system

Abstract

We provide the system. [Solution] A means of collecting traffic data from various sensors in the communication infrastructure, A method for analyzing collected traffic data and predicting congestion, A means for calculating the optimal route based on the prediction results and presenting the route information to the moving object, A means of collecting waste data from waste containers and generating efficient waste collection routes, A means for presenting the generated route information to a data collection device and automatically sorting recyclable resources, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern cities, there are problems that traffic congestion and inefficient waste management have an adverse impact on the quality of residents' lives. Traffic congestion causes an increase in travel time and economic losses, and inadequate waste management causes waste of resources and environmental problems. It is required to effectively solve these problems and improve the efficiency of the whole city.

Means for Solving the Problems

[0005] This invention aims to solve the aforementioned problems through a new urban management system utilizing communication infrastructure and artificial intelligence technology. In traffic management, traffic data is collected from various sensors in the communication infrastructure, analyzed using artificial intelligence to predict congestion, and the optimal travel route is calculated and presented. Regarding waste management, the system generates efficient collection routes based on data from waste containers and has a function to automatically sort recyclable resources. In this way, the invention provides a system that simultaneously achieves smooth traffic flow and efficient waste disposal.

[0006] "Communication infrastructure" refers to all networks and devices installed in a city for the purpose of collecting, transmitting, and processing information.

[0007] A "sensor" refers to a device that detects physical or chemical changes and outputs that information as an electrical signal.

[0008] "Traffic data" refers to data that includes information about the movement of vehicles and people on roads.

[0009] "AI (Artificial Intelligence)" refers to technology that gives computer systems the ability to automatically perform specific tasks and imitate human intelligence.

[0010] "Congestion forecasting" refers to the process of estimating future traffic congestion based on collected data.

[0011] "Route calculation" refers to the process of calculating the most efficient route from a given point to a destination.

[0012] A "waste container" refers to a facility or device for temporarily storing waste.

[0013] An "efficient collection route" refers to a path designed to collect waste with maximum efficiency while minimizing resources and time.

[0014] "Collection equipment" refers to a machine or device used to collect, transport, and process waste.

[0015] "Recyclable resources" refers to materials and substances that can be reused and whose handling can reduce the environmental load.

[0016] "Automatic sorting" refers to a process in which machines or systems identify and separate different types of objects without manual intervention.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] 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.

[0022] 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, and the like.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system that uses communication infrastructure and artificial intelligence (AI) technology to streamline traffic management and waste management in urban environments.

[0039] Traffic management system

[0040] The server first collects traffic data over time from various sensors and cameras installed throughout the city. This includes vehicle flow, speed, and intersection congestion. The server analyzes this data using AI algorithms to predict traffic congestion. For example, the server predicts congestion on a specific road for the next hour and generates the optimal detour route based on that information. This allows users to receive quick and efficient routes via their smartphones or in-car devices.

[0041] As a concrete example, when a user departs for their morning commute, the server analyzes traffic data at that time and sends an alternative route to the user's device if the usual route is congested. By accepting this suggestion, the user can shorten their commute time.

[0042] Waste management system

[0043] The terminal measures the volume and type of waste in real time using sensors attached to waste containers installed in each home and building. The measured data is sent to a server, where the server's AI calculates the optimal waste collection route based on the collected information. It also has a function to automatically sort recyclable resources.

[0044] For example, a terminal identifies locations in a specific area where garbage collection is needed, and the server generates an efficient route that includes those locations and sends it to the collection truck. This allows the collection team, as the user, to carry out their work without waste.

[0045] This invention will improve the quality of life for residents by streamlining both urban traffic and waste management.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server collects real-time traffic data through sensors and cameras placed throughout the city. This data includes information such as the number of vehicles on the road, their speed, and the status of traffic lights.

[0049] Step 2:

[0050] The server inputs the collected traffic data into an AI algorithm for analysis. This analysis evaluates the current traffic situation and identifies areas where congestion is expected.

[0051] Step 3:

[0052] The server calculates the optimal detour route for the user based on identified congested areas. This calculation utilizes real-time road conditions and traffic forecasts.

[0053] Step 4:

[0054] The terminal provides the user with route information received from the server. Users can then efficiently travel by following the suggested route via their smartphone or navigation system.

[0055] Step 5:

[0056] The terminals are installed in trash cans in homes and buildings, and sensors monitor the type and amount of waste. This data is transmitted to a server at regular intervals.

[0057] Step 6:

[0058] The server analyzes the received waste data and generates an efficient collection route. This calculation takes into account factors such as when the waste container is full and the availability of recyclable resources.

[0059] Step 7:

[0060] The terminal provides the collection team's devices with the optimal waste collection route obtained from the server. Users can then follow these instructions and perform their tasks efficiently.

[0061] (Example 1)

[0062] 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."

[0063] Improving the efficiency of traffic and waste management in urban environments is a challenge. Conventional systems lack the accuracy and speed to predict traffic congestion and effective waste collection routes, resulting in insufficient convenience for residents and inadequate environmental protection. To address this, real-time data analysis and efficient route generation are necessary.

[0064] 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.

[0065] In this invention, the server includes means for collecting transportation data from sensor devices of a communication system and storing it in a database; means for analyzing the collected transportation data with an artificial intelligence program and predicting traffic congestion; and means for deriving an optimal travel route based on the prediction results and displaying the route information on a mobile terminal. This enables real-time optimization of traffic and efficient waste collection.

[0066] A "communication system" refers to the technological infrastructure for transmitting and receiving data, and includes the function of transmitting information between sensor devices and mobile terminals.

[0067] A "sensor device" refers to a device that can detect various physical or state changes, convert them into digital data, and transmit them to a communication system.

[0068] "Transportation data" refers to a dataset that includes information about traffic flow, vehicle speed, intersection congestion, and so on.

[0069] A "database" refers to a system that organizes and stores collected information, making it readily accessible when needed.

[0070] An "artificial intelligence program" is a computer program used for data analysis and prediction, and is particularly aimed at identifying patterns and trends using machine learning techniques.

[0071] "Traffic congestion" refers to a condition where vehicles cannot move at their normal speed on a road.

[0072] A "travel route" refers to the optimized route for travel from a starting point to a destination.

[0073] A "mobile terminal" is a device primarily used to display information usable while on the move, and it has the function of connecting to a communication system to receive predicted routes and instructions.

[0074] "Waste container" refers to equipment or devices used to temporarily store waste for collection and processing.

[0075] "Waste data" refers to a dataset containing measurement information about the quantity and type of waste.

[0076] "Collection equipment" refers to transport devices and machinery used to efficiently collect waste.

[0077] "Reusable resources" refer to materials and substances within waste that can be reprocessed and reused.

[0078] This invention provides a system in which servers, terminals, and users cooperate to efficiently manage urban traffic and waste. This system collects transportation and waste data from sensor devices via a communication system installed in the city and utilizes an artificial intelligence program to process this data.

[0079] In traffic management, the server stores transportation data collected from sensor devices in a database in real time and analyzes it using artificial intelligence programs. Specifically, AI frameworks such as TENSORFLOW® and PyTorch are used to run models that predict traffic congestion. Furthermore, the optimal travel route is generated based on the analyzed data and displayed on a mobile terminal. This mobile terminal functions as the user's smartphone or in-vehicle device.

[0080] Meanwhile, in waste management, the terminal acquires waste data in real time from sensor devices attached to waste containers. This data is sent to a server, where an AI algorithm is used to create the optimal waste collection route. The server also automatically sorts reusable resources and outputs route information to the collection equipment.

[0081] For example, in traffic optimization, users can check and use the optimal route suggested by the server on their mobile device before departure. In waste management, drivers of collection equipment can operate according to efficient collection routes transmitted from the server.

[0082] Examples of prompts include, "Suggest the optimal commute route based on current traffic data," and "Calculate the most efficient collection route based on the latest waste data for the specified area." This allows the system to improve the efficiency of urban traffic and waste management.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects transportation data from sensor devices installed throughout the city. Specifically, it stores real-time information such as vehicle flow, speed, and congestion levels in a database. The input is digital signals from the sensors, and the output is the stored database entries.

[0086] Step 2:

[0087] The server analyzes the accumulated transportation data using an artificial intelligence program. Specifically, it uses TensorFlow or PyTorch to analyze the data and build a traffic congestion prediction model. The input is the data collected in the previous step, and the output is the predicted congestion information.

[0088] Step 3:

[0089] The server generates the optimal travel route based on the analysis results. Here, route calculation is performed by combining the analyzed traffic congestion information and geographical information. The input is predicted traffic congestion information and geographical information, and the output is optimized route information. Specifically, the route is generated using a map API.

[0090] Step 4:

[0091] The user receives route information sent from the server on their mobile device. Specifically, the route information is displayed on the mobile device (smartphone or in-vehicle device), and the user selects a route based on that information. The input is optimized route information, and the output is the actual travel route selected by the user.

[0092] Step 5:

[0093] The terminal collects waste data from sensor devices attached to waste containers. It measures the quantity and type of waste using sensors and records this data. The input is the physical measurement from the sensors, and the output is digital data transmitted to the server.

[0094] Step 6:

[0095] The server analyzes waste data transmitted from terminals and calculates efficient waste collection routes. Specifically, it processes the collected data using linear programming and heuristic algorithms. The input is waste data, and the output is collection route information.

[0096] Step 7:

[0097] Users of the collection equipment receive collection routes provided by the server and use them in their actual work. Specifically, they import the route into the navigation system, and the driver efficiently collects waste by following visual instructions. The input is collection route information, and the output is the actual waste collection work.

[0098] (Application Example 1)

[0099] 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."

[0100] Traffic congestion and inefficient waste management in cities are factors that reduce the quality of life for residents. To solve this problem, it is necessary to integrate traffic and waste management data to achieve more efficient route optimization. However, current systems often manage these aspects individually, and integrated efficiency improvements are not sufficiently achieved. Therefore, there is a need for technology that enables comprehensive urban management using autonomous vehicles.

[0101] 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.

[0102] In this invention, the server includes means for collecting traffic data from various sensors in the communication infrastructure, means for analyzing the collected traffic data and predicting congestion, and means for integrating traffic data and waste management data to adjust routes in real time in order to optimize the operation of autonomous vehicles. This makes it possible to provide a system that can efficiently manage both traffic and waste collection in an urban environment.

[0103] "Various sensors for communication infrastructure" is a general term for multiple types of measuring devices installed in urban environments to collect traffic data and waste management data.

[0104] "Traffic data" refers to information about road conditions within a city, such as the flow of vehicles, their speed, and the level of congestion at intersections.

[0105] A "machine learning algorithm" is a type of artificial intelligence technology used to analyze collected data and predict traffic flow and congestion.

[0106] "Means of collecting waste data from waste containers" refers to devices and methods for measuring the quantity and type of waste and transmitting the information to a server.

[0107] "Means of adjusting routes" refers to methods for optimizing the operating routes of autonomous vehicles based on real-time data to ensure safe and efficient travel.

[0108] "Means for automatically sorting reusable resources" refers to technologies that automatically identify collected waste and sort out reusable resources.

[0109] This invention aims to construct a system for efficiently managing traffic and waste in urban environments. The core of the system is a server that collects traffic and waste data from various sensors connected to the communication infrastructure.

[0110] The server uses machine learning algorithms to predict traffic flow using traffic data. This involves utilizing data analysis platforms such as TensorFlow to perform data clustering and generate predictive models. From the collected data, it automatically calculates the optimal detour route to avoid congestion and transmits this information to the onboard computers and scheduling systems of autonomous vehicles.

[0111] Furthermore, in managing waste data, sensors attached to waste containers measure the volume and type of waste and send this information to a server. Based on the collected data, the server automatically generates routes for sorting recyclable resources for garbage collection vehicles. This improves the efficiency of waste collection. In particular, by efficiently collecting waste while avoiding traffic congestion in urban areas, it enables waste-free operation.

[0112] As a concrete example, during the morning commute, a server analyzes traffic data and notifies the in-vehicle device if the usual route is congested. This allows the autonomous vehicle to avoid congestion and use an alternative route to reach its destination. Additionally, based on the weekend garbage collection schedule, data from garbage containers in specific areas is evaluated in real time, and the collection route is automatically adjusted.

[0113] An example of a prompt message is, "Based on current traffic data, please provide the optimal route from this point to the destination." This prompt works in conjunction with a generative AI model to always suggest a feasible route based on the latest traffic conditions.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server collects traffic and waste data from various sensors in the communication infrastructure. This includes vehicle counts and speeds from cameras and LiDAR sensors placed on roads, and waste volume information from waste container sensors. The data is transmitted to the server in real time and stored in a database.

[0117] Step 2:

[0118] The server analyzes collected traffic data and uses a generative AI model to predict congestion. Historical traffic data and weather information are used as input, and the output shows the expected level of congestion at a specific location. Specifically, TensorFlow is used to train a deep learning model, and the predictions are updated in real time.

[0119] Step 3:

[0120] The server calculates the optimal travel route based on congestion prediction results. It takes into account the rapidly changing traffic conditions to select a route that allows the user to reach their destination safely and quickly. The input is congestion prediction data from the server, and the output is route information. The operation includes route searching using the Dijkstra algorithm.

[0121] Step 4:

[0122] The terminal, specifically the in-vehicle computer, applies the route information received from the server to the driving system. The route may be displayed on the in-vehicle display as the vehicle continuously adjusts its optimal route and continues driving. The input here is the route information received from the server, and the output is the vehicle's operation control.

[0123] Step 5:

[0124] The server generates efficient waste collection routes based on waste data. It automatically adjusts the optimal collection route based on the quantity and type of waste. Input data consists of collection information from waste sensors, and output is the operation schedule for collection vehicles. This information can be used by the waste collection team.

[0125] Step 6:

[0126] The server utilizes a generative AI model to create prompts that integrate traffic data and waste management data. These prompts propose real-time solutions based on traffic conditions through prompt statements. For example, it might generate a prompt such as, "Based on current traffic data, please provide the optimal route from this point to the destination," and then provide information based on that prompt.

[0127] 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.

[0128] This invention is a system that highly streamlines traffic management and waste management using communication infrastructure, artificial intelligence (AI), and an emotion engine. Furthermore, this system customizes services based on the user's emotional state, providing a more personalized user experience.

[0129] Traffic management system

[0130] The server collects traffic data in real time from multiple sensors and cameras within the city. Using AI, it analyzes this data to predict traffic flow and congestion. Based on the predicted data, it generates the optimal route and proposes it to the user. Furthermore, an emotion engine installed on the user's device analyzes the user's voice, facial expressions, and other biometric data to identify their emotional state. For example, if the server determines that the user is feeling stressed, it will provide customized route suggestions tailored to that situation, such as prioritizing scenic routes.

[0131] For example, if a user is on their way home and the emotion engine determines that the user is tired, the server will suggest a more relaxing route than their usual commute. For instance, it might choose a route that goes through a park or a road with less traffic.

[0132] Waste management system

[0133] The terminals are installed in garbage containers in homes and buildings, and sensors measure the type and amount of waste. This data is sent to a server, where AI calculates the most efficient collection route. Furthermore, an emotion engine can be used to suggest collection routes that take into account the user's emotions within the garbage collection team. For example, during busy periods, an efficiency-focused route can be suggested, while routes that consider the environment and health can be selected when there is more time available.

[0134] This invention makes urban life more convenient and comfortable by providing services tailored to the individual circumstances and feelings of users in both transportation and waste management.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The server collects real-time traffic data from sensors and cameras installed throughout the city. This data includes vehicle speed, number, and traffic light status.

[0138] Step 2:

[0139] The server feeds the collected traffic data into an AI algorithm to analyze traffic flow. Based on this analysis, congestion is predicted and the optimal detour route is calculated.

[0140] Step 3:

[0141] The device uses an emotion engine to analyze the user's voice, facial expressions, and other biometric information, and evaluates the user's emotional state in real time.

[0142] Step 4:

[0143] Based on information obtained from the emotion engine, the server creates route suggestions tailored to the user's emotional state. For example, if the user is highly stressed, it will suggest a safe and peaceful route.

[0144] Step 5:

[0145] The user's device displays a route suggested by the server, and the user can travel based on that route. The user can choose whether or not to accept the suggested route.

[0146] Step 6:

[0147] The terminal acquires the type and amount of waste in real time through sensors installed in the waste container. This data is periodically transmitted to the server.

[0148] Step 7:

[0149] The server uses AI to analyze the received waste data and generate efficient and emotionally considerate waste collection routes. The optimal route is suggested, taking into account the emotional state of the collection team.

[0150] Step 8:

[0151] The garbage collection team, acting as users, collects waste according to route information provided by the terminal. The server can update instructions in real time as the situation changes.

[0152] (Example 2)

[0153] 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".

[0154] In urban life, traffic congestion and efficient waste management are challenges, and there is a demand for services that cater to the individual emotional states of users. Currently, there is no system in place to comprehensively manage and coordinate these elements, making it difficult to realize optimal routes and waste disposal routes that take into account individual circumstances and emotions.

[0155] 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.

[0156] In this invention, the server includes means for collecting movement information from various detection devices of the communication infrastructure, means for analyzing the collected movement information and predicting traffic congestion, and means for identifying the emotional state of the user and adjusting the travel route and waste collection route based on the emotional state. This makes it possible to provide an optimal travel route that takes the user's emotions into consideration and to improve the efficiency of waste disposal.

[0157] A "communication infrastructure" is a technical foundation that enables the transmission and reception of data, and it plays a role in connecting various detection devices with a central data processing unit.

[0158] A "detection device" is a device that senses information from the outside world and transmits it as data to a communication infrastructure, and includes sensors and cameras.

[0159] "Mobility information" refers to data related to moving objects, such as traffic flow, vehicle location, and speed.

[0160] "Analysis" refers to the process of processing and analyzing collected data to transform it into meaningful information.

[0161] "Traffic congestion" refers to a situation where vehicles or people are overcrowded in a particular area or time period.

[0162] A "travel route" refers to the path a moving object takes to reach its destination, and its purpose is to be optimized.

[0163] "Waste information" refers to data that indicates the type and quantity of waste, and is fundamental information for waste management based on that data.

[0164] "Data collection equipment" refers to devices that receive data and perform instructed tasks, and includes waste disposal equipment.

[0165] "Emotional state" refers to information that indicates the user's psychological or physiological state, and is used to identify and adjust services accordingly.

[0166] This invention provides a system for streamlining traffic management and waste management, enabling integrated services that take into account the emotional state of servers, terminals, and users.

[0167] Traffic management system

[0168] The server collects movement information from various detection devices installed throughout the city via the communication infrastructure. The hardware used includes various sensors and cameras, and the data collected from these is processed in real time. Specifically, software such as "OpenCV" and "TensorFlow" is used to analyze the data and predict traffic flow and congestion. Based on the analysis results, the server calculates the optimal route and presents the route information to the user's mobile device using "Google® Maps API" and other tools.

[0169] The user's device is equipped with an emotion engine that analyzes biometric data through speech recognition and facial expression analysis technologies. Specific examples include the use of technologies such as "AWS® Rekognition" and "Microsoft® Azure® Emotion Recognition." If the emotion engine detects that the user is experiencing stress, the server will suggest scenic routes or less congested roads to improve the user's travel experience.

[0170] As a concrete example, here is an example of a prompt: "AI model, please generate a sentence suggesting a recommended travel route for when the user is relaxed. Also, please explain the advantages of that route."

[0171] Waste management system

[0172] The terminal is connected to the waste container and measures waste information using sensors. This information is transmitted to a server using the communication protocol "MQTT". The server analyzes the received waste information using an AI algorithm to calculate the most efficient collection route. Software such as "OpenAI®" and "SciKit-learn" are used in this process.

[0173] Furthermore, by using an emotion engine installed in the waste collection team's terminals to analyze the emotional state of the members, the optimality of the route can be improved. Based on the emotion analysis results, the server can, for example, select a route that allows members to enjoy natural scenery during their free time, thus balancing efficiency and comfort.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The server collects movement information from various detection devices via the communication infrastructure. Inputs include data such as traffic flow, vehicle position, and speed, while output is a real-time movement information dataset. The server processes this data using OpenCV and TensorFlow to perform initial data cleaning. Specifically, this involves noise reduction and data interpolation.

[0177] Step 2:

[0178] The server analyzes collected travel information to predict traffic congestion. The input is the dataset from Step 1, and the output is predicted traffic congestion data. The server uses TensorFlow or PyTorch to execute learning algorithms and build a prediction model using a neural network. This model is then used to perform specific actions such as analyzing traffic congestion by time of day and region.

[0179] Step 3:

[0180] The server calculates the optimal route based on the prediction results. The input is the prediction data from step 2, and the output is the optimal route information presented to the user. The server uses the "Google Maps API" to calculate the optimal route from the user's current location to the destination and sends that route information to the user's device. Specifically, it provides time-based route information and alternative routes at each intersection.

[0181] Step 4:

[0182] An emotion engine installed in the user's device analyzes biometric data such as the user's voice and facial expressions to identify their emotional state. The input is the user's voice and facial expression data, and the output is metrics indicating stress levels and emotional state. The device utilizes "AWS Rekognition" and "Microsoft Azure Emotion Recognition" to evaluate the user's emotional state based on biometric information. Specifically, it checks whether the user is smiling and whether their heart rate is normal.

[0183] Step 5:

[0184] The server adjusts the route based on the emotion analysis results to provide the user with the best possible service. The input is the route information from step 3 and the emotion state data from step 4, and the output is the optimized route information. The server takes the emotion data into consideration and prioritizes routes that are relaxing or have good scenery, and generates a new route. Specifically, it adjusts the route priority order and sends a customized suggestion to the user's terminal.

[0185] Step 6:

[0186] The terminal collects waste information from waste containers and transmits the data to a server. The input is data indicating the type and volume of waste, and the output is a data package sent to the server. The terminal uses an "Ultrasonic Sensor" and a "Weight Sensor" to measure the amount of waste in the container. Specifically, it analyzes the signals from the sensors and transmits them to the server as digital data.

[0187] Step 7:

[0188] The server analyzes waste information and calculates efficient collection routes. The input is waste data from step 6, and the output is information on the optimal waste collection route. The server uses "OpenAI" and "SciKit-learn" to analyze the received data and optimize collection routes according to the amount and type of waste. Specifically, it determines which route the collection vehicle should take and transmits that information to the collection device.

[0189] Step 8:

[0190] The server analyzes the emotional state of the garbage collection team and adjusts the collection route accordingly. The input is emotional data from the team members, and the output is collection route information adjusted based on their emotional state. The server analyzes the stress levels and fatigue of the collection team and changes the route as needed. Software used for this includes the "Emotion API." Specific actions include changing to less stressful routes and inserting rest points.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] Traffic congestion and waste management are serious problems in modern cities, resulting in wasted time and resources. Furthermore, these management systems typically provide generic routes without considering the emotional state of users, and are not always optimal for individual users. Therefore, the objective of this invention is to improve the quality of urban life by streamlining traffic and waste management and providing personalized services that take into account the emotional state of users.

[0194] 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.

[0195] In this invention, the server includes means for collecting traffic data from various sensors in the communication infrastructure, means for analyzing the collected traffic data and predicting congestion, and means for analyzing the user's emotional state using an emotion detection device mounted on a mobile vehicle. This makes it possible to customize and present the optimal route based on the user's emotions.

[0196] "Communication infrastructure" refers to a network structure that enables the transmission and reception of data, and serves as a foundation for exchanging information with sensors and mobile devices.

[0197] A "sensor" is a device that detects external physical quantities, converts them into electrical signals, and provides them as information.

[0198] "Traffic data" refers to information about the flow, speed, and location of vehicles on roads.

[0199] "Analysis" is the process by which a system interprets information and finds meaning based on collected data.

[0200] "Mobile entities" refer to vehicles and devices used as means of transportation.

[0201] An "emotion detection device" is a technology that determines a user's emotional state based on their voice, facial expressions, and other biosignals.

[0202] "Waste data" refers to information that indicates the type and quantity of waste contained in a waste container.

[0203] An "efficient waste collection route" refers to a collection path designed to produce the greatest effect with the fewest resources.

[0204] "Recyclable resources" are waste materials that can be reused and are intended for recycling in order to reduce environmental impact.

[0205] This invention is a system that utilizes sensors, communication infrastructure, AI, and an emotion engine to improve the efficiency of traffic management and waste management.

[0206] First, the server collects traffic data from various sensors and cameras installed throughout the city. By utilizing communication infrastructure and database systems, this data can be acquired in real time. The collected data is processed by an AI module to analyze traffic flow and predict future congestion. For this purpose, machine learning platforms such as Google Cloud AI and TensorFlow are suitable. This allows the server to calculate the optimal route based on the predictions and present it to the user's mobile device. Furthermore, it's possible to analyze the user's emotional state using emotion detection devices and suggest routes for sightseeing or relaxing paths.

[0207] In waste management, terminals collect data on the type and quantity of waste via various sensors installed in homes and buildings. This data is transmitted to a server, where AI is used to calculate the optimal waste collection route. Real-time data processing technology is utilized in this calculation. The generated route is presented to the collection equipment, facilitating the automatic sorting of recyclable resources.

[0208] As a concrete example, consider a user driving a self-driving car. If the emotion engine analyzes that the user is in a happy mood, the server will present the user with a prompt such as, "Based on the information gathered by the AI, today is a perfect day for sightseeing! We will guide you to some enjoyable tourist spots."

[0209] Such systems enable optimization tailored to individual circumstances and emotions, resulting in efficient and personalized traffic and waste management.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server collects traffic data through sensors and cameras within the city. Real-time data collection takes place here. Input data includes traffic density, vehicle speed, and location information, which is transmitted to the server via the communication infrastructure. The server stores this data in storage in an appropriate format.

[0213] Step 2:

[0214] The server inputs the collected traffic data into an AI module to analyze traffic flow. This analysis process utilizes machine learning libraries such as TensorFlow. The AI ​​then uses the collected data patterns to predict future congestion. The output of this step is predictive data showing future traffic conditions.

[0215] Step 3:

[0216] The server calculates the optimal route based on the predicted data. The route calculation algorithm evaluates multiple paths and their conditions to determine the shortest or most comfortable route. The output is the proposed optimal route, which is sent to the user's device.

[0217] Step 4:

[0218] An emotion detection device installed in the user's terminal analyzes the user's biometric data (such as facial expressions and voice) to identify their emotional state. This analysis result is transmitted to a server. The input data is biometric data, and the output is an estimated emotion.

[0219] Step 5:

[0220] The server customizes the route based on the estimated emotions. For example, if the server determines that the user wants to relax, it will choose a route with good scenery. The output is the customized route, which is also suggested to the user.

[0221] Step 6:

[0222] In waste management, terminals collect waste data through sensors. This data includes information about the type and quantity of waste. The input data from the sensors is sent to a server and processed to optimize collection routes.

[0223] Step 7:

[0224] The server processes the collected waste data using AI to generate efficient collection routes. It calculates the optimal route while updating the data in real time. The output is the efficient waste collection route, which is displayed on the collection device.

[0225] Step 8:

[0226] If a user requests a sightseeing drive, the system will suggest tourist attractions via prompt messages. These prompt messages may include phrases such as, "It's a perfect day for sightseeing! We'll guide you to some enjoyable tourist spots." The output is a prompt message generated using a text-based AI model.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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".

[0243] This invention is a system that uses communication infrastructure and artificial intelligence (AI) technology to streamline traffic management and waste management in urban environments.

[0244] Traffic management system

[0245] The server first collects traffic data over time from various sensors and cameras installed throughout the city. This includes vehicle flow, speed, and intersection congestion. The server analyzes this data using AI algorithms to predict traffic congestion. For example, the server predicts congestion on a specific road for the next hour and generates the optimal detour route based on that information. This allows users to receive quick and efficient routes via their smartphones or in-car devices.

[0246] As a concrete example, when a user departs for their morning commute, the server analyzes traffic data at that time and sends an alternative route to the user's device if the usual route is congested. By accepting this suggestion, the user can shorten their commute time.

[0247] Waste management system

[0248] The terminal measures the volume and type of waste in real time using sensors attached to waste containers installed in each home and building. The measured data is sent to a server, where the server's AI calculates the optimal waste collection route based on the collected information. It also has a function to automatically sort recyclable resources.

[0249] For example, a terminal identifies locations in a specific area where garbage collection is needed, and the server generates an efficient route that includes those locations and sends it to the collection truck. This allows the collection team, as the user, to carry out their work without waste.

[0250] This invention will improve the quality of life for residents by streamlining both urban traffic and waste management.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server collects real-time traffic data through sensors and cameras placed throughout the city. This data includes information such as the number of vehicles on the road, their speed, and the status of traffic lights.

[0254] Step 2:

[0255] The server inputs the collected traffic data into an AI algorithm for analysis. This analysis evaluates the current traffic situation and identifies areas where congestion is expected.

[0256] Step 3:

[0257] The server calculates the optimal detour route for the user based on identified congested areas. This calculation utilizes real-time road conditions and traffic forecasts.

[0258] Step 4:

[0259] The terminal provides the user with route information received from the server. Users can then efficiently travel by following the suggested route via their smartphone or navigation system.

[0260] Step 5:

[0261] The terminals are installed in trash cans in homes and buildings, and sensors monitor the type and amount of waste. This data is transmitted to a server at regular intervals.

[0262] Step 6:

[0263] The server analyzes the received waste data and generates an efficient collection route. This calculation takes into account factors such as when the waste container is full and the availability of recyclable resources.

[0264] Step 7:

[0265] The terminal provides the collection team's devices with the optimal waste collection route obtained from the server. Users can then follow these instructions and perform their tasks efficiently.

[0266] (Example 1)

[0267] 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."

[0268] Improving the efficiency of traffic and waste management in urban environments is a challenge. Conventional systems lack the accuracy and speed to predict traffic congestion and effective waste collection routes, resulting in insufficient convenience for residents and inadequate environmental protection. To address this, real-time data analysis and efficient route generation are necessary.

[0269] 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.

[0270] In this invention, the server includes means for collecting transportation data from sensor devices of a communication system and storing it in a database; means for analyzing the collected transportation data with an artificial intelligence program and predicting traffic congestion; and means for deriving an optimal travel route based on the prediction results and displaying the route information on a mobile terminal. This enables real-time optimization of traffic and efficient waste collection.

[0271] A "communication system" refers to the technological infrastructure for transmitting and receiving data, and includes the function of transmitting information between sensor devices and mobile terminals.

[0272] A "sensor device" refers to a device that can detect various physical or state changes, convert them into digital data, and transmit them to a communication system.

[0273] "Transportation data" refers to a dataset that includes information about traffic flow, vehicle speed, intersection congestion, and so on.

[0274] A "database" refers to a system that organizes and stores collected information, making it readily accessible when needed.

[0275] An "artificial intelligence program" is a computer program used for data analysis and prediction, and is particularly aimed at identifying patterns and trends using machine learning techniques.

[0276] "Traffic congestion" refers to a condition where vehicles cannot move at their normal speed on a road.

[0277] A "travel route" refers to the optimized route for travel from a starting point to a destination.

[0278] A "mobile terminal" is a device primarily used to display information usable while on the move, and it has the function of connecting to a communication system to receive predicted routes and instructions.

[0279] "Waste container" refers to equipment or devices used to temporarily store waste for collection and processing.

[0280] "Waste data" refers to a dataset containing measurement information about the quantity and type of waste.

[0281] "Collection equipment" refers to transport devices and machinery used to efficiently collect waste.

[0282] "Reusable resources" refer to materials and substances within waste that can be reprocessed and reused.

[0283] This invention provides a system in which servers, terminals, and users cooperate to efficiently manage urban traffic and waste. This system collects transportation and waste data from sensor devices via a communication system installed in the city and utilizes an artificial intelligence program to process this data.

[0284] In traffic management, the server accumulates the transportation data collected from the sensor devices in the database in real time and analyzes it using an artificial intelligence program. Specifically, AI frameworks such as TensorFlow and PyTorch are utilized, and a model for predicting traffic congestion is executed. Furthermore, an optimal travel route is generated based on the analyzed data and displayed on the mobile terminal. This mobile terminal functions as the user's smartphone or in-vehicle device.

[0285] On the other hand, in waste management, the terminal obtains waste data in real time from the sensor devices attached to the waste containers. This data is sent to the server, and an optimal waste collection route is created using AI algorithms. Also, the server automatically sorts reusable resources and outputs route information to the collection equipment.

[0286] As a specific example, in traffic optimization, the user can check and use the optimal route proposed by the server on the mobile terminal before departure. Also, in waste management, the driver of the collection equipment can drive according to the efficient collection route sent from the server.

[0287] Examples of prompt sentences include "Please propose an optimal commuting route based on the current traffic data." and "Please calculate the most efficient collection route based on the latest garbage data in the specified area." Thus, the system can achieve the efficiency improvement of urban traffic and waste management.

[0288] The flow of the specific process in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The server collects transportation data from sensor devices installed throughout the city. Specifically, it stores real-time information such as vehicle flow, speed, and congestion levels in a database. The input is digital signals from the sensors, and the output is the stored database entries.

[0291] Step 2:

[0292] The server analyzes the accumulated transportation data using an artificial intelligence program. Specifically, it uses TensorFlow or PyTorch to analyze the data and build a traffic congestion prediction model. The input is the data collected in the previous step, and the output is the predicted congestion information.

[0293] Step 3:

[0294] The server generates the optimal travel route based on the analysis results. Here, route calculation is performed by combining the analyzed traffic congestion information and geographical information. The input is predicted traffic congestion information and geographical information, and the output is optimized route information. Specifically, the route is generated using a map API.

[0295] Step 4:

[0296] The user receives route information sent from the server on their mobile device. Specifically, the route information is displayed on the mobile device (smartphone or in-vehicle device), and the user selects a route based on that information. The input is optimized route information, and the output is the actual travel route selected by the user.

[0297] Step 5:

[0298] The terminal collects waste data from sensor devices attached to waste containers. It measures the quantity and type of waste using sensors and records this data. The input is the physical measurement from the sensors, and the output is digital data transmitted to the server.

[0299] Step 6:

[0300] The server analyzes waste data transmitted from terminals and calculates efficient waste collection routes. Specifically, it processes the collected data using linear programming and heuristic algorithms. The input is waste data, and the output is collection route information.

[0301] Step 7:

[0302] Users of the collection equipment receive collection routes provided by the server and use them in their actual work. Specifically, they import the route into the navigation system, and the driver efficiently collects waste by following visual instructions. The input is collection route information, and the output is the actual waste collection work.

[0303] (Application Example 1)

[0304] 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."

[0305] Traffic congestion and inefficient waste management in cities are factors that reduce the quality of life for residents. To solve this problem, it is necessary to integrate traffic and waste management data to achieve more efficient route optimization. However, current systems often manage these aspects individually, and integrated efficiency improvements are not sufficiently achieved. Therefore, there is a need for technology that enables comprehensive urban management using autonomous vehicles.

[0306] 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.

[0307] In this invention, the server includes means for collecting traffic data from various sensors of the communication infrastructure, means for analyzing the collected traffic data and predicting congestion, and means for integrating traffic data and waste management data to adjust the route in real time in order to optimize the operation of autonomous vehicles. This makes it possible to provide a system that can efficiently manage both traffic and waste collection in an urban environment.

[0308] The "various sensors of the communication infrastructure" is a general term for a plurality of types of measuring devices installed to collect traffic data and waste management data in an urban environment.

[0309] "Traffic data" refers to information regarding road conditions within a city, such as vehicle flow, speed, and the congestion level at intersections.

[0310] The "machine learning algorithm" is a type of artificial intelligence technology used to analyze the collected data and predict traffic flow and congestion.

[0311] The "means for collecting waste data from waste containers" refers to devices or methods for measuring the quantity and type of waste and transmitting information to the server.

[0312] The "means for adjusting the route" is a method for optimizing the operating route of an autonomous vehicle based on real-time data to achieve safe and efficient movement.

[0313] The "means for automatically sorting reusable resources" is a technology for automatically identifying the collected waste and selecting reusable resources.

[0314] In this invention, a system for efficiently performing traffic and waste management in an urban environment is constructed. The core of the system is the server, which collects traffic data and waste data from various sensors connected to the communication infrastructure.

[0315] The server uses machine learning algorithms to predict traffic flow using traffic data. This involves utilizing data analysis platforms such as TensorFlow to perform data clustering and generate predictive models. From the collected data, it automatically calculates the optimal detour route to avoid congestion and transmits this information to the onboard computers and scheduling systems of autonomous vehicles.

[0316] Furthermore, in managing waste data, sensors attached to waste containers measure the volume and type of waste and send this information to a server. Based on the collected data, the server automatically generates routes for sorting recyclable resources for garbage collection vehicles. This improves the efficiency of waste collection. In particular, by efficiently collecting waste while avoiding traffic congestion in urban areas, it enables waste-free operation.

[0317] As a concrete example, during the morning commute, a server analyzes traffic data and notifies the in-vehicle device if the usual route is congested. This allows the autonomous vehicle to avoid congestion and use an alternative route to reach its destination. Additionally, based on the weekend garbage collection schedule, data from garbage containers in specific areas is evaluated in real time, and the collection route is automatically adjusted.

[0318] An example of a prompt message is, "Based on current traffic data, please provide the optimal route from this point to the destination." This prompt works in conjunction with a generative AI model to always suggest a feasible route based on the latest traffic conditions.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The server collects traffic and waste data from various sensors in the communication infrastructure. This includes vehicle counts and speeds from cameras and LiDAR sensors placed on roads, and waste volume information from waste container sensors. The data is transmitted to the server in real time and stored in a database.

[0322] Step 2:

[0323] The server analyzes collected traffic data and uses a generative AI model to predict congestion. Historical traffic data and weather information are used as input, and the output shows the expected level of congestion at a specific location. Specifically, TensorFlow is used to train a deep learning model, and the predictions are updated in real time.

[0324] Step 3:

[0325] The server calculates the optimal travel route based on congestion prediction results. It takes into account the rapidly changing traffic conditions to select a route that allows the user to reach their destination safely and quickly. The input is congestion prediction data from the server, and the output is route information. The operation includes route searching using the Dijkstra algorithm.

[0326] Step 4:

[0327] The terminal, specifically the in-vehicle computer, applies the route information received from the server to the driving system. The route may be displayed on the in-vehicle display as the vehicle continuously adjusts its optimal route and continues driving. The input here is the route information received from the server, and the output is the vehicle's operation control.

[0328] Step 5:

[0329] The server generates efficient waste collection routes based on waste data. It automatically adjusts the optimal collection route based on the quantity and type of waste. Input data consists of collection information from waste sensors, and output is the operation schedule for collection vehicles. This information can be used by the waste collection team.

[0330] Step 6:

[0331] The server utilizes a generative AI model to create prompts that integrate traffic data and waste management data. These prompts propose real-time solutions based on traffic conditions through prompt statements. For example, it might generate a prompt such as, "Based on current traffic data, please provide the optimal route from this point to the destination," and then provide information based on that prompt.

[0332] 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.

[0333] This invention is a system that highly streamlines traffic management and waste management using communication infrastructure, artificial intelligence (AI), and an emotion engine. Furthermore, this system customizes services based on the user's emotional state, providing a more personalized user experience.

[0334] Traffic management system

[0335] The server collects traffic data in real time from multiple sensors and cameras within the city. Using AI, it analyzes this data to predict traffic flow and congestion. Based on the predicted data, it generates the optimal route and proposes it to the user. Furthermore, an emotion engine installed on the user's device analyzes the user's voice, facial expressions, and other biometric data to identify their emotional state. For example, if the server determines that the user is feeling stressed, it will provide customized route suggestions tailored to that situation, such as prioritizing scenic routes.

[0336] For example, if a user is on their way home and the emotion engine determines that the user is tired, the server will suggest a more relaxing route than their usual commute. For instance, it might choose a route that goes through a park or a road with less traffic.

[0337] Waste management system

[0338] The terminals are installed in garbage containers in homes and buildings, and sensors measure the type and amount of waste. This data is sent to a server, where AI calculates the most efficient collection route. Furthermore, an emotion engine can be used to suggest collection routes that take into account the user's emotions within the garbage collection team. For example, during busy periods, an efficiency-focused route can be suggested, while routes that consider the environment and health can be selected when there is more time available.

[0339] This invention makes urban life more convenient and comfortable by providing services tailored to the individual circumstances and feelings of users in both transportation and waste management.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The server collects real-time traffic data from sensors and cameras installed throughout the city. This data includes vehicle speed, number, and traffic light status.

[0343] Step 2:

[0344] The server feeds the collected traffic data into an AI algorithm to analyze traffic flow. Based on this analysis, congestion is predicted and the optimal detour route is calculated.

[0345] Step 3:

[0346] The device uses an emotion engine to analyze the user's voice, facial expressions, and other biometric information, and evaluates the user's emotional state in real time.

[0347] Step 4:

[0348] Based on information obtained from the emotion engine, the server creates route suggestions tailored to the user's emotional state. For example, if the user is highly stressed, it will suggest a safe and peaceful route.

[0349] Step 5:

[0350] The user's device displays a route suggested by the server, and the user can travel based on that route. The user can choose whether or not to accept the suggested route.

[0351] Step 6:

[0352] The terminal acquires the type and amount of waste in real time through sensors installed in the waste container. This data is periodically transmitted to the server.

[0353] Step 7:

[0354] The server uses AI to analyze the received waste data and generate efficient and emotionally considerate waste collection routes. The optimal route is suggested, taking into account the emotional state of the collection team.

[0355] Step 8:

[0356] The garbage collection team, acting as users, collects waste according to route information provided by the terminal. The server can update instructions in real time as the situation changes.

[0357] (Example 2)

[0358] 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".

[0359] In urban life, traffic congestion and efficient waste management are challenges, and there is a demand for services that cater to the individual emotional states of users. Currently, there is no system in place to comprehensively manage and coordinate these elements, making it difficult to realize optimal routes and waste disposal routes that take into account individual circumstances and emotions.

[0360] 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.

[0361] In this invention, the server includes means for collecting movement information from various detection devices of the communication infrastructure, means for analyzing the collected movement information and predicting traffic congestion, and means for identifying the emotional state of the user and adjusting the travel route and waste collection route based on the emotional state. This makes it possible to provide an optimal travel route that takes the user's emotions into consideration and to improve the efficiency of waste disposal.

[0362] A "communication infrastructure" is a technical foundation that enables the transmission and reception of data, and it plays a role in connecting various detection devices with a central data processing unit.

[0363] A "detection device" is a device that senses information from the outside world and transmits it as data to a communication infrastructure, and includes sensors and cameras.

[0364] "Mobility information" refers to data related to moving objects, such as traffic flow, vehicle location, and speed.

[0365] "Analysis" refers to the process of processing and analyzing collected data to transform it into meaningful information.

[0366] "Traffic congestion" refers to a situation where vehicles or people are overcrowded in a particular area or time period.

[0367] A "travel route" refers to the path a moving object takes to reach its destination, and its purpose is to be optimized.

[0368] "Waste information" refers to data that indicates the type and quantity of waste, and is fundamental information for waste management based on that data.

[0369] "Data collection equipment" refers to devices that receive data and perform instructed tasks, and includes waste disposal equipment.

[0370] "Emotional state" refers to information that indicates the user's psychological or physiological state, and is used to identify and adjust services accordingly.

[0371] This invention provides a system for streamlining traffic management and waste management, enabling integrated services that take into account the emotional state of servers, terminals, and users.

[0372] Traffic management system

[0373] The server collects movement information from various detection devices installed throughout the city via the communication infrastructure. The hardware used includes various sensors and cameras, and the data collected from these is processed in real time. Specifically, software such as "OpenCV" and "TensorFlow" is used to analyze the data and predict traffic flow and congestion. Based on the analysis results, the server calculates the optimal route and presents the route information to the user's mobile device using "Google Maps API" and other tools.

[0374] The user's device is equipped with an emotion engine that analyzes biometric data through speech recognition and facial expression analysis technologies. Specific examples include the use of technologies such as "AWS Rekognition" and "Microsoft Azure Emotion Recognition." If the emotion engine detects that the user is experiencing stress, the server will suggest scenic routes or less congested roads to improve the user's travel experience.

[0375] As a concrete example, here is an example of a prompt: "AI model, please generate a sentence suggesting a recommended travel route for when the user is relaxed. Also, please explain the advantages of that route."

[0376] Waste management system

[0377] The terminal is connected to the waste container and measures waste information using sensors. This information is transmitted to a server using the communication protocol "MQTT". The server analyzes the received waste information using an AI algorithm to calculate the most efficient collection route. Software such as "OpenAI" and "SciKit-learn" are used in this process.

[0378] Furthermore, by using an emotion engine installed in the waste collection team's terminals to analyze the emotional state of the members, the optimality of the route can be improved. Based on the emotion analysis results, the server can, for example, select a route that allows members to enjoy natural scenery during their free time, thus balancing efficiency and comfort.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The server collects movement information from various detection devices via the communication infrastructure. Inputs include data such as traffic flow, vehicle position, and speed, while output is a real-time movement information dataset. The server processes this data using OpenCV and TensorFlow to perform initial data cleaning. Specifically, this involves noise reduction and data interpolation.

[0382] Step 2:

[0383] The server analyzes collected travel information to predict traffic congestion. The input is the dataset from Step 1, and the output is predicted traffic congestion data. The server uses TensorFlow or PyTorch to execute learning algorithms and build a prediction model using a neural network. This model is then used to perform specific actions such as analyzing traffic congestion by time of day and region.

[0384] Step 3:

[0385] The server calculates the optimal route based on the prediction results. The input is the prediction data from step 2, and the output is the optimal route information presented to the user. The server uses the "Google Maps API" to calculate the optimal route from the user's current location to the destination and sends that route information to the user's device. Specifically, it provides time-based route information and alternative routes at each intersection.

[0386] Step 4:

[0387] An emotion engine installed in the user's device analyzes biometric data such as the user's voice and facial expressions to identify their emotional state. The input is the user's voice and facial expression data, and the output is metrics indicating stress levels and emotional state. The device utilizes "AWS Rekognition" and "Microsoft Azure Emotion Recognition" to evaluate the user's emotional state based on biometric information. Specifically, it checks whether the user is smiling and whether their heart rate is normal.

[0388] Step 5:

[0389] The server adjusts the route based on the emotion analysis results to provide the user with the best possible service. The input is the route information from step 3 and the emotion state data from step 4, and the output is the optimized route information. The server takes the emotion data into consideration and prioritizes routes that are relaxing or have good scenery, and generates a new route. Specifically, it adjusts the route priority order and sends a customized suggestion to the user's terminal.

[0390] Step 6:

[0391] The terminal collects waste information from waste containers and transmits the data to a server. The input is data indicating the type and volume of waste, and the output is a data package sent to the server. The terminal uses an "Ultrasonic Sensor" and a "Weight Sensor" to measure the amount of waste in the container. Specifically, it analyzes the signals from the sensors and transmits them to the server as digital data.

[0392] Step 7:

[0393] The server analyzes waste information and calculates efficient collection routes. The input is waste data from step 6, and the output is information on the optimal waste collection route. The server uses "OpenAI" and "SciKit-learn" to analyze the received data and optimize collection routes according to the amount and type of waste. Specifically, it determines which route the collection vehicle should take and transmits that information to the collection device.

[0394] Step 8:

[0395] The server analyzes the emotional state of the garbage collection team and adjusts the collection route accordingly. The input is emotional data from the team members, and the output is collection route information adjusted based on their emotional state. The server analyzes the stress levels and fatigue of the collection team and changes the route as needed. Software used for this includes the "Emotion API." Specific actions include changing to less stressful routes and inserting rest points.

[0396] (Application Example 2)

[0397] 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 as the "terminal".

[0398] Traffic congestion and waste management are serious problems in modern cities, resulting in wasted time and resources. Furthermore, these management systems typically provide generic routes without considering the emotional state of users, and are not always optimal for individual users. Therefore, the objective of this invention is to improve the quality of urban life by streamlining traffic and waste management and providing personalized services that take into account the emotional state of users.

[0399] 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.

[0400] In this invention, the server includes means for collecting traffic data from various sensors in the communication infrastructure, means for analyzing the collected traffic data and predicting congestion, and means for analyzing the user's emotional state using an emotion detection device mounted on a mobile vehicle. This makes it possible to customize and present the optimal route based on the user's emotions.

[0401] "Communication infrastructure" refers to a network structure that enables the transmission and reception of data, and serves as a foundation for exchanging information with sensors and mobile devices.

[0402] A "sensor" is a device that detects external physical quantities, converts them into electrical signals, and provides them as information.

[0403] "Traffic data" refers to information about the flow, speed, and location of vehicles on roads.

[0404] "Analysis" is the process by which a system interprets information and finds meaning based on collected data.

[0405] "Mobile entities" refer to vehicles and devices used as means of transportation.

[0406] An "emotion detection device" is a technology that determines a user's emotional state based on their voice, facial expressions, and other biosignals.

[0407] "Waste data" refers to information that indicates the type and quantity of waste contained in a waste container.

[0408] An "efficient waste collection route" refers to a collection path designed to produce the greatest effect with the fewest resources.

[0409] "Recyclable resources" are waste materials that can be reused and are intended for recycling in order to reduce environmental impact.

[0410] This invention is a system that utilizes sensors, communication infrastructure, AI, and an emotion engine to improve the efficiency of traffic management and waste management.

[0411] First, the server collects traffic data from various sensors and cameras installed throughout the city. By utilizing communication infrastructure and database systems, this data can be acquired in real time. The collected data is processed by an AI module to analyze traffic flow and predict future congestion. For this purpose, machine learning platforms such as Google Cloud AI and TensorFlow are suitable. This allows the server to calculate the optimal route based on the predictions and present it to the user's mobile device. Furthermore, it's possible to analyze the user's emotional state using emotion detection devices and suggest routes for sightseeing or relaxing paths.

[0412] In waste management, terminals collect data on the type and quantity of waste via various sensors installed in homes and buildings. This data is transmitted to a server, where AI is used to calculate the optimal waste collection route. Real-time data processing technology is utilized in this calculation. The generated route is presented to the collection equipment, facilitating the automatic sorting of recyclable resources.

[0413] As a concrete example, consider a user driving a self-driving car. If the emotion engine analyzes that the user is in a happy mood, the server will present the user with a prompt such as, "Based on the information gathered by the AI, today is a perfect day for sightseeing! We will guide you to some enjoyable tourist spots."

[0414] Such systems enable optimization tailored to individual circumstances and emotions, resulting in efficient and personalized traffic and waste management.

[0415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0416] Step 1:

[0417] The server collects traffic data through sensors and cameras within the city. Real-time data collection takes place here. Input data includes traffic density, vehicle speed, and location information, which is transmitted to the server via the communication infrastructure. The server stores this data in storage in an appropriate format.

[0418] Step 2:

[0419] The server inputs the collected traffic data into an AI module to analyze traffic flow. This analysis process utilizes machine learning libraries such as TensorFlow. The AI ​​then uses the collected data patterns to predict future congestion. The output of this step is predictive data showing future traffic conditions.

[0420] Step 3:

[0421] The server calculates the optimal route based on the predicted data. The route calculation algorithm evaluates multiple paths and their conditions to determine the shortest or most comfortable route. The output is the proposed optimal route, which is sent to the user's device.

[0422] Step 4:

[0423] An emotion detection device installed in the user's terminal analyzes the user's biometric data (such as facial expressions and voice) to identify their emotional state. This analysis result is transmitted to a server. The input data is biometric data, and the output is an estimated emotion.

[0424] Step 5:

[0425] The server customizes the route based on the estimated emotions. For example, if the server determines that the user wants to relax, it will choose a route with good scenery. The output is the customized route, which is also suggested to the user.

[0426] Step 6:

[0427] In waste management, terminals collect waste data through sensors. This data includes information about the type and quantity of waste. The input data from the sensors is sent to a server and processed to optimize collection routes.

[0428] Step 7:

[0429] The server processes the collected waste data using AI to generate efficient collection routes. It calculates the optimal route while updating the data in real time. The output is the efficient waste collection route, which is displayed on the collection device.

[0430] Step 8:

[0431] If a user requests a sightseeing drive, the system will suggest tourist attractions via prompt messages. These prompt messages may include phrases such as, "It's a perfect day for sightseeing! We'll guide you to some enjoyable tourist spots." The output is a prompt message generated using a text-based AI model.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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".

[0448] This invention is a system that uses communication infrastructure and artificial intelligence (AI) technology to streamline traffic management and waste management in urban environments.

[0449] Traffic management system

[0450] The server first collects traffic data over time from various sensors and cameras installed throughout the city. This includes vehicle flow, speed, and intersection congestion. The server analyzes this data using AI algorithms to predict traffic congestion. For example, the server predicts congestion on a specific road for the next hour and generates the optimal detour route based on that information. This allows users to receive quick and efficient routes via their smartphones or in-car devices.

[0451] As a concrete example, when a user departs for their morning commute, the server analyzes traffic data at that time and sends an alternative route to the user's device if the usual route is congested. By accepting this suggestion, the user can shorten their commute time.

[0452] Waste management system

[0453] The terminal measures the volume and type of waste in real time using sensors attached to waste containers installed in each home and building. The measured data is sent to a server, where the server's AI calculates the optimal waste collection route based on the collected information. It also has a function to automatically sort recyclable resources.

[0454] For example, a terminal identifies locations in a specific area where garbage collection is needed, and the server generates an efficient route that includes those locations and sends it to the collection truck. This allows the collection team, as the user, to carry out their work without waste.

[0455] This invention will improve the quality of life for residents by streamlining both urban traffic and waste management.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The server collects real-time traffic data through sensors and cameras placed throughout the city. This data includes information such as the number of vehicles on the road, their speed, and the status of traffic lights.

[0459] Step 2:

[0460] The server inputs the collected traffic data into an AI algorithm for analysis. This analysis evaluates the current traffic situation and identifies areas where congestion is expected.

[0461] Step 3:

[0462] The server calculates the optimal detour route for the user based on identified congested areas. This calculation utilizes real-time road conditions and traffic forecasts.

[0463] Step 4:

[0464] The terminal provides the user with route information received from the server. Users can then efficiently travel by following the suggested route via their smartphone or navigation system.

[0465] Step 5:

[0466] The terminals are installed in trash cans in homes and buildings, and sensors monitor the type and amount of waste. This data is transmitted to a server at regular intervals.

[0467] Step 6:

[0468] The server analyzes the received waste data and generates an efficient collection route. This calculation takes into account factors such as when the waste container is full and the availability of recyclable resources.

[0469] Step 7:

[0470] The terminal provides the collection team's devices with the optimal waste collection route obtained from the server. Users can then follow these instructions and perform their tasks efficiently.

[0471] (Example 1)

[0472] 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."

[0473] Improving the efficiency of traffic and waste management in urban environments is a challenge. Conventional systems lack the accuracy and speed to predict traffic congestion and effective waste collection routes, resulting in insufficient convenience for residents and inadequate environmental protection. To address this, real-time data analysis and efficient route generation are necessary.

[0474] 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.

[0475] In this invention, the server includes means for collecting transportation data from sensor devices of a communication system and storing it in a database; means for analyzing the collected transportation data with an artificial intelligence program and predicting traffic congestion; and means for deriving an optimal travel route based on the prediction results and displaying the route information on a mobile terminal. This enables real-time optimization of traffic and efficient waste collection.

[0476] A "communication system" refers to the technological infrastructure for transmitting and receiving data, and includes the function of transmitting information between sensor devices and mobile terminals.

[0477] A "sensor device" refers to a device that can detect various physical or state changes, convert them into digital data, and transmit them to a communication system.

[0478] "Transportation data" refers to a dataset that includes information about traffic flow, vehicle speed, intersection congestion, and so on.

[0479] A "database" refers to a system that organizes and stores collected information, making it readily accessible when needed.

[0480] An "artificial intelligence program" is a computer program used for data analysis and prediction, and is particularly aimed at identifying patterns and trends using machine learning techniques.

[0481] "Traffic congestion" refers to a condition where vehicles cannot move at their normal speed on a road.

[0482] A "travel route" refers to the optimized route for travel from a starting point to a destination.

[0483] A "mobile terminal" is a device primarily used to display information usable while on the move, and it has the function of connecting to a communication system to receive predicted routes and instructions.

[0484] "Waste container" refers to equipment or devices used to temporarily store waste for collection and processing.

[0485] "Waste data" refers to a dataset containing measurement information about the quantity and type of waste.

[0486] "Collection equipment" refers to transport devices and machinery used to efficiently collect waste.

[0487] "Reusable resources" refer to materials and substances within waste that can be reprocessed and reused.

[0488] This invention provides a system in which servers, terminals, and users cooperate to efficiently manage urban traffic and waste. This system collects transportation and waste data from sensor devices via a communication system installed in the city and utilizes an artificial intelligence program to process this data.

[0489] In traffic management, the server stores transportation data collected from sensor devices in real time in a database and analyzes it using artificial intelligence programs. Specifically, AI frameworks such as TensorFlow and PyTorch are used to run models that predict traffic congestion. Furthermore, the optimal travel route is generated based on the analyzed data and displayed on a mobile terminal. This mobile terminal functions as the user's smartphone or in-car device.

[0490] Meanwhile, in waste management, the terminal acquires waste data in real time from sensor devices attached to waste containers. This data is sent to a server, where an AI algorithm is used to create the optimal waste collection route. The server also automatically sorts reusable resources and outputs route information to the collection equipment.

[0491] For example, in traffic optimization, users can check and use the optimal route suggested by the server on their mobile device before departure. In waste management, drivers of collection equipment can operate according to efficient collection routes transmitted from the server.

[0492] Examples of prompts include, "Suggest the optimal commute route based on current traffic data," and "Calculate the most efficient collection route based on the latest waste data for the specified area." This allows the system to improve the efficiency of urban traffic and waste management.

[0493] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0494] Step 1:

[0495] The server collects transportation data from sensor devices installed throughout the city. Specifically, it stores real-time information such as vehicle flow, speed, and congestion levels in a database. The input is digital signals from the sensors, and the output is the stored database entries.

[0496] Step 2:

[0497] The server analyzes the accumulated transportation data using an artificial intelligence program. Specifically, it uses TensorFlow or PyTorch to analyze the data and build a traffic congestion prediction model. The input is the data collected in the previous step, and the output is the predicted congestion information.

[0498] Step 3:

[0499] The server generates the optimal travel route based on the analysis results. Here, route calculation is performed by combining the analyzed traffic congestion information and geographical information. The input is predicted traffic congestion information and geographical information, and the output is optimized route information. Specifically, the route is generated using a map API.

[0500] Step 4:

[0501] The user receives route information sent from the server on their mobile device. Specifically, the route information is displayed on the mobile device (smartphone or in-vehicle device), and the user selects a route based on that information. The input is optimized route information, and the output is the actual travel route selected by the user.

[0502] Step 5:

[0503] The terminal collects waste data from sensor devices attached to waste containers. It measures the quantity and type of waste using sensors and records this data. The input is the physical measurement from the sensors, and the output is digital data transmitted to the server.

[0504] Step 6:

[0505] The server analyzes waste data transmitted from terminals and calculates efficient waste collection routes. Specifically, it processes the collected data using linear programming and heuristic algorithms. The input is waste data, and the output is collection route information.

[0506] Step 7:

[0507] Users of the collection equipment receive collection routes provided by the server and use them in their actual work. Specifically, they import the route into the navigation system, and the driver efficiently collects waste by following visual instructions. The input is collection route information, and the output is the actual waste collection work.

[0508] (Application Example 1)

[0509] 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."

[0510] Traffic congestion and inefficient waste management in cities are factors that reduce the quality of life for residents. To solve this problem, it is necessary to integrate traffic and waste management data to achieve more efficient route optimization. However, current systems often manage these aspects individually, and integrated efficiency improvements are not sufficiently achieved. Therefore, there is a need for technology that enables comprehensive urban management using autonomous vehicles.

[0511] 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.

[0512] In this invention, the server includes means for collecting traffic data from various sensors in the communication infrastructure, means for analyzing the collected traffic data and predicting congestion, and means for integrating traffic data and waste management data to adjust routes in real time in order to optimize the operation of autonomous vehicles. This makes it possible to provide a system that can efficiently manage both traffic and waste collection in an urban environment.

[0513] "Various sensors for communication infrastructure" is a general term for multiple types of measuring devices installed in urban environments to collect traffic data and waste management data.

[0514] "Traffic data" refers to information about road conditions within a city, such as the flow of vehicles, their speed, and the level of congestion at intersections.

[0515] A "machine learning algorithm" is a type of artificial intelligence technology used to analyze collected data and predict traffic flow and congestion.

[0516] "Means of collecting waste data from waste containers" refers to devices and methods for measuring the quantity and type of waste and transmitting the information to a server.

[0517] "Means of adjusting routes" refers to methods for optimizing the operating routes of autonomous vehicles based on real-time data to ensure safe and efficient travel.

[0518] "Means for automatically sorting reusable resources" refers to technologies that automatically identify collected waste and sort out reusable resources.

[0519] This invention aims to construct a system for efficiently managing traffic and waste in urban environments. The core of the system is a server that collects traffic and waste data from various sensors connected to the communication infrastructure.

[0520] The server uses machine learning algorithms to predict traffic flow using traffic data. This involves utilizing data analysis platforms such as TensorFlow to perform data clustering and generate predictive models. From the collected data, it automatically calculates the optimal detour route to avoid congestion and transmits this information to the onboard computers and scheduling systems of autonomous vehicles.

[0521] Furthermore, in managing waste data, sensors attached to waste containers measure the volume and type of waste and send this information to a server. Based on the collected data, the server automatically generates routes for sorting recyclable resources for garbage collection vehicles. This improves the efficiency of waste collection. In particular, by efficiently collecting waste while avoiding traffic congestion in urban areas, it enables waste-free operation.

[0522] As a concrete example, during the morning commute, a server analyzes traffic data and notifies the in-vehicle device if the usual route is congested. This allows the autonomous vehicle to avoid congestion and use an alternative route to reach its destination. Additionally, based on the weekend garbage collection schedule, data from garbage containers in specific areas is evaluated in real time, and the collection route is automatically adjusted.

[0523] An example of a prompt message is, "Based on current traffic data, please provide the optimal route from this point to the destination." This prompt works in conjunction with a generative AI model to always suggest a feasible route based on the latest traffic conditions.

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] The server collects traffic and waste data from various sensors in the communication infrastructure. This includes vehicle counts and speeds from cameras and LiDAR sensors placed on roads, and waste volume information from waste container sensors. The data is transmitted to the server in real time and stored in a database.

[0527] Step 2:

[0528] The server analyzes collected traffic data and uses a generative AI model to predict congestion. Historical traffic data and weather information are used as input, and the output shows the expected level of congestion at a specific location. Specifically, TensorFlow is used to train a deep learning model, and the predictions are updated in real time.

[0529] Step 3:

[0530] The server calculates the optimal travel route based on congestion prediction results. It takes into account the rapidly changing traffic conditions to select a route that allows the user to reach their destination safely and quickly. The input is congestion prediction data from the server, and the output is route information. The operation includes route searching using the Dijkstra algorithm.

[0531] Step 4:

[0532] The terminal, specifically the in-vehicle computer, applies the route information received from the server to the driving system. The route may be displayed on the in-vehicle display as the vehicle continuously adjusts its optimal route and continues driving. The input here is the route information received from the server, and the output is the vehicle's operation control.

[0533] Step 5:

[0534] The server generates efficient waste collection routes based on waste data. It automatically adjusts the optimal collection route based on the quantity and type of waste. Input data consists of collection information from waste sensors, and output is the operation schedule for collection vehicles. This information can be used by the waste collection team.

[0535] Step 6:

[0536] The server utilizes a generative AI model to create prompts that integrate traffic data and waste management data. These prompts propose real-time solutions based on traffic conditions through prompt statements. For example, it might generate a prompt such as, "Based on current traffic data, please provide the optimal route from this point to the destination," and then provide information based on that prompt.

[0537] 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.

[0538] This invention is a system that highly streamlines traffic management and waste management using communication infrastructure, artificial intelligence (AI), and an emotion engine. Furthermore, this system customizes services based on the user's emotional state, providing a more personalized user experience.

[0539] Traffic management system

[0540] The server collects traffic data in real time from multiple sensors and cameras within the city. Using AI, it analyzes this data to predict traffic flow and congestion. Based on the predicted data, it generates the optimal route and proposes it to the user. Furthermore, an emotion engine installed on the user's device analyzes the user's voice, facial expressions, and other biometric data to identify their emotional state. For example, if the server determines that the user is feeling stressed, it will provide customized route suggestions tailored to that situation, such as prioritizing scenic routes.

[0541] For example, if a user is on their way home and the emotion engine determines that the user is tired, the server will suggest a more relaxing route than their usual commute. For instance, it might choose a route that goes through a park or a road with less traffic.

[0542] Waste management system

[0543] The terminals are installed in garbage containers in homes and buildings, and sensors measure the type and amount of waste. This data is sent to a server, where AI calculates the most efficient collection route. Furthermore, an emotion engine can be used to suggest collection routes that take into account the user's emotions within the garbage collection team. For example, during busy periods, an efficiency-focused route can be suggested, while routes that consider the environment and health can be selected when there is more time available.

[0544] This invention makes urban life more convenient and comfortable by providing services tailored to the individual circumstances and feelings of users in both transportation and waste management.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The server collects real-time traffic data from sensors and cameras installed throughout the city. This data includes vehicle speed, number, and traffic light status.

[0548] Step 2:

[0549] The server feeds the collected traffic data into an AI algorithm to analyze traffic flow. Based on this analysis, congestion is predicted and the optimal detour route is calculated.

[0550] Step 3:

[0551] The device uses an emotion engine to analyze the user's voice, facial expressions, and other biometric information, and evaluates the user's emotional state in real time.

[0552] Step 4:

[0553] Based on information obtained from the emotion engine, the server creates route suggestions tailored to the user's emotional state. For example, if the user is highly stressed, it will suggest a safe and peaceful route.

[0554] Step 5:

[0555] The user's device displays a route suggested by the server, and the user can travel based on that route. The user can choose whether or not to accept the suggested route.

[0556] Step 6:

[0557] The terminal acquires the type and amount of waste in real time through sensors installed in the waste container. This data is periodically transmitted to the server.

[0558] Step 7:

[0559] The server uses AI to analyze the received waste data and generate efficient and emotionally considerate waste collection routes. The optimal route is suggested, taking into account the emotional state of the collection team.

[0560] Step 8:

[0561] The garbage collection team, acting as users, collects waste according to route information provided by the terminal. The server can update instructions in real time as the situation changes.

[0562] (Example 2)

[0563] 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."

[0564] In urban life, traffic congestion and efficient waste management are challenges, and there is a demand for services that cater to the individual emotional states of users. Currently, there is no system in place to comprehensively manage and coordinate these elements, making it difficult to realize optimal routes and waste disposal routes that take into account individual circumstances and emotions.

[0565] 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.

[0566] In this invention, the server includes means for collecting movement information from various detection devices of the communication infrastructure, means for analyzing the collected movement information and predicting traffic congestion, and means for identifying the emotional state of the user and adjusting the travel route and waste collection route based on the emotional state. This makes it possible to provide an optimal travel route that takes the user's emotions into consideration and to improve the efficiency of waste disposal.

[0567] A "communication infrastructure" is a technical foundation that enables the transmission and reception of data, and it plays a role in connecting various detection devices with a central data processing unit.

[0568] A "detection device" is a device that senses information from the outside world and transmits it as data to a communication infrastructure, and includes sensors and cameras.

[0569] "Mobility information" refers to data related to moving objects, such as traffic flow, vehicle location, and speed.

[0570] "Analysis" refers to the process of processing and analyzing collected data to transform it into meaningful information.

[0571] "Traffic congestion" refers to a situation where vehicles or people are overcrowded in a particular area or time period.

[0572] A "travel route" refers to the path a moving object takes to reach its destination, and its purpose is to be optimized.

[0573] "Waste information" refers to data that indicates the type and quantity of waste, and is fundamental information for waste management based on that data.

[0574] "Data collection equipment" refers to devices that receive data and perform instructed tasks, and includes waste disposal equipment.

[0575] "Emotional state" refers to information that indicates the user's psychological or physiological state, and is used to identify and adjust services accordingly.

[0576] This invention provides a system for streamlining traffic management and waste management, enabling integrated services that take into account the emotional state of servers, terminals, and users.

[0577] Traffic management system

[0578] The server collects movement information from various detection devices installed throughout the city via the communication infrastructure. The hardware used includes various sensors and cameras, and the data collected from these is processed in real time. Specifically, software such as "OpenCV" and "TensorFlow" is used to analyze the data and predict traffic flow and congestion. Based on the analysis results, the server calculates the optimal route and presents the route information to the user's mobile device using "Google Maps API" and other tools.

[0579] The user's device is equipped with an emotion engine that analyzes biometric data through speech recognition and facial expression analysis technologies. Specific examples include the use of technologies such as "AWS Rekognition" and "Microsoft Azure Emotion Recognition." If the emotion engine detects that the user is experiencing stress, the server will suggest scenic routes or less congested roads to improve the user's travel experience.

[0580] As a concrete example, here is an example of a prompt: "AI model, please generate a sentence suggesting a recommended travel route for when the user is relaxed. Also, please explain the advantages of that route."

[0581] Waste management system

[0582] The terminal is connected to the waste container and measures waste information using sensors. This information is transmitted to a server using the communication protocol "MQTT". The server analyzes the received waste information using an AI algorithm to calculate the most efficient collection route. Software such as "OpenAI" and "SciKit-learn" are used in this process.

[0583] Furthermore, by using an emotion engine installed in the waste collection team's terminals to analyze the emotional state of the members, the optimality of the route can be improved. Based on the emotion analysis results, the server can, for example, select a route that allows members to enjoy natural scenery during their free time, thus balancing efficiency and comfort.

[0584] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0585] Step 1:

[0586] The server collects movement information from various detection devices via the communication infrastructure. Inputs include data such as traffic flow, vehicle position, and speed, while output is a real-time movement information dataset. The server processes this data using OpenCV and TensorFlow to perform initial data cleaning. Specifically, this involves noise reduction and data interpolation.

[0587] Step 2:

[0588] The server analyzes collected travel information to predict traffic congestion. The input is the dataset from Step 1, and the output is predicted traffic congestion data. The server uses TensorFlow or PyTorch to execute learning algorithms and build a prediction model using a neural network. This model is then used to perform specific actions such as analyzing traffic congestion by time of day and region.

[0589] Step 3:

[0590] The server calculates the optimal route based on the prediction results. The input is the prediction data from step 2, and the output is the optimal route information presented to the user. The server uses the "Google Maps API" to calculate the optimal route from the user's current location to the destination and sends that route information to the user's device. Specifically, it provides time-based route information and alternative routes at each intersection.

[0591] Step 4:

[0592] An emotion engine installed in the user's device analyzes biometric data such as the user's voice and facial expressions to identify their emotional state. The input is the user's voice and facial expression data, and the output is metrics indicating stress levels and emotional state. The device utilizes "AWS Rekognition" and "Microsoft Azure Emotion Recognition" to evaluate the user's emotional state based on biometric information. Specifically, it checks whether the user is smiling and whether their heart rate is normal.

[0593] Step 5:

[0594] The server adjusts the route based on the emotion analysis results to provide the user with the best possible service. The input is the route information from step 3 and the emotion state data from step 4, and the output is the optimized route information. The server takes the emotion data into consideration and prioritizes routes that are relaxing or have good scenery, and generates a new route. Specifically, it adjusts the route priority order and sends a customized suggestion to the user's terminal.

[0595] Step 6:

[0596] The terminal collects waste information from waste containers and transmits the data to a server. The input is data indicating the type and volume of waste, and the output is a data package sent to the server. The terminal uses an "Ultrasonic Sensor" and a "Weight Sensor" to measure the amount of waste in the container. Specifically, it analyzes the signals from the sensors and transmits them to the server as digital data.

[0597] Step 7:

[0598] The server analyzes waste information and calculates efficient collection routes. The input is waste data from step 6, and the output is information on the optimal waste collection route. The server uses "OpenAI" and "SciKit-learn" to analyze the received data and optimize collection routes according to the amount and type of waste. Specifically, it determines which route the collection vehicle should take and transmits that information to the collection device.

[0599] Step 8:

[0600] The server analyzes the emotional state of the garbage collection team and adjusts the collection route accordingly. The input is emotional data from the team members, and the output is collection route information adjusted based on their emotional state. The server analyzes the stress levels and fatigue of the collection team and changes the route as needed. Software used for this includes the "Emotion API." Specific actions include changing to less stressful routes and inserting rest points.

[0601] (Application Example 2)

[0602] 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."

[0603] Traffic congestion and waste management are serious problems in modern cities, resulting in wasted time and resources. Furthermore, these management systems typically provide generic routes without considering the emotional state of users, and are not always optimal for individual users. Therefore, the objective of this invention is to improve the quality of urban life by streamlining traffic and waste management and providing personalized services that take into account the emotional state of users.

[0604] 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.

[0605] In this invention, the server includes means for collecting traffic data from various sensors in the communication infrastructure, means for analyzing the collected traffic data and predicting congestion, and means for analyzing the user's emotional state using an emotion detection device mounted on a mobile vehicle. This makes it possible to customize and present the optimal route based on the user's emotions.

[0606] "Communication infrastructure" refers to a network structure that enables the transmission and reception of data, and serves as a foundation for exchanging information with sensors and mobile devices.

[0607] A "sensor" is a device that detects external physical quantities, converts them into electrical signals, and provides them as information.

[0608] "Traffic data" refers to information about the flow, speed, and location of vehicles on roads.

[0609] "Analysis" is the process by which a system interprets information and finds meaning based on collected data.

[0610] "Mobile entities" refer to vehicles and devices used as means of transportation.

[0611] An "emotion detection device" is a technology that determines a user's emotional state based on their voice, facial expressions, and other biosignals.

[0612] "Waste data" refers to information that indicates the type and quantity of waste contained in a waste container.

[0613] An "efficient waste collection route" refers to a collection path designed to produce the greatest effect with the fewest resources.

[0614] "Recyclable resources" are waste materials that can be reused and are intended for recycling in order to reduce environmental impact.

[0615] This invention is a system that utilizes sensors, communication infrastructure, AI, and an emotion engine to improve the efficiency of traffic management and waste management.

[0616] First, the server collects traffic data from various sensors and cameras installed throughout the city. By utilizing communication infrastructure and database systems, this data can be acquired in real time. The collected data is processed by an AI module to analyze traffic flow and predict future congestion. For this purpose, machine learning platforms such as Google Cloud AI and TensorFlow are suitable. This allows the server to calculate the optimal route based on the predictions and present it to the user's mobile device. Furthermore, it's possible to analyze the user's emotional state using emotion detection devices and suggest routes for sightseeing or relaxing paths.

[0617] In waste management, terminals collect data on the type and quantity of waste via various sensors installed in homes and buildings. This data is transmitted to a server, where AI is used to calculate the optimal waste collection route. Real-time data processing technology is utilized in this calculation. The generated route is presented to the collection equipment, facilitating the automatic sorting of recyclable resources.

[0618] As a concrete example, consider a user driving a self-driving car. If the emotion engine analyzes that the user is in a happy mood, the server will present the user with a prompt such as, "Based on the information gathered by the AI, today is a perfect day for sightseeing! We will guide you to some enjoyable tourist spots."

[0619] Such systems enable optimization tailored to individual circumstances and emotions, resulting in efficient and personalized traffic and waste management.

[0620] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0621] Step 1:

[0622] The server collects traffic data through sensors and cameras within the city. Real-time data collection takes place here. Input data includes traffic density, vehicle speed, and location information, which is transmitted to the server via the communication infrastructure. The server stores this data in storage in an appropriate format.

[0623] Step 2:

[0624] The server inputs the collected traffic data into an AI module to analyze traffic flow. This analysis process utilizes machine learning libraries such as TensorFlow. The AI ​​then uses the collected data patterns to predict future congestion. The output of this step is predictive data showing future traffic conditions.

[0625] Step 3:

[0626] The server calculates the optimal route based on the predicted data. The route calculation algorithm evaluates multiple paths and their conditions to determine the shortest or most comfortable route. The output is the proposed optimal route, which is sent to the user's device.

[0627] Step 4:

[0628] An emotion detection device installed in the user's terminal analyzes the user's biometric data (such as facial expressions and voice) to identify their emotional state. This analysis result is transmitted to a server. The input data is biometric data, and the output is an estimated emotion.

[0629] Step 5:

[0630] The server customizes the route based on the estimated emotions. For example, if the server determines that the user wants to relax, it will choose a route with good scenery. The output is the customized route, which is also suggested to the user.

[0631] Step 6:

[0632] In waste management, terminals collect waste data through sensors. This data includes information about the type and quantity of waste. The input data from the sensors is sent to a server and processed to optimize collection routes.

[0633] Step 7:

[0634] The server processes the collected waste data using AI to generate efficient collection routes. It calculates the optimal route while updating the data in real time. The output is the efficient waste collection route, which is displayed on the collection device.

[0635] Step 8:

[0636] If a user requests a sightseeing drive, the system will suggest tourist attractions via prompt messages. These prompt messages may include phrases such as, "It's a perfect day for sightseeing! We'll guide you to some enjoyable tourist spots." The output is a prompt message generated using a text-based AI model.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] [Fourth Embodiment]

[0641] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0642] 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.

[0643] 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).

[0644] 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.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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".

[0654] This invention is a system that uses communication infrastructure and artificial intelligence (AI) technology to streamline traffic management and waste management in urban environments.

[0655] Traffic management system

[0656] The server first collects traffic data over time from various sensors and cameras installed throughout the city. This includes vehicle flow, speed, and intersection congestion. The server analyzes this data using AI algorithms to predict traffic congestion. For example, the server predicts congestion on a specific road for the next hour and generates the optimal detour route based on that information. This allows users to receive quick and efficient routes via their smartphones or in-car devices.

[0657] As a concrete example, when a user departs for their morning commute, the server analyzes traffic data at that time and sends an alternative route to the user's device if the usual route is congested. By accepting this suggestion, the user can shorten their commute time.

[0658] Waste management system

[0659] The terminal measures the volume and type of waste in real time using sensors attached to waste containers installed in each home and building. The measured data is sent to a server, where the server's AI calculates the optimal waste collection route based on the collected information. It also has a function to automatically sort recyclable resources.

[0660] For example, a terminal identifies locations in a specific area where garbage collection is needed, and the server generates an efficient route that includes those locations and sends it to the collection truck. This allows the collection team, as the user, to carry out their work without waste.

[0661] This invention will improve the quality of life for residents by streamlining both urban traffic and waste management.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The server collects real-time traffic data through sensors and cameras placed throughout the city. This data includes information such as the number of vehicles on the road, their speed, and the status of traffic lights.

[0665] Step 2:

[0666] The server inputs the collected traffic data into an AI algorithm for analysis. This analysis evaluates the current traffic situation and identifies areas where congestion is expected.

[0667] Step 3:

[0668] The server calculates the optimal detour route for the user based on identified congested areas. This calculation utilizes real-time road conditions and traffic forecasts.

[0669] Step 4:

[0670] The terminal provides the user with route information received from the server. Users can then efficiently travel by following the suggested route via their smartphone or navigation system.

[0671] Step 5:

[0672] The terminals are installed in trash cans in homes and buildings, and sensors monitor the type and amount of waste. This data is transmitted to a server at regular intervals.

[0673] Step 6:

[0674] The server analyzes the received waste data and generates an efficient collection route. This calculation takes into account factors such as when the waste container is full and the availability of recyclable resources.

[0675] Step 7:

[0676] The terminal provides the collection team's devices with the optimal waste collection route obtained from the server. Users can then follow these instructions and perform their tasks efficiently.

[0677] (Example 1)

[0678] 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".

[0679] Improving the efficiency of traffic and waste management in urban environments is a challenge. Conventional systems lack the accuracy and speed to predict traffic congestion and effective waste collection routes, resulting in insufficient convenience for residents and inadequate environmental protection. To address this, real-time data analysis and efficient route generation are necessary.

[0680] 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.

[0681] In this invention, the server includes means for collecting transportation data from sensor devices of a communication system and storing it in a database; means for analyzing the collected transportation data with an artificial intelligence program and predicting traffic congestion; and means for deriving an optimal travel route based on the prediction results and displaying the route information on a mobile terminal. This enables real-time optimization of traffic and efficient waste collection.

[0682] A "communication system" refers to the technological infrastructure for transmitting and receiving data, and includes the function of transmitting information between sensor devices and mobile terminals.

[0683] A "sensor device" refers to a device that can detect various physical or state changes, convert them into digital data, and transmit them to a communication system.

[0684] "Transportation data" refers to a dataset that includes information about traffic flow, vehicle speed, intersection congestion, and so on.

[0685] A "database" refers to a system that organizes and stores collected information, making it readily accessible when needed.

[0686] An "artificial intelligence program" is a computer program used for data analysis and prediction, and is particularly aimed at identifying patterns and trends using machine learning techniques.

[0687] "Traffic congestion" refers to a condition where vehicles cannot move at their normal speed on a road.

[0688] A "travel route" refers to the optimized route for travel from a starting point to a destination.

[0689] A "mobile terminal" is a device primarily used to display information usable while on the move, and it has the function of connecting to a communication system to receive predicted routes and instructions.

[0690] "Waste container" refers to equipment or devices used to temporarily store waste for collection and processing.

[0691] "Waste data" refers to a dataset containing measurement information about the quantity and type of waste.

[0692] "Collection equipment" refers to transport devices and machinery used to efficiently collect waste.

[0693] "Reusable resources" refer to materials and substances within waste that can be reprocessed and reused.

[0694] This invention provides a system in which servers, terminals, and users cooperate to efficiently manage urban traffic and waste. This system collects transportation and waste data from sensor devices via a communication system installed in the city and utilizes an artificial intelligence program to process this data.

[0695] In traffic management, the server stores transportation data collected from sensor devices in real time in a database and analyzes it using artificial intelligence programs. Specifically, AI frameworks such as TensorFlow and PyTorch are used to run models that predict traffic congestion. Furthermore, the optimal travel route is generated based on the analyzed data and displayed on a mobile terminal. This mobile terminal functions as the user's smartphone or in-car device.

[0696] Meanwhile, in waste management, the terminal acquires waste data in real time from sensor devices attached to waste containers. This data is sent to a server, where an AI algorithm is used to create the optimal waste collection route. The server also automatically sorts reusable resources and outputs route information to the collection equipment.

[0697] For example, in traffic optimization, users can check and use the optimal route suggested by the server on their mobile device before departure. In waste management, drivers of collection equipment can operate according to efficient collection routes transmitted from the server.

[0698] Examples of prompts include, "Suggest the optimal commute route based on current traffic data," and "Calculate the most efficient collection route based on the latest waste data for the specified area." This allows the system to improve the efficiency of urban traffic and waste management.

[0699] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0700] Step 1:

[0701] The server collects transportation data from sensor devices installed throughout the city. Specifically, it stores real-time information such as vehicle flow, speed, and congestion levels in a database. The input is digital signals from the sensors, and the output is the stored database entries.

[0702] Step 2:

[0703] The server analyzes the accumulated transportation data using an artificial intelligence program. Specifically, it uses TensorFlow or PyTorch to analyze the data and build a traffic congestion prediction model. The input is the data collected in the previous step, and the output is the predicted congestion information.

[0704] Step 3:

[0705] The server generates the optimal travel route based on the analysis results. Here, route calculation is performed by combining the analyzed traffic congestion information and geographical information. The input is predicted traffic congestion information and geographical information, and the output is optimized route information. Specifically, the route is generated using a map API.

[0706] Step 4:

[0707] The user receives route information sent from the server on their mobile device. Specifically, the route information is displayed on the mobile device (smartphone or in-vehicle device), and the user selects a route based on that information. The input is optimized route information, and the output is the actual travel route selected by the user.

[0708] Step 5:

[0709] The terminal collects waste data from sensor devices attached to waste containers. It measures the quantity and type of waste using sensors and records this data. The input is the physical measurement from the sensors, and the output is digital data transmitted to the server.

[0710] Step 6:

[0711] The server analyzes waste data transmitted from terminals and calculates efficient waste collection routes. Specifically, it processes the collected data using linear programming and heuristic algorithms. The input is waste data, and the output is collection route information.

[0712] Step 7:

[0713] Users of the collection equipment receive collection routes provided by the server and use them in their actual work. Specifically, they import the route into the navigation system, and the driver efficiently collects waste by following visual instructions. The input is collection route information, and the output is the actual waste collection work.

[0714] (Application Example 1)

[0715] 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".

[0716] Traffic congestion and inefficient waste management in cities are factors that reduce the quality of life for residents. To solve this problem, it is necessary to integrate traffic and waste management data to achieve more efficient route optimization. However, current systems often manage these aspects individually, and integrated efficiency improvements are not sufficiently achieved. Therefore, there is a need for technology that enables comprehensive urban management using autonomous vehicles.

[0717] 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.

[0718] In this invention, the server includes means for collecting traffic data from various sensors in the communication infrastructure, means for analyzing the collected traffic data and predicting congestion, and means for integrating traffic data and waste management data to adjust routes in real time in order to optimize the operation of autonomous vehicles. This makes it possible to provide a system that can efficiently manage both traffic and waste collection in an urban environment.

[0719] "Various sensors for communication infrastructure" is a general term for multiple types of measuring devices installed in urban environments to collect traffic data and waste management data.

[0720] "Traffic data" refers to information about road conditions within a city, such as the flow of vehicles, their speed, and the level of congestion at intersections.

[0721] A "machine learning algorithm" is a type of artificial intelligence technology used to analyze collected data and predict traffic flow and congestion.

[0722] "Means of collecting waste data from waste containers" refers to devices and methods for measuring the quantity and type of waste and transmitting the information to a server.

[0723] "Means of adjusting routes" refers to methods for optimizing the operating routes of autonomous vehicles based on real-time data to ensure safe and efficient travel.

[0724] "Means for automatically sorting reusable resources" refers to technologies that automatically identify collected waste and sort out reusable resources.

[0725] This invention aims to construct a system for efficiently managing traffic and waste in urban environments. The core of the system is a server that collects traffic and waste data from various sensors connected to the communication infrastructure.

[0726] The server uses machine learning algorithms to predict traffic flow using traffic data. This involves utilizing data analysis platforms such as TensorFlow to perform data clustering and generate predictive models. From the collected data, it automatically calculates the optimal detour route to avoid congestion and transmits this information to the onboard computers and scheduling systems of autonomous vehicles.

[0727] Furthermore, in managing waste data, sensors attached to waste containers measure the volume and type of waste and send this information to a server. Based on the collected data, the server automatically generates routes for sorting recyclable resources for garbage collection vehicles. This improves the efficiency of waste collection. In particular, by efficiently collecting waste while avoiding traffic congestion in urban areas, it enables waste-free operation.

[0728] As a concrete example, during the morning commute, a server analyzes traffic data and notifies the in-vehicle device if the usual route is congested. This allows the autonomous vehicle to avoid congestion and use an alternative route to reach its destination. Additionally, based on the weekend garbage collection schedule, data from garbage containers in specific areas is evaluated in real time, and the collection route is automatically adjusted.

[0729] An example of a prompt message is, "Based on current traffic data, please provide the optimal route from this point to the destination." This prompt works in conjunction with a generative AI model to always suggest a feasible route based on the latest traffic conditions.

[0730] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0731] Step 1:

[0732] The server collects traffic and waste data from various sensors in the communication infrastructure. This includes vehicle counts and speeds from cameras and LiDAR sensors placed on roads, and waste volume information from waste container sensors. The data is transmitted to the server in real time and stored in a database.

[0733] Step 2:

[0734] The server analyzes collected traffic data and uses a generative AI model to predict congestion. Historical traffic data and weather information are used as input, and the output shows the expected level of congestion at a specific location. Specifically, TensorFlow is used to train a deep learning model, and the predictions are updated in real time.

[0735] Step 3:

[0736] The server calculates the optimal travel route based on congestion prediction results. It takes into account the rapidly changing traffic conditions to select a route that allows the user to reach their destination safely and quickly. The input is congestion prediction data from the server, and the output is route information. The operation includes route searching using the Dijkstra algorithm.

[0737] Step 4:

[0738] The terminal, specifically the in-vehicle computer, applies the route information received from the server to the driving system. The route may be displayed on the in-vehicle display as the vehicle continuously adjusts its optimal route and continues driving. The input here is the route information received from the server, and the output is the vehicle's operation control.

[0739] Step 5:

[0740] The server generates efficient waste collection routes based on waste data. It automatically adjusts the optimal collection route based on the quantity and type of waste. Input data consists of collection information from waste sensors, and output is the operation schedule for collection vehicles. This information can be used by the waste collection team.

[0741] Step 6:

[0742] The server utilizes a generative AI model to create prompts that integrate traffic data and waste management data. These prompts propose real-time solutions based on traffic conditions through prompt statements. For example, it might generate a prompt such as, "Based on current traffic data, please provide the optimal route from this point to the destination," and then provide information based on that prompt.

[0743] 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.

[0744] This invention is a system that highly streamlines traffic management and waste management using communication infrastructure, artificial intelligence (AI), and an emotion engine. Furthermore, this system customizes services based on the user's emotional state, providing a more personalized user experience.

[0745] Traffic management system

[0746] The server collects traffic data in real time from multiple sensors and cameras within the city. Using AI, it analyzes this data to predict traffic flow and congestion. Based on the predicted data, it generates the optimal route and proposes it to the user. Furthermore, an emotion engine installed on the user's device analyzes the user's voice, facial expressions, and other biometric data to identify their emotional state. For example, if the server determines that the user is feeling stressed, it will provide customized route suggestions tailored to that situation, such as prioritizing scenic routes.

[0747] For example, if a user is on their way home and the emotion engine determines that the user is tired, the server will suggest a more relaxing route than their usual commute. For instance, it might choose a route that goes through a park or a road with less traffic.

[0748] Waste management system

[0749] The terminals are installed in garbage containers in homes and buildings, and sensors measure the type and amount of waste. This data is sent to a server, where AI calculates the most efficient collection route. Furthermore, an emotion engine can be used to suggest collection routes that take into account the user's emotions within the garbage collection team. For example, during busy periods, an efficiency-focused route can be suggested, while routes that consider the environment and health can be selected when there is more time available.

[0750] This invention makes urban life more convenient and comfortable by providing services tailored to the individual circumstances and feelings of users in both transportation and waste management.

[0751] The following describes the processing flow.

[0752] Step 1:

[0753] The server collects real-time traffic data from sensors and cameras installed throughout the city. This data includes vehicle speed, number, and traffic light status.

[0754] Step 2:

[0755] The server feeds the collected traffic data into an AI algorithm to analyze traffic flow. Based on this analysis, congestion is predicted and the optimal detour route is calculated.

[0756] Step 3:

[0757] The device uses an emotion engine to analyze the user's voice, facial expressions, and other biometric information, and evaluates the user's emotional state in real time.

[0758] Step 4:

[0759] Based on information obtained from the emotion engine, the server creates route suggestions tailored to the user's emotional state. For example, if the user is highly stressed, it will suggest a safe and peaceful route.

[0760] Step 5:

[0761] The user's device displays a route suggested by the server, and the user can travel based on that route. The user can choose whether or not to accept the suggested route.

[0762] Step 6:

[0763] The terminal acquires the type and amount of waste in real time through sensors installed in the waste container. This data is periodically transmitted to the server.

[0764] Step 7:

[0765] The server uses AI to analyze the received waste data and generate efficient and emotionally considerate waste collection routes. The optimal route is suggested, taking into account the emotional state of the collection team.

[0766] Step 8:

[0767] The garbage collection team, acting as users, collects waste according to route information provided by the terminal. The server can update instructions in real time as the situation changes.

[0768] (Example 2)

[0769] 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".

[0770] In urban life, traffic congestion and efficient waste management are challenges, and there is a demand for services that cater to the individual emotional states of users. Currently, there is no system in place to comprehensively manage and coordinate these elements, making it difficult to realize optimal routes and waste disposal routes that take into account individual circumstances and emotions.

[0771] 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.

[0772] In this invention, the server includes means for collecting movement information from various detection devices of the communication infrastructure, means for analyzing the collected movement information and predicting traffic congestion, and means for identifying the emotional state of the user and adjusting the travel route and waste collection route based on the emotional state. This makes it possible to provide an optimal travel route that takes the user's emotions into consideration and to improve the efficiency of waste disposal.

[0773] A "communication infrastructure" is a technical foundation that enables the transmission and reception of data, and it plays a role in connecting various detection devices with a central data processing unit.

[0774] A "detection device" is a device that senses information from the outside world and transmits it as data to a communication infrastructure, and includes sensors and cameras.

[0775] "Mobility information" refers to data related to moving objects, such as traffic flow, vehicle location, and speed.

[0776] "Analysis" refers to the process of processing and analyzing collected data to transform it into meaningful information.

[0777] "Traffic congestion" refers to a situation where vehicles or people are overcrowded in a particular area or time period.

[0778] A "travel route" refers to the path a moving object takes to reach its destination, and its purpose is to be optimized.

[0779] "Waste information" refers to data that indicates the type and quantity of waste, and is fundamental information for waste management based on that data.

[0780] "Data collection equipment" refers to devices that receive data and perform instructed tasks, and includes waste disposal equipment.

[0781] "Emotional state" refers to information that indicates the user's psychological or physiological state, and is used to identify and adjust services accordingly.

[0782] This invention provides a system for streamlining traffic management and waste management, enabling integrated services that take into account the emotional state of servers, terminals, and users.

[0783] Traffic management system

[0784] The server collects movement information from various detection devices installed throughout the city via the communication infrastructure. The hardware used includes various sensors and cameras, and the data collected from these is processed in real time. Specifically, software such as "OpenCV" and "TensorFlow" is used to analyze the data and predict traffic flow and congestion. Based on the analysis results, the server calculates the optimal route and presents the route information to the user's mobile device using "Google Maps API" and other tools.

[0785] The user's device is equipped with an emotion engine that analyzes biometric data through speech recognition and facial expression analysis technologies. Specific examples include the use of technologies such as "AWS Rekognition" and "Microsoft Azure Emotion Recognition." If the emotion engine detects that the user is experiencing stress, the server will suggest scenic routes or less congested roads to improve the user's travel experience.

[0786] As a concrete example, here is an example of a prompt: "AI model, please generate a sentence suggesting a recommended travel route for when the user is relaxed. Also, please explain the advantages of that route."

[0787] Waste management system

[0788] The terminal is connected to the waste container and measures waste information using sensors. This information is transmitted to a server using the communication protocol "MQTT". The server analyzes the received waste information using an AI algorithm to calculate the most efficient collection route. Software such as "OpenAI" and "SciKit-learn" are used in this process.

[0789] Furthermore, by using an emotion engine installed in the waste collection team's terminals to analyze the emotional state of the members, the optimality of the route can be improved. Based on the emotion analysis results, the server can, for example, select a route that allows members to enjoy natural scenery during their free time, thus balancing efficiency and comfort.

[0790] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0791] Step 1:

[0792] The server collects movement information from various detection devices via the communication infrastructure. Inputs include data such as traffic flow, vehicle position, and speed, while output is a real-time movement information dataset. The server processes this data using OpenCV and TensorFlow to perform initial data cleaning. Specifically, this involves noise reduction and data interpolation.

[0793] Step 2:

[0794] The server analyzes collected travel information to predict traffic congestion. The input is the dataset from Step 1, and the output is predicted traffic congestion data. The server uses TensorFlow or PyTorch to execute learning algorithms and build a prediction model using a neural network. This model is then used to perform specific actions such as analyzing traffic congestion by time of day and region.

[0795] Step 3:

[0796] The server calculates the optimal route based on the prediction results. The input is the prediction data from step 2, and the output is the optimal route information presented to the user. The server uses the "Google Maps API" to calculate the optimal route from the user's current location to the destination and sends that route information to the user's device. Specifically, it provides time-based route information and alternative routes at each intersection.

[0797] Step 4:

[0798] An emotion engine installed in the user's device analyzes biometric data such as the user's voice and facial expressions to identify their emotional state. The input is the user's voice and facial expression data, and the output is metrics indicating stress levels and emotional state. The device utilizes "AWS Rekognition" and "Microsoft Azure Emotion Recognition" to evaluate the user's emotional state based on biometric information. Specifically, it checks whether the user is smiling and whether their heart rate is normal.

[0799] Step 5:

[0800] The server adjusts the route based on the emotion analysis results to provide the user with the best possible service. The input is the route information from step 3 and the emotion state data from step 4, and the output is the optimized route information. The server takes the emotion data into consideration and prioritizes routes that are relaxing or have good scenery, and generates a new route. Specifically, it adjusts the route priority order and sends a customized suggestion to the user's terminal.

[0801] Step 6:

[0802] The terminal collects waste information from waste containers and transmits the data to a server. The input is data indicating the type and volume of waste, and the output is a data package sent to the server. The terminal uses an "Ultrasonic Sensor" and a "Weight Sensor" to measure the amount of waste in the container. Specifically, it analyzes the signals from the sensors and transmits them to the server as digital data.

[0803] Step 7:

[0804] The server analyzes waste information and calculates efficient collection routes. The input is waste data from step 6, and the output is information on the optimal waste collection route. The server uses "OpenAI" and "SciKit-learn" to analyze the received data and optimize collection routes according to the amount and type of waste. Specifically, it determines which route the collection vehicle should take and transmits that information to the collection device.

[0805] Step 8:

[0806] The server analyzes the emotional state of the garbage collection team and adjusts the collection route accordingly. The input is emotional data from the team members, and the output is collection route information adjusted based on their emotional state. The server analyzes the stress levels and fatigue of the collection team and changes the route as needed. Software used for this includes the "Emotion API." Specific actions include changing to less stressful routes and inserting rest points.

[0807] (Application Example 2)

[0808] 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".

[0809] Traffic congestion and waste management are serious problems in modern cities, resulting in wasted time and resources. Furthermore, these management systems typically provide generic routes without considering the emotional state of users, and are not always optimal for individual users. Therefore, the objective of this invention is to improve the quality of urban life by streamlining traffic and waste management and providing personalized services that take into account the emotional state of users.

[0810] 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.

[0811] In this invention, the server includes means for collecting traffic data from various sensors in the communication infrastructure, means for analyzing the collected traffic data and predicting congestion, and means for analyzing the user's emotional state using an emotion detection device mounted on a mobile vehicle. This makes it possible to customize and present the optimal route based on the user's emotions.

[0812] "Communication infrastructure" refers to a network structure that enables the transmission and reception of data, and serves as a foundation for exchanging information with sensors and mobile devices.

[0813] A "sensor" is a device that detects external physical quantities, converts them into electrical signals, and provides them as information.

[0814] "Traffic data" refers to information about the flow, speed, and location of vehicles on roads.

[0815] "Analysis" is the process by which a system interprets information and finds meaning based on collected data.

[0816] "Mobile entities" refer to vehicles and devices used as means of transportation.

[0817] An "emotion detection device" is a technology that determines a user's emotional state based on their voice, facial expressions, and other biosignals.

[0818] "Waste data" refers to information that indicates the type and quantity of waste contained in a waste container.

[0819] An "efficient waste collection route" refers to a collection path designed to produce the greatest effect with the fewest resources.

[0820] "Recyclable resources" are waste materials that can be reused and are intended for recycling in order to reduce environmental impact.

[0821] This invention is a system that utilizes sensors, communication infrastructure, AI, and an emotion engine to improve the efficiency of traffic management and waste management.

[0822] First, the server collects traffic data from various sensors and cameras installed throughout the city. By utilizing communication infrastructure and database systems, this data can be acquired in real time. The collected data is processed by an AI module to analyze traffic flow and predict future congestion. For this purpose, machine learning platforms such as Google Cloud AI and TensorFlow are suitable. This allows the server to calculate the optimal route based on the predictions and present it to the user's mobile device. Furthermore, it's possible to analyze the user's emotional state using emotion detection devices and suggest routes for sightseeing or relaxing paths.

[0823] In waste management, terminals collect data on the type and quantity of waste via various sensors installed in homes and buildings. This data is transmitted to a server, where AI is used to calculate the optimal waste collection route. Real-time data processing technology is utilized in this calculation. The generated route is presented to the collection equipment, facilitating the automatic sorting of recyclable resources.

[0824] As a concrete example, consider a user driving a self-driving car. If the emotion engine analyzes that the user is in a happy mood, the server will present the user with a prompt such as, "Based on the information gathered by the AI, today is a perfect day for sightseeing! We will guide you to some enjoyable tourist spots."

[0825] Such systems enable optimization tailored to individual circumstances and emotions, resulting in efficient and personalized traffic and waste management.

[0826] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0827] Step 1:

[0828] The server collects traffic data through sensors and cameras within the city. Real-time data collection takes place here. Input data includes traffic density, vehicle speed, and location information, which is transmitted to the server via the communication infrastructure. The server stores this data in storage in an appropriate format.

[0829] Step 2:

[0830] The server inputs the collected traffic data into an AI module to analyze traffic flow. This analysis process utilizes machine learning libraries such as TensorFlow. The AI ​​then uses the collected data patterns to predict future congestion. The output of this step is predictive data showing future traffic conditions.

[0831] Step 3:

[0832] The server calculates the optimal route based on the predicted data. The route calculation algorithm evaluates multiple paths and their conditions to determine the shortest or most comfortable route. The output is the proposed optimal route, which is sent to the user's device.

[0833] Step 4:

[0834] An emotion detection device installed in the user's terminal analyzes the user's biometric data (such as facial expressions and voice) to identify their emotional state. This analysis result is transmitted to a server. The input data is biometric data, and the output is an estimated emotion.

[0835] Step 5:

[0836] The server customizes the route based on the estimated emotions. For example, if the server determines that the user wants to relax, it will choose a route with good scenery. The output is the customized route, which is also suggested to the user.

[0837] Step 6:

[0838] In waste management, terminals collect waste data through sensors. This data includes information about the type and quantity of waste. The input data from the sensors is sent to a server and processed to optimize collection routes.

[0839] Step 7:

[0840] The server processes the collected waste data using AI to generate efficient collection routes. It calculates the optimal route while updating the data in real time. The output is the efficient waste collection route, which is displayed on the collection device.

[0841] Step 8:

[0842] If a user requests a sightseeing drive, the system will suggest tourist attractions via prompt messages. These prompt messages may include phrases such as, "It's a perfect day for sightseeing! We'll guide you to some enjoyable tourist spots." The output is a prompt message generated using a text-based AI model.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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."

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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 as being incorporated by reference.

[0864] The following is further disclosed regarding the embodiments described above.

[0865] (Claim 1)

[0866] A means of collecting traffic data from various sensors in the communication infrastructure,

[0867] A method for analyzing collected traffic data and predicting congestion,

[0868] A means for calculating the optimal route based on the prediction results and presenting the route information to the moving object,

[0869] A means of collecting waste data from waste containers and generating efficient waste collection routes,

[0870] A means for presenting the generated route information to a data collection device and automatically sorting recyclable resources,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, which predicts traffic flow using a machine learning algorithm in the analysis of traffic data.

[0874] (Claim 3)

[0875] The system according to claim 1, which optimizes the garbage collection route in real time based on data from various sensors when generating the garbage collection route.

[0876] "Example 1"

[0877] (Claim 1)

[0878] A means of collecting transport data from sensor devices in a communication system and storing it in a database,

[0879] A method for predicting traffic congestion by analyzing collected transportation data with an artificial intelligence program,

[0880] A means for deriving the optimal travel route based on the predicted results and displaying the route information on a mobile terminal,

[0881] A means of obtaining waste data from waste containers and creating efficient waste collection routes,

[0882] A means for outputting the created route information to a device and automatically selecting reusable resources,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, which uses machine learning techniques to predict traffic flow and congestion in the analysis of transportation data.

[0886] (Claim 3)

[0887] The system according to claim 1, which optimizes the waste collection route over time based on data from a sensor device in creating the waste collection route.

[0888] "Application Example 1"

[0889] (Claim 1)

[0890] A means of collecting traffic data from various sensors in the communication infrastructure,

[0891] A method for analyzing collected traffic data and predicting congestion,

[0892] A means for calculating the optimal route based on the prediction results and presenting the route information to the moving object,

[0893] A means for collecting waste data from waste containers and generating efficient waste collection routes,

[0894] A means for presenting generated route information to a work device and automatically sorting reusable resources,

[0895] To optimize the operation of autonomous vehicles, a means of integrating traffic data and waste management data to adjust routes in real time,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, which predicts traffic flow using a machine learning algorithm in the analysis of traffic data.

[0899] (Claim 3)

[0900] The system according to claim 1, which optimizes the garbage collection route in real time based on data from various sensors when generating the garbage collection route.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] A means for collecting movement information from various detection devices of the communication infrastructure,

[0904] A method for analyzing collected travel information and predicting traffic congestion,

[0905] A means for calculating the optimal route based on the prediction results and presenting the route information to a mobile device,

[0906] A means for collecting waste information from waste containers and generating efficient waste collection routes,

[0907] A means for presenting generated route information to a device and automatically selecting reusable resources,

[0908] A means for identifying the emotional state of users and adjusting travel routes and waste collection routes based on that emotional state,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, which predicts the flow of movement using a learning algorithm in the analysis of movement information.

[0912] (Claim 3)

[0913] The system according to claim 1, which optimizes waste collection routes in real time based on information from various detection devices when generating waste collection routes.

[0914] "Application example 2 when combining with an emotional engine"

[0915] (Claim 1)

[0916] A means of collecting traffic data from various sensors in the communication infrastructure,

[0917] A method for analyzing collected traffic data and predicting congestion,

[0918] A means for calculating the optimal route based on the prediction results and presenting the route information to the moving object,

[0919] A means of analyzing the user's emotional state using an emotion detection device mounted on a mobile device,

[0920] A means of customizing and presenting routes that are relaxing or pass through tourist spots based on the user's emotions,

[0921] A means of collecting waste data from waste containers and generating efficient waste collection routes,

[0922] A means for presenting the generated route information to a data collection device and automatically sorting recyclable resources,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, which predicts traffic flow using a machine learning algorithm in the analysis of traffic data.

[0926] (Claim 3)

[0927] The system according to claim 1, which optimizes the garbage collection route in real time based on data from various sensors when generating the garbage collection route. [Explanation of symbols]

[0928] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting traffic data from various sensors in the communication infrastructure, A method for analyzing collected traffic data and predicting congestion, A means for calculating the optimal route based on the prediction results and presenting the route information to the moving object, A means of collecting waste data from waste containers and generating efficient waste collection routes, A means for presenting the generated route information to a data collection device and automatically sorting recyclable resources, A system that includes this.

2. The system according to claim 1, which predicts traffic flow using a machine learning algorithm in the analysis of traffic data.

3. The system according to claim 1, which optimizes the garbage collection route in real time based on data from various sensors when generating the garbage collection route.

Images