system

Abstract

We provide the system. [Solution] A means of setting the travel route, A means of collecting and analyzing user behavior history data, A means of predicting traffic congestion and estimating the time it will take for it to clear, A means of suggesting nearby rest areas that match the user's preferences, A means of displaying information about the proposed rest area, A system that includes this.

Description

Technical Field

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Traffic jams that occur during long-distance driving increase the physical and mental burden on drivers. Especially when caught in a traffic jam on the way back, not only is unnecessary time wasted, but a comfortable return home is also hindered, so it is required to effectively utilize the traffic jam time. In addition, it is difficult to propose rest breaks and sightseeing spots for traffic jam avoidance based on the preferences of users, and no efficient solutions have been provided by the conventional technologies.

Means for Solving the Problems

[0005] This invention provides a navigation system that automatically sets driving routes, which collects user activity history data and uses that data to suggest rest stops and tourist destinations tailored to the user's preferences, thereby enabling effective use of time spent in traffic congestion. Specifically, it includes means for predicting traffic congestion, estimating the time it will take for it to clear, and then suggesting rest stops and tourist spots suitable for the user. This allows users to engage in activities aligned with their hobbies and interests while timing their visits to coincide with traffic congestion clearing, and to return home comfortably to their destination. Furthermore, the information on suggested spots is based on advertising bids, ensuring that the system operates economically while meeting the needs of the user.

[0006] "Means for setting a driving route" refers to technical means for automatically calculating and guiding the user to a destination specified by the user.

[0007] "Means for collecting and analyzing user behavior history data" refers to technical means for acquiring data on users' past behavior and preferences, and using that data to analyze users' hobbies and tendencies.

[0008] "Means for predicting traffic congestion and estimating the time it will take to resolve it" refers to technical means for analyzing traffic information to predict where congestion will occur and how long it will take to resolve it.

[0009] "Means for suggesting nearby rest areas according to user preferences" refers to technical means for selecting available rest areas and tourist destinations based on user preferences and presenting that information.

[0010] "Means for displaying information on suggested rest areas" refers to technical means for visually showing users detailed information about suggested rest areas and tourist destinations.

[0011] "Methods for determining based on bids from advertisers" refers to technical means for determining the display order of proposed rest areas and tourist destinations based on the bid amounts submitted by advertisers. [Brief explanation of the drawing]

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

[0013] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

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

[0018] 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).

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] In an embodiment of the present invention, a system is provided that suggests an optimal rest stop to a user while they are driving, while avoiding traffic congestion. This system mainly consists of three elements: a server, a terminal, and a user.

[0034] First, the user uses their device to search for a route to their destination. During this process, the device obtains a standard route from the user's current location to their destination.

[0035] Next, the server collects and analyzes user behavior data to understand user preferences. This includes past search and visit history, and uses AI algorithms to identify user interests. Based on this information, the server learns the types of recreation and relaxation styles preferred by the user.

[0036] The server then uses current traffic information to check congestion along the set route and uses an AI model to estimate the time it will take for the congestion to clear. For example, it uses historical traffic data and real-time traffic information to predict which sections are experiencing congestion and when that congestion will clear.

[0037] If traffic conditions are determined to be congested, the server searches for nearby rest stops. As part of the search, filtering is performed to reflect the user's preferences, allowing the server to select the most suitable location for the user. This information is sent to the device and displayed in a user-friendly format.

[0038] The terminal displays detailed information about suggested rest stops to the user. Based on this information, the user can select a spot they wish to visit. Once a selection is made, the terminal guides the user to the optimal route to that location, making efficient use of time spent in traffic.

[0039] Furthermore, this system includes a mechanism that allows advertisers to change the display order through bidding, making it possible to make specific spots more visible. By integrating this, the server creates a system that not only provides a better user experience but also has the potential to generate economic benefits.

[0040] Thus, the present invention reduces the user's driving burden while providing meaningful time based on hobbies and interests, and ensuring a comfortable journey home.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user sets a destination on the car navigation terminal, and the terminal obtains a standard driving route from the current location to the destination.

[0044] Step 2:

[0045] The server collects user behavior history data linked to Yahoo IDs, analyzes it using AI, and identifies the user's hobbies and preferences. The collected data includes past search history and browsing history.

[0046] Step 3:

[0047] The server checks traffic conditions based on current route information. In particular, it identifies congestion points along the route and uses an AI model to predict the time it will take for each congestion to clear.

[0048] Step 4:

[0049] To make the most of time spent in traffic, the server selects personalized rest stops from the surrounding area based on the user's preferences. This selection takes into account the estimated time required to clear the traffic and the user's behavioral history.

[0050] Step 5:

[0051] The terminal displays a list of rest spots sent from the server, along with their details, to the user, allowing them to select a spot they wish to visit.

[0052] Step 6:

[0053] The user selects a rest spot that interests them, and the device guides them to their desired location by displaying the optimal route to that spot.

[0054] Step 7:

[0055] The server adjusts the priority of displayed rest spots based on bids from advertisers, aiming to generate economic revenue.

[0056] (Example 1)

[0057] 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."

[0058] In modern transportation systems, users face the challenge of significantly increased travel times to their destinations due to frequent traffic congestion. Furthermore, there is the difficulty of reducing fatigue associated with monotonous drives and providing fulfilling rest periods tailored to user preferences. Additionally, advertisers require effective placement of their advertisements.

[0059] 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.

[0060] In this invention, the server includes means for setting a route, means for collecting and analyzing the user's behavior history, means for predicting traffic conditions and estimating the time it will take for them to clear, means for suggesting appropriate rest stops based on the user's preferences, and means for determining the display order based on advertiser bids. As a result, users can receive suggestions for the optimal route and rest stops, enabling them to shorten travel time and enjoy more fulfilling rest periods. Advertisers can also achieve more effective ad placement.

[0061] "Means of setting a route" refers to a function that determines the optimal driving route based on the user's current location and destination.

[0062] "Means for collecting and analyzing user behavior history" refers to a system that analyzes users' preferences and interests based on their past usage history and location information.

[0063] "Means for predicting traffic conditions and estimating the time it takes to resolve them" refers to a function that uses real-time data and historical traffic data to predict the occurrence of traffic congestion and calculate the time it will take for it to resolve.

[0064] "Means of suggesting appropriate rest locations based on user preferences" refers to a system that selects and suggests rest spots that suit the user based on behavioral history and preference analysis.

[0065] "Means of presenting detailed information about proposed rest areas" refers to a function that visually displays specific information about the selected rest area to the user.

[0066] "Means of determining display order based on advertiser bids" refers to a system for dynamically determining the order of elements displayed according to the advertiser's bid amount.

[0067] This invention is a system designed to enable users to reach their destinations comfortably and efficiently. This system primarily consists of a server, terminals, and users, each working together to process information. The details are described below.

[0068] The server provides real-time traffic information to assist with route planning. This utilizes map applications such as Google Maps and Apple Maps, as well as traffic information APIs. The server integrates these information sources to plan routes and analyze traffic conditions.

[0069] The terminal functions as the user interface, sending information about the user's current location and destination to the server. Furthermore, the terminal displays detailed information about suggested rest stops received from the server and provides optimal route guidance based on the user's selection.

[0070] The server uses databases and machine learning algorithms to collect and analyze user behavior history. For example, it uses K-Means clustering to analyze user interests and preferences and suggests appropriate resting places based on that information.

[0071] Traffic congestion is predicted by the server using a time-series forecasting model. By combining historical traffic data with real-time data, the server can estimate the time it takes for congestion to occur and resolve.

[0072] The display of proposed rest areas is determined by advertiser bids. The server adjusts the priority of rest areas based on the advertiser's bid amount. This ensures that users receive information about the most noteworthy rest areas.

[0073] A concrete example of this system is when a user is planning a long-distance drive and it is determined that the route to their destination is congested. In such cases, the server suggests recreational spots to avoid the predicted congestion. These spots allow for optimal rest stops based on the user's past visit history and preferences.

[0074] An example of a prompt message might be, "Please suggest suitable rest stops while avoiding traffic congestion on the route to the destination." This allows the user to utilize the information provided by the system and spend their time productively.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user inputs the route to their destination using a terminal. The terminal obtains the user's current location using GPS and sends it to the server along with the destination information. The server calculates a standard route using a map application and returns it to the terminal. The input is the current location and destination, and the output is the initial standard route.

[0078] Step 2:

[0079] The server retrieves user behavior history data from a database and analyzes it using machine learning algorithms. For example, it uses K-Means clustering to detect user preferences and identify their interests in recreation and rest areas. In this process, the input is behavior history data, and the output is user preference information.

[0080] Step 3:

[0081] The server acquires real-time traffic data through a traffic information API. Using a time-series forecasting model, it predicts traffic congestion from the acquired data and estimates the time it will take for the congestion to clear. The input is real-time traffic data, and the output is congestion prediction and clearing time.

[0082] Step 4:

[0083] The server searches for rest stops along the route and filters them based on the user's preferences. It selects a suitable rest stop and sends its details to the terminal. The input is route information and user preferences, and the output is suggested rest stop information.

[0084] Step 5:

[0085] The terminal displays information about suggested rest stops sent from the server to the user. The user selects a rest stop they wish to visit from the displayed list and inputs their selection into the terminal. The input is the information about the suggested rest stops, and the output is the user's selection.

[0086] Step 6:

[0087] The server searches for the optimal route based on the rest stops selected by the user and sends the guidance information to the terminal. The terminal then presents the newly set route to the user and provides navigation. The input is the rest stops selected by the user, and the output is the updated route guidance.

[0088] Step 7:

[0089] The server determines the display order of proposed rest areas based on the advertiser's bid information. The server adjusts the display order and sends that information to the terminal. The input is the advertiser's bid information, and the output is the adjusted display order.

[0090] (Application Example 1)

[0091] 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."

[0092] In autonomous vehicles, there is a need to avoid traffic congestion during operation, suggest appropriate rest stops according to the user's preferences, and provide a smooth and comfortable driving experience. Furthermore, it is necessary to build a system that can generate economic benefits by prioritizing the suggestion of specific rest stops.

[0093] 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.

[0094] In this invention, the server includes a device for setting a travel route, a device for collecting and analyzing user behavior history information, a device for predicting traffic congestion and estimating the time of its resolution, a device for suggesting nearby rest areas according to the user's preferences, a device for displaying information on the suggested rest areas, a function for automatically guiding the user to the suggested rest areas, and a function for adjusting the order in which rest areas are presented based on advertising bidding information. This makes it possible to suggest the most suitable rest areas for the user and maximize economic benefits.

[0095] A "route setting device" is a device that automatically calculates and sets the optimal route from the vehicle's current location to its destination.

[0096] A "device for collecting and analyzing user behavior history information" is a device that collects users' past movement data and visit history, and analyzes this data to identify users' preferences and patterns.

[0097] A "device for predicting traffic congestion and estimating the time it will clear" is a device that analyzes real-time and historical traffic data to predict the current state of congestion and estimate the time it will clear.

[0098] A "device that suggests nearby resting places according to the user's preferences" is a device that takes the user's preferences into consideration and selects and suggests an appropriate location from among the available resting places in the surrounding area.

[0099] A "device for displaying information on proposed rest areas" is a device that visually presents detailed information about proposed rest areas to the user to aid in their understanding.

[0100] The "function to automatically guide the vehicle to a suggested resting place" refers to providing control and navigation functions to automatically guide the vehicle to the selected resting place.

[0101] The "function to adjust the order in which rest locations are presented based on advertising bid information" is a function that adjusts the order in which rest locations are displayed preferentially based on the advertiser's bid information.

[0102] The system implementing this invention is based on a server installed in an autonomous vehicle and a terminal operated by the user. The server runs on the Google Cloud Platform and uses the Google Maps API to acquire traffic information. It also uses BigQuery to analyze user behavior history and employs TENSORFLOW® for implementing AI algorithms. The Amazon Forecast service from AWS® is used for traffic congestion prediction. The server integrates these tools to suggest the optimal resting place based on traffic conditions and user preferences.

[0103] The terminal visually displays information about suggested rest stops to the user, allowing the user to make a selection. It also has the function to instruct the autonomous driving system on the route to the selected rest stop. The information displayed on the terminal uses an intuitive and highly visible interface so that the user can easily understand it even while driving.

[0104] Users can leverage these features to select the optimal route and rest stops in real time, minimizing wasted time due to traffic congestion. In particular, a generative AI model analyzes the user's past behavior patterns and recommends rest stops that are likely to interest them.

[0105] As a concrete example, a user on a family trip might be suggested a new route to avoid traffic, along with a zoo to visit for a break. In this case, the prompt would be something like, "Where should an animal-loving user go for a break if they want to avoid traffic on the Tomei Expressway for two hours starting at 4 PM?" The server would then select the most suitable candidate locations.

[0106] This system will enable a comfortable and efficient travel experience while also providing effective information tailored to the needs of advertisers from a business perspective.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server uses the Google Maps API to calculate a standard route from the starting point to the destination based on the destination entered by the user. The input is the user-specified starting point and destination, and the output is multiple route options between them. Data calculations are then performed to select the most efficient route from these options.

[0110] Step 2:

[0111] Users provide their browsing history using their devices. The server uses BigQuery to collect and organize the user's past visit history data, which is the input. Keywords related to the user's preferences are extracted from the analyzed data. This output information is then reflected in subsequent rest stop suggestions.

[0112] Step 3:

[0113] The server uses Amazon Forecast from AWS to infer real-time and historical traffic data and obtain current and predicted congestion levels. The input is traffic data from various locations, and the output is a list of congested sections and their predicted clearing times. This generates data to optimize user travel.

[0114] Step 4:

[0115] The server uses a generative AI model to combine user preference data with traffic congestion information. Using user preference keywords and congestion information as input, it performs data calculations to generate a list of optimal nearby rest stop candidates. The output is a list of suggested rest stops.

[0116] Step 5:

[0117] The terminal visually displays a list of rest stop options provided by the server. The input is a list of rest stops, and the information design is carefully crafted to facilitate user selection. The output is the user's selection action.

[0118] Step 6:

[0119] The user selects their desired rest spot from the locations displayed on the terminal. This selection information is sent back from the terminal to the server and used for the next process.

[0120] Step 7:

[0121] The server sets a new route for the autonomous driving system to guide the vehicle to the selected rest stop. The inputs are the user's selection information and the vehicle's current location, and the output generates an updated destination and the optimal route to it. This information is then sent to the vehicle's navigation system.

[0122] This process allows users to have a travel experience that avoids traffic jams while incorporating breaks that suit their interests.

[0123] 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.

[0124] An embodiment of the present invention is a navigation system that provides a driving route and enables personalized rest stop suggestions that take into account the user's emotional state. This system incorporates an emotion engine that recognizes the user's emotions in real time and optimizes the selection of rest stops based on those emotions.

[0125] First, the user sets a driving route to their destination via the navigation terminal. During this process, the terminal obtains the route from the user's current location to the destination. Along with general traffic information, the user's activity history data is sent to the server.

[0126] The server learns the user's hobbies and preferences based on this behavioral history data, and further analyzes the user's current emotional state in real time through an emotion engine. This analysis uses technologies such as speech recognition and facial recognition to determine the user's stress level and mood.

[0127] Based on conventional traffic information, the server grasps the congestion status along the route and estimates the time it will take for congestion to clear if it is anticipated. Furthermore, to mitigate the user stress expected due to this congestion, it suggests rest stops according to the emotional state recognized by the emotion engine. For example, if the analysis indicates that the user is feeling fatigued or stressed, it will prioritize suggesting spots that prioritize relaxation.

[0128] The terminal displays a list of rest locations provided by the server and prompts the user to make a selection. Based on the information presented, the user decides which spot to visit, and the terminal provides directions to the selected rest location.

[0129] Furthermore, the server can adjust the display order of rest stops based on bids from advertisers, thereby generating advertising revenue. This system allows users to visit rest stops that are optimal for their emotional state, resulting in a more comfortable driving experience while reducing the stress caused by traffic congestion.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user enters their destination into the navigation terminal and performs a route search. The terminal then obtains a standard driving route from the starting point to the destination.

[0133] Step 2:

[0134] The server collects user behavior data and uses AI to learn user preferences. This includes past visit and search history to identify what kind of activities the user prefers.

[0135] Step 3:

[0136] The server uses an emotion engine to recognize the user's emotions from voice and facial expression data. Through real-time analysis, it determines the user's emotional state (for example, whether they are tired or stressed).

[0137] Step 4:

[0138] The server references real-time traffic data to check for congestion information along the specified route. It analyzes past patterns and current traffic conditions to predict where congestion will occur and how long it will take for it to clear.

[0139] Step 5:

[0140] The server combines the user's emotional state with traffic information to select suitable spots for rest and sightseeing from the surrounding area. For example, if the user is feeling stressed, it will suggest facilities with relaxation effects.

[0141] Step 6:

[0142] The terminal displays location information provided by the server to the user, including an overview and location information for each spot. The user can then choose the location that interests them most from the presented options.

[0143] Step 7:

[0144] The user confirms the selected rest stop on the device and enters their intention to "visit" into the device. The device immediately displays a detailed route to the selected spot and begins guiding the user there.

[0145] Step 8:

[0146] The server maximizes advertising revenue by adjusting the display order to highlight specific spots, taking into account advertiser bidding information. This function is implemented as a means of delivering relevant information to users while generating the funds necessary to maintain the system.

[0147] (Example 2)

[0148] 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".

[0149] Conventional navigation systems simply set routes based on traffic information and could not suggest rest stops while considering the user's emotional state or individual stress level. Furthermore, the ability to utilize advertising value in the order in which rest stops were displayed was limited, making it difficult to optimize the user experience.

[0150] 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.

[0151] In this invention, the server includes means for collecting and analyzing the user's behavioral history and emotional state; means for predicting traffic conditions and estimating congestion relief time; means for suggesting personalized rest stops based on the user's emotional state; and means for adjusting the display order of rest stops based on bids from advertisers. This makes it possible to suggest the optimal rest stop according to the user's emotional state and to set a display order that maximizes the value of advertising.

[0152] A "travel route" refers to the path a user takes to reach their destination.

[0153] "User activity history" refers to a collection of information about past driving history and places visited.

[0154] "Emotional state" refers to information that indicates the user's psychological state, including stress levels and mood.

[0155] "Traffic conditions" refers to information related to traffic, such as road congestion and traffic restrictions.

[0156] "Congestion relief time" refers to the estimated time it will take for traffic congestion to clear up.

[0157] A "personalized rest stop" is a resting spot selected to suit the user's specific needs and emotional state.

[0158] An "advertiser" is someone who provides advertisements for specific products or services and bids for the order in which they are displayed.

[0159] This invention provides a navigation system that takes into account the user's emotional state, and provides personalized settings for driving routes, traffic conditions, and rest stops. The main components necessary for implementing this system are a navigation terminal, an emotion engine, a communication network, and a server.

[0160] The user first sets a destination using a navigation terminal. This terminal uses a location information acquisition device (e.g., GPS) to obtain the user's current location and calculates the route to the set destination.

[0161] The terminal sends route information, user behavior history data, and real-time sentiment information to the server. To obtain this sentiment information, speech recognition software or facial recognition software (e.g., general software for speech recognition, facial recognition API) is used.

[0162] The server receives this data and analyzes the user's emotional state via an emotion engine. Based on this analysis, the server predicts congestion levels and selects the most suitable resting place for the user's emotions. In this process, a traffic information API is used to estimate road congestion levels and the time it will take for traffic to clear.

[0163] The device then displays a list of rest locations that match the user's emotional state. This includes adjusting the display order based on advertisers' bidding information. Based on the information presented, the user can select a rest location they wish to visit.

[0164] For example, if a user wants to "reconnect with nature and refresh themselves" during a long drive, the system is configured to promptly suggest nearby parks and gardens. An example of a prompt to input into the generating AI model would be, "Please suggest relaxing places or spots that suit your interests so that I can find the best rest stop considering your current emotional state." Through this process, the user can enjoy a more comfortable and personalized driving experience.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user sets a destination using a navigation terminal. The input includes the starting point and destination, and the current location information is obtained. The output is candidate route information to the destination. Here, a GPS device is used to obtain precise location information, and a map application is used to calculate the route.

[0168] Step 2:

[0169] The device transmits route information, user behavior history data, and sentiment data collected via voice or camera to the server. The input consists of the aforementioned behavior history and real-time sentiment data, which are used to generate data packages for the server. The output is the user profile information transmitted to the server.

[0170] Step 3:

[0171] The server analyzes the received data and uses an emotion engine to identify the user's emotional state. The input is emotional data received from the terminal. Data analysis software (e.g., emotion analysis algorithm) is used to classify the user's emotional state as stress, relaxation, etc. The output is the evaluation result of the analyzed emotional state.

[0172] Step 4:

[0173] The server uses a traffic information API to predict traffic conditions on the user's current route. Route information and historical traffic pattern data are used as input. After analysis, the output includes a congestion prediction and an estimated time for congestion to clear.

[0174] Step 5:

[0175] The server selects the optimal rest stop based on the user's emotional state and traffic conditions. The input consists of an evaluation of the emotional state and traffic forecast information. A generative AI model is used to evaluate candidate rest stops and generate a ranking. The output is a list of the most suitable rest stops for the user.

[0176] Step 6:

[0177] The terminal displays a list of rest stops received from the server to the user. The input is rest stop information obtained from the server. This information is prioritized based on bidding information from advertisers. The output is a list of rest stop candidates formatted for user display.

[0178] Step 7:

[0179] The user selects a destination from a list of suggested rest stops. The input is the list of rest stop options displayed on the terminal. After the selection is complete, the terminal begins providing detailed route guidance to the selected rest stop. The output is detailed navigation information.

[0180] (Application Example 2)

[0181] 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".

[0182] Users of autonomous vehicles often experience monotonous journeys over long periods and may feel stressed and fatigued due to traffic congestion. As a result, they may not enjoy a comfortable travel experience. Furthermore, these factors affect satisfaction during travel, and new approaches are needed to improve the user's driving experience.

[0183] 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.

[0184] In this invention, the server includes means for collecting and analyzing user behavior history data, means for detecting the user's emotional state, and means for optimizing suggested rest locations based on the emotional state. This enables users to rest at optimal rest locations according to their emotional state, thereby reducing stress during travel and providing a comfortable driving experience.

[0185] "Means for setting the travel route" refers to a function that automatically calculates and sets the optimal travel route to the destination.

[0186] "Means for collecting and analyzing user behavior history data" refers to the process of collecting data on users' past travel history, interests, and preferences, and analyzing user trends based on that data.

[0187] "Methods for predicting traffic congestion and estimating the time it will take to resolve it" refers to technologies that analyze traffic flow to predict future congestion and the time it will take for it to resolve.

[0188] "A means of suggesting nearby rest areas according to user preferences" refers to a function that selects and recommends appropriate rest areas based on the user's preferences.

[0189] "Means for displaying information on proposed rest areas" refers to an interface that visually provides users with detailed information about the selected rest areas.

[0190] "Means for detecting the user's emotional state" refers to a function that analyzes the user's current emotions in real time using technologies such as speech recognition and facial recognition.

[0191] "A means of optimizing rest locations based on emotional state" refers to a system that selects rest locations taking into account the user's emotional state and presents the optimal option.

[0192] The system for carrying out this invention consists of three components: a user, a terminal, and a server. The system aims to provide the user with a comfortable travel experience by integrating emotion recognition technology and navigation technology.

[0193] First, the user sets a travel route to their destination using a device. The device has the function of acquiring the user's current location and destination information and sending it to the server. The user's activity history data is also sent to the server at the same time, which is used to analyze the user's preferences and travel trends.

[0194] Next, the server uses the user's behavioral history data to analyze their hobbies and preferences, and further analyzes their emotional state in real time via an emotion engine. By utilizing speech recognition and facial recognition, it grasps the user's stress level and mood, and based on that, suggests the most suitable resting place.

[0195] The server analyzes traffic information in real time, predicts congestion levels, and estimates the time it will take for congestion to clear. This predicted data, along with the user's emotional state, is used to design routes that ensure a comfortable travel experience.

[0196] The terminal displays information about rest stops provided by the server and prompts the user to make a selection. Based on the information presented, the user decides which rest stop to visit, and the terminal provides detailed directions to the selected rest stop.

[0197] This system allows users to take breaks according to their emotional state at the time, reducing stress and providing a comfortable driving experience. For example, if the system detects that the user is stressed, the server can suggest nearby relaxation spots and play relaxing music on the device.

[0198] An example of a prompt message is: "Develop a system that analyzes the emotions of passengers in an autonomous vehicle and suggests locations where they can relax."

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The terminal receives destination information set by the user and uses GPS to determine the user's current location. The input information consists of the user's destination and current location. This information is sent to the server, which provides the data necessary for calculating the travel route.

[0202] Step 2:

[0203] The server calculates the optimal route based on the received destination and current location data. During this process, it obtains real-time traffic information from a traffic database and performs congestion predictions. This results in a route that takes congestion into account.

[0204] Step 3:

[0205] The server analyzes the user's behavioral history data to extract their hobbies and preferences. Input data includes past travel history and interest / preference-related information. The output is an individual preference pattern that reflects the user's tendencies.

[0206] Step 4:

[0207] The server receives video and audio data from the terminal and uses an emotion engine to analyze the user's emotional state in real time. The resulting stress level and mood state are then output.

[0208] Step 5:

[0209] The server lists optimal rest locations that match the user's preferences based on the analyzed emotional state. Several candidate locations are output, taking into account the user's interests and emotional state.

[0210] Step 6:

[0211] The terminal displays rest location information obtained from the server and presents options to the user. It facilitates interaction until the user makes a selection and confirms the final selected location.

[0212] Step 7:

[0213] The device displays detailed directions to the selected rest stop, assisting the user in reaching the destination comfortably. It provides navigation to the chosen rest stop and plays music that responds to the user's mood.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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".

[0230] In an embodiment of the present invention, a system is provided that suggests an optimal rest stop to a user while they are driving, while avoiding traffic congestion. This system mainly consists of three elements: a server, a terminal, and a user.

[0231] First, the user uses their device to search for a route to their destination. During this process, the device obtains a standard route from the user's current location to their destination.

[0232] Next, the server collects and analyzes user behavior data to understand user preferences. This includes past search and visit history, and uses AI algorithms to identify user interests. Based on this information, the server learns the types of recreation and relaxation styles preferred by the user.

[0233] The server then uses current traffic information to check congestion along the configured route and uses an AI model to estimate the time it will take for the congestion to clear. For example, it uses historical traffic data and real-time traffic information to predict which sections are experiencing congestion and when that congestion will clear.

[0234] If traffic conditions are determined to be congested, the server searches for nearby rest stops. As part of the search, filtering is performed to reflect the user's preferences, allowing the server to select the most suitable location for the user. This information is sent to the device and displayed in a user-friendly format.

[0235] The terminal displays detailed information about suggested rest stops to the user. Based on this information, the user can select a spot they wish to visit. Once a selection is made, the terminal guides the user to the optimal route to that location, making efficient use of time spent in traffic.

[0236] Furthermore, this system includes a mechanism that allows advertisers to change the display order through bidding, making it possible to make specific spots more visible. By integrating this, the server creates a system that not only provides a better user experience but also has the potential to generate economic benefits.

[0237] Thus, the present invention reduces the user's driving burden while providing meaningful time based on hobbies and interests, and ensuring a comfortable journey home.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user sets a destination on the car navigation terminal, and the terminal obtains a standard driving route from the current location to the destination.

[0241] Step 2:

[0242] The server collects user behavior history data linked to Yahoo IDs, analyzes it using AI, and identifies the user's hobbies and preferences. The collected data includes past search history and browsing history.

[0243] Step 3:

[0244] The server checks traffic conditions based on current route information. In particular, it identifies congestion points along the route and uses an AI model to predict the time it will take for each congestion to clear.

[0245] Step 4:

[0246] To make the most of time spent in traffic, the server selects personalized rest stops from the surrounding area based on the user's preferences. This selection takes into account the estimated time required to clear the traffic and the user's behavioral history.

[0247] Step 5:

[0248] The terminal displays a list of rest spots sent from the server, along with their details, to the user, allowing them to select a spot they wish to visit.

[0249] Step 6:

[0250] The user selects a rest spot that interests them, and the device guides the user to their desired location by displaying the optimal route to that spot.

[0251] Step 7:

[0252] The server adjusts the priority of displayed rest spots based on bids from advertisers, aiming to generate economic revenue.

[0253] (Example 1)

[0254] 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 as the "terminal".

[0255] In modern transportation systems, users face the challenge of significantly increased travel times due to frequent traffic congestion. Furthermore, it's difficult to alleviate fatigue associated with monotonous drives and provide fulfilling rest periods tailored to user preferences. Additionally, advertisers require effective placement of their advertisements.

[0256] 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.

[0257] In this invention, the server includes means for setting a route, means for collecting and analyzing the user's behavior history, means for predicting traffic conditions and estimating the time it will take for them to clear, means for suggesting appropriate rest stops based on the user's preferences, and means for determining the display order based on advertiser bids. As a result, users can receive suggestions for the optimal route and rest stops, enabling them to shorten travel time and enjoy more fulfilling rest periods. Advertisers can also achieve more effective ad placement.

[0258] "Means of setting a route" refers to a function that determines the optimal driving route based on the user's current location and destination.

[0259] "Means for collecting and analyzing user behavior history" refers to a system that analyzes users' preferences and interests based on their past usage history and location information.

[0260] "Means for predicting traffic conditions and estimating the time it takes to resolve them" refers to a function that uses real-time data and historical traffic data to predict the occurrence of traffic congestion and calculate the time it will take for it to resolve.

[0261] "Means of suggesting appropriate rest locations based on user preferences" refers to a system that selects and suggests rest spots that suit the user based on behavioral history and preference analysis.

[0262] "Means of presenting detailed information about proposed rest areas" refers to a function that visually displays specific information about the selected rest area to the user.

[0263] "Means of determining display order based on advertiser bids" refers to a system for dynamically determining the order of elements displayed according to the advertiser's bid amount.

[0264] This invention is a system designed to enable users to reach their destinations comfortably and efficiently. This system primarily consists of a server, terminals, and users, each working together to process information. Details are provided below.

[0265] The server provides real-time traffic information to assist with route planning. This utilizes map applications such as Google Maps and Apple Maps, as well as traffic information APIs. The server integrates these information sources to plan routes and analyze traffic conditions.

[0266] The terminal functions as the user interface, sending information about the user's current location and destination to the server. Furthermore, the terminal displays detailed information about suggested rest stops received from the server and provides optimal route guidance based on the user's selection.

[0267] The server uses databases and machine learning algorithms to collect and analyze user behavior history. For example, it uses K-Means clustering to analyze user interests and preferences and suggests appropriate resting places based on that information.

[0268] Traffic congestion is predicted by the server using a time-series forecasting model. By combining historical traffic data with real-time data, the server can estimate the time it takes for congestion to occur and resolve.

[0269] The display of proposed rest areas is determined by advertiser bids. The server adjusts the priority of rest areas based on the advertiser's bid amount. This ensures that users receive information about the most noteworthy rest areas.

[0270] A concrete example of this system is when a user is planning a long-distance drive and it is determined that the route to their destination is congested. In such cases, the server suggests recreational spots to avoid the expected congestion. These spots allow for optimal rest stops based on the user's past visit history and preferences.

[0271] An example of a prompt message might be, "Please suggest suitable rest stops while avoiding traffic congestion on the route to the destination." This allows the user to utilize the information provided by the system and spend their time productively.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user inputs the route to their destination using a terminal. The terminal obtains the user's current location using GPS and sends it to the server along with the destination information. The server calculates a standard route using a map application and returns it to the terminal. The input is the current location and destination, and the output is the initial standard route.

[0275] Step 2:

[0276] The server retrieves the user's behavior history data from the database and analyzes it using machine learning algorithms. For example, by using K-Means clustering, it detects the user's preferences and identifies the user's interest in recreation and rest areas. In this process, the input is the behavior history data, and the output is the user's preference information.

[0277] Step 3:

[0278] The server obtains real-time traffic data through the traffic information API. Using a time series prediction model, it predicts traffic congestion from the acquired data and estimates the time to clear it. The input is the real-time traffic data, and the output is the congestion prediction and the clearance time.

[0279] Step 4:

[0280] The server searches for rest spots on the route and filters them based on the user's preference information. It selects appropriate rest areas and sends their detailed information to the terminal. The input is the route information and the user's preferences, and the output is the proposed rest area information. <0\000888>

[0281] Step 5:

[0282] The terminal displays the information of the proposed rest areas sent from the server to the user. The user selects the rest areas they want to visit from the displayed list and inputs their selection into the terminal. The input is the information of the proposed rest areas, and the output is the user's selection.

[0283] Step 6:

[0284] Based on the rest areas selected by the user, the server searches for the optimal route and sends guidance information to the terminal. The terminal presents the newly set route to the user and provides navigation. The input is the rest areas selected by the user, and the output is the updated route guidance.

[0285] Step 7:

[0286] The server determines the display order of the proposed rest areas based on the bid information of the advertisers. The server adjusts the display order and transmits the information to the terminal. The input is the bid information of the advertisers, and the output is the adjusted display order.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In an autonomous vehicle, it is required to propose an appropriate rest area according to the user's preferences while avoiding traffic jams during operation, and to provide a smooth and comfortable driving experience. In addition, it is necessary to construct a system that can generate economic benefits by preferentially presenting specific rest areas.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes a device for setting a driving route, a device for collecting and analyzing the user's behavior history information, a device for predicting the traffic jam situation and estimating the resolution time, a device for proposing surrounding rest areas according to the user's preferences, a device for displaying the information of the proposed rest areas, a function for automatically executing the guidance to the proposed rest areas, and a function for adjusting the presentation order of the rest areas based on the bid information of the advertisements. As a result, it becomes possible to propose the optimal rest area for the user and maximize the economic effect.

[0292] The "device for setting a driving route" is a device for automatically calculating and setting the optimal route from the current position of the vehicle to the destination.

[0293] The "device for collecting and analyzing the user's behavior history information" is a device for collecting the user's past movement data and visit history, and identifying the user's preferences and patterns by analyzing this data.

[0294] A "device for predicting traffic congestion and estimating the time it will clear" is a device that analyzes real-time and historical traffic data to predict the current state of congestion and estimate the time it will clear.

[0295] A "device that suggests nearby resting places according to the user's preferences" is a device that takes the user's preferences into consideration and selects and suggests an appropriate location from among the available resting places in the surrounding area.

[0296] A "device for displaying information on proposed rest areas" is a device that visually presents detailed information about proposed rest areas to the user to aid in their understanding.

[0297] The "function to automatically guide the vehicle to a suggested resting place" refers to providing control and navigation functions to automatically guide the vehicle to the selected resting place.

[0298] The "function to adjust the order in which rest locations are presented based on advertising bid information" is a function that adjusts the order in which rest locations are displayed preferentially based on the advertiser's bid information.

[0299] The system implementing this invention is based on a server installed in an autonomous vehicle and a terminal operated by the user. The server runs on the Google Cloud Platform and uses the Google Maps API to acquire traffic information. It also uses BigQuery to analyze the user's behavior history and employs TensorFlow to implement AI algorithms. AWS's Amazon Forecast service is used to predict traffic congestion. The server integrates these tools to suggest the optimal resting place based on traffic conditions and user preferences.

[0300] The terminal visually displays the proposed rest area information to the user and enables the user to select it. It also has a function to instruct the autonomous driving system on the route to the selected rest area. Information presentation on the terminal is carried out using an intuitive and highly visible interface so that the user can easily understand it even while driving.

[0301] Users can utilize these functions to select the optimal route and rest locations in real-time, minimizing the waste of time due to traffic congestion. In particular, a mechanism is introduced where the generative AI model analyzes the user's past behavior patterns and recommends rest areas that the user is likely to be interested in.

[0302] As a specific example, when a user during a family trip is proposed a new route to avoid traffic congestion along with a zoo to visit for a break, the prompt sentence at this time is input to the server in the form of "When a user who likes animals wants to avoid traffic congestion on the Tomei Expressway for 2 hours starting at 16:00, where should they go for a rest?", and the optimal candidate locations are selected.

[0303] This system enables the realization of a comfortable and efficient travel experience while also being able to effectively provide information that meets the needs of advertisers from a business perspective.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The server calculates the standard route from the departure point to the destination based on the destination input by the user using the Google Maps API. At this time, the input is the departure point and destination specified by the user, and the output is a plurality of route proposals between them. Data calculations are performed to select the most efficient route from the route proposals.

[0307] Step 2:

[0308] Users provide their browsing history using their devices. The server uses BigQuery to collect and organize the user's past visit history data, which is the input. Keywords related to the user's preferences are extracted from the analyzed data. This output information is then reflected in subsequent rest stop suggestions.

[0309] Step 3:

[0310] The server uses Amazon Forecast from AWS to infer real-time and historical traffic data and obtain current and predicted congestion levels. The input is traffic data from various locations, and the output is a list of congested sections and their predicted clearing times. This generates data to optimize user travel.

[0311] Step 4:

[0312] The server uses a generative AI model to combine user preference data with traffic congestion information. Using user preference keywords and congestion information as input, it performs data calculations to generate a list of optimal nearby rest stop candidates. The output is a list of suggested rest stops.

[0313] Step 5:

[0314] The terminal visually displays a list of rest stop options provided by the server. It receives a list of rest stops as input, and the information design is carefully crafted to facilitate user selection. The output is the user's selection action.

[0315] Step 6:

[0316] The user selects their desired rest spot from the locations displayed on the terminal. This selection information is sent back from the terminal to the server and used for the next process.

[0317] Step 7:

[0318] The server sets a new route for the autonomous driving system to guide the vehicle to the selected rest stop. The inputs are the user's selection information and the vehicle's current location, and the output generates an updated destination and the optimal route to it. This information is then sent to the vehicle's navigation system.

[0319] This process allows users to have a travel experience that avoids traffic jams while incorporating breaks that suit their interests.

[0320] 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.

[0321] An embodiment of the present invention is a navigation system that provides a driving route and enables personalized rest stop suggestions that take into account the user's emotional state. This system incorporates an emotion engine that recognizes the user's emotions in real time and optimizes the selection of rest stops based on those emotions.

[0322] First, the user sets a driving route to their destination via the navigation terminal. During this process, the terminal obtains the route from the user's current location to the destination. Along with general traffic information, the user's activity history data is sent to the server.

[0323] The server learns the user's hobbies and preferences based on this behavioral history data, and further analyzes the user's current emotional state in real time through an emotion engine. This analysis uses technologies such as speech recognition and facial recognition to determine the user's stress level and mood.

[0324] Based on conventional traffic information, the server grasps the congestion status along the route and estimates the time it will take for congestion to clear if it is anticipated. Furthermore, to mitigate the user stress expected due to this congestion, it suggests rest stops according to the emotional state recognized by the emotion engine. For example, if the analysis indicates that the user is feeling fatigued or stressed, it will prioritize suggesting spots that prioritize relaxation.

[0325] The terminal displays a list of rest locations provided by the server and prompts the user to make a selection. Based on the information presented, the user decides which spot to visit, and the terminal provides directions to the selected rest location.

[0326] Furthermore, the server can adjust the display order of rest stops based on bids from advertisers, thereby generating advertising revenue. This system allows users to visit rest stops that are optimal for their emotional state, resulting in a more comfortable driving experience while reducing the stress caused by traffic congestion.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The user enters their destination into the navigation terminal and performs a route search. The terminal then obtains a standard driving route from the starting point to the destination.

[0330] Step 2:

[0331] The server collects user behavior data and uses AI to learn user preferences. This includes past visit and search history to identify what kind of activities the user prefers.

[0332] Step 3:

[0333] The server uses an emotion engine to recognize the user's emotions from voice and facial expression data. Through real-time analysis, it determines the user's emotional state (for example, whether they are tired or stressed).

[0334] Step 4:

[0335] The server references real-time traffic data to check for congestion information along the specified route. It analyzes past patterns and current traffic conditions to predict where congestion will occur and how long it will take for it to clear.

[0336] Step 5:

[0337] The server combines the user's emotional state with traffic information to select suitable spots for rest and sightseeing from the surrounding area. For example, if the user is feeling stressed, it will suggest facilities with relaxation effects.

[0338] Step 6:

[0339] The terminal displays location information provided by the server to the user, including an overview and location information for each spot. The user can then choose the location that interests them most from the presented options.

[0340] Step 7:

[0341] The user confirms the selected rest stop on the device and enters their intention to "visit" into the device. The device immediately displays a detailed route to the selected spot and begins guiding the user there.

[0342] Step 8:

[0343] The server maximizes advertising revenue by adjusting the display order to highlight specific spots, taking into account advertiser bidding information. This function is implemented as a means of delivering relevant information to users while generating the funds necessary to maintain the system.

[0344] (Example 2)

[0345] 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".

[0346] Conventional navigation systems simply set routes based on traffic information and could not suggest rest stops while considering the user's emotional state or individual stress level. Furthermore, the ability to utilize advertising value in the order in which rest stops were displayed was limited, making it difficult to optimize the user experience.

[0347] 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.

[0348] In this invention, the server includes means for collecting and analyzing the user's behavioral history and emotional state; means for predicting traffic conditions and estimating congestion relief time; means for suggesting personalized rest stops based on the user's emotional state; and means for adjusting the display order of rest stops based on bids from advertisers. This makes it possible to suggest the optimal rest stop according to the user's emotional state and to set a display order that maximizes the value of advertising.

[0349] A "travel route" refers to the path a user takes to reach their destination.

[0350] "User activity history" refers to a collection of information about past driving history and places visited.

[0351] "Emotional state" refers to information that indicates the user's psychological state, including stress levels and mood.

[0352] "Traffic conditions" refers to information related to traffic, such as road congestion and traffic restrictions.

[0353] "Congestion relief time" refers to the estimated time it will take for traffic congestion to clear up.

[0354] A "personalized rest stop" is a resting spot selected to suit the user's specific needs and emotional state.

[0355] An "advertiser" is someone who provides advertisements for specific products or services and bids for the order in which they are displayed.

[0356] This invention provides a navigation system that takes into account the user's emotional state, and provides personalized settings for driving routes, traffic conditions, and rest stops. The main components necessary for implementing this system are a navigation terminal, an emotion engine, a communication network, and a server.

[0357] The user first sets a destination using a navigation terminal. This terminal uses a location information acquisition device (e.g., GPS) to obtain the user's current location and calculates the route to the set destination.

[0358] The terminal sends route information, user behavior history data, and real-time sentiment information to the server. To obtain this sentiment information, speech recognition software or facial recognition software (e.g., general software for speech recognition, facial recognition API) is used.

[0359] The server receives this data and analyzes the user's emotional state via an emotion engine. Based on this analysis, the server predicts congestion levels and selects the most suitable resting place for the user's emotions. In this process, a traffic information API is used to estimate road congestion levels and the time it will take for traffic to clear.

[0360] The device then displays a list of rest locations that match the user's emotional state. This includes adjusting the display order based on advertisers' bidding information. Based on the information presented, the user can select a rest location they wish to visit.

[0361] For example, if a user wants to "reconnect with nature and refresh themselves" during a long drive, the system is configured to promptly suggest nearby parks and gardens. An example of a prompt to input into the generating AI model would be, "Please suggest relaxing places or spots that suit your interests so that I can find the best rest stop considering your current emotional state." Through this process, the user can enjoy a more comfortable and personalized driving experience.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The user sets a destination using a navigation terminal. The input includes the starting point and destination, and the current location information is obtained. The output is candidate route information to the destination. Here, a GPS device is used to obtain precise location information, and a map application is used to calculate the route.

[0365] Step 2:

[0366] The device transmits route information, user behavior history data, and sentiment data collected via voice or camera to the server. The input consists of the aforementioned behavior history and real-time sentiment data, which are used to generate data packages for the server. The output is the user profile information transmitted to the server.

[0367] Step 3:

[0368] The server analyzes the received data and uses an emotion engine to identify the user's emotional state. The input is emotional data received from the terminal. Data analysis software (e.g., emotion analysis algorithm) is used to classify the user's emotional state as stress, relaxation, etc. The output is the evaluation result of the analyzed emotional state.

[0369] Step 4:

[0370] The server uses a traffic information API to predict traffic conditions on the user's current route. Route information and historical traffic pattern data are used as input. After analysis, the output includes a congestion prediction and an estimated time for congestion to clear.

[0371] Step 5:

[0372] The server selects the optimal rest stop based on the user's emotional state and traffic conditions. The input consists of an evaluation of the emotional state and traffic forecast information. A generative AI model is used to evaluate candidate rest stops and generate a ranking. The output is a list of the most suitable rest stops for the user.

[0373] Step 6:

[0374] The terminal displays a list of rest stops received from the server to the user. The input is rest stop information obtained from the server. This information is prioritized based on bidding information from advertisers. The output is a list of rest stop candidates formatted for user display.

[0375] Step 7:

[0376] The user selects a destination from a list of suggested rest stops. The input is the list of rest stop options displayed on the terminal. After the selection is complete, the terminal begins providing detailed route guidance to the selected rest stop. The output is detailed navigation information.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0379] Users of autonomous vehicles often experience monotonous journeys over long periods and may feel stressed and fatigued due to traffic congestion. As a result, they may not enjoy a comfortable travel experience. Furthermore, these factors affect satisfaction during travel, and new approaches are needed to improve the user's driving experience.

[0380] 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.

[0381] In this invention, the server includes means for collecting and analyzing user behavior history data, means for detecting the user's emotional state, and means for optimizing suggested rest locations based on the emotional state. This enables users to rest at optimal rest locations according to their emotional state, thereby reducing stress during travel and providing a comfortable driving experience.

[0382] "Means for setting the travel route" refers to a function that automatically calculates and sets the optimal travel route to the destination.

[0383] "Means for collecting and analyzing user behavior history data" refers to the process of collecting data on users' past travel history, interests, and preferences, and analyzing user trends based on that data.

[0384] "Methods for predicting traffic congestion and estimating the time it will take to resolve it" refers to technologies that analyze traffic flow to predict future congestion and the time it will take for it to resolve.

[0385] "A means of suggesting nearby rest areas according to user preferences" refers to a function that selects and recommends appropriate rest areas based on the user's preferences.

[0386] "Means for displaying information on proposed rest areas" refers to an interface that visually provides users with detailed information about the selected rest areas.

[0387] "Means for detecting the user's emotional state" refers to a function that analyzes the user's current emotions in real time using technologies such as speech recognition and facial recognition.

[0388] "A means of optimizing rest locations based on emotional state" refers to a system that selects rest locations taking into account the user's emotional state and presents the optimal option.

[0389] The system for carrying out this invention consists of three components: a user, a terminal, and a server. The system aims to provide the user with a comfortable travel experience by integrating emotion recognition technology and navigation technology.

[0390] First, the user sets a travel route to their destination using a device. The device has the function of acquiring the user's current location and destination information and sending it to the server. The user's activity history data is also sent to the server at the same time, which is used to analyze the user's preferences and travel trends.

[0391] Next, the server uses the user's behavioral history data to analyze their hobbies and preferences, and further analyzes their emotional state in real time via an emotion engine. By utilizing speech recognition and facial recognition, it grasps the user's stress level and mood, and based on that, suggests the most suitable resting place.

[0392] The server analyzes traffic information in real time, predicts congestion levels, and estimates the time it will take for congestion to clear. This predicted data, along with the user's emotional state, is used to design routes that ensure a comfortable travel experience.

[0393] The terminal displays information about rest stops provided by the server and prompts the user to make a selection. Based on the information presented, the user decides which rest stop to visit, and the terminal provides detailed directions to the selected rest stop.

[0394] This system allows users to take breaks according to their emotional state at the time, reducing stress and providing a comfortable driving experience. For example, if the system detects that the user is stressed, the server can suggest nearby relaxation spots and play relaxing music on the device.

[0395] An example of a prompt message is: "Develop a system that analyzes the emotions of passengers in an autonomous vehicle and suggests locations where they can relax."

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The terminal receives destination information set by the user and uses GPS to determine the user's current location. The input information consists of the user's destination and current location. This information is sent to the server, which provides the data necessary for calculating the travel route.

[0399] Step 2:

[0400] The server calculates the optimal route based on the received destination and current location data. During this process, it obtains real-time traffic information from a traffic database and performs congestion predictions. This results in a route that takes congestion into account.

[0401] Step 3:

[0402] The server analyzes the user's behavioral history data to extract their hobbies and preferences. Input data includes past travel history and interest / preference-related information. The output is an individual preference pattern that reflects the user's tendencies.

[0403] Step 4:

[0404] The server receives video and audio data from the terminal and uses an emotion engine to analyze the user's emotional state in real time. The resulting stress level and mood state are then output.

[0405] Step 5:

[0406] The server lists optimal rest locations that match the user's preferences based on the analyzed emotional state. Several candidate locations are output, taking into account the user's interests and emotional state.

[0407] Step 6:

[0408] The terminal displays rest location information obtained from the server and presents options to the user. It facilitates interaction until the user makes a selection and confirms the final selected location.

[0409] Step 7:

[0410] The device displays detailed directions to the selected rest stop, assisting the user in reaching the destination comfortably. It provides navigation to the chosen rest stop and plays music that responds to the user's mood.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] In an embodiment of the present invention, a system is provided that suggests an optimal rest stop to a user while they are driving, while avoiding traffic congestion. This system mainly consists of three elements: a server, a terminal, and a user.

[0428] First, the user uses their device to search for a route to their destination. During this process, the device obtains a standard route from the user's current location to their destination.

[0429] Next, the server collects and analyzes user behavior data to understand user preferences. This includes past search and visit history, and uses AI algorithms to identify user interests. Based on this information, the server learns the types of recreation and relaxation styles preferred by the user.

[0430] The server then uses current traffic information to check congestion along the configured route and uses an AI model to estimate the time it will take for the congestion to clear. For example, it uses historical traffic data and real-time traffic information to predict which sections are experiencing congestion and when that congestion will clear.

[0431] If traffic conditions are determined to be congested, the server searches for nearby rest stops. As part of the search, filtering is performed to reflect the user's preferences, allowing the server to select the most suitable location for the user. This information is sent to the device and displayed in a user-friendly format.

[0432] The terminal displays detailed information about suggested rest stops to the user. Based on this information, the user can select a spot they wish to visit. Once a selection is made, the terminal guides the user to the optimal route to that location, making efficient use of time spent in traffic.

[0433] Furthermore, this system includes a mechanism that allows advertisers to change the display order through bidding, making it possible to make specific spots more visible. By integrating this, the server creates a system that not only provides a better user experience but also has the potential to generate economic benefits.

[0434] Thus, the present invention reduces the user's driving burden while providing meaningful time based on hobbies and interests, and ensuring a comfortable journey home.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The user sets a destination on the car navigation terminal, and the terminal obtains a standard driving route from the current location to the destination.

[0438] Step 2:

[0439] The server collects user behavior history data linked to Yahoo IDs, analyzes it using AI, and identifies the user's hobbies and preferences. The collected data includes past search history and browsing history.

[0440] Step 3:

[0441] The server checks traffic conditions based on current route information. In particular, it identifies congestion points along the route and uses an AI model to predict the time it will take for each congestion to clear.

[0442] Step 4:

[0443] To make the most of time spent in traffic, the server selects personalized rest stops from the surrounding area based on the user's preferences. This selection takes into account the estimated time required to clear the traffic and the user's behavioral history.

[0444] Step 5:

[0445] The terminal displays a list of rest spots sent from the server, along with their details, to the user, allowing them to select a spot they wish to visit.

[0446] Step 6:

[0447] The user selects a rest spot that interests them, and the device guides the user to their desired location by displaying the optimal route to that spot.

[0448] Step 7:

[0449] The server adjusts the priority of displayed rest spots based on bids from advertisers, aiming to generate economic revenue.

[0450] (Example 1)

[0451] 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."

[0452] In modern transportation systems, users face the challenge of significantly increased travel times due to frequent traffic congestion. Furthermore, it's difficult to alleviate fatigue associated with monotonous drives and provide fulfilling rest periods tailored to user preferences. Additionally, advertisers require effective placement of their advertisements.

[0453] 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.

[0454] In this invention, the server includes means for setting a route, means for collecting and analyzing the user's behavior history, means for predicting traffic conditions and estimating the time it will take for them to clear, means for suggesting appropriate rest stops based on the user's preferences, and means for determining the display order based on advertiser bids. As a result, users can receive suggestions for the optimal route and rest stops, enabling them to shorten travel time and enjoy more fulfilling rest periods. Advertisers can also achieve more effective ad placement.

[0455] "Means of setting a route" refers to a function that determines the optimal driving route based on the user's current location and destination.

[0456] "Means for collecting and analyzing user behavior history" refers to a system that analyzes users' preferences and interests based on their past usage history and location information.

[0457] "Means for predicting traffic conditions and estimating the time it takes to resolve them" refers to a function that uses real-time data and historical traffic data to predict the occurrence of traffic congestion and calculate the time it will take for it to resolve.

[0458] "Means of suggesting appropriate rest locations based on user preferences" refers to a system that selects and suggests rest spots that suit the user based on behavioral history and preference analysis.

[0459] "Means of presenting detailed information about proposed rest areas" refers to a function that visually displays specific information about the selected rest area to the user.

[0460] "Means of determining display order based on advertiser bids" refers to a system for dynamically determining the order of elements displayed according to the advertiser's bid amount.

[0461] This invention is a system designed to enable users to reach their destinations comfortably and efficiently. This system primarily consists of a server, terminals, and users, each working together to process information. Details are provided below.

[0462] The server provides real-time traffic information to assist with route planning. This utilizes map applications such as Google Maps and Apple Maps, as well as traffic information APIs. The server integrates these information sources to plan routes and analyze traffic conditions.

[0463] The terminal functions as the user interface, sending information about the user's current location and destination to the server. Furthermore, the terminal displays detailed information about suggested rest stops received from the server and provides optimal route guidance based on the user's selection.

[0464] The server uses databases and machine learning algorithms to collect and analyze user behavior history. For example, it uses K-Means clustering to analyze user interests and preferences and suggests appropriate resting places based on that information.

[0465] Traffic congestion is predicted by the server using a time-series forecasting model. By combining historical traffic data with real-time data, the server can estimate the time it takes for congestion to occur and resolve.

[0466] The display of proposed rest areas is determined by advertiser bids. The server adjusts the priority of rest areas based on the advertiser's bid amount. This ensures that users receive information about the most noteworthy rest areas.

[0467] A concrete example of this system is when a user is planning a long-distance drive and it is determined that the route to their destination is congested. In such cases, the server suggests recreational spots to avoid the expected congestion. These spots allow for optimal rest stops based on the user's past visit history and preferences.

[0468] An example of a prompt message might be, "Please suggest suitable rest stops while avoiding traffic congestion on the route to the destination." This allows the user to utilize the information provided by the system and spend their time productively.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user inputs the route to their destination using a terminal. The terminal obtains the user's current location using GPS and sends it to the server along with the destination information. The server calculates a standard route using a map application and returns it to the terminal. The input is the current location and destination, and the output is the initial standard route.

[0472] Step 2:

[0473] The server retrieves user behavior history data from a database and analyzes it using machine learning algorithms. For example, it uses K-Means clustering to detect user preferences and identify their interests in recreation and rest areas. In this process, the input is behavior history data, and the output is user preference information.

[0474] Step 3:

[0475] The server acquires real-time traffic data through a traffic information API. Using a time-series prediction model, it predicts traffic congestion from the acquired data and estimates the time it will take for the congestion to clear. The input is real-time traffic data, and the output is congestion prediction and clearing time.

[0476] Step 4:

[0477] The server searches for rest stops along the route and filters them based on the user's preferences. It selects a suitable rest stop and sends its details to the terminal. The input is route information and user preferences, and the output is suggested rest stop information.

[0478] Step 5:

[0479] The terminal displays information about suggested rest stops sent from the server to the user. The user selects a rest stop they wish to visit from the displayed list and inputs their selection into the terminal. The input is the information about the suggested rest stops, and the output is the user's selection.

[0480] Step 6:

[0481] The server searches for the optimal route based on the rest stops selected by the user and sends the guidance information to the terminal. The terminal then presents the newly set route to the user and provides navigation. The input is the rest stops selected by the user, and the output is the updated route guidance.

[0482] Step 7:

[0483] The server determines the display order of proposed rest areas based on the advertiser's bid information. The server adjusts the display order and sends that information to the terminal. The input is the advertiser's bid information, and the output is the adjusted display order.

[0484] (Application Example 1)

[0485] 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."

[0486] In autonomous vehicles, there is a need to avoid traffic congestion during operation, suggest appropriate rest stops according to the user's preferences, and provide a smooth and comfortable driving experience. Furthermore, it is necessary to build a system that can generate economic benefits by prioritizing the presentation of specific rest stops.

[0487] 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.

[0488] In this invention, the server includes a device for setting a travel route, a device for collecting and analyzing user behavior history information, a device for predicting traffic congestion and estimating the time of its resolution, a device for suggesting nearby rest areas according to the user's preferences, a device for displaying information on the suggested rest areas, a function for automatically guiding the user to the suggested rest areas, and a function for adjusting the order in which rest areas are presented based on advertising bidding information. This makes it possible to suggest the most suitable rest areas for the user and maximize economic benefits.

[0489] A "route setting device" is a device that automatically calculates and sets the optimal route from the vehicle's current location to its destination.

[0490] A "device for collecting and analyzing user behavior history information" is a device that collects users' past movement data and visit history, and analyzes this data to identify users' preferences and patterns.

[0491] A "device for predicting traffic congestion and estimating the time it will clear" is a device that analyzes real-time and historical traffic data to predict the current state of congestion and estimate the time it will clear.

[0492] A "device that suggests nearby resting places according to the user's preferences" is a device that takes the user's preferences into consideration and selects and suggests an appropriate location from among the available resting places in the surrounding area.

[0493] A "device for displaying information on proposed rest areas" is a device that visually presents detailed information about proposed rest areas to the user to aid in their understanding.

[0494] The "function to automatically guide the vehicle to a suggested resting place" refers to providing control and navigation functions to automatically guide the vehicle to the selected resting place.

[0495] The "function to adjust the order in which rest locations are presented based on advertising bid information" is a function that adjusts the order in which rest locations are displayed preferentially based on the advertiser's bid information.

[0496] The system implementing this invention is based on a server installed in an autonomous vehicle and a terminal operated by the user. The server runs on the Google Cloud Platform and uses the Google Maps API to acquire traffic information. It also uses BigQuery to analyze the user's behavior history and employs TensorFlow to implement AI algorithms. AWS's Amazon Forecast service is used to predict traffic congestion. The server integrates these tools to suggest the optimal resting place based on traffic conditions and user preferences.

[0497] The terminal visually displays information about suggested rest stops to the user, allowing the user to select one. It also has the function to instruct the autonomous driving system on the route to the selected rest stop. The information displayed on the terminal uses an intuitive and highly visible interface so that the user can easily understand it even while driving.

[0498] Users can leverage these features to select the optimal route and rest stops in real time, minimizing wasted time due to traffic congestion. In particular, a generative AI model analyzes the user's past behavior patterns and recommends rest stops that are likely to interest them.

[0499] As a concrete example, a user on a family trip might be suggested a new route to avoid traffic, along with a zoo to visit for a break. In this case, the prompt would be something like, "Where should an animal-loving user go for a break if they want to avoid traffic on the Tomei Expressway for two hours starting at 4 PM?" The server would then select the most suitable candidate locations.

[0500] This system will enable a comfortable and efficient travel experience while also providing effective information tailored to the needs of advertisers from a business perspective.

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] The server uses the Google Maps API to calculate a standard route from the starting point to the destination based on the destination entered by the user. The input is the user-specified starting point and destination, and the output is multiple route options between them. Data calculations are then performed to select the most efficient route from these options.

[0504] Step 2:

[0505] Users provide their browsing history using their devices. The server uses BigQuery to collect and organize the user's past visit history data, which is the input. Keywords related to the user's preferences are extracted from the analyzed data. This output information is then reflected in subsequent rest stop suggestions.

[0506] Step 3:

[0507] The server uses Amazon Forecast from AWS to infer real-time and historical traffic data and obtain current and predicted congestion levels. The input is traffic data from various locations, and the output is a list of congested sections and their predicted clearing times. This generates data to optimize user travel.

[0508] Step 4:

[0509] The server uses a generative AI model to combine user preference data with traffic congestion information. Using user preference keywords and congestion information as input, it performs data calculations to generate a list of optimal nearby rest stop candidates. The output is a list of suggested rest stops.

[0510] Step 5:

[0511] The terminal visually displays a list of rest stop options provided by the server. The input is a list of rest stops, and the information design is carefully crafted to facilitate user selection. The output is the user's selection action.

[0512] Step 6:

[0513] The user selects their desired rest spot from the locations displayed on the terminal. This selection information is sent back from the terminal to the server and used for the next process.

[0514] Step 7:

[0515] The server sets a new route for the autonomous driving system to guide the vehicle to the selected rest stop. The inputs are the user's selection information and the vehicle's current location, and the output generates an updated destination and the optimal route to it. This information is then sent to the vehicle's navigation system.

[0516] This process allows users to have a travel experience that avoids traffic jams while incorporating breaks that suit their interests.

[0517] 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.

[0518] An embodiment of the present invention is a navigation system that provides a driving route and enables personalized rest stop suggestions that take into account the user's emotional state. This system incorporates an emotion engine that recognizes the user's emotions in real time and optimizes the selection of rest stops based on those emotions.

[0519] First, the user sets a driving route to their destination via the navigation terminal. During this process, the terminal obtains the route from the user's current location to the destination. Along with general traffic information, the user's activity history data is sent to the server.

[0520] The server learns the user's hobbies and preferences based on this behavioral history data, and further analyzes the user's current emotional state in real time through an emotion engine. This analysis uses technologies such as speech recognition and facial recognition to determine the user's stress level and mood.

[0521] Based on conventional traffic information, the server identifies congestion along the route and estimates the time it will take for congestion to clear if it is anticipated. Furthermore, to mitigate the user stress expected due to this congestion, it suggests rest stops based on the emotional state recognized by the emotion engine. For example, if the analysis indicates that the user is feeling fatigued or stressed, the server prioritizes suggesting spots that emphasize relaxation.

[0522] The terminal displays a list of rest locations provided by the server and prompts the user to make a selection. Based on the information presented, the user decides which spot to visit, and the terminal provides directions to the selected rest location.

[0523] Furthermore, the server can adjust the display order of rest stops based on bids from advertisers, thereby generating advertising revenue. This system allows users to visit rest stops that are optimal for their emotional state, resulting in a more comfortable driving experience while reducing the stress caused by traffic congestion.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] The user enters their destination into the navigation terminal and performs a route search. The terminal then obtains a standard driving route from the starting point to the destination.

[0527] Step 2:

[0528] The server collects user behavior data and uses AI to learn user preferences. This includes past visit and search history to identify what kind of activities the user prefers.

[0529] Step 3:

[0530] The server uses an emotion engine to recognize the user's emotions from voice and facial expression data. Through real-time analysis, it determines the user's emotional state (for example, whether they are tired or stressed).

[0531] Step 4:

[0532] The server references real-time traffic data to check for congestion information along the specified route. It analyzes past patterns and current traffic conditions to predict where congestion will occur and how long it will take for it to clear.

[0533] Step 5:

[0534] The server combines the user's emotional state with traffic information to select suitable spots for rest and sightseeing from the surrounding area. For example, if the user is feeling stressed, it will suggest facilities with relaxation effects.

[0535] Step 6:

[0536] The terminal displays location information provided by the server to the user, including an overview and location information for each spot. The user can then choose the location that interests them most from the presented options.

[0537] Step 7:

[0538] The user confirms the selected rest stop on the device and enters their intention to "visit" into the device. The device immediately displays a detailed route to the selected spot and begins guiding the user there.

[0539] Step 8:

[0540] The server maximizes advertising revenue by adjusting the display order to highlight specific spots, taking into account advertiser bidding information. This function is implemented as a means of delivering relevant information to users while generating the funds necessary to maintain the system.

[0541] (Example 2)

[0542] 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."

[0543] Conventional navigation systems simply set routes based on traffic information and could not suggest rest stops while considering the user's emotional state or individual stress level. Furthermore, the ability to utilize advertising value in the order in which rest stops were displayed was limited, making it difficult to optimize the user experience.

[0544] 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.

[0545] In this invention, the server includes means for collecting and analyzing the user's behavioral history and emotional state; means for predicting traffic conditions and estimating congestion relief time; means for suggesting personalized rest stops based on the user's emotional state; and means for adjusting the display order of rest stops based on bids from advertisers. This makes it possible to suggest the optimal rest stop according to the user's emotional state and to set a display order that maximizes the value of advertising.

[0546] A "travel route" refers to the path a user takes to reach their destination.

[0547] "User activity history" refers to a collection of information about past driving history and places visited.

[0548] "Emotional state" refers to information that indicates the user's psychological state, including stress levels and mood.

[0549] "Traffic conditions" refers to information related to traffic, such as road congestion and traffic restrictions.

[0550] "Congestion relief time" refers to the estimated time it will take for traffic congestion to clear up.

[0551] A "personalized rest stop" is a resting spot selected to suit the user's specific needs and emotional state.

[0552] An "advertiser" is someone who provides advertisements for specific products or services and bids for the order in which they are displayed.

[0553] This invention provides a navigation system that takes into account the user's emotional state, and provides personalized settings for driving routes, traffic conditions, and rest stops. The main components necessary for implementing this system are a navigation terminal, an emotion engine, a communication network, and a server.

[0554] The user first sets a destination using a navigation terminal. This terminal uses a location information acquisition device (e.g., GPS) to obtain the user's current location and calculates the route to the set destination.

[0555] The terminal sends route information, user behavior history data, and real-time sentiment information to the server. To obtain this sentiment information, speech recognition software or facial recognition software (e.g., general-purpose software for speech recognition, facial recognition API) is used.

[0556] The server receives this data and analyzes the user's emotional state via an emotion engine. Based on this analysis, the server predicts congestion levels and selects the most suitable resting place for the user's emotions. In this process, a traffic information API is used to estimate road congestion levels and the time it will take for traffic to clear.

[0557] The device then displays a list of rest locations that match the user's emotional state. This includes adjusting the display order based on advertisers' bidding information. Based on the information presented, the user can select a rest location they wish to visit.

[0558] For example, if a user wants to "reconnect with nature and refresh themselves" during a long drive, the system is configured to promptly suggest nearby parks and gardens. An example of a prompt to input into the generating AI model would be, "Please suggest relaxing places or spots that suit your interests so that I can find the best rest stop considering your current emotional state." Through this process, the user can enjoy a more comfortable and personalized driving experience.

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] The user sets a destination using a navigation terminal. The input includes the starting point and destination, and the current location information is obtained. The output is candidate route information to the destination. Here, a GPS device is used to obtain precise location information, and a map application is used to calculate the route.

[0562] Step 2:

[0563] The device transmits route information, user behavior history data, and sentiment data collected via voice or camera to the server. The input consists of the aforementioned behavior history and real-time sentiment data, which are used to generate data packages for the server. The output is the user profile information transmitted to the server.

[0564] Step 3:

[0565] The server analyzes the received data and uses an emotion engine to identify the user's emotional state. The input is emotional data received from the terminal. Data analysis software (e.g., emotion analysis algorithm) is used to classify the user's emotional state as stress, relaxation, etc. The output is the evaluation result of the analyzed emotional state.

[0566] Step 4:

[0567] The server uses a traffic information API to predict traffic conditions on the user's current route. Route information and historical traffic pattern data are used as input. After analysis, the output includes a congestion prediction and an estimated time for congestion to clear.

[0568] Step 5:

[0569] The server selects the optimal rest stop based on the user's emotional state and traffic conditions. The input consists of an evaluation of the emotional state and traffic forecast information. A generative AI model is used to evaluate candidate rest stops and generate a ranking. The output is a list of the most suitable rest stops for the user.

[0570] Step 6:

[0571] The terminal displays a list of rest stops received from the server to the user. The input is rest stop information obtained from the server. This information is prioritized based on bidding information from advertisers. The output is a list of rest stop candidates formatted for user display.

[0572] Step 7:

[0573] The user selects a destination from a list of suggested rest stops. The input is the list of rest stop options displayed on the terminal. After the selection is complete, the terminal begins providing detailed route guidance to the selected rest stop. The output is detailed navigation information.

[0574] (Application Example 2)

[0575] 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."

[0576] Users of autonomous vehicles often experience monotonous journeys over long periods and may feel stressed and fatigued due to traffic congestion. As a result, they may not enjoy a comfortable travel experience. Furthermore, these factors affect satisfaction during travel, highlighting the need for new approaches to improve the user's driving experience.

[0577] 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.

[0578] In this invention, the server includes means for collecting and analyzing user behavior history data, means for detecting the user's emotional state, and means for optimizing suggested rest locations based on the emotional state. This enables users to rest at optimal rest locations according to their emotional state, thereby reducing stress during travel and providing a comfortable driving experience.

[0579] "Means for setting the travel route" refers to a function that automatically calculates and sets the optimal travel route to the destination.

[0580] "Means for collecting and analyzing user behavior history data" refers to the process of collecting data on users' past travel history, interests, and preferences, and analyzing user trends based on that data.

[0581] "Methods for predicting traffic congestion and estimating the time it will take to resolve it" refers to technologies that analyze traffic flow to predict future congestion and the time it will take for it to resolve.

[0582] "A means of suggesting nearby rest areas according to user preferences" refers to a function that selects and recommends appropriate rest areas based on the user's preferences.

[0583] "Means for displaying information on proposed rest areas" refers to an interface that visually provides users with detailed information about the selected rest areas.

[0584] "Means for detecting the user's emotional state" refers to a function that analyzes the user's current emotions in real time using technologies such as speech recognition and facial recognition.

[0585] "A means of optimizing rest locations based on emotional state" refers to a system that selects rest locations taking into account the user's emotional state and presents the optimal option.

[0586] The system for carrying out this invention consists of three components: a user, a terminal, and a server. The system aims to provide the user with a comfortable travel experience by integrating emotion recognition technology and navigation technology.

[0587] First, the user sets a travel route to their destination using a terminal. The terminal has the function of acquiring the user's current location and destination information and sending it to the server. The user's activity history data is also sent to the server at the same time, which is used to analyze the user's preferences and travel trends.

[0588] Next, the server uses the user's behavioral history data to analyze their hobbies and preferences, and further analyzes their emotional state in real time via an emotion engine. By utilizing speech recognition and facial recognition, it grasps the user's stress level and mood, and based on that, suggests the most suitable resting place.

[0589] The server analyzes traffic information in real time, predicts congestion levels, and estimates the time it will take for congestion to clear. This predicted data, along with the user's emotional state, is used to design routes that ensure a comfortable travel experience.

[0590] The terminal displays information about rest stops provided by the server and prompts the user to make a selection. Based on the information presented, the user decides which rest stop to visit, and the terminal provides detailed directions to the selected rest stop.

[0591] This system allows users to take breaks according to their emotional state at the time, reducing stress and providing a comfortable driving experience. For example, if the system detects that the user is stressed, the server can suggest nearby relaxation spots and play relaxing music on the device.

[0592] An example of a prompt is: "Develop a system that analyzes the emotions of passengers in an autonomous vehicle and suggests locations where they can relax."

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The terminal receives destination information set by the user and uses GPS to determine the user's current location. The input information consists of the user's destination and current location. This information is sent to the server, which provides the data necessary for calculating the travel route.

[0596] Step 2:

[0597] The server calculates the optimal route based on the received destination and current location data. During this process, it obtains real-time traffic information from a traffic database and performs congestion predictions. This results in a route that takes congestion into account.

[0598] Step 3:

[0599] The server analyzes the user's behavioral history data to extract their hobbies and preferences. Input data includes past travel history and interest / preference-related information. The output is an individual preference pattern that reflects the user's tendencies.

[0600] Step 4:

[0601] The server receives video and audio data from the terminal and uses an emotion engine to analyze the user's emotional state in real time. The resulting stress level and mood state are then output.

[0602] Step 5:

[0603] The server lists optimal rest locations that match the user's preferences based on the analyzed emotional state. Several candidate locations are output, taking into account the user's interests and emotional state.

[0604] Step 6:

[0605] The terminal displays rest location information obtained from the server and presents options to the user. It facilitates interaction until the user makes a selection and confirms the final selected location.

[0606] Step 7:

[0607] The device displays detailed directions to the selected rest stop, assisting the user in reaching the destination comfortably. It provides navigation to the chosen rest stop and plays music that responds to the user's mood.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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".

[0625] In an embodiment of the present invention, a system is provided that suggests an optimal rest stop to a user while they are driving, while avoiding traffic congestion. This system mainly consists of three elements: a server, a terminal, and a user.

[0626] First, the user uses their device to search for a route to their destination. During this process, the device obtains a standard route from the user's current location to their destination.

[0627] Next, the server collects and analyzes user behavior data to understand user preferences. This includes past search and visit history, and uses AI algorithms to identify user interests. Based on this information, the server learns the types of recreation and relaxation styles preferred by the user.

[0628] The server then uses current traffic information to check congestion along the configured route and uses an AI model to estimate the time it will take for the congestion to clear. For example, it uses historical traffic data and real-time traffic information to predict which sections are experiencing congestion and when that congestion will clear.

[0629] If traffic conditions are determined to be congested, the server searches for nearby rest stops. As part of the search, filtering is performed to reflect the user's preferences, allowing the server to select the most suitable location for the user. This information is sent to the device and displayed in a user-friendly format.

[0630] The terminal displays detailed information about suggested rest stops to the user. Based on this information, the user can select a spot they wish to visit. Once a selection is made, the terminal guides the user to the optimal route to that location, making efficient use of time spent in traffic.

[0631] Furthermore, this system includes a mechanism that allows advertisers to change the display order through bidding, making it possible to make specific spots more visible. By integrating this, the server creates a system that not only provides a better user experience but also has the potential to generate economic benefits.

[0632] Thus, the present invention reduces the user's driving burden while providing meaningful time based on hobbies and interests, and ensuring a comfortable journey home.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The user sets a destination on the car navigation terminal, and the terminal obtains a standard driving route from the current location to the destination.

[0636] Step 2:

[0637] The server collects user behavior history data linked to Yahoo IDs, analyzes it using AI, and identifies the user's hobbies and preferences. The collected data includes past search history and browsing history.

[0638] Step 3:

[0639] The server checks traffic conditions based on current route information. In particular, it identifies congestion points along the route and uses an AI model to predict the time it will take for each congestion to clear.

[0640] Step 4:

[0641] To make the most of time spent in traffic, the server selects personalized rest stops from the surrounding area based on the user's preferences. This selection takes into account the estimated time required to clear the traffic and the user's behavioral history.

[0642] Step 5:

[0643] The terminal displays a list of rest spots sent from the server, along with their details, to the user, allowing them to select a spot they wish to visit.

[0644] Step 6:

[0645] The user selects a rest spot that interests them, and the device guides the user to their desired location by displaying the optimal route to that spot.

[0646] Step 7:

[0647] The server adjusts the priority of displayed rest spots based on bids from advertisers, aiming to generate economic revenue.

[0648] (Example 1)

[0649] 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".

[0650] In modern transportation systems, users face the challenge of significantly increased travel times due to frequent traffic congestion. Furthermore, it's difficult to alleviate fatigue associated with monotonous drives and provide fulfilling rest periods tailored to user preferences. Additionally, advertisers require effective placement of their advertisements.

[0651] 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.

[0652] In this invention, the server includes means for setting a route, means for collecting and analyzing the user's behavior history, means for predicting traffic conditions and estimating the time it will take for them to clear, means for suggesting appropriate rest stops based on the user's preferences, and means for determining the display order based on advertiser bids. As a result, users can receive suggestions for the optimal route and rest stops, enabling them to shorten travel time and enjoy more fulfilling rest periods. Advertisers can also achieve more effective ad placement.

[0653] "Means of setting a route" refers to a function that determines the optimal driving route based on the user's current location and destination.

[0654] "Means for collecting and analyzing user behavior history" refers to a system that analyzes users' preferences and interests based on their past usage history and location information.

[0655] "Means for predicting traffic conditions and estimating the time it takes to resolve them" refers to a function that uses real-time data and historical traffic data to predict the occurrence of traffic congestion and calculate the time it will take for it to resolve.

[0656] "Means of suggesting appropriate rest locations based on user preferences" refers to a system that selects and suggests rest spots that suit the user based on behavioral history and preference analysis.

[0657] "Means of presenting detailed information about proposed rest areas" refers to a function that visually displays specific information about the selected rest area to the user.

[0658] "Means of determining display order based on advertiser bids" refers to a system for dynamically determining the order of elements displayed according to the advertiser's bid amount.

[0659] This invention is a system designed to enable users to reach their destinations comfortably and efficiently. This system primarily consists of a server, terminals, and users, each working together to process information. Details are provided below.

[0660] The server provides real-time traffic information to assist with route planning. This utilizes map applications such as Google Maps and Apple Maps, as well as traffic information APIs. The server integrates these information sources to plan routes and analyze traffic conditions.

[0661] The terminal functions as the user interface, sending information about the user's current location and destination to the server. Furthermore, the terminal displays detailed information about suggested rest stops received from the server and provides optimal route guidance based on the user's selection.

[0662] The server uses databases and machine learning algorithms to collect and analyze user behavior history. For example, it uses K-Means clustering to analyze user interests and preferences and suggests appropriate resting places based on that information.

[0663] Traffic congestion is predicted by the server using a time-series forecasting model. By combining historical traffic data with real-time data, the server can estimate the time it takes for congestion to occur and resolve.

[0664] The display of proposed rest areas is determined by advertiser bids. The server adjusts the priority of rest areas based on the advertiser's bid amount. This ensures that users receive information about the most noteworthy rest areas.

[0665] A concrete example of this system is when a user is planning a long-distance drive and it is determined that the route to their destination is congested. In such cases, the server suggests recreational spots to avoid the expected congestion. These spots allow for optimal rest stops based on the user's past visit history and preferences.

[0666] An example of a prompt message might be, "Please suggest suitable rest stops while avoiding traffic congestion on the route to the destination." This allows the user to utilize the information provided by the system and spend their time productively.

[0667] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0668] Step 1:

[0669] The user inputs the route to their destination using a terminal. The terminal obtains the user's current location using GPS and sends it to the server along with the destination information. The server calculates a standard route using a map application and returns it to the terminal. The input is the current location and destination, and the output is the initial standard route.

[0670] Step 2:

[0671] The server retrieves user behavior history data from a database and analyzes it using machine learning algorithms. For example, it uses K-Means clustering to detect user preferences and identify their interests in recreation and rest areas. In this process, the input is behavior history data, and the output is user preference information.

[0672] Step 3:

[0673] The server acquires real-time traffic data through a traffic information API. Using a time-series prediction model, it predicts traffic congestion from the acquired data and estimates the time it will take for the congestion to clear. The input is real-time traffic data, and the output is congestion prediction and clearing time.

[0674] Step 4:

[0675] The server searches for rest stops along the route and filters them based on the user's preferences. It selects a suitable rest stop and sends its details to the terminal. The input is route information and user preferences, and the output is suggested rest stop information.

[0676] Step 5:

[0677] The terminal displays information about suggested rest stops sent from the server to the user. The user selects a rest stop they wish to visit from the displayed list and inputs their selection into the terminal. The input is the information about the suggested rest stops, and the output is the user's selection.

[0678] Step 6:

[0679] The server searches for the optimal route based on the rest stops selected by the user and sends the guidance information to the terminal. The terminal then presents the newly set route to the user and provides navigation. The input is the rest stops selected by the user, and the output is the updated route guidance.

[0680] Step 7:

[0681] The server determines the display order of proposed rest areas based on the advertiser's bid information. The server adjusts the display order and sends that information to the terminal. The input is the advertiser's bid information, and the output is the adjusted display order.

[0682] (Application Example 1)

[0683] 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".

[0684] In autonomous vehicles, there is a need to avoid traffic congestion during operation, suggest appropriate rest stops according to the user's preferences, and provide a smooth and comfortable driving experience. Furthermore, it is necessary to build a system that can generate economic benefits by prioritizing the presentation of specific rest stops.

[0685] 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.

[0686] In this invention, the server includes a device for setting a travel route, a device for collecting and analyzing user behavior history information, a device for predicting traffic congestion and estimating the time of its resolution, a device for suggesting nearby rest areas according to the user's preferences, a device for displaying information on the suggested rest areas, a function for automatically guiding the user to the suggested rest areas, and a function for adjusting the order in which rest areas are presented based on advertising bidding information. This makes it possible to suggest the most suitable rest areas for the user and maximize economic benefits.

[0687] A "route setting device" is a device that automatically calculates and sets the optimal route from the vehicle's current location to its destination.

[0688] A "device for collecting and analyzing user behavior history information" is a device that collects users' past movement data and visit history, and analyzes this data to identify users' preferences and patterns.

[0689] A "device for predicting traffic congestion and estimating the time it will clear" is a device that analyzes real-time and historical traffic data to predict the current state of congestion and estimate the time it will clear.

[0690] A "device that suggests nearby resting places according to the user's preferences" is a device that takes the user's preferences into consideration and selects and suggests an appropriate location from among the available resting places in the surrounding area.

[0691] A "device for displaying information on proposed rest areas" is a device that visually presents detailed information about proposed rest areas to the user to aid in their understanding.

[0692] The "function to automatically guide the vehicle to a suggested resting place" refers to providing control and navigation functions to automatically guide the vehicle to the selected resting place.

[0693] The "function to adjust the order in which rest locations are presented based on advertising bid information" is a function that adjusts the order in which rest locations are displayed preferentially based on the advertiser's bid information.

[0694] The system implementing this invention is based on a server installed in an autonomous vehicle and a terminal operated by the user. The server runs on the Google Cloud Platform and uses the Google Maps API to acquire traffic information. It also uses BigQuery to analyze the user's behavior history and employs TensorFlow to implement AI algorithms. AWS's Amazon Forecast service is used to predict traffic congestion. The server integrates these tools to suggest the optimal resting place based on traffic conditions and user preferences.

[0695] The terminal visually displays information about suggested rest stops to the user, allowing the user to select one. It also has the function to instruct the autonomous driving system on the route to the selected rest stop. The information displayed on the terminal uses an intuitive and highly visible interface so that the user can easily understand it even while driving.

[0696] Users can leverage these features to select the optimal route and rest stops in real time, minimizing wasted time due to traffic congestion. In particular, a generative AI model analyzes the user's past behavior patterns and recommends rest stops that are likely to interest them.

[0697] As a concrete example, a user on a family trip might be suggested a new route to avoid traffic, along with a zoo to visit for a break. In this case, the prompt would be something like, "Where should an animal-loving user go for a break if they want to avoid traffic on the Tomei Expressway for two hours starting at 4 PM?" The server would then select the most suitable candidate locations.

[0698] This system will enable a comfortable and efficient travel experience while also providing effective information tailored to the needs of advertisers from a business perspective.

[0699] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0700] Step 1:

[0701] The server uses the Google Maps API to calculate a standard route from the starting point to the destination based on the destination entered by the user. The input is the user-specified starting point and destination, and the output is multiple route options between them. Data calculations are then performed to select the most efficient route from these options.

[0702] Step 2:

[0703] Users provide their browsing history using their devices. The server uses BigQuery to collect and organize the user's past visit history data, which is the input. Keywords related to the user's preferences are extracted from the analyzed data. This output information is then reflected in subsequent rest stop suggestions.

[0704] Step 3:

[0705] The server uses Amazon Forecast from AWS to infer real-time and historical traffic data and obtain current and predicted congestion levels. The input is traffic data from various locations, and the output is a list of congested sections and their predicted clearing times. This generates data to optimize user travel.

[0706] Step 4:

[0707] The server uses a generative AI model to combine user preference data with traffic congestion information. Using user preference keywords and congestion information as input, it performs data calculations to generate a list of optimal nearby rest stop candidates. The output is a list of suggested rest stops.

[0708] Step 5:

[0709] The terminal visually displays a list of rest stop options provided by the server. The input is a list of rest stops, and the information design is carefully crafted to facilitate user selection. The output is the user's selection action.

[0710] Step 6:

[0711] The user selects their desired rest spot from the locations displayed on the terminal. This selection information is sent back from the terminal to the server and used for the next process.

[0712] Step 7:

[0713] The server sets a new route for the autonomous driving system to guide the vehicle to the selected rest stop. The inputs are the user's selection information and the vehicle's current location, and the output generates an updated destination and the optimal route to it. This information is then sent to the vehicle's navigation system.

[0714] This process allows users to have a travel experience that avoids traffic jams while incorporating breaks that suit their interests.

[0715] 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.

[0716] An embodiment of the present invention is a navigation system that provides a driving route and enables personalized rest stop suggestions that take into account the user's emotional state. This system incorporates an emotion engine that recognizes the user's emotions in real time and optimizes the selection of rest stops based on those emotions.

[0717] First, the user sets a driving route to their destination via the navigation terminal. During this process, the terminal obtains the route from the user's current location to the destination. Along with general traffic information, the user's activity history data is sent to the server.

[0718] The server learns the user's hobbies and preferences based on this behavioral history data, and further analyzes the user's current emotional state in real time through an emotion engine. This analysis uses technologies such as speech recognition and facial recognition to determine the user's stress level and mood.

[0719] Based on conventional traffic information, the server identifies congestion along the route and estimates the time it will take for congestion to clear if it is anticipated. Furthermore, to mitigate the user stress expected due to this congestion, it suggests rest stops based on the emotional state recognized by the emotion engine. For example, if the analysis indicates that the user is feeling fatigued or stressed, the server prioritizes suggesting spots that emphasize relaxation.

[0720] The terminal displays a list of rest locations provided by the server and prompts the user to make a selection. Based on the information presented, the user decides which spot to visit, and the terminal provides directions to the selected rest location.

[0721] Furthermore, the server can adjust the display order of rest stops based on bids from advertisers, thereby generating advertising revenue. This system allows users to visit rest stops that are optimal for their emotional state, resulting in a more comfortable driving experience while reducing the stress caused by traffic congestion.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The user enters their destination into the navigation terminal and performs a route search. The terminal then obtains a standard driving route from the starting point to the destination.

[0725] Step 2:

[0726] The server collects user behavior data and uses AI to learn user preferences. This includes past visit and search history to identify what kind of activities the user prefers.

[0727] Step 3:

[0728] The server uses an emotion engine to recognize the user's emotions from voice and facial expression data. Through real-time analysis, it determines the user's emotional state (for example, whether they are tired or stressed).

[0729] Step 4:

[0730] The server references real-time traffic data to check for congestion information along the specified route. It analyzes past patterns and current traffic conditions to predict where congestion will occur and how long it will take for it to clear.

[0731] Step 5:

[0732] The server combines the user's emotional state with traffic information to select suitable spots for rest and sightseeing from the surrounding area. For example, if the user is feeling stressed, it will suggest facilities with relaxation effects.

[0733] Step 6:

[0734] The terminal displays location information provided by the server to the user, including an overview and location information for each spot. The user can then choose the location that interests them most from the presented options.

[0735] Step 7:

[0736] The user confirms the selected rest stop on the device and enters their intention to "visit" into the device. The device immediately displays a detailed route to the selected spot and begins guiding the user there.

[0737] Step 8:

[0738] The server maximizes advertising revenue by adjusting the display order to highlight specific spots, taking into account advertiser bidding information. This function is implemented as a means of delivering relevant information to users while generating the funds necessary to maintain the system.

[0739] (Example 2)

[0740] 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".

[0741] Conventional navigation systems simply set routes based on traffic information and could not suggest rest stops while considering the user's emotional state or individual stress level. Furthermore, the ability to utilize advertising value in the order in which rest stops were displayed was limited, making it difficult to optimize the user experience.

[0742] 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.

[0743] In this invention, the server includes means for collecting and analyzing the user's behavioral history and emotional state; means for predicting traffic conditions and estimating congestion relief time; means for suggesting personalized rest stops based on the user's emotional state; and means for adjusting the display order of rest stops based on bids from advertisers. This makes it possible to suggest the optimal rest stop according to the user's emotional state and to set a display order that maximizes the value of advertising.

[0744] A "travel route" refers to the path a user takes to reach their destination.

[0745] "User activity history" refers to a collection of information about past driving history and places visited.

[0746] "Emotional state" refers to information that indicates the user's psychological state, including stress levels and mood.

[0747] "Traffic conditions" refers to information related to traffic, such as road congestion and traffic restrictions.

[0748] "Congestion relief time" refers to the estimated time it will take for traffic congestion to clear up.

[0749] A "personalized rest stop" is a resting spot selected to suit the user's specific needs and emotional state.

[0750] An "advertiser" is someone who provides advertisements for specific products or services and bids for the order in which they are displayed.

[0751] This invention provides a navigation system that takes into account the user's emotional state, and provides personalized settings for driving routes, traffic conditions, and rest stops. The main components necessary for implementing this system are a navigation terminal, an emotion engine, a communication network, and a server.

[0752] The user first sets a destination using a navigation terminal. This terminal uses a location information acquisition device (e.g., GPS) to obtain the user's current location and calculates the route to the set destination.

[0753] The terminal sends route information, user behavior history data, and real-time sentiment information to the server. To obtain this sentiment information, speech recognition software or facial recognition software (e.g., general-purpose software for speech recognition, facial recognition API) is used.

[0754] The server receives this data and analyzes the user's emotional state via an emotion engine. Based on this analysis, the server predicts congestion levels and selects the most suitable resting place for the user's emotions. In this process, a traffic information API is used to estimate road congestion levels and the time it will take for traffic to clear.

[0755] The device then displays a list of rest locations that match the user's emotional state. This includes adjusting the display order based on advertisers' bidding information. Based on the information presented, the user can select a rest location they wish to visit.

[0756] For example, if a user wants to "reconnect with nature and refresh themselves" during a long drive, the system is configured to promptly suggest nearby parks and gardens. An example of a prompt to input into the generating AI model would be, "Please suggest relaxing places or spots that suit your interests so that I can find the best rest stop considering your current emotional state." Through this process, the user can enjoy a more comfortable and personalized driving experience.

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] The user sets a destination using a navigation terminal. The input includes the starting point and destination, and the current location information is obtained. The output is candidate route information to the destination. Here, a GPS device is used to obtain precise location information, and a map application is used to calculate the route.

[0760] Step 2:

[0761] The device transmits route information, user behavior history data, and sentiment data collected via voice or camera to the server. The input consists of the aforementioned behavior history and real-time sentiment data, which are used to generate data packages for the server. The output is the user profile information transmitted to the server.

[0762] Step 3:

[0763] The server analyzes the received data and uses an emotion engine to identify the user's emotional state. The input is emotional data received from the terminal. Data analysis software (e.g., emotion analysis algorithm) is used to classify the user's emotional state as stress, relaxation, etc. The output is the evaluation result of the analyzed emotional state.

[0764] Step 4:

[0765] The server uses a traffic information API to predict traffic conditions on the user's current route. Route information and historical traffic pattern data are used as input. After analysis, the output includes a congestion prediction and an estimated time for congestion to clear.

[0766] Step 5:

[0767] The server selects the optimal rest stop based on the user's emotional state and traffic conditions. The input consists of an evaluation of the emotional state and traffic forecast information. A generative AI model is used to evaluate candidate rest stops and generate a ranking. The output is a list of the most suitable rest stops for the user.

[0768] Step 6:

[0769] The terminal displays a list of rest stops received from the server to the user. The input is rest stop information obtained from the server. This information is prioritized based on bidding information from advertisers. The output is a list of rest stop candidates formatted for user display.

[0770] Step 7:

[0771] The user selects a destination from a list of suggested rest stops. The input is the list of rest stop options displayed on the terminal. After the selection is complete, the terminal begins providing detailed route guidance to the selected rest stop. The output is detailed navigation information.

[0772] (Application Example 2)

[0773] 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".

[0774] Users of autonomous vehicles often experience monotonous journeys over long periods and may feel stressed and fatigued due to traffic congestion. As a result, they may not enjoy a comfortable travel experience. Furthermore, these factors affect satisfaction during travel, highlighting the need for new approaches to improve the user's driving experience.

[0775] 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.

[0776] In this invention, the server includes means for collecting and analyzing user behavior history data, means for detecting the user's emotional state, and means for optimizing suggested rest locations based on the emotional state. This enables users to rest at optimal rest locations according to their emotional state, thereby reducing stress during travel and providing a comfortable driving experience.

[0777] "Means for setting the travel route" refers to a function that automatically calculates and sets the optimal travel route to the destination.

[0778] "Means for collecting and analyzing user behavior history data" refers to the process of collecting data on users' past travel history, interests, and preferences, and analyzing user trends based on that data.

[0779] "Methods for predicting traffic congestion and estimating the time it will take to resolve it" refers to technologies that analyze traffic flow to predict future congestion and the time it will take for it to resolve.

[0780] "A means of suggesting nearby rest areas according to user preferences" refers to a function that selects and recommends appropriate rest areas based on the user's preferences.

[0781] "Means for displaying information on proposed rest areas" refers to an interface that visually provides users with detailed information about the selected rest areas.

[0782] "Means for detecting the user's emotional state" refers to a function that analyzes the user's current emotions in real time using technologies such as speech recognition and facial recognition.

[0783] "A means of optimizing rest locations based on emotional state" refers to a system that selects rest locations taking into account the user's emotional state and presents the optimal option.

[0784] The system for carrying out this invention consists of three components: a user, a terminal, and a server. The system aims to provide the user with a comfortable travel experience by integrating emotion recognition technology and navigation technology.

[0785] First, the user sets a travel route to their destination using a terminal. The terminal has the function of acquiring the user's current location and destination information and sending it to the server. The user's activity history data is also sent to the server at the same time, which is used to analyze the user's preferences and travel trends.

[0786] Next, the server uses the user's behavioral history data to analyze their hobbies and preferences, and further analyzes their emotional state in real time via an emotion engine. By utilizing speech recognition and facial recognition, it grasps the user's stress level and mood, and based on that, suggests the most suitable resting place.

[0787] The server analyzes traffic information in real time, predicts congestion levels, and estimates the time it will take for congestion to clear. This predicted data, along with the user's emotional state, is used to design routes that ensure a comfortable travel experience.

[0788] The terminal displays information about rest stops provided by the server and prompts the user to make a selection. Based on the information presented, the user decides which rest stop to visit, and the terminal provides detailed directions to the selected rest stop.

[0789] This system allows users to take breaks according to their emotional state at the time, reducing stress and providing a comfortable driving experience. For example, if the system detects that the user is stressed, the server can suggest nearby relaxation spots and play relaxing music on the device.

[0790] An example of a prompt is: "Develop a system that analyzes the emotions of passengers in an autonomous vehicle and suggests locations where they can relax."

[0791] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0792] Step 1:

[0793] The terminal receives destination information set by the user and uses GPS to determine the user's current location. The input information consists of the user's destination and current location. This information is sent to the server, which provides the data necessary for calculating the travel route.

[0794] Step 2:

[0795] The server calculates the optimal route based on the received destination and current location data. During this process, it obtains real-time traffic information from a traffic database and performs congestion predictions. This results in a route that takes congestion into account.

[0796] Step 3:

[0797] The server analyzes the user's behavioral history data to extract their hobbies and preferences. Input data includes past travel history and interest / preference-related information. The output is an individual preference pattern that reflects the user's tendencies.

[0798] Step 4:

[0799] The server receives video and audio data from the terminal and uses an emotion engine to analyze the user's emotional state in real time. The resulting stress level and mood state are then output.

[0800] Step 5:

[0801] The server lists optimal rest locations that match the user's preferences based on the analyzed emotional state. Several candidate locations are output, taking into account the user's interests and emotional state.

[0802] Step 6:

[0803] The terminal displays rest location information obtained from the server and presents options to the user. It facilitates interaction until the user makes a selection and confirms the final selected location.

[0804] Step 7:

[0805] The device displays detailed directions to the selected rest stop, assisting the user in reaching the destination comfortably. It provides navigation to the chosen rest stop and plays music that responds to the user's mood.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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."

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A means of setting the travel route,

[0830] A means of collecting and analyzing user behavior history data,

[0831] A means of predicting traffic congestion and estimating the time it will take for it to clear,

[0832] A means of suggesting nearby rest areas that match the user's preferences,

[0833] A means of displaying information about the proposed rest area,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for guiding the user to a suggested rest stop in order to avoid congestion along the route.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for determining the display order of proposed rest areas based on bids from advertisers.

[0839] "Example 1"

[0840] (Claim 1)

[0841] Means for setting a route,

[0842] A means of collecting and analyzing user behavior history,

[0843] A means of predicting traffic conditions and estimating the time it will take for them to resolve,

[0844] A means of suggesting appropriate rest areas based on user preferences,

[0845] A means of presenting detailed information about the proposed rest area,

[0846] A means of determining the display order based on advertisers' bids,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, comprising means for guiding users to a proposed rest area in order to avoid traffic congestion.

[0850] (Claim 3)

[0851] The system according to claim 1, comprising means for adjusting guidance to users based on proposed rest areas.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A device for setting the travel route,

[0855] A device that collects and analyzes user behavior history information,

[0856] A device that predicts traffic congestion and estimates the time it will clear up,

[0857] A device that suggests nearby resting places according to the user's preferences,

[0858] A device that displays information about the proposed rest area,

[0859] A function that automatically guides you to suggested resting places,

[0860] A function that adjusts the order in which rest areas are displayed based on advertising bidding information,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which automatically adjusts the route to rest areas in an autonomous vehicle, taking into account the user's preferences.

[0864] (Claim 3)

[0865] The system according to claim 1, which uses an artificial intelligence model to predict user preferences and analyze traffic conditions.

[0866] "Example 2 of combining an emotion engine"

[0867] (Claim 1)

[0868] A means of setting the travel route,

[0869] A means of collecting and analyzing user behavior history and emotional state,

[0870] A means of predicting traffic conditions and estimating the time it will take for congestion to subside,

[0871] A means of suggesting personalized rest points based on the user's emotional state,

[0872] A means of displaying information about the proposed rest stops,

[0873] A means of adjusting the display order of rest stops based on bids from advertisers,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising means for avoiding congestion along the route and guiding the user to a suggested rest stop, taking into account the user's emotional state.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising means for analyzing the user's emotional state in real time using emotion recognition technology.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A means of setting the travel route,

[0882] A means of collecting and analyzing user behavior history data,

[0883] A means of predicting traffic congestion and estimating the time it will take for it to clear,

[0884] A means of suggesting nearby rest areas that match the user's preferences,

[0885] A means of displaying information about the proposed rest area,

[0886] A means for detecting the emotional state of the user,

[0887] A means of optimizing suggested rest points based on emotional state,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, comprising means for guiding the user to a suggested rest stop in order to avoid congestion along the route.

[0891] (Claim 3)

[0892] The system according to claim 1, comprising means for determining the display order of proposed rest areas based on bids from advertisers. [Explanation of Symbols]

[0893] 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 setting the travel route, A means of collecting and analyzing user behavior history data, A means of predicting traffic congestion and estimating the time it will take for it to clear, A means of suggesting nearby rest areas that match the user's preferences, A means of displaying information about the proposed rest area, A system that includes this.

2. The system according to claim 1, further comprising means for guiding the user to a suggested rest stop in order to avoid congestion along the route.

3. The system according to claim 1, comprising means for determining the display order of proposed rest areas based on bids from advertisers.

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