system

Abstract

Provide a system. 【Solution means】 Means for receiving image information transmitted from a user terminal and extracting position information and time information from the image information; Means for generating a movement route based on the extracted position information and time information; Means for storing a history of visited locations based on the generated movement route; Means for proposing unvisited points of interest based on the history; Means for receiving and aggregating evaluation information of the proposed points; Means for determining popularity based on the aggregated evaluation information; Means for generating a reward for the proposer of a proposal whose popularity meets the criteria; A data processing system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is not easy for travelers to appropriately plan their travel routes at their destinations, and they are required to cope with arrival time and stay time constraints or unexpected troubles. However, existing model courses are fixed and difficult to arrange according to individual needs. Also, for travelers who are not proficient in foreign languages, their use is further restricted. There is a need for a system that can utilize travel experiences for revisits and create more fulfilling plans.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates travel routes using image data captured by travelers and suggests unvisited points of interest based on that travel history. This system utilizes location and time information from images to generate new travel routes. Furthermore, it collects evaluation information on the suggested routes and determines their popularity, thereby encouraging continued travel participation. This makes it possible to provide travel plans that meet diverse needs and improve the travel experience.

[0006] A "user terminal" is a device that a user operates to send and receive data over the internet.

[0007] "Image information" refers to the visual information and metadata contained in photographs and video data taken with cameras, smartphones, etc.

[0008] "Location information" is data that indicates a specific point on Earth, and is usually digital information expressed as latitude and longitude.

[0009] "Time information" refers to data that indicates the time when a particular event or data occurred or was collected.

[0010] A "travel route" is a set of information related to the sequence of locations a user has visited within a specific period of time.

[0011] "Visited locations history" is a record that includes a list of places the user has visited in the past, and may also include the order of those visits and the duration of each visit.

[0012] A "point of interest" is a tourist destination, commercial facility, or similar distinctive location that is worth visiting for the user.

[0013] A "suggestion" is information provided to encourage a user to take a specific action or make a specific choice.

[0014] "Evaluation information" refers to the feedback given by users regarding specific experiences or proposed routes, and includes indicators such as satisfaction and convenience.

[0015] "Popularity" is an indicator showing the degree to which proposed routes or visited locations are highly evaluated by other users.

[0016] "Reward" is an incentive or benefit given in return for proposals or contributions provided by users.

Brief Description of the Drawings

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

Mode for Carrying Out the Invention

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

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

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

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

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

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0034] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention is a data processing system that automatically generates travel routes using image information taken by travelers and suggests points of interest. This system mainly consists of a user terminal and a server.

[0039] Users send image information taken during their travels to the server via their device. This image information includes location and time information, which the server uses for analysis.

[0040] The server analyzes the received location and time information to generate the user's travel route. The generated travel route is a chronological history of the places the user has visited and is recorded as a travel diary that can be used as a reference for future trips.

[0041] Furthermore, based on this travel diary, the server lists points of interest that the user has not yet visited. These include popular tourist destinations and highly-rated restaurants. The server collects relevant reviews and rating data from the internet and suggests the most suitable points of interest.

[0042] The user's terminal displays new points of interest sent from the server. The user can then incorporate this information into their next travel plans.

[0043] As a concrete example, the system generates travel routes using photos taken by a user while sightseeing in city B, and suggests unvisited historical buildings and popular local cafes within city B. This allows users to discover new places and create itineraries based on their individual interests.

[0044] The server aggregates user ratings and calculates popularity. Proposers who receive high ratings are rewarded through the system. In this way, high-quality travel experiences are shared, and active participation among users is encouraged. This invention is useful as a traveler planning support tool and improves the quality of the travel experience.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user selects images taken during their trip and sends a bulk upload request from their device to the server.

[0048] Step 2:

[0049] The device reads location and time information from each selected image and sends it to the server along with this metadata.

[0050] Step 3:

[0051] The server analyzes the received image data and extracts location and time information. This provides the basic data needed to reconstruct the user's movement path.

[0052] Step 4:

[0053] The server uses the extracted data to generate a chronological record of the user's travel route and saves it to the user's account as a travel diary.

[0054] Step 5:

[0055] The server analyzes the user's travel diary and identifies unvisited points of interest based on the history of places visited. This involves referencing review sites and tourist destination databases.

[0056] Step 6:

[0057] The server uses an AI algorithm to propose a new travel route from previously unvisited points and sends the results to the user's terminal.

[0058] Step 7:

[0059] The device visualizes the suggested route received from the server on a map and displays it to the user, along with detailed information. The user can then use this information to plan their next trip.

[0060] Step 8:

[0061] After experiencing the suggested route, the user evaluates it and sends the evaluation information to the server as feedback.

[0062] Step 9:

[0063] The server analyzes the collected user ratings, identifies the highest-rated route, and calculates and distributes rewards to the person who proposed it.

[0064] Step 10:

[0065] When a reward is generated, the server sends that information as a notification to the proposer's user terminal to encourage participation.

[0066] (Example 1)

[0067] 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."

[0068] Modern travelers seek efficient and personalized travel plans based on their interests and past travel history, but manually gathering information and making decisions presents time-consuming and laborious challenges. Furthermore, to ensure travelers fully utilize their destinations, there is a need for mechanisms that effectively leverage relevant evaluation data and provide appropriate incentives to providers.

[0069] 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.

[0070] In this invention, the server includes means for receiving image data transmitted from a user device and extracting location data and time data from the image data; means for generating a route based on the extracted data and recording a history of visited areas; and means for suggesting unexplored areas of interest using the recorded history and a generated AI model. This enables travelers to receive efficient and personalized travel suggestions, allowing them to make the most of their travel experience.

[0071] A "user device" is a portable electronic device used by travelers to transmit image data to a server.

[0072] "Image data" refers to digital files containing visual information, such as photographs and videos, taken with a user's device.

[0073] "Location data" refers to geographical information that indicates the location where the image data was taken, and is usually expressed in the form of latitude and longitude.

[0074] "Time data" refers to information indicating the date and time the image data was taken.

[0075] A "route" is the path generated by a user following the points they have visited in order.

[0076] "Visited area history" refers to chronological information of locations recorded based on the user's travel routes.

[0077] "Points of interest" refer to places such as tourist attractions, facilities, and restaurants that the user has not yet visited and that are likely to attract the interest of travelers.

[0078] A "suggestion" is a recommendation for a new place to visit, based on the user's browsing history and preference analysis.

[0079] "Evaluation data" refers to feedback information collected from users regarding the suggested areas of interest, and includes data on satisfaction levels and experiences.

[0080] "Popularity" is an indicator that shows the acceptability and level of evaluation of a proposal, calculated by aggregating evaluation data.

[0081] "Reward" refers to an incentive given to the originator of a proposal who provides high-quality suggestions to users.

[0082] "Visual presentation" refers to the act of making information easier for users to understand by using visual elements such as maps and images.

[0083] A "generative AI model" is a type of artificial intelligence technology that learns from large amounts of data to analyze user interests and travel patterns, and then makes optimal suggestions based on that analysis.

[0084] This invention is a system that generates individual travel routes and suggests places of interest using image data taken by travelers. This system mainly consists of a user terminal and a server.

[0085] Users take image data using their smartphones or cameras while traveling. The user's device transmits this image data to a server via a dedicated application. The images contain location and time data, which the server uses for analysis.

[0086] The server extracts location and time data to generate the user's travel route. This route is recorded as a chronological history of the areas the user has visited. This allows users to efficiently plan further destinations based on their past travel history.

[0087] Furthermore, the server uses the generated travel routes and AI models to suggest potential destinations the user hasn't yet visited. This process employs methods to collect and analyze tourism and rating data from the internet. For example, popular tourist spots and highly-rated restaurants may be suggested as potential destinations.

[0088] The suggested areas of interest are visually displayed on the user's device application along with a map. Based on this, users can create new travel plans and easily decide on their next destination.

[0089] As a concrete example, a server can generate travel routes using images taken by a user while traveling in a tourist city, and suggest unvisited museums and popular cafes within that city. This allows users to efficiently visit destinations of interest and enjoy new cultural experiences.

[0090] By utilizing generative AI models, the accuracy of suggestions and their personalization to match user needs are achieved. Similarly, user feedback is used to calculate popularity and provide rewards, ensuring that high-quality suggestions are continuously delivered.

[0091] An example of a prompt message would be: "Describe a system that suggests new travel routes and points of interest based on image information taken by travelers. As a specific example, consider a case where the system uses images taken by a user in city B to suggest places to visit."

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] Users take pictures using their smartphones while traveling. Location and time information are automatically added as metadata to the captured images. Users send the image data to a server via a dedicated app. The inputs are image data, location information, and time information. The output is the image data and its metadata stored on the server.

[0095] Step 2:

[0096] The server analyzes the received image data and extracts location and time information. At this stage, the image metadata is stored in a database and organized chronologically. The input is image data sent by the user, and the output is the organized location and time information stored in the database.

[0097] Step 3:

[0098] The server generates the user's travel route using extracted location and time information. This process arranges visited locations sequentially, taking time chronological order into consideration, and plots the route on a map. The input is location and time information from a database, and the output is user-specific travel route data.

[0099] Step 4:

[0100] The server utilizes a generative AI model to suggest unvisited areas of interest based on the user's travel history and interests. Here, it analyzes data on tourist destinations and restaurants collected from the internet to select suitable candidates for the user. The input consists of generated travel route data and tourist information from an external database, while the output is a list of suggested areas of interest.

[0101] Step 5:

[0102] The server sends the suggested interests to the user's device. The user's device displays the received data on the app screen and visualizes it on a map. The input is the list of interests sent from the server, and the output is the suggested information displayed on the user's device.

[0103] Step 6:

[0104] After visiting a suggested location, users rate their experience and submit feedback through the app. The server aggregates this rating information and calculates the popularity ranking. The input is user ratings, and the output is popularity scoring data.

[0105] (Application Example 1)

[0106] 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."

[0107] Traditional travel planning systems struggled to generate customized travel plans based on actual experiences, using information gathered from places visited and photos taken by users. Furthermore, they lacked mechanisms to collect real-time evaluations of destinations and suggest information useful for future trips. Additionally, suggestions for unvisited locations and products in the virtual space were insufficient, limiting opportunities for new discoveries based on user interests.

[0108] 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.

[0109] In this invention, the server includes means for receiving image data transmitted from a user device and extracting location and time information from the image data; means for generating a route based on the extracted location and time information; means for storing a record of visited locations based on the generated route; and means for displaying information on proposed unvisited areas in a form accessible from the user terminal and identifying recommended products and tourist spots in the virtual space. This makes it possible to efficiently generate travel routes from images taken by the user, discover new points of interest in the virtual space based on the travel history, and utilize this information for travel planning and product selection.

[0110] A "user device" is a terminal used by a user that has the function of capturing and transmitting image data.

[0111] "Image data" refers to visual information captured by a user, including location and time information.

[0112] "Location information" refers to data that indicates geographical coordinates, and is information used to identify the location where image data was taken.

[0113] "Time information" refers to data that indicates the date and time a specific event occurred, and is used to determine the time when image data was captured.

[0114] A "route" is a chronological list of locations visited by a user, representing their movement pattern.

[0115] "Visited locations record" refers to data that stores information about multiple locations visited by the user, and is used to refer to past travel history.

[0116] "Areas of interest" refer to information about places or products that a user has not yet visited but may be interested in.

[0117] "Evaluation information" refers to data that includes feedback and reviews on each area of ​​interest provided by users or others.

[0118] "Popularity" is an index that indicates the general level of appreciation for each area of ​​interest, calculated based on collected evaluation information.

[0119] A "virtual space" is a virtual environment created by a computer that transcends physical limitations, providing users with products and tourist destinations.

[0120] A "recommended product" is a product or service that is selected based on the user's areas of interest and is recommended for purchase or visit.

[0121] This invention is a system that generates travel routes using image data captured by the user and suggests unvisited points of interest and recommended products. This system consists of a user device, a server, and a cloud service.

[0122] The user device is responsible for capturing image data and sending that data to the server. This process extracts location and time information from the images using an image analysis API such as Google Cloud Vision.

[0123] The server generates a route based on the received data. Here, it utilizes location services such as the Google Maps API to identify the user's visited locations and constructs a route chronologically based on these locations. The generated route is stored as a record of the places the user has visited.

[0124] Furthermore, the server uses this record to suggest unvisited locations that the user might be interested in. When making suggestions, it collects reviews and ratings information publicly available on the internet and uses this to calculate the popularity of the recommended locations and products.

[0125] The user terminal reappears, displaying unvisited locations and recommended products suggested by the server in a virtual space. Users can easily access this information via their smartphones or smart glasses to help plan their next trips and influence their purchasing decisions.

[0126] As a concrete example, when a user uploads an image taken at a tourist destination, recommended tourist spots they haven't yet visited in that area, as well as popular items in virtual stores, are suggested. Based on the generative AI model, when recommending unvisited locations within a specific region, a prompt message such as "Please suggest places and products the user hasn't visited yet based on the image taken at the tourist destination. Please prioritize suggesting popular spots and products with good reviews" is used.

[0127] This allows users to efficiently discover new areas of interest based on the images they have taken, and to gain richer travel and shopping experiences.

[0128] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0129] Step 1:

[0130] The user takes image data and sends it to the server via their device. The input is the image taken by the user, and the output is the image data sent to the server. The user's device captures the image and uploads the data to the server using a communication protocol.

[0131] Step 2:

[0132] The server extracts location and time information from the received image data. The input is image data, and the output is location and time information associated with the image. The server analyzes the image using an image analysis API such as Google Cloud Vision to identify the location data and the date and time the image was taken.

[0133] Step 3:

[0134] The server generates a visit route based on extracted location and time information. The input is location and time information, and the output is the generated visit route. The server uses the Google Maps API to connect the visited locations in chronological order and construct the user's travel route.

[0135] Step 4:

[0136] The server saves a record of visited locations based on the visit route. The input is the generated visit route, and the output is the record of visited locations stored in the database. The server creates entries to save the visit route as a persistent record.

[0137] Step 5:

[0138] The server suggests unvisited locations of interest based on records of visited locations. The input is a record of visited locations, and the output is a list of unvisited locations suggested to the user. The server collects online reviews and rating data to identify unvisited locations that might be of interest and generates suggestions.

[0139] Step 6:

[0140] The server collects evaluation information for proposed points of interest and calculates their popularity. The input is user and other users' evaluation information, and the output is the calculated popularity score for the points of interest. The server aggregates the collected evaluation information, calculates the average score, and determines the popularity score.

[0141] Step 7:

[0142] The user terminal displays unvisited locations and recommended products suggested by the server. The input is the server's suggestions, and the output is the recommendations displayed on the user terminal. The user terminal processes the received suggestions and provides information to the user visually.

[0143] Step 8:

[0144] Users can view recommended locations and products in a virtual space and use this information to plan their next trip. The input is the information displayed on the user's terminal, and the output is the planned next destination. Users review the information, select locations of interest, and adjust their travel plan.

[0145] 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.

[0146] This invention relates to a data processing system incorporating an emotion engine to enhance the user's travel experience. The system consists of a user terminal, a server, and an emotion engine.

[0147] Users send images they take during their trip from their device to the server. During this process, the emotion engine analyzes the image data to recognize the user's emotional state. This emotional state is inferred from facial expressions such as smiles, surprise, and boredom, and corresponding emotion data is generated.

[0148] The server generates travel routes based on the location and time information of the received images. In addition to this data, it utilizes sentiment data provided by the sentiment engine to analyze what emotions the user experienced at different points during their trip. This enables the suggestion of more personalized travel routes.

[0149] Furthermore, the server references the user's emotional history to identify unvisited points of interest that are likely to trigger specific emotions. This emotional data is incorporated into the evaluation information, serving as user feedback that can be helpful to other users.

[0150] For example, if a user smiles frequently while visiting historical buildings during their trip, the emotion engine will use this information to prioritize other historical points of interest in future suggestions. This results in suggestions that match the user's preferences, making their travel experience more fulfilling.

[0151] Finally, the server aggregates user evaluation information along with sentiment data to determine the popularity of each point of interest. Based on this popularity ranking, users who receive high ratings are rewarded, thereby encouraging their use of the system. This invention aims to further improve satisfaction compared to conventional systems by supporting the traveler's experience from an emotional perspective as well.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] Users select images taken during their trip and upload them to the server via their device. The images include location and time information.

[0155] Step 2:

[0156] The emotion engine analyzes image data transmitted from the user's device to identify the emotional state the user is exhibiting. This result is then generated as emotion data.

[0157] Step 3:

[0158] The server extracts location and time information from uploaded images to generate the user's visit history. Furthermore, sentiment data is also recorded.

[0159] Step 4:

[0160] The server identifies unvisited points of interest that are likely to pique the user's interest, based on their visit history and sentiment data. Reviews and ratings from other travelers are also used as references in this identification process.

[0161] Step 5:

[0162] The server generates a new travel route based on unvisited points of interest and sends it to the user's terminal. This route is personalized according to the user's mood.

[0163] Step 6:

[0164] The device visualizes the received travel route as a map and presents it to the user along with detailed information. The user can then use this information to plan their next trip.

[0165] Step 7:

[0166] After experiencing the suggested travel route, users send their impressions and ratings to the server. The ratings include feedback on the emotional impact they experienced at the places they visited.

[0167] Step 8:

[0168] The server analyzes the collected evaluation and sentiment data to calculate the popularity of each point of interest. This popularity ranking is also valuable information for other users.

[0169] Step 9:

[0170] The server distributes rewards to the originator of a proposal if its popularity exceeds a certain threshold. The reward is calculated based on the proposer's past performance.

[0171] Step 10:

[0172] The server sends a notification regarding rewards to the user's terminal to encourage participation and contribution to the system. This notification is intended to acknowledge the user's achievements.

[0173] (Example 2)

[0174] 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".

[0175] Modern travelers seek more personalized travel experiences, but existing travel guidance systems struggle to adequately provide suggestions based on their emotions and personal preferences. Furthermore, travel suggestions and plans that utilize user emotional data are rarely implemented, and the potential for increasing traveler satisfaction has not been fully explored. This invention aims to improve the traveler's experience by analyzing the emotional state of users using images they have taken and providing personalized travel suggestions based on this analysis.

[0176] 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.

[0177] In this invention, the server includes means for receiving image information transmitted from a user terminal and extracting location and time information from the image information; means for inferring the user's emotional state using image analysis technology and generating emotional data; and means for storing a history of visited locations based on the generated travel route and emotional data. This makes it possible to personalize travel suggestions based on the user's emotions and provide a more personalized travel experience.

[0178] A "user terminal" refers to a device used by a user to capture and transmit image data, and includes, for example, smartphones and tablets.

[0179] "Image information" refers to digital data transmitted by a user's terminal, represented as a still image.

[0180] "Location information" refers to data representing geographical coordinates associated with image information.

[0181] "Time information" refers to data indicating the date and time the image information was acquired.

[0182] A "travel route" refers to the sequence of places a user visited during their trip, based on location and time information.

[0183] "Emotional data" refers to data that represents the user's emotional state, inferred through the analysis of image information.

[0184] "Visited location history" refers to a data file that records information about places the user has visited in the past.

[0185] "Unvisited points of interest" refer to places that are not included in the user's visit history but which may be of interest to the user.

[0186] "Evaluation information" refers to data that represents the feedback and ratings that users give to suggested points of interest.

[0187] "Popularity" refers to an index that shows the general evaluation and favorability of points of interest, derived from aggregating evaluation information.

[0188] "Reward" refers to the benefits or value provided to the originator of a proposal that meets a certain level of popularity.

[0189] This invention is a data processing system for improving the user's travel experience, and it consists of multiple components.

[0190] First, users take pictures of their travel destinations using their own devices, such as smartphones or tablets. These devices are equipped with a function to save the captured images and their metadata (location information, time information). Users can then send this image data from their devices to a server.

[0191] Next, the server receives the image data sent from the terminal and transfers it to an image analysis system called an emotion engine. The emotion engine utilizes image processing libraries such as OpenCV or TENSORFLOW® to analyze the user's facial expressions in the image, infer their emotional state, and generate corresponding emotion data.

[0192] The generated emotion data, along with the location and time information contained in the images, is stored on the server and used to create the user's travel route. The server utilizes technologies such as Geographic Information Systems (GIS) to map what emotions were felt at which locations.

[0193] Furthermore, the server utilizes a generative AI model based on this data to identify and suggest unvisited points of interest, taking into account the user's past emotional history. These suggestions are customized based on the user's past emotional data, personalizing the travel experience.

[0194] As a concrete example, a prompt such as "Generate suggestions for the next travel destination based on the user's emotional data for each place they have visited" could be considered. This would allow users to receive travel suggestions tailored to their individual interests and emotional states, which is expected to significantly improve their travel satisfaction.

[0195] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0196] Step 1:

[0197] Users take pictures using their own devices, such as smartphones or tablets, while traveling. These images are automatically tagged with location and time information. Users then send this data directly from their devices to the server. The input includes images, location information, and time information, while the output is a series of data received by the server.

[0198] Step 2:

[0199] The server receives image information sent from the user's terminal, along with location and time information. The received data is stored in the system's storage. The input is images and metadata from the user, and the output is storage in the server's data storage.

[0200] Step 3:

[0201] The server inputs the stored image data into the emotion engine. The emotion engine then analyzes the images using image analysis libraries (e.g., OpenCV, TensorFlow). It infers the user's emotional state from their facial expressions and generates emotion data. The input to this process is an image, and the output is the emotion data obtained from its analysis.

[0202] Step 4:

[0203] Based on the generated sentiment data, location information, and time information, the server creates the user's travel path. Geographic Information System (GIS) technology is used for this path generation. The input is sentiment data, location information, and time information, and the output is the user's travel path.

[0204] Step 5:

[0205] The server updates the user's visit history using the generated travel routes and sentiment data. Furthermore, it uses a generative AI model based on past sentiment data to identify and suggest unvisited points of interest. The input to this process is travel routes and sentiment data, and the output is a suggestion for the next travel destination.

[0206] Step 6:

[0207] Users evaluate whether they would actually visit the suggested travel destinations. This evaluation is sent to the server and used for system improvements and popularity analysis. The input is user evaluation feedback, and the output is improved suggestions and popularity data based on that feedback.

[0208] Through the processing performed at each step, this system can realize personalized travel suggestions based on the user's emotions.

[0209] (Application Example 2)

[0210] 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".

[0211] Traditional systems failed to adequately provide personalized recommendations based on the preferences of travelers and visitors, limiting the quality of experiences at travel destinations and stores. Furthermore, it was difficult to provide recommendations that reflected visitors' behavior and emotions in real time, highlighting the need for a system that could contribute to discovering new points of interest and increasing purchasing intent.

[0212] 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.

[0213] In this invention, the server includes means for receiving image information transmitted from a user terminal and extracting location and time information from the image information, means for analyzing the visitor's facial expression data and recognizing their emotional state, and means for recommending products and services based on the recognized emotional state. This makes it possible to provide visitors with more appropriate experiences and products that match their emotions and preferences.

[0214] A "user terminal" is an electronic device used by a user, and is a device for transmitting image information and location information to a server.

[0215] "Image information" refers to visual data transmitted from a user's device, which is used to recognize location information and emotional states.

[0216] "Location information" refers to data indicating the geographical location contained in image information, and is used to generate the user's travel route.

[0217] "Time information" refers to data indicating the date and time the image information was acquired, and is used as time-series data in generating travel routes.

[0218] "Travel route" refers to data that shows a user's travel history, generated based on location and time information.

[0219] A "visited location" refers to a place that the user has actually visited and is a geographical point recorded as part of their travel route.

[0220] "Emotional state" refers to data that indicates the type and intensity of emotions obtained by analyzing the visitor's facial expression data.

[0221] "Recommending products or services" is the act of selecting and suggesting products or services to a user based on their perceived emotional state.

[0222] "Evaluation information" refers to data showing user reactions to suggested points of interest and products.

[0223] "Popularity" is an index that compiles received evaluation information and quantifies the popularity of a particular point or product.

[0224] "Rewards" refer to incentives offered to the originator of a proposal that meets the popularity criteria.

[0225] This invention realizes a system that provides a personalized experience based on the visitor's emotions, utilizing a user terminal, a server, and an emotion engine. The user terminal includes smart glasses or a smartphone, which acquire image information. When the user views a specific product or service, facial expression data is collected via the camera and analyzed using the emotion engine.

[0226] The server extracts location and time information from the collected image data and generates the visitor's travel route. The travel route is recorded as visited points, and unvisited points of interest are suggested based on the accumulated history. At this point, the system considers the visitor's emotional state, identifies the user's preferences and interests from the facial expressions they displayed, and recommends the most suitable products and services.

[0227] In this process, machine learning frameworks such as "TensorFlow" are used to process the data obtained from facial expression analysis in real time and accurately grasp the emotional state. For example, if a user smiles when they see a product in a store, the server will immediately recommend related products.

[0228] As a concrete example, if a user smiles in the literature section of a bookstore, the server will use this data to present a list of other literary works. This allows the user to discover more interesting products.

[0229] An example of a prompt to input into the generative AI model would be, "When you capture the moment a customer smiles in front of a specific product, generate a list of related products." This would enable real-time recommendations based on emotions.

[0230] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0231] Step 1:

[0232] The user walks around the store using smart glasses or a smartphone. The user's device acquires image information using a camera and sends that image information to a server. The input is the image information acquired within the store, and the output is the transmission of the image information to the server.

[0233] Step 2:

[0234] The server extracts location and time information from the received image data. During this process, the image metadata is analyzed, and the location is obtained as GPS data, while the time is the time the image was taken. The input is image data sent from the user's terminal, and the output is location and time information.

[0235] Step 3:

[0236] The server analyzes image information using an emotion engine to recognize the visitor's emotional state. Specifically, a facial recognition model is executed using TensorFlow to identify emotions from facial expressions such as smiles and surprise. The input is image information stored on the server, and the output is the recognized emotional state.

[0237] Step 4:

[0238] The server integrates extracted location, time, and emotional states to generate a travel path. This creates a history of what emotions a visitor displayed at each location. The inputs are location, time, and emotional states, and the output is the generated travel path.

[0239] Step 5:

[0240] The server suggests unvisited points of interest to the user based on the generated travel path and emotional history. It refers to a database of past emotional states and related points of interest to identify new points that might pique similar interest. The input is the travel path and emotional history, and the output is unvisited points of interest.

[0241] Step 6:

[0242] When a user visits a suggested point of interest, the server retrieves and aggregates evaluation information. This evaluation information is collected based on the user's expressed emotions, level of boredom, etc., and stored in a database. The input is the user's evaluation information, and the output is the aggregated evaluation data.

[0243] Step 7:

[0244] The server calculates popularity based on aggregated evaluation information and rewards proposers who meet the criteria. Popularity is calculated from the sum of evaluation information for each interest point, and the system determines whether the reward conditions are met. The input is aggregated evaluation information, and the output is the popularity evaluation result and the generation of rewards.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] [Second Embodiment]

[0249] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0250] 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.

[0251] 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).

[0252] 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.

[0253] 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.

[0254] 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).

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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".

[0261] This invention is a data processing system that automatically generates travel routes using image information taken by travelers and suggests points of interest. This system mainly consists of a user terminal and a server.

[0262] Users send image information taken during their travels to the server via their device. This image information includes location and time information, which the server uses for analysis.

[0263] The server analyzes the received location and time information to generate the user's travel route. The generated travel route is a chronological history of the places the user has visited and is recorded as a travel diary that can be used as a reference for future trips.

[0264] Furthermore, based on this travel diary, the server lists points of interest that the user has not yet visited. These include popular tourist destinations and highly-rated restaurants. The server collects relevant reviews and rating data from the internet and suggests the most suitable points of interest.

[0265] The user's terminal displays new points of interest sent from the server. The user can then incorporate this information into their next travel plans.

[0266] As a concrete example, the system generates travel routes using photos taken by a user while sightseeing in city B, and suggests unvisited historical buildings and popular local cafes within city B. This allows users to discover new places and create itineraries based on their individual interests.

[0267] The server aggregates user ratings and calculates popularity. Proposers who receive high ratings are rewarded through the system. In this way, high-quality travel experiences are shared, and active participation among users is encouraged. This invention is useful as a traveler planning support tool and improves the quality of the travel experience.

[0268] The following describes the processing flow.

[0269] Step 1:

[0270] The user selects images taken during their trip and sends a bulk upload request from their device to the server.

[0271] Step 2:

[0272] The terminal reads the location information and time information from each selected image and transmits them to the server in a form that includes these metadata.

[0273] Step 3:

[0274] The server analyzes the received image data and extracts the location information and time information. This ensures the basic data for reconstructing the user's movement route.

[0275] Step 4:

[0276] The server generates the user's movement route in chronological order using the extracted data and saves it as a travel diary in the user's account.

[0277] Step 5:

[0278] The server analyzes the user's travel diary and identifies unvisited points of interest based on the history of visited locations. This involves referring to databases of review sites and tourist attractions.

[0279] Step 6:

[0280] The server uses an AI algorithm to propose a new movement route from the unvisited points and transmits the result to the user terminal.

[0281] Step 7:

[0282] The terminal visualizes the proposed route received from the server on a map and displays it to the user, including detailed information. The user can utilize this for their next travel plan.

[0283] Step 8:

[0284] After experiencing the proposed route, the user evaluates it and transmits the evaluation information to the server as feedback.

[0285] Step 9:

[0286] The server analyzes the evaluation information of the gathered users, identifies the most highly evaluated route, and calculates and distributes a reward to the proposer thereof.

[0287] Step 10:

[0288] When a reward occurs, the server transmits the information as a notification to the user terminal of the proposer to encourage participation.

[0289] (Example 1)

[0290] Next, 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".

[0291] Modern travelers are seeking efficient and personalized travel plans based on their interests and past visit histories, but manually collecting information and making decisions is a time-consuming and laborious task. Also, in order for travelers to make the most of their destinations, a mechanism for effectively utilizing relevant evaluation data and providing appropriate incentives to providers is required.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0293] In this invention, the server includes means for receiving image data transmitted from a user device, extracting position data and time data from the image data, generating a route based on the extracted data, and recording a history of the visited area, and means for proposing unexplored objects of interest using the recorded history and a generated AI model. As a result, travelers can receive efficient and personalized travel proposals and can make the most of their travel experiences.

[0294] The "user device" is a portable electronic device used by a traveler for transmitting image data to the server. <s <s

[0295] "Image data" refers to digital files containing visual information, such as photographs and videos, taken with a user's device.

[0296] "Location data" refers to geographical information that indicates the location where the image data was taken, and is usually expressed in the form of latitude and longitude.

[0297] "Time data" refers to information indicating the date and time the image data was taken.

[0298] A "route" is the path generated by a user following the points they have visited in order.

[0299] "Visited area history" refers to chronological information of locations recorded based on the user's travel routes.

[0300] "Points of interest" refer to places such as tourist attractions, facilities, and restaurants that the user has not yet visited and that are likely to attract the interest of travelers.

[0301] A "suggestion" is a recommendation for a new place to visit, based on the user's browsing history and preference analysis.

[0302] "Evaluation data" refers to feedback information collected from users regarding the suggested areas of interest, and includes data on satisfaction levels and experiences.

[0303] "Popularity" is an indicator that shows the acceptability and level of evaluation of a proposal, calculated by aggregating evaluation data.

[0304] "Reward" refers to an incentive given to the originator of a proposal who provides high-quality suggestions to users.

[0305] "Visual presentation" refers to the act of making information easier for users to understand by using visual elements such as maps and images.

[0306] The "generative AI model" is a type of artificial intelligence technology that learns a large amount of data, analyzes the user's interests and travel patterns, and makes optimal proposals based on this.

[0307] The present invention is a system that uses image data taken by travelers to generate individual travel routes and propose places of interest. This system is mainly composed of a user terminal and a server.

[0308] The user takes image data using a smartphone or camera during the trip. The user terminal transmits this image data to the server via a dedicated application. The images contain location data and time data, and the server uses these data for analysis.

[0309] The server extracts the location data and time data and generates the user's movement route. This movement route is recorded as a chronological history of the areas the user has visited. As a result, based on the user's own past travel history, it is possible to efficiently plan points to visit further.

[0310] Furthermore, the server uses the generated movement route and the generative AI model to propose unvisited objects of interest to the user. In this process, a method of collecting and analyzing tourism data and evaluation data on the Internet is adopted. For example, popular tourist attractions and highly rated restaurants are proposed as objects of interest.

[0311] The proposed objects of interest are visually displayed together with a map on the application of the user terminal. The user can create a new travel plan based on this and easily determine the next destination.

[0312] As a specific example, using images taken by the user during a trip to a tourist city, the server can generate a movement route and propose unvisited museums and popular cafes within that city. As a result, the user can efficiently visit destinations of interest and enjoy new cultural experiences.

[0313] By utilizing generative AI models, the accuracy of suggestions and their personalization to match user needs are achieved. Similarly, user feedback is used to calculate popularity and provide rewards, ensuring that high-quality suggestions are continuously delivered.

[0314] An example of a prompt message would be: "Describe a system that suggests new travel routes and points of interest based on image information taken by travelers. As a specific example, consider a case where the system uses images taken by a user in city B to suggest places to visit."

[0315] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0316] Step 1:

[0317] Users take pictures using their smartphones while traveling. Location and time information are automatically added as metadata to the captured images. Users send the image data to a server via a dedicated app. The inputs are image data, location information, and time information. The output is the image data and its metadata stored on the server.

[0318] Step 2:

[0319] The server analyzes the received image data and extracts location and time information. At this stage, the image metadata is stored in a database and organized chronologically. The input is image data sent by the user, and the output is the organized location and time information stored in the database.

[0320] Step 3:

[0321] The server generates the user's travel route using extracted location and time information. This process arranges visited locations sequentially, taking time chronological order into consideration, and plots the route on a map. The input is location and time information from a database, and the output is user-specific travel route data.

[0322] Step 4:

[0323] The server utilizes a generative AI model to suggest unvisited areas of interest based on the user's travel history and interests. Here, it analyzes data on tourist destinations and restaurants collected from the internet to select suitable candidates for the user. The input consists of generated travel route data and tourist information from an external database, while the output is a list of suggested areas of interest.

[0324] Step 5:

[0325] The server sends the suggested interests to the user's device. The user's device displays the received data on the app screen and visualizes it on a map. The input is the list of interests sent from the server, and the output is the suggested information displayed on the user's device.

[0326] Step 6:

[0327] After visiting a suggested location, users rate their experience and submit feedback through the app. The server aggregates this rating information and calculates the popularity ranking. The input is user ratings, and the output is popularity scoring data.

[0328] (Application Example 1)

[0329] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0330] Traditional travel planning systems struggled to generate customized travel plans based on actual experiences, using information gathered from places visited and photos taken by users. Furthermore, they lacked mechanisms to collect real-time evaluations of destinations and suggest information useful for future trips. Additionally, suggestions for unvisited locations and products in the virtual space were insufficient, limiting opportunities for new discoveries based on user interests.

[0331] 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.

[0332] In this invention, the server includes means for receiving image data transmitted from a user device and extracting location and time information from the image data; means for generating a route based on the extracted location and time information; means for storing a record of visited locations based on the generated route; and means for displaying information on proposed unvisited areas in a form accessible from the user terminal and identifying recommended products and tourist spots in the virtual space. This makes it possible to efficiently generate travel routes from images taken by the user, discover new points of interest in the virtual space based on the travel history, and utilize this information for travel planning and product selection.

[0333] A "user device" is a terminal used by a user that has the function of capturing and transmitting image data.

[0334] "Image data" refers to visual information captured by a user, including location and time information.

[0335] "Location information" refers to data that indicates geographical coordinates, and is information used to identify the location where image data was taken.

[0336] "Time information" refers to data that indicates the date and time a specific event occurred, and is used to determine the time when image data was captured.

[0337] A "route" is a chronological list of locations visited by a user, representing their movement pattern.

[0338] "Visited locations record" refers to data that stores information about multiple locations visited by the user, and is used to refer to past travel history.

[0339] "Areas of interest" refer to information about places or products that a user has not yet visited but may be interested in.

[0340] "Evaluation information" refers to data that includes feedback and reviews on each area of ​​interest provided by users or others.

[0341] "Popularity" is an index that indicates the general level of appreciation for each area of ​​interest, calculated based on collected evaluation information.

[0342] A "virtual space" is a virtual environment created by a computer that transcends physical limitations, providing users with products and tourist destinations.

[0343] A "recommended product" is a product or service that is selected based on the user's areas of interest and is recommended for purchase or visit.

[0344] This invention is a system that generates travel routes using image data captured by the user and suggests unvisited points of interest and recommended products. This system consists of a user device, a server, and a cloud service.

[0345] The user's device is responsible for capturing image data and sending it to the server. This process extracts location and time information from the images using an image analysis API such as Google Cloud Vision.

[0346] The server generates a route based on the received data. Here, it utilizes location services such as the Google Maps API to identify the user's visited locations and constructs a route chronologically based on these locations. The generated route is stored as a record of the places the user has visited.

[0347] Furthermore, the server uses this record to suggest unvisited locations that the user might be interested in. When making suggestions, it collects reviews and ratings information publicly available on the internet and uses this to calculate the popularity of the recommended locations and products.

[0348] The user terminal reappears, displaying unvisited locations and recommended products suggested by the server in a virtual space. Users can easily access this information via their smartphones or smart glasses to help plan their next trips and influence their purchasing decisions.

[0349] As a concrete example, when a user uploads an image taken at a tourist destination, recommended tourist spots they haven't yet visited in that area, as well as popular items in virtual stores, are suggested. Based on the generative AI model, when recommending unvisited locations within a specific region, a prompt message such as "Please suggest places and products the user hasn't visited yet based on the image taken at the tourist destination. Please prioritize suggesting popular spots and products with good reviews" is used.

[0350] This allows users to efficiently discover new areas of interest based on the images they have taken, and to gain richer travel and shopping experiences.

[0351] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0352] Step 1:

[0353] The user takes image data and sends it to the server via their device. The input is the image taken by the user, and the output is the image data sent to the server. The user's device captures the image and uploads the data to the server using a communication protocol.

[0354] Step 2:

[0355] The server extracts location and time information from the received image data. The input is image data, and the output is location and time information associated with the image. The server analyzes the image using an image analysis API such as Google Cloud Vision to identify the location data and the date and time the image was taken.

[0356] Step 3:

[0357] The server generates a visit route based on extracted location and time information. The input is location and time information, and the output is the generated visit route. The server uses the Google Maps API to connect the visited locations in chronological order and construct the user's travel route.

[0358] Step 4:

[0359] The server saves a record of visited locations based on the visit route. The input is the generated visit route, and the output is the record of visited locations stored in the database. The server creates entries to save the visit route as a persistent record.

[0360] Step 5:

[0361] The server suggests unvisited locations of interest based on records of visited locations. The input is a record of visited locations, and the output is a list of unvisited locations suggested to the user. The server collects online reviews and rating data to identify unvisited locations that might be of interest and generates suggestions.

[0362] Step 6:

[0363] The server collects evaluation information for proposed points of interest and calculates their popularity. The input is user and other users' evaluation information, and the output is the calculated popularity score for the points of interest. The server aggregates the collected evaluation information, calculates the average score, and determines the popularity score.

[0364] Step 7:

[0365] The user terminal displays unvisited locations and recommended products suggested by the server. The input is the server's suggestions, and the output is the recommendations displayed on the user terminal. The user terminal processes the received suggestions and provides information to the user visually.

[0366] Step 8:

[0367] Users can view recommended locations and products in a virtual space and use this information to plan their next trip. The input is the information displayed on the user's terminal, and the output is the planned next destination. Users review the information, select locations of interest, and adjust their travel plan.

[0368] 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.

[0369] This invention relates to a data processing system incorporating an emotion engine to enhance the user's travel experience. The system consists of a user terminal, a server, and an emotion engine.

[0370] Users send images they take during their trip from their device to the server. During this process, the emotion engine analyzes the image data to recognize the user's emotional state. This emotional state is inferred from facial expressions such as smiles, surprise, and boredom, and corresponding emotion data is generated.

[0371] The server generates travel routes based on the location and time information of the received images. In addition to this data, it utilizes sentiment data provided by the sentiment engine to analyze what emotions the user experienced at different points during their trip. This enables the suggestion of more personalized travel routes.

[0372] Furthermore, the server references the user's emotional history to identify unvisited points of interest that are likely to trigger specific emotions. This emotional data is incorporated into the evaluation information, serving as user feedback that can be helpful to other users.

[0373] For example, if a user smiles frequently while visiting historical buildings during their trip, the emotion engine will use this information to prioritize other historical points of interest in future suggestions. This results in suggestions that match the user's preferences, making their travel experience more fulfilling.

[0374] Finally, the server aggregates user evaluation information along with sentiment data to determine the popularity of each point of interest. Based on this popularity ranking, users who receive high ratings are rewarded, thereby encouraging their use of the system. This invention aims to further improve satisfaction compared to conventional systems by supporting the traveler's experience from an emotional perspective as well.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] Users select images taken during their trip and upload them to the server via their device. The images include location and time information.

[0378] Step 2:

[0379] The emotion engine analyzes image data transmitted from the user's device to identify the emotional state the user is exhibiting. This result is then generated as emotion data.

[0380] Step 3:

[0381] The server extracts location and time information from uploaded images to generate the user's visit history. Furthermore, sentiment data is also recorded.

[0382] Step 4:

[0383] The server identifies unvisited points of interest that are likely to pique the user's interest, based on their visit history and sentiment data. Reviews and ratings from other travelers are also used as references in this identification process.

[0384] Step 5:

[0385] The server generates a new travel route based on unvisited points of interest and sends it to the user's terminal. This route is personalized according to the user's mood.

[0386] Step 6:

[0387] The device visualizes the received travel route as a map and presents it to the user along with detailed information. The user can then use this information to plan their next trip.

[0388] Step 7:

[0389] After experiencing the suggested travel route, users send their impressions and ratings to the server. The ratings include feedback on the emotional impact they experienced at the places they visited.

[0390] Step 8:

[0391] The server analyzes the collected evaluation and sentiment data to calculate the popularity of each point of interest. This popularity ranking is also valuable information for other users.

[0392] Step 9:

[0393] The server distributes rewards to the originator of a proposal if its popularity exceeds a certain threshold. The reward is calculated based on the proposer's past performance.

[0394] Step 10:

[0395] The server sends a notification regarding rewards to the user's terminal to encourage participation and contribution to the system. This notification is intended to acknowledge the user's achievements.

[0396] (Example 2)

[0397] 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".

[0398] Modern travelers seek more personalized travel experiences, but existing travel guidance systems struggle to adequately provide suggestions based on their emotions and personal preferences. Furthermore, travel suggestions and plans that utilize user emotional data are rarely implemented, and the potential for increasing traveler satisfaction has not been fully explored. This invention aims to improve the traveler's experience by analyzing the emotional state of users using images they have taken and providing personalized travel suggestions based on this analysis.

[0399] 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.

[0400] In this invention, the server includes means for receiving image information transmitted from a user terminal and extracting location and time information from the image information; means for inferring the user's emotional state using image analysis technology and generating emotional data; and means for storing a history of visited locations based on the generated travel route and emotional data. This makes it possible to personalize travel suggestions based on the user's emotions and provide a more personalized travel experience.

[0401] A "user terminal" refers to a device used by a user to capture and transmit image data, and includes, for example, smartphones and tablets.

[0402] "Image information" refers to digital data transmitted by a user's terminal, represented as a still image.

[0403] "Location information" refers to data representing geographical coordinates associated with image information.

[0404] "Time information" refers to data indicating the date and time the image information was acquired.

[0405] A "travel route" refers to the sequence of places a user visited during their trip, based on location and time information.

[0406] "Emotional data" refers to data that represents the user's emotional state, inferred through the analysis of image information.

[0407] "Visited location history" refers to a data file that records information about places the user has visited in the past.

[0408] "Unvisited points of interest" refer to places that are not included in the user's visit history but which may be of interest to the user.

[0409] "Evaluation information" refers to data that represents the feedback and ratings that users give to suggested points of interest.

[0410] "Popularity" refers to an index that shows the general evaluation and favorability of points of interest, derived from aggregating evaluation information.

[0411] "Reward" refers to the benefits or value provided to the originator of a proposal that meets a certain level of popularity.

[0412] This invention is a data processing system for improving the user's travel experience, and it consists of multiple components.

[0413] First, users take pictures of their travel destinations using their own devices, such as smartphones or tablets. These devices are equipped with a function to save the captured images and their metadata (location information, time information). Users can then send this image data from their devices to a server.

[0414] Next, the server receives the image data sent from the terminal and transfers it to an image analysis system called an emotion engine. The emotion engine utilizes image processing libraries such as OpenCV or TensorFlow to analyze the user's facial expressions in the image, infer their emotional state, and generate corresponding emotion data.

[0415] The generated emotion data, along with the location and time information contained in the images, is stored on the server and used to create the user's travel route. The server utilizes technologies such as Geographic Information Systems (GIS) to map what emotions were felt at which locations.

[0416] Furthermore, the server utilizes a generative AI model based on this data to identify and suggest unvisited points of interest, taking into account the user's past emotional history. These suggestions are customized based on the user's past emotional data, personalizing the travel experience.

[0417] As a concrete example, a prompt such as "Generate suggestions for the next travel destination based on the user's emotional data for each place they have visited" could be considered. This would allow users to receive travel suggestions tailored to their individual interests and emotional states, which is expected to significantly improve their travel satisfaction.

[0418] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0419] Step 1:

[0420] Users take pictures using their own devices, such as smartphones or tablets, while traveling. These images are automatically tagged with location and time information. Users then send this data directly from their devices to the server. The input includes images, location information, and time information, while the output is a series of data received by the server.

[0421] Step 2:

[0422] The server receives image information sent from the user's terminal, along with location and time information. The received data is stored in the system's storage. The input is the image and metadata from the user, and the output is storage in the server's data storage.

[0423] Step 3:

[0424] The server inputs the stored image data into the emotion engine. The emotion engine then analyzes the images using image analysis libraries (e.g., OpenCV, TensorFlow). It infers the user's emotional state from their facial expressions and generates emotion data. The input to this process is an image, and the output is the emotion data obtained from its analysis.

[0425] Step 4:

[0426] Based on the generated sentiment data, location information, and time information, the server creates the user's travel path. Geographic Information System (GIS) technology is used for this path generation. The input is sentiment data, location information, and time information, and the output is the user's travel path.

[0427] Step 5:

[0428] The server updates the user's visit history using the generated travel routes and sentiment data. Furthermore, it uses a generative AI model based on past sentiment data to identify and suggest unvisited points of interest. The input to this process is travel routes and sentiment data, and the output is a suggestion for the next travel destination.

[0429] Step 6:

[0430] Users evaluate whether they would actually visit the suggested travel destinations. This evaluation is sent to the server and used for system improvements and popularity analysis. The input is user evaluation feedback, and the output is improved suggestions and popularity data based on that feedback.

[0431] Through the processing performed at each step, this system can realize personalized travel suggestions based on the user's emotions.

[0432] (Application Example 2)

[0433] 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."

[0434] Traditional systems failed to adequately provide personalized recommendations based on the preferences of travelers and visitors, limiting the quality of experiences at travel destinations and stores. Furthermore, it was difficult to provide recommendations that reflected visitors' behavior and emotions in real time, highlighting the need for a system that could contribute to discovering new points of interest and increasing purchasing intent.

[0435] 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.

[0436] In this invention, the server includes means for receiving image information transmitted from a user terminal and extracting location and time information from the image information, means for analyzing the visitor's facial expression data and recognizing their emotional state, and means for recommending products and services based on the recognized emotional state. This makes it possible to provide visitors with more appropriate experiences and products that match their emotions and preferences.

[0437] A "user terminal" is an electronic device used by a user, and is a device for transmitting image information and location information to a server.

[0438] "Image information" refers to visual data transmitted from a user's device, which is used to recognize location information and emotional states.

[0439] "Location information" refers to data indicating the geographical location contained in image information, and is used to generate the user's travel route.

[0440] "Time information" refers to data indicating the date and time the image information was acquired, and is used as time-series data in generating travel routes.

[0441] "Travel route" refers to data that shows a user's travel history, generated based on location and time information.

[0442] A "visited location" refers to a place that the user has actually visited and is a geographical point recorded as part of their travel route.

[0443] "Emotional state" refers to data that indicates the type and intensity of emotions obtained by analyzing the visitor's facial expression data.

[0444] "Recommending products or services" is the act of selecting and suggesting products or services to a user based on their perceived emotional state.

[0445] "Evaluation information" refers to data showing user reactions to suggested points of interest and products.

[0446] "Popularity" is an index that compiles received evaluation information and quantifies the popularity of a particular point or product.

[0447] "Rewards" refer to incentives offered to the originator of a proposal that meets the popularity criteria.

[0448] This invention realizes a system that provides a personalized experience based on the visitor's emotions, utilizing a user terminal, a server, and an emotion engine. The user terminal includes smart glasses or a smartphone, which acquire image information. When the user views a specific product or service, facial expression data is collected via the camera and analyzed using the emotion engine.

[0449] The server extracts location and time information from the collected image data and generates the visitor's travel route. The travel route is recorded as visited points, and unvisited points of interest are suggested based on the accumulated history. At this point, the system considers the visitor's emotional state, identifies the user's preferences and interests from the facial expressions they displayed, and recommends the most suitable products and services.

[0450] In this process, machine learning frameworks such as "TensorFlow" are used to process the data obtained from facial expression analysis in real time and accurately grasp the emotional state. For example, if a user smiles when they see a product in a store, the server will immediately recommend related products.

[0451] As a concrete example, if a user smiles in the literature section of a bookstore, the server will use this data to present a list of other literary works. This allows the user to discover more interesting products.

[0452] An example of a prompt to input into the generative AI model would be, "When you capture the moment a customer smiles in front of a specific product, generate a list of related products." This would enable real-time recommendations based on emotions.

[0453] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0454] Step 1:

[0455] The user walks around the store using smart glasses or a smartphone. The user's device acquires image information using a camera and sends that image information to a server. The input is the image information acquired within the store, and the output is the transmission of the image information to the server.

[0456] Step 2:

[0457] The server extracts location and time information from the received image data. During this process, the image metadata is analyzed, and the location is obtained as GPS data, while the time is the time the image was taken. The input is image data sent from the user's terminal, and the output is location and time information.

[0458] Step 3:

[0459] The server analyzes image information using an emotion engine to recognize the visitor's emotional state. Specifically, a facial recognition model is executed using TensorFlow to identify emotions from facial expressions such as smiles and surprise. The input is image information stored on the server, and the output is the recognized emotional state.

[0460] Step 4:

[0461] The server integrates extracted location, time, and emotional states to generate a travel path. This creates a history of what emotions a visitor displayed at each location. The inputs are location, time, and emotional states, and the output is the generated travel path.

[0462] Step 5:

[0463] The server suggests unvisited points of interest to the user based on the generated travel path and emotional history. It refers to a database of past emotional states and related points of interest to identify new points that might pique similar interest. The input is the travel path and emotional history, and the output is unvisited points of interest.

[0464] Step 6:

[0465] When a user visits a suggested point of interest, the server retrieves and aggregates evaluation information. This evaluation information is collected based on the user's expressed emotions, level of boredom, etc., and stored in a database. The input is the user's evaluation information, and the output is the aggregated evaluation data.

[0466] Step 7:

[0467] The server calculates popularity based on aggregated evaluation information and rewards proposers who meet the criteria. Popularity is calculated from the sum of evaluation information for each interest point, and the system determines whether the reward conditions are met. The input is aggregated evaluation information, and the output is the popularity evaluation result and the generation of rewards.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] [Third Embodiment]

[0472] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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).

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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".

[0484] This invention is a data processing system that automatically generates travel routes using image information taken by travelers and suggests points of interest. This system mainly consists of a user terminal and a server.

[0485] Users send image information taken during their travels to the server via their device. This image information includes location and time information, which the server uses for analysis.

[0486] The server analyzes the received location and time information to generate the user's travel route. The generated travel route is a chronological history of the places the user has visited and is recorded as a travel diary that can be used as a reference for future trips.

[0487] Furthermore, based on this travel diary, the server lists points of interest that the user has not yet visited. These include popular tourist destinations and highly-rated restaurants. The server collects relevant reviews and rating data from the internet and suggests the most suitable points of interest.

[0488] The user's terminal displays new points of interest sent from the server. The user can then incorporate this information into their next travel plans.

[0489] As a concrete example, the system generates travel routes using photos taken by a user while sightseeing in city B, and suggests unvisited historical buildings and popular local cafes within city B. This allows users to discover new places and create itineraries based on their individual interests.

[0490] The server aggregates user ratings and calculates popularity. Proposers who receive high ratings are rewarded through the system. In this way, high-quality travel experiences are shared, and active participation among users is encouraged. This invention is useful as a traveler planning support tool and improves the quality of the travel experience.

[0491] The following describes the processing flow.

[0492] Step 1:

[0493] The user selects images taken during their trip and sends a bulk upload request from their device to the server.

[0494] Step 2:

[0495] The device reads location and time information from each selected image and sends it to the server along with this metadata.

[0496] Step 3:

[0497] The server analyzes the received image data and extracts location and time information. This provides the basic data needed to reconstruct the user's movement path.

[0498] Step 4:

[0499] The server uses the extracted data to generate a chronological record of the user's travel route and saves it to the user's account as a travel diary.

[0500] Step 5:

[0501] The server analyzes the user's travel diary and identifies unvisited points of interest based on the history of places visited. This involves referencing review sites and tourist destination databases.

[0502] Step 6:

[0503] The server uses an AI algorithm to propose a new travel route from previously unvisited points and sends the results to the user's terminal.

[0504] Step 7:

[0505] The device visualizes the suggested route received from the server on a map and displays it to the user, along with detailed information. The user can then use this information to plan their next trip.

[0506] Step 8:

[0507] After experiencing the suggested route, the user evaluates it and sends the evaluation information to the server as feedback.

[0508] Step 9:

[0509] The server analyzes the collected user ratings, identifies the highest-rated route, and calculates and distributes rewards to the person who proposed it.

[0510] Step 10:

[0511] When a reward is generated, the server sends that information as a notification to the proposer's user terminal to encourage participation.

[0512] (Example 1)

[0513] 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."

[0514] Modern travelers seek efficient and personalized travel plans based on their interests and past travel history, but manually gathering information and making decisions presents time-consuming and laborious challenges. Furthermore, to ensure travelers fully utilize their destinations, there is a need for mechanisms that effectively leverage relevant evaluation data and provide appropriate incentives to providers.

[0515] 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.

[0516] In this invention, the server includes means for receiving image data transmitted from a user device and extracting location data and time data from the image data; means for generating a route based on the extracted data and recording a history of visited areas; and means for suggesting unexplored areas of interest using the recorded history and a generated AI model. This enables travelers to receive efficient and personalized travel suggestions, allowing them to make the most of their travel experience.

[0517] A "user device" is a portable electronic device used by travelers to transmit image data to a server.

[0518] "Image data" refers to digital files containing visual information, such as photographs and videos, taken with a user's device.

[0519] "Location data" refers to geographical information that indicates the location where the image data was taken, and is usually expressed in the form of latitude and longitude.

[0520] "Time data" refers to information indicating the date and time the image data was taken.

[0521] A "route" is the path generated by a user following the points they have visited in order.

[0522] "Visited area history" refers to chronological information of locations recorded based on the user's travel routes.

[0523] "Points of interest" refer to places such as tourist attractions, facilities, and restaurants that the user has not yet visited and that are likely to attract the interest of travelers.

[0524] A "suggestion" is a recommendation for a new place to visit, based on the user's browsing history and preference analysis.

[0525] "Evaluation data" refers to feedback information collected from users regarding the suggested areas of interest, and includes data on satisfaction levels and experiences.

[0526] "Popularity" is an indicator that shows the acceptability and level of evaluation of a proposal, calculated by aggregating evaluation data.

[0527] "Reward" refers to an incentive given to the originator of a proposal who provides high-quality suggestions to users.

[0528] "Visual presentation" refers to the act of making information easier for users to understand by using visual elements such as maps and images.

[0529] A "generative AI model" is a type of artificial intelligence technology that learns from large amounts of data to analyze user interests and travel patterns, and then makes optimal suggestions based on that analysis.

[0530] This invention is a system that generates individual travel routes and suggests places of interest using image data taken by travelers. This system mainly consists of a user terminal and a server.

[0531] Users take image data using their smartphones or cameras while traveling. The user's device transmits this image data to a server via a dedicated application. The images contain location and time data, which the server uses for analysis.

[0532] The server extracts location and time data to generate the user's travel route. This route is recorded as a chronological history of the areas the user has visited. This allows users to efficiently plan further destinations based on their past travel history.

[0533] Furthermore, the server uses the generated travel routes and AI models to suggest potential destinations the user hasn't yet visited. This process employs methods to collect and analyze tourism and rating data from the internet. For example, popular tourist spots and highly-rated restaurants may be suggested as potential destinations.

[0534] The suggested areas of interest are visually displayed on the user's device application along with a map. Based on this, users can create new travel plans and easily decide on their next destination.

[0535] As a concrete example, a server can generate travel routes using images taken by a user while traveling in a tourist city, and suggest unvisited museums and popular cafes within that city. This allows users to efficiently visit destinations of interest and enjoy new cultural experiences.

[0536] By utilizing generative AI models, the accuracy of suggestions and their personalization to match user needs are achieved. Similarly, user feedback is used to calculate popularity and provide rewards, ensuring that high-quality suggestions are continuously delivered.

[0537] An example of a prompt message would be: "Describe a system that suggests new travel routes and points of interest based on image information taken by travelers. As a specific example, consider a case where the system uses images taken by a user in city B to suggest places to visit."

[0538] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0539] Step 1:

[0540] Users take pictures using their smartphones while traveling. Location and time information are automatically added as metadata to the captured images. Users send the image data to a server via a dedicated app. The inputs are image data, location information, and time information. The output is the image data and its metadata stored on the server.

[0541] Step 2:

[0542] The server analyzes the received image data and extracts location and time information. At this stage, the image metadata is stored in a database and organized chronologically. The input is image data sent by the user, and the output is the organized location and time information stored in the database.

[0543] Step 3:

[0544] The server generates the user's travel route using extracted location and time information. This process arranges visited locations sequentially, taking time chronological order into consideration, and plots the route on a map. The input is location and time information from a database, and the output is user-specific travel route data.

[0545] Step 4:

[0546] The server utilizes a generative AI model to suggest unvisited areas of interest based on the user's travel history and interests. Here, it analyzes data on tourist destinations and restaurants collected from the internet to select suitable candidates for the user. The input consists of generated travel route data and tourist information from an external database, while the output is a list of suggested areas of interest.

[0547] Step 5:

[0548] The server sends the suggested interests to the user's device. The user's device displays the received data on the app screen and visualizes it on a map. The input is the list of interests sent from the server, and the output is the suggested information displayed on the user's device.

[0549] Step 6:

[0550] After visiting a suggested location, users rate their experience and submit feedback through the app. The server aggregates this rating information and calculates the popularity ranking. The input is user ratings, and the output is popularity scoring data.

[0551] (Application Example 1)

[0552] 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."

[0553] Traditional travel planning systems struggled to generate customized travel plans based on actual experiences, using information gathered from places visited and photos taken by users. Furthermore, they lacked mechanisms to collect real-time evaluations of destinations and suggest information useful for future trips. Additionally, suggestions for unvisited locations and products in the virtual space were insufficient, limiting opportunities for new discoveries based on user interests.

[0554] 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.

[0555] In this invention, the server includes means for receiving image data transmitted from a user device and extracting location and time information from the image data; means for generating a route based on the extracted location and time information; means for storing a record of visited locations based on the generated route; and means for displaying information on proposed unvisited areas in a form accessible from the user terminal and identifying recommended products and tourist spots in the virtual space. This makes it possible to efficiently generate travel routes from images taken by the user, discover new points of interest in the virtual space based on the travel history, and utilize this information for travel planning and product selection.

[0556] A "user device" is a terminal used by a user that has the function of capturing and transmitting image data.

[0557] "Image data" refers to visual information captured by a user, including location and time information.

[0558] "Location information" refers to data that indicates geographical coordinates, and is information used to identify the location where image data was taken.

[0559] "Time information" refers to data that indicates the date and time a specific event occurred, and is used to determine the time when image data was captured.

[0560] A "route" is a chronological list of locations visited by a user, representing their movement pattern.

[0561] "Visited locations record" refers to data that stores information about multiple locations visited by the user, and is used to refer to past travel history.

[0562] "Areas of interest" refer to information about places or products that a user has not yet visited but may be interested in.

[0563] "Evaluation information" refers to data that includes feedback and reviews on each area of ​​interest provided by users or others.

[0564] "Popularity" is an index that indicates the general level of appreciation for each area of ​​interest, calculated based on collected evaluation information.

[0565] A "virtual space" is a virtual environment created by a computer that transcends physical limitations, providing users with products and tourist destinations.

[0566] A "recommended product" is a product or service that is selected based on the user's areas of interest and is recommended for purchase or visit.

[0567] This invention is a system that generates travel routes using image data captured by the user and suggests unvisited points of interest and recommended products. This system consists of a user device, a server, and a cloud service.

[0568] The user's device is responsible for capturing image data and sending it to the server. This process extracts location and time information from the images using an image analysis API such as Google Cloud Vision.

[0569] The server generates a route based on the received data. Here, it utilizes location services such as the Google Maps API to identify the user's visited locations and constructs a route chronologically based on these locations. The generated route is stored as a record of the places the user has visited.

[0570] Furthermore, the server uses this record to suggest unvisited locations that the user might be interested in. When making suggestions, it collects reviews and ratings information publicly available on the internet and uses this to calculate the popularity of the recommended locations and products.

[0571] The user terminal reappears, displaying unvisited locations and recommended products suggested by the server in a virtual space. Users can easily access this information via their smartphones or smart glasses to help plan their next trips and influence their purchasing decisions.

[0572] As a concrete example, when a user uploads an image taken at a tourist destination, recommended tourist spots they haven't yet visited in that area, as well as popular items in virtual stores, are suggested. Based on the generative AI model, when recommending unvisited locations within a specific region, a prompt message such as "Please suggest places and products the user hasn't visited yet based on the image taken at the tourist destination. Please prioritize suggesting popular spots and products with good reviews" is used.

[0573] This allows users to efficiently discover new areas of interest based on the images they have taken, and to gain richer travel and shopping experiences.

[0574] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0575] Step 1:

[0576] The user takes image data and sends it to the server via their device. The input is the image taken by the user, and the output is the image data sent to the server. The user's device captures the image and uploads the data to the server using a communication protocol.

[0577] Step 2:

[0578] The server extracts location and time information from the received image data. The input is image data, and the output is location and time information associated with the image. The server analyzes the image using an image analysis API such as Google Cloud Vision to identify the location data and the date and time the image was taken.

[0579] Step 3:

[0580] The server generates a visit route based on extracted location and time information. The input is location and time information, and the output is the generated visit route. The server uses the Google Maps API to connect the visited locations in chronological order and construct the user's travel route.

[0581] Step 4:

[0582] The server saves a record of visited locations based on the visit route. The input is the generated visit route, and the output is the record of visited locations stored in the database. The server creates entries to save the visit route as a persistent record.

[0583] Step 5:

[0584] The server suggests unvisited locations of interest based on records of visited locations. The input is a record of visited locations, and the output is a list of unvisited locations suggested to the user. The server collects online reviews and rating data to identify unvisited locations that might be of interest and generates suggestions.

[0585] Step 6:

[0586] The server collects evaluation information for proposed points of interest and calculates their popularity. The input is user and other users' evaluation information, and the output is the calculated popularity score for the points of interest. The server aggregates the collected evaluation information, calculates the average score, and determines the popularity score.

[0587] Step 7:

[0588] The user terminal displays unvisited locations and recommended products suggested by the server. The input is the server's suggestions, and the output is the recommendations displayed on the user terminal. The user terminal processes the received suggestions and provides information to the user visually.

[0589] Step 8:

[0590] Users can view recommended locations and products in a virtual space and use this information to plan their next trip. The input is the information displayed on the user's terminal, and the output is the planned next destination. Users review the information, select locations of interest, and adjust their travel plan.

[0591] 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.

[0592] This invention relates to a data processing system incorporating an emotion engine to enhance the user's travel experience. The system consists of a user terminal, a server, and an emotion engine.

[0593] Users send images they take during their trip from their device to the server. During this process, the emotion engine analyzes the image data to recognize the user's emotional state. This emotional state is inferred from facial expressions such as smiles, surprise, and boredom, and corresponding emotion data is generated.

[0594] The server generates travel routes based on the location and time information of the received images. In addition to this data, it utilizes sentiment data provided by the sentiment engine to analyze what emotions the user experienced at different points during their trip. This enables the suggestion of more personalized travel routes.

[0595] Furthermore, the server references the user's emotional history to identify unvisited points of interest that are likely to trigger specific emotions. This emotional data is incorporated into the evaluation information, serving as user feedback that can be helpful to other users.

[0596] For example, if a user smiles frequently while visiting historical buildings during their trip, the emotion engine will use this information to prioritize other historical points of interest in future suggestions. This results in suggestions that match the user's preferences, making their travel experience more fulfilling.

[0597] Finally, the server aggregates user evaluation information along with sentiment data to determine the popularity of each point of interest. Based on this popularity ranking, users who receive high ratings are rewarded, thereby encouraging their use of the system. This invention aims to further improve satisfaction compared to conventional systems by supporting the traveler's experience from an emotional perspective as well.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] Users select images taken during their trip and upload them to the server via their device. The images include location and time information.

[0601] Step 2:

[0602] The emotion engine analyzes image data transmitted from the user's device to identify the emotional state the user is exhibiting. This result is then generated as emotion data.

[0603] Step 3:

[0604] The server extracts location and time information from uploaded images to generate the user's visit history. Furthermore, sentiment data is also recorded.

[0605] Step 4:

[0606] The server identifies unvisited points of interest that are likely to pique the user's interest, based on their visit history and sentiment data. Reviews and ratings from other travelers are also used as references in this identification process.

[0607] Step 5:

[0608] The server generates a new travel route based on unvisited points of interest and sends it to the user's terminal. This route is personalized according to the user's mood.

[0609] Step 6:

[0610] The device visualizes the received travel route as a map and presents it to the user along with detailed information. The user can then use this information to plan their next trip.

[0611] Step 7:

[0612] After experiencing the suggested travel route, users send their impressions and ratings to the server. The ratings include feedback on the emotional impact they experienced at the places they visited.

[0613] Step 8:

[0614] The server analyzes the collected evaluation and sentiment data to calculate the popularity of each point of interest. This popularity ranking is also valuable information for other users.

[0615] Step 9:

[0616] The server distributes rewards to the originator of a proposal if its popularity exceeds a certain threshold. The reward is calculated based on the proposer's past performance.

[0617] Step 10:

[0618] The server sends a notification regarding rewards to the user's terminal to encourage participation and contribution to the system. This notification is intended to acknowledge the user's achievements.

[0619] (Example 2)

[0620] 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."

[0621] Modern travelers seek more personalized travel experiences, but existing travel guidance systems struggle to adequately provide suggestions based on their emotions and personal preferences. Furthermore, travel suggestions and plans that utilize user emotional data are rarely implemented, and the potential for increasing traveler satisfaction has not been fully explored. This invention aims to improve the traveler's experience by analyzing the emotional state of users using images they have taken and providing personalized travel suggestions based on this analysis.

[0622] 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.

[0623] In this invention, the server includes means for receiving image information transmitted from a user terminal and extracting location and time information from the image information; means for inferring the user's emotional state using image analysis technology and generating emotional data; and means for storing a history of visited locations based on the generated travel route and emotional data. This makes it possible to personalize travel suggestions based on the user's emotions and provide a more personalized travel experience.

[0624] A "user terminal" refers to a device used by a user to capture and transmit image data, and includes, for example, smartphones and tablets.

[0625] "Image information" refers to digital data transmitted by a user's terminal, represented as a still image.

[0626] "Location information" refers to data representing geographical coordinates associated with image information.

[0627] "Time information" refers to data indicating the date and time the image information was acquired.

[0628] A "travel route" refers to the sequence of places a user visited during their trip, based on location and time information.

[0629] "Emotional data" refers to data that represents the user's emotional state, inferred through the analysis of image information.

[0630] "Visited location history" refers to a data file that records information about places the user has visited in the past.

[0631] "Unvisited points of interest" refer to places that are not included in the user's visit history but which may be of interest to the user.

[0632] "Evaluation information" refers to data that represents the feedback and ratings that users give to suggested points of interest.

[0633] "Popularity" refers to an index that shows the general evaluation and favorability of points of interest, derived from aggregating evaluation information.

[0634] "Reward" refers to the benefits or value provided to the originator of a proposal that meets a certain level of popularity.

[0635] This invention is a data processing system for improving the user's travel experience, and it consists of multiple components.

[0636] First, users take pictures of their travel destinations using their own devices, such as smartphones or tablets. These devices are equipped with a function to save the captured images and their metadata (location information, time information). Users can then send this image data from their devices to a server.

[0637] Next, the server receives the image data sent from the terminal and transfers it to an image analysis system called an emotion engine. The emotion engine utilizes image processing libraries such as OpenCV or TensorFlow to analyze the user's facial expressions in the image, infer their emotional state, and generate corresponding emotion data.

[0638] The generated emotion data, along with the location and time information contained in the images, is stored on the server and used to create the user's travel route. The server utilizes technologies such as Geographic Information Systems (GIS) to map what emotions were felt at which locations.

[0639] Furthermore, the server utilizes a generative AI model based on this data to identify and suggest unvisited points of interest, taking into account the user's past emotional history. These suggestions are customized based on the user's past emotional data, personalizing the travel experience.

[0640] As a concrete example, a prompt such as "Generate suggestions for the next travel destination based on the user's emotional data for each place they have visited" could be considered. This would allow users to receive travel suggestions tailored to their individual interests and emotional states, which is expected to significantly improve their travel satisfaction.

[0641] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0642] Step 1:

[0643] Users take pictures using their own devices, such as smartphones or tablets, while traveling. These images are automatically tagged with location and time information. Users then send this data directly from their devices to the server. The input includes images, location information, and time information, while the output is a series of data received by the server.

[0644] Step 2:

[0645] The server receives image information sent from the user's terminal, along with location and time information. The received data is stored in the system's storage. The input is the image and metadata from the user, and the output is storage in the server's data storage.

[0646] Step 3:

[0647] The server inputs the stored image data into the emotion engine. The emotion engine then analyzes the images using image analysis libraries (e.g., OpenCV, TensorFlow). It infers the user's emotional state from their facial expressions and generates emotion data. The input to this process is an image, and the output is the emotion data obtained from its analysis.

[0648] Step 4:

[0649] Based on the generated sentiment data, location information, and time information, the server creates the user's travel path. Geographic Information System (GIS) technology is used for this path generation. The input is sentiment data, location information, and time information, and the output is the user's travel path.

[0650] Step 5:

[0651] The server updates the user's visit history using the generated travel routes and sentiment data. Furthermore, it uses a generative AI model based on past sentiment data to identify and suggest unvisited points of interest. The input to this process is travel routes and sentiment data, and the output is a suggestion for the next travel destination.

[0652] Step 6:

[0653] Users evaluate whether they would actually visit the suggested travel destinations. This evaluation is sent to the server and used for system improvements and popularity analysis. The input is user evaluation feedback, and the output is improved suggestions and popularity data based on that feedback.

[0654] Through the processing performed at each step, this system can realize personalized travel suggestions based on the user's emotions.

[0655] (Application Example 2)

[0656] 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."

[0657] Traditional systems failed to adequately provide personalized recommendations based on the preferences of travelers and visitors, limiting the quality of experiences at travel destinations and stores. Furthermore, it was difficult to provide recommendations that reflected visitors' behavior and emotions in real time, highlighting the need for a system that could contribute to discovering new points of interest and increasing purchasing intent.

[0658] 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.

[0659] In this invention, the server includes means for receiving image information transmitted from a user terminal and extracting location and time information from the image information, means for analyzing the visitor's facial expression data and recognizing their emotional state, and means for recommending products and services based on the recognized emotional state. This makes it possible to provide visitors with more appropriate experiences and products that match their emotions and preferences.

[0660] A "user terminal" is an electronic device used by a user, and is a device for transmitting image information and location information to a server.

[0661] "Image information" refers to visual data transmitted from a user's device, which is used to recognize location information and emotional states.

[0662] "Location information" refers to data indicating the geographical location contained in image information, and is used to generate the user's travel route.

[0663] "Time information" refers to data indicating the date and time the image information was acquired, and is used as time-series data in generating travel routes.

[0664] "Travel route" refers to data that shows a user's travel history, generated based on location and time information.

[0665] A "visited location" refers to a place that the user has actually visited and is a geographical point recorded as part of their travel route.

[0666] "Emotional state" refers to data that indicates the type and intensity of emotions obtained by analyzing the visitor's facial expression data.

[0667] "Recommending products or services" is the act of selecting and suggesting products or services to a user based on their perceived emotional state.

[0668] "Evaluation information" refers to data showing user reactions to suggested points of interest and products.

[0669] "Popularity" is an index that compiles received evaluation information and quantifies the popularity of a particular point or product.

[0670] "Rewards" refer to incentives offered to the originator of a proposal that meets the popularity criteria.

[0671] This invention realizes a system that provides a personalized experience based on the visitor's emotions, utilizing a user terminal, a server, and an emotion engine. The user terminal includes smart glasses or a smartphone, which acquire image information. When the user views a specific product or service, facial expression data is collected via the camera and analyzed using the emotion engine.

[0672] The server extracts location and time information from the collected image data and generates the visitor's travel route. The travel route is recorded as visited points, and unvisited points of interest are suggested based on the accumulated history. At this point, the system considers the visitor's emotional state, identifies the user's preferences and interests from the facial expressions they displayed, and recommends the most suitable products and services.

[0673] In this process, machine learning frameworks such as "TensorFlow" are used to process the data obtained from facial expression analysis in real time and accurately grasp the emotional state. For example, if a user smiles when they see a product in a store, the server will immediately recommend related products.

[0674] As a concrete example, if a user smiles in the literature section of a bookstore, the server will use this data to present a list of other literary works. This allows the user to discover more interesting products.

[0675] An example of a prompt to input into the generative AI model would be, "When you capture the moment a customer smiles in front of a specific product, generate a list of related products." This would enable real-time recommendations based on emotions.

[0676] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0677] Step 1:

[0678] The user walks around the store using smart glasses or a smartphone. The user's device acquires image information using a camera and sends that image information to a server. The input is the image information acquired within the store, and the output is the transmission of the image information to the server.

[0679] Step 2:

[0680] The server extracts location and time information from the received image data. During this process, the image metadata is analyzed, and the location is obtained as GPS data, while the time is the time the image was taken. The input is image data sent from the user's terminal, and the output is location and time information.

[0681] Step 3:

[0682] The server analyzes image information using an emotion engine to recognize the visitor's emotional state. Specifically, a facial recognition model is executed using TensorFlow to identify emotions from facial expressions such as smiles and surprise. The input is image information stored on the server, and the output is the recognized emotional state.

[0683] Step 4:

[0684] The server integrates extracted location, time, and emotional states to generate a travel path. This creates a history of what emotions a visitor displayed at each location. The inputs are location, time, and emotional states, and the output is the generated travel path.

[0685] Step 5:

[0686] The server suggests unvisited points of interest to the user based on the generated travel path and emotional history. It refers to a database of past emotional states and related points of interest to identify new points that might pique similar interest. The input is the travel path and emotional history, and the output is unvisited points of interest.

[0687] Step 6:

[0688] When a user visits a suggested point of interest, the server retrieves and aggregates evaluation information. This evaluation information is collected based on the user's expressed emotions, level of boredom, etc., and stored in a database. The input is the user's evaluation information, and the output is the aggregated evaluation data.

[0689] Step 7:

[0690] The server calculates popularity based on aggregated evaluation information and rewards proposers who meet the criteria. Popularity is calculated from the sum of evaluation information for each interest point, and the system determines whether the reward conditions are met. The input is aggregated evaluation information, and the output is the popularity evaluation result and the generation of rewards.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] [Fourth Embodiment]

[0695] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0696] 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.

[0697] 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).

[0698] 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.

[0699] 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.

[0700] 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).

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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".

[0708] This invention is a data processing system that automatically generates travel routes using image information taken by travelers and suggests points of interest. This system mainly consists of a user terminal and a server.

[0709] Users send image information taken during their travels to the server via their device. This image information includes location and time information, which the server uses for analysis.

[0710] The server analyzes the received location and time information to generate the user's travel route. The generated travel route is a chronological history of the places the user has visited and is recorded as a travel diary that can be used as a reference for future trips.

[0711] Furthermore, based on this travel diary, the server lists points of interest that the user has not yet visited. These include popular tourist destinations and highly-rated restaurants. The server collects relevant reviews and rating data from the internet and suggests the most suitable points of interest.

[0712] The user's terminal displays new points of interest sent from the server. The user can then incorporate this information into their next travel plans.

[0713] As a concrete example, the system generates travel routes using photos taken by a user while sightseeing in city B, and suggests unvisited historical buildings and popular local cafes within city B. This allows users to discover new places and create itineraries based on their individual interests.

[0714] The server aggregates user ratings and calculates popularity. Proposers who receive high ratings are rewarded through the system. In this way, high-quality travel experiences are shared, and active participation among users is encouraged. This invention is useful as a traveler planning support tool and improves the quality of the travel experience.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The user selects images taken during their trip and sends a bulk upload request from their device to the server.

[0718] Step 2:

[0719] The device reads location and time information from each selected image and sends it to the server along with this metadata.

[0720] Step 3:

[0721] The server analyzes the received image data and extracts location and time information. This provides the basic data needed to reconstruct the user's movement path.

[0722] Step 4:

[0723] The server uses the extracted data to generate a chronological record of the user's travel route and saves it to the user's account as a travel diary.

[0724] Step 5:

[0725] The server analyzes the user's travel diary and identifies unvisited points of interest based on the history of places visited. This involves referencing review sites and tourist destination databases.

[0726] Step 6:

[0727] The server uses an AI algorithm to propose a new travel route from previously unvisited points and sends the results to the user's terminal.

[0728] Step 7:

[0729] The device visualizes the suggested route received from the server on a map and displays it to the user, along with detailed information. The user can then use this information to plan their next trip.

[0730] Step 8:

[0731] After experiencing the suggested route, the user evaluates it and sends the evaluation information to the server as feedback.

[0732] Step 9:

[0733] The server analyzes the collected user ratings, identifies the highest-rated route, and calculates and distributes rewards to the person who proposed it.

[0734] Step 10:

[0735] When a reward is generated, the server sends that information as a notification to the proposer's user terminal to encourage participation.

[0736] (Example 1)

[0737] 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".

[0738] Modern travelers seek efficient and personalized travel plans based on their interests and past travel history, but manually gathering information and making decisions presents time-consuming and laborious challenges. Furthermore, to ensure travelers fully utilize their destinations, there is a need for mechanisms that effectively leverage relevant evaluation data and provide appropriate incentives to providers.

[0739] 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.

[0740] In this invention, the server includes means for receiving image data transmitted from a user device and extracting location data and time data from the image data; means for generating a route based on the extracted data and recording a history of visited areas; and means for suggesting unexplored areas of interest using the recorded history and a generated AI model. This enables travelers to receive efficient and personalized travel suggestions, allowing them to make the most of their travel experience.

[0741] A "user device" is a portable electronic device used by travelers to transmit image data to a server.

[0742] "Image data" refers to digital files containing visual information, such as photographs and videos, taken with a user's device.

[0743] "Location data" refers to geographical information that indicates the location where the image data was taken, and is usually expressed in the form of latitude and longitude.

[0744] "Time data" refers to information indicating the date and time the image data was taken.

[0745] A "route" is the path generated by a user following the points they have visited in order.

[0746] "Visited area history" refers to chronological information of locations recorded based on the user's travel routes.

[0747] "Points of interest" refer to places such as tourist attractions, facilities, and restaurants that the user has not yet visited and that are likely to attract the interest of travelers.

[0748] A "suggestion" is a recommendation for a new place to visit, based on the user's browsing history and preference analysis.

[0749] "Evaluation data" refers to feedback information collected from users regarding the suggested areas of interest, and includes data on satisfaction levels and experiences.

[0750] "Popularity" is an indicator that shows the acceptability and level of evaluation of a proposal, calculated by aggregating evaluation data.

[0751] "Reward" refers to an incentive given to the originator of a proposal who provides high-quality suggestions to users.

[0752] "Visual presentation" refers to the act of making information easier for users to understand by using visual elements such as maps and images.

[0753] A "generative AI model" is a type of artificial intelligence technology that learns from large amounts of data to analyze user interests and travel patterns, and then makes optimal suggestions based on that analysis.

[0754] This invention is a system that generates individual travel routes and suggests places of interest using image data taken by travelers. This system mainly consists of a user terminal and a server.

[0755] Users take image data using their smartphones or cameras while traveling. The user's device transmits this image data to a server via a dedicated application. The images contain location and time data, which the server uses for analysis.

[0756] The server extracts location and time data to generate the user's travel route. This route is recorded as a chronological history of the areas the user has visited. This allows users to efficiently plan further destinations based on their past travel history.

[0757] Furthermore, the server uses the generated travel routes and AI models to suggest potential destinations the user hasn't yet visited. This process employs methods to collect and analyze tourism and rating data from the internet. For example, popular tourist spots and highly-rated restaurants may be suggested as potential destinations.

[0758] The suggested areas of interest are visually displayed on the user's device application along with a map. Based on this, users can create new travel plans and easily decide on their next destination.

[0759] As a concrete example, a server can generate travel routes using images taken by a user while traveling in a tourist city, and suggest unvisited museums and popular cafes within that city. This allows users to efficiently visit destinations of interest and enjoy new cultural experiences.

[0760] By utilizing generative AI models, the accuracy of suggestions and their personalization to match user needs are achieved. Similarly, user feedback is used to calculate popularity and provide rewards, ensuring that high-quality suggestions are continuously delivered.

[0761] An example of a prompt message would be: "Describe a system that suggests new travel routes and points of interest based on image information taken by travelers. As a specific example, consider a case where the system uses images taken by a user in city B to suggest places to visit."

[0762] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0763] Step 1:

[0764] Users take pictures using their smartphones while traveling. Location and time information are automatically added as metadata to the captured images. Users send the image data to a server via a dedicated app. The inputs are image data, location information, and time information. The output is the image data and its metadata stored on the server.

[0765] Step 2:

[0766] The server analyzes the received image data and extracts location and time information. At this stage, the image metadata is stored in a database and organized chronologically. The input is image data sent by the user, and the output is the organized location and time information stored in the database.

[0767] Step 3:

[0768] The server generates the user's travel route using extracted location and time information. This process arranges visited locations sequentially, taking time chronological order into consideration, and plots the route on a map. The input is location and time information from a database, and the output is user-specific travel route data.

[0769] Step 4:

[0770] The server utilizes a generative AI model to suggest unvisited areas of interest based on the user's travel history and interests. Here, it analyzes data on tourist destinations and restaurants collected from the internet to select suitable candidates for the user. The input consists of generated travel route data and tourist information from an external database, while the output is a list of suggested areas of interest.

[0771] Step 5:

[0772] The server sends the suggested interests to the user's device. The user's device displays the received data on the app screen and visualizes it on a map. The input is the list of interests sent from the server, and the output is the suggested information displayed on the user's device.

[0773] Step 6:

[0774] After visiting a suggested location, users rate their experience and submit feedback through the app. The server aggregates this rating information and calculates the popularity ranking. The input is user ratings, and the output is popularity scoring data.

[0775] (Application Example 1)

[0776] 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".

[0777] Traditional travel planning systems struggled to generate customized travel plans based on actual experiences, using information gathered from places visited and photos taken by users. Furthermore, they lacked mechanisms to collect real-time evaluations of destinations and suggest information useful for future trips. Additionally, suggestions for unvisited locations and products in the virtual space were insufficient, limiting opportunities for new discoveries based on user interests.

[0778] 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.

[0779] In this invention, the server includes means for receiving image data transmitted from a user device and extracting location and time information from the image data; means for generating a route based on the extracted location and time information; means for storing a record of visited locations based on the generated route; and means for displaying information on proposed unvisited areas in a form accessible from the user terminal and identifying recommended products and tourist spots in the virtual space. This makes it possible to efficiently generate travel routes from images taken by the user, discover new points of interest in the virtual space based on the travel history, and utilize this information for travel planning and product selection.

[0780] A "user device" is a terminal used by a user that has the function of capturing and transmitting image data.

[0781] "Image data" refers to visual information captured by a user, including location and time information.

[0782] "Location information" refers to data that indicates geographical coordinates, and is information used to identify the location where image data was taken.

[0783] "Time information" refers to data that indicates the date and time a specific event occurred, and is used to determine the time when image data was captured.

[0784] A "route" is a chronological list of locations visited by a user, representing their movement pattern.

[0785] "Visited locations record" refers to data that stores information about multiple locations visited by the user, and is used to refer to past travel history.

[0786] "Areas of interest" refer to information about places or products that a user has not yet visited but may be interested in.

[0787] "Evaluation information" refers to data that includes feedback and reviews on each area of ​​interest provided by users or others.

[0788] "Popularity" is an index that indicates the general level of appreciation for each area of ​​interest, calculated based on collected evaluation information.

[0789] A "virtual space" is a virtual environment created by a computer that transcends physical limitations, providing users with products and tourist destinations.

[0790] A "recommended product" is a product or service that is selected based on the user's areas of interest and is recommended for purchase or visit.

[0791] This invention is a system that generates travel routes using image data captured by the user and suggests unvisited points of interest and recommended products. This system consists of a user device, a server, and a cloud service.

[0792] The user's device is responsible for capturing image data and sending it to the server. This process extracts location and time information from the images using an image analysis API such as Google Cloud Vision.

[0793] The server generates a route based on the received data. Here, it utilizes location services such as the Google Maps API to identify the user's visited locations and constructs a route chronologically based on these locations. The generated route is stored as a record of the places the user has visited.

[0794] Furthermore, the server uses this record to suggest unvisited locations that the user might be interested in. When making suggestions, it collects reviews and ratings information publicly available on the internet and uses this to calculate the popularity of the recommended locations and products.

[0795] The user terminal reappears, displaying unvisited locations and recommended products suggested by the server in a virtual space. Users can easily access this information via their smartphones or smart glasses to help plan their next trips and influence their purchasing decisions.

[0796] As a concrete example, when a user uploads an image taken at a tourist destination, recommended tourist spots they haven't yet visited in that area, as well as popular items in virtual stores, are suggested. Based on the generative AI model, when recommending unvisited locations within a specific region, a prompt message such as "Please suggest places and products the user hasn't visited yet based on the image taken at the tourist destination. Please prioritize suggesting popular spots and products with good reviews" is used.

[0797] This allows users to efficiently discover new areas of interest based on the images they have taken, and to gain richer travel and shopping experiences.

[0798] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0799] Step 1:

[0800] The user takes image data and sends it to the server via their device. The input is the image taken by the user, and the output is the image data sent to the server. The user's device captures the image and uploads the data to the server using a communication protocol.

[0801] Step 2:

[0802] The server extracts location and time information from the received image data. The input is image data, and the output is location and time information associated with the image. The server analyzes the image using an image analysis API such as Google Cloud Vision to identify the location data and the date and time the image was taken.

[0803] Step 3:

[0804] The server generates a visit route based on extracted location and time information. The input is location and time information, and the output is the generated visit route. The server uses the Google Maps API to connect the visited locations in chronological order and construct the user's travel route.

[0805] Step 4:

[0806] The server saves a record of visited locations based on the visit route. The input is the generated visit route, and the output is the record of visited locations stored in the database. The server creates entries to save the visit route as a persistent record.

[0807] Step 5:

[0808] The server suggests unvisited locations of interest based on records of visited locations. The input is a record of visited locations, and the output is a list of unvisited locations suggested to the user. The server collects online reviews and rating data to identify unvisited locations that might be of interest and generates suggestions.

[0809] Step 6:

[0810] The server collects evaluation information for proposed points of interest and calculates their popularity. The input is user and other users' evaluation information, and the output is the calculated popularity score for the points of interest. The server aggregates the collected evaluation information, calculates the average score, and determines the popularity score.

[0811] Step 7:

[0812] The user terminal displays unvisited locations and recommended products suggested by the server. The input is the server's suggestions, and the output is the recommendations displayed on the user terminal. The user terminal processes the received suggestions and provides information to the user visually.

[0813] Step 8:

[0814] Users can view recommended locations and products in a virtual space and use this information to plan their next trip. The input is the information displayed on the user's terminal, and the output is the planned next destination. Users review the information, select locations of interest, and adjust their travel plan.

[0815] 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.

[0816] This invention relates to a data processing system incorporating an emotion engine to enhance the user's travel experience. The system consists of a user terminal, a server, and an emotion engine.

[0817] Users send images they take during their trip from their device to the server. During this process, the emotion engine analyzes the image data to recognize the user's emotional state. This emotional state is inferred from facial expressions such as smiles, surprise, and boredom, and corresponding emotion data is generated.

[0818] The server generates travel routes based on the location and time information of the received images. In addition to this data, it utilizes sentiment data provided by the sentiment engine to analyze what emotions the user experienced at different points during their trip. This enables the suggestion of more personalized travel routes.

[0819] Furthermore, the server references the user's emotional history to identify unvisited points of interest that are likely to trigger specific emotions. This emotional data is incorporated into the evaluation information, serving as user feedback that can be helpful to other users.

[0820] For example, if a user smiles frequently while visiting historical buildings during their trip, the emotion engine will use this information to prioritize other historical points of interest in future suggestions. This results in suggestions that match the user's preferences, making their travel experience more fulfilling.

[0821] Finally, the server aggregates user evaluation information along with sentiment data to determine the popularity of each point of interest. Based on this popularity ranking, users who receive high ratings are rewarded, thereby encouraging their use of the system. This invention aims to further improve satisfaction compared to conventional systems by supporting the traveler's experience from an emotional perspective as well.

[0822] The following describes the processing flow.

[0823] Step 1:

[0824] Users select images taken during their trip and upload them to the server via their device. The images include location and time information.

[0825] Step 2:

[0826] The emotion engine analyzes image data transmitted from the user's device to identify the emotional state the user is exhibiting. This result is then generated as emotion data.

[0827] Step 3:

[0828] The server extracts location and time information from uploaded images to generate the user's visit history. Furthermore, sentiment data is also recorded.

[0829] Step 4:

[0830] The server identifies unvisited points of interest that are likely to pique the user's interest, based on their visit history and sentiment data. Reviews and ratings from other travelers are also used as references in this identification process.

[0831] Step 5:

[0832] The server generates a new travel route based on unvisited points of interest and sends it to the user's terminal. This route is personalized according to the user's mood.

[0833] Step 6:

[0834] The device visualizes the received travel route as a map and presents it to the user along with detailed information. The user can then use this information to plan their next trip.

[0835] Step 7:

[0836] After experiencing the suggested travel route, users send their impressions and ratings to the server. The ratings include feedback on the emotional impact they experienced at the places they visited.

[0837] Step 8:

[0838] The server analyzes the collected evaluation and sentiment data to calculate the popularity of each point of interest. This popularity ranking is also valuable information for other users.

[0839] Step 9:

[0840] The server distributes rewards to the originator of a proposal if its popularity exceeds a certain threshold. The reward is calculated based on the proposer's past performance.

[0841] Step 10:

[0842] The server sends a notification regarding rewards to the user's terminal to encourage participation and contribution to the system. This notification is intended to acknowledge the user's achievements.

[0843] (Example 2)

[0844] 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".

[0845] Modern travelers seek more personalized travel experiences, but existing travel guidance systems struggle to adequately provide suggestions based on their emotions and personal preferences. Furthermore, travel suggestions and plans that utilize user emotional data are rarely implemented, and the potential for increasing traveler satisfaction has not been fully explored. This invention aims to improve the traveler's experience by analyzing the emotional state of users using images they have taken and providing personalized travel suggestions based on this analysis.

[0846] 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.

[0847] In this invention, the server includes means for receiving image information transmitted from a user terminal and extracting location and time information from the image information; means for inferring the user's emotional state using image analysis technology and generating emotional data; and means for storing a history of visited locations based on the generated travel route and emotional data. This makes it possible to personalize travel suggestions based on the user's emotions and provide a more personalized travel experience.

[0848] A "user terminal" refers to a device used by a user to capture and transmit image data, and includes, for example, smartphones and tablets.

[0849] "Image information" refers to digital data transmitted by a user's terminal, represented as a still image.

[0850] "Location information" refers to data representing geographical coordinates associated with image information.

[0851] "Time information" refers to data indicating the date and time the image information was acquired.

[0852] A "travel route" refers to the sequence of places a user visited during their trip, based on location and time information.

[0853] "Emotional data" refers to data that represents the user's emotional state, inferred through the analysis of image information.

[0854] "Visited location history" refers to a data file that records information about places the user has visited in the past.

[0855] "Unvisited points of interest" refer to places that are not included in the user's visit history but which may be of interest to the user.

[0856] "Evaluation information" refers to data that represents the feedback and ratings that users give to suggested points of interest.

[0857] "Popularity" refers to an index that shows the general evaluation and favorability of points of interest, derived from aggregating evaluation information.

[0858] "Reward" refers to the benefits or value provided to the originator of a proposal that meets a certain level of popularity.

[0859] This invention is a data processing system for improving the user's travel experience, and it consists of multiple components.

[0860] First, users take pictures of their travel destinations using their own devices, such as smartphones or tablets. These devices are equipped with a function to save the captured images and their metadata (location information, time information). Users can then send this image data from their devices to a server.

[0861] Next, the server receives the image data sent from the terminal and transfers it to an image analysis system called an emotion engine. The emotion engine utilizes image processing libraries such as OpenCV or TensorFlow to analyze the user's facial expressions in the image, infer their emotional state, and generate corresponding emotion data.

[0862] The generated emotion data, along with the location and time information contained in the images, is stored on the server and used to create the user's travel route. The server utilizes technologies such as Geographic Information Systems (GIS) to map what emotions were felt at which locations.

[0863] Furthermore, the server utilizes a generative AI model based on this data to identify and suggest unvisited points of interest, taking into account the user's past emotional history. These suggestions are customized based on the user's past emotional data, personalizing the travel experience.

[0864] As a concrete example, a prompt such as "Generate suggestions for the next travel destination based on the user's emotional data for each place they have visited" could be considered. This would allow users to receive travel suggestions tailored to their individual interests and emotional states, which is expected to significantly improve their travel satisfaction.

[0865] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0866] Step 1:

[0867] Users take pictures using their own devices, such as smartphones or tablets, while traveling. These images are automatically tagged with location and time information. Users then send this data directly from their devices to the server. The input includes images, location information, and time information, while the output is a series of data received by the server.

[0868] Step 2:

[0869] The server receives image information sent from the user's terminal, along with location and time information. The received data is stored in the system's storage. The input is the image and metadata from the user, and the output is storage in the server's data storage.

[0870] Step 3:

[0871] The server inputs the stored image data into the emotion engine. The emotion engine then analyzes the images using image analysis libraries (e.g., OpenCV, TensorFlow). It infers the user's emotional state from their facial expressions and generates emotion data. The input to this process is an image, and the output is the emotion data obtained from its analysis.

[0872] Step 4:

[0873] Based on the generated sentiment data, location information, and time information, the server creates the user's travel path. Geographic Information System (GIS) technology is used for this path generation. The input is sentiment data, location information, and time information, and the output is the user's travel path.

[0874] Step 5:

[0875] The server updates the user's visit history using the generated travel routes and sentiment data. Furthermore, it uses a generative AI model based on past sentiment data to identify and suggest unvisited points of interest. The input to this process is travel routes and sentiment data, and the output is a suggestion for the next travel destination.

[0876] Step 6:

[0877] Users evaluate whether they would actually visit the suggested travel destinations. This evaluation is sent to the server and used for system improvements and popularity analysis. The input is user evaluation feedback, and the output is improved suggestions and popularity data based on that feedback.

[0878] Through the processing performed at each step, this system can realize personalized travel suggestions based on the user's emotions.

[0879] (Application Example 2)

[0880] 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".

[0881] Traditional systems failed to adequately provide personalized recommendations based on the preferences of travelers and visitors, limiting the quality of experiences at travel destinations and stores. Furthermore, it was difficult to provide recommendations that reflected visitors' behavior and emotions in real time, highlighting the need for a system that could contribute to discovering new points of interest and increasing purchasing intent.

[0882] 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.

[0883] In this invention, the server includes means for receiving image information transmitted from a user terminal and extracting location and time information from the image information, means for analyzing the visitor's facial expression data and recognizing their emotional state, and means for recommending products and services based on the recognized emotional state. This makes it possible to provide visitors with more appropriate experiences and products that match their emotions and preferences.

[0884] A "user terminal" is an electronic device used by a user, and is a device for transmitting image information and location information to a server.

[0885] "Image information" refers to visual data transmitted from a user's device, which is used to recognize location information and emotional states.

[0886] "Location information" refers to data indicating the geographical location contained in image information, and is used to generate the user's travel route.

[0887] "Time information" refers to data indicating the date and time the image information was acquired, and is used as time-series data in generating travel routes.

[0888] "Travel route" refers to data that shows a user's travel history, generated based on location and time information.

[0889] A "visited location" refers to a place that the user has actually visited and is a geographical point recorded as part of their travel route.

[0890] "Emotional state" refers to data that indicates the type and intensity of emotions obtained by analyzing the visitor's facial expression data.

[0891] "Recommending products or services" is the act of selecting and suggesting products or services to a user based on their perceived emotional state.

[0892] "Evaluation information" refers to data showing user reactions to suggested points of interest and products.

[0893] "Popularity" is an index that compiles received evaluation information and quantifies the popularity of a particular point or product.

[0894] "Rewards" refer to incentives offered to the originator of a proposal that meets the popularity criteria.

[0895] This invention realizes a system that provides a personalized experience based on the visitor's emotions, utilizing a user terminal, a server, and an emotion engine. The user terminal includes smart glasses or a smartphone, which acquire image information. When the user views a specific product or service, facial expression data is collected via the camera and analyzed using the emotion engine.

[0896] The server extracts location and time information from the collected image data and generates the visitor's travel route. The travel route is recorded as visited points, and unvisited points of interest are suggested based on the accumulated history. At this point, the system considers the visitor's emotional state, identifies the user's preferences and interests from the facial expressions they displayed, and recommends the most suitable products and services.

[0897] In this process, machine learning frameworks such as "TensorFlow" are used to process the data obtained from facial expression analysis in real time and accurately grasp the emotional state. For example, if a user smiles when they see a product in a store, the server will immediately recommend related products.

[0898] As a concrete example, if a user smiles in the literature section of a bookstore, the server will use this data to present a list of other literary works. This allows the user to discover more interesting products.

[0899] An example of a prompt to input into the generative AI model would be, "When you capture the moment a customer smiles in front of a specific product, generate a list of related products." This would enable real-time recommendations based on emotions.

[0900] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0901] Step 1:

[0902] The user walks around the store using smart glasses or a smartphone. The user's device acquires image information using a camera and sends that image information to a server. The input is the image information acquired within the store, and the output is the transmission of the image information to the server.

[0903] Step 2:

[0904] The server extracts location and time information from the received image data. During this process, the image metadata is analyzed, and the location is obtained as GPS data, while the time is the time the image was captured. The input is image data sent from the user's terminal, and the output is location and time information.

[0905] Step 3:

[0906] The server analyzes image information using an emotion engine to recognize the visitor's emotional state. Specifically, a facial recognition model is executed using TensorFlow to identify emotions from facial expressions such as smiles and surprise. The input is image information stored on the server, and the output is the recognized emotional state.

[0907] Step 4:

[0908] The server integrates extracted location, time, and emotional states to generate a travel path. This creates a history of what emotions a visitor exhibited at each location. The inputs are location, time, and emotional states, and the output is the generated travel path.

[0909] Step 5:

[0910] The server suggests unvisited points of interest to the user based on the generated travel path and emotional history. It refers to a database of past emotional states and related points of interest to identify new points that might pique similar interest. The input is the travel path and emotional history, and the output is unvisited points of interest.

[0911] Step 6:

[0912] When a user visits a suggested point of interest, the server retrieves and aggregates evaluation information. This evaluation information is collected based on the user's expressed emotions, level of boredom, etc., and stored in a database. The input is the user's evaluation information, and the output is the aggregated evaluation data.

[0913] Step 7:

[0914] The server calculates popularity based on aggregated evaluation information and rewards proposers who meet the criteria. Popularity is calculated from the sum of evaluation information for each interest point, and the system determines whether the reward conditions are met. The input is aggregated evaluation information, and the output is the popularity evaluation result and the generation of rewards.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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."

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] The following is further disclosed regarding the embodiments described above.

[0937] (Claim 1)

[0938] A means for receiving image information transmitted from a user terminal and extracting location information and time information from said image information,

[0939] A means for generating a travel route based on extracted location and time information,

[0940] A means for saving a history of visited locations based on the generated travel route,

[0941] Based on this history, a means of suggesting unvisited points of interest,

[0942] A means for receiving and aggregating the evaluation information of the proposed points,

[0943] A means for determining popularity based on the aggregated evaluation information,

[0944] A means of generating rewards for the originators of proposals that meet the popularity criteria,

[0945] A data processing system that includes this.

[0946] (Claim 2)

[0947] The data processing system according to claim 1, further comprising means for visualizing and presenting the generated travel path to the user.

[0948] (Claim 3)

[0949] The data processing system according to claim 1, further comprising means for displaying information on proposed unvisited locations in multiple languages.

[0950] "Example 1"

[0951] (Claim 1)

[0952] A means for receiving image data transmitted from a user device and extracting location data and time data from said image data,

[0953] A means for generating a route based on extracted location data and time data,

[0954] A means for recording the history of visited areas based on the generated route,

[0955] A means of proposing unexplored areas of interest based on the history,

[0956] A means for receiving and aggregating evaluation data for the proposed subject,

[0957] A means for calculating popularity based on the aggregated evaluation data,

[0958] A means of providing rewards to providers of proposals that meet the popularity criteria,

[0959] Means for visually presenting the generated paths and objects of interest,

[0960] A means of analyzing data and user history and making personalized suggestions using a generative AI model,

[0961] A system that includes this.

[0962] (Claim 2)

[0963] The system according to claim 1, further comprising means for displaying information on proposed unvisited areas in multiple languages.

[0964] (Claim 3)

[0965] The system according to claim 1, comprising a learning process for improving the accuracy of suggestions based on user evaluations.

[0966] "Application Example 1"

[0967] (Claim 1)

[0968] A means for receiving image data transmitted from a user device and extracting location information and time information from said image data,

[0969] A means for generating a route based on extracted location and time information,

[0970] A means for saving a record of visited locations based on the generated route,

[0971] Based on the record, a means of suggesting unvisited areas of interest,

[0972] A means for receiving and aggregating evaluation information for the proposed domain,

[0973] A means for determining popularity based on the aggregated evaluation information,

[0974] A means of generating rewards for the originators of proposals that meet the popularity criteria,

[0975] A means of displaying information on proposed unvisited areas in a format accessible from the user's terminal, and identifying recommended products and tourist spots within the virtual space,

[0976] A system that includes this.

[0977] (Claim 2)

[0978] The system according to claim 1, further comprising means for displaying and presenting the generated route to the user.

[0979] (Claim 3)

[0980] The system according to claim 1, further comprising means for displaying information regarding proposed unvisited areas in multiple languages.

[0981] "Example 2 of combining an emotion engine"

[0982] (Claim 1)

[0983] A means for receiving image information transmitted from a user terminal and extracting location information and time information from said image information,

[0984] A means for generating a travel route based on extracted location and time information,

[0985] A means for inferring a user's emotional state and generating emotional data using image analysis technology,

[0986] A means for saving a history of visited locations based on generated travel routes and sentiment data,

[0987] A means of suggesting unvisited points of interest based on the history and sentiment data,

[0988] A means for receiving and aggregating the evaluation information of the proposed points,

[0989] A means for determining popularity based on the aggregated evaluation information,

[0990] A means of generating rewards for the originators of proposals that meet the popularity criteria,

[0991] A system that includes this.

[0992] (Claim 2)

[0993] The system according to claim 1, further comprising means for visualizing and presenting to the user the generated movement path and emotion data.

[0994] (Claim 3)

[0995] The system according to claim 1, further comprising means for displaying information on proposed unvisited locations in multiple languages.

[0996] "Application example 2 when combining with an emotional engine"

[0997] (Claim 1)

[0998] A means for receiving image information transmitted from a user terminal and extracting location information and time information from said image information,

[0999] A means for generating a travel route based on extracted location and time information,

[1000] A means for saving a history of visited locations based on the generated travel route,

[1001] Based on this history, a means of suggesting unvisited points of interest,

[1002] A means for receiving and aggregating the evaluation information of the proposed points,

[1003] A means for determining popularity based on the aggregated evaluation information,

[1004] A means of analyzing visitors' facial expression data to recognize their emotional state,

[1005] A means of recommending products and services based on recognized emotional states,

[1006] A means of generating rewards for the originators of proposals that meet the popularity criteria,

[1007] A system that includes this.

[1008] (Claim 2)

[1009] The system according to claim 1, further comprising means for visualizing and presenting the generated travel path to the user.

[1010] (Claim 3)

[1011] The system according to claim 1, further comprising means for displaying information on proposed unvisited locations in multiple languages. [Explanation of Symbols]

[1012] 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 for receiving image information transmitted from a user terminal and extracting location information and time information from said image information, A means for generating a travel route based on extracted location and time information, A means for saving a history of visited locations based on the generated travel route, Based on this history, a means of suggesting unvisited points of interest, A means for receiving and aggregating the evaluation information of the proposed points, A means for determining popularity based on the aggregated evaluation information, A means of generating rewards for the originators of proposals that meet the popularity criteria, A data processing system that includes this.

2. The data processing system according to claim 1, further comprising means for visualizing and presenting the generated travel path to the user.

3. The data processing system according to claim 1, further comprising means for displaying information on proposed unvisited locations in multiple languages.

Images