800] Means for acquiring and integrating movement information,

[0801] A means for simulating transportation routes using the integrated information and evaluating their profitability,

[0802] A means of considering the introduction of autonomous driving technology based on evaluation results,

[0803] A means of providing users with optimal route information that takes into account current traffic conditions and future demand forecasts,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, comprising a method for forecasting traffic demand using a large-scale language model.

[0807] (Claim 3)

[0808] The system according to claim 1, obtained from a human flow information infrastructure.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] Means for acquiring and integrating movement information,

[0812] A means for simulating transportation planning using the integrated information and evaluating its profitability,

[0813] A means of considering the introduction of autonomous driving technology based on evaluation results,

[0814] A means of recognizing the user's emotional state and adjusting the transportation plan based on that state,

[0815] ...

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, comprising a method for forecasting traffic demand using a large-scale language model.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the movement information is obtained from a human flow information base.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] Means for acquiring and integrating movement information,

[0824] A means for simulating transportation routes using the integrated information and evaluating their profitability,

[0825] A means of considering the introduction of an autonomous driving system based on the evaluation results,

[0826] Means for recognizing and analyzing emotional states,

[0827] A means for adjusting the transportation plan based on the said emotion analysis,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein the simulation means includes a technology for predicting traffic demand using a large-scale language model, and dynamically selects routes based on sentiment data.

[0831] (Claim 3)

[0832] The system according to claim 1, wherein the movement information is obtained from a demographic database. [Explanation of symbols]

[0833] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring and integrating movement data, A means for simulating transportation routes using the integrated data and evaluating their profitability, A means of considering the introduction of autonomous driving technology based on evaluation results, A system that includes this.

2. The system according to claim 1, wherein the simulation means comprises a method for predicting traffic demand using a large-scale language model.

3. The system according to claim 1, wherein the movement data is obtained from a human flow database.

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