system

Abstract

Provide a system. 【Solution means】 Analyze the image of an individual's clothes obtained from a terminal, Means for estimating an individual's preference based on the image, Means for automatically generating a travel itinerary based on the preference, Means for reflecting an individual's constraints in the itinerary, Means for visually displaying the generated itinerary, Means for recommending items based on attributes within a commercial facility based on the image of clothes obtained from a terminal, Means for providing an optimal route using the location information within the commercial facility, A 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Conventional travel planning often cannot fully reflect individual user preferences and constraints, and the proposed plans have been a problem in that they cannot meet user expectations. In particular, for users seeking a travel experience that takes into account their own hobbies and personal conditions, not only does the burden in advance preparation increase, but also the fun of finding new experiences is often impaired.

Means for Solving the Problems

[0005] This invention provides a system that acquires images of an individual's clothing from a terminal and estimates their preferences using image analysis technology. Furthermore, it automatically generates a travel itinerary based on the estimated preferences and proposes a travel plan suitable for the user by reflecting individual constraints. In addition, the generated itinerary is displayed visually, allowing the user to intuitively check the travel plan. Moreover, by utilizing user feedback data, travel history, and usage information, the system continuously improves future plans, enabling more accurate suggestions.

[0006] A "terminal" is a portable electronic device that a user can carry and is equipped with functions for taking pictures and sending and receiving information.

[0007] "Images of personal clothing" refer to digital data containing visual information of clothing worn by a specific person.

[0008] "Analysis" is the process of extracting specific information from given data and interpreting or evaluating it.

[0009] "Preferences" refer to characteristics that indicate a person's particular likes or interests.

[0010] "Estimation" refers to the act of deriving predictions or hypotheses from certain information through observation or analysis.

[0011] A "travel itinerary" is a plan that arranges destinations and activities to be visited over a specific period of time in chronological order.

[0012] "Automated generation" refers to the process by which a machine or program produces a specific output without requiring human intervention.

[0013] "Constraints" refer to limitations or requirements that should be considered under specific circumstances.

[0014] "Visual display" means presenting information in a way that can be visually confirmed using images and graphics.

[0015] "Feedback" refers to information for reference in subsequent improvements by providing evaluations and comments on certain actions or results.

[0016] "Movement history" refers to a record of data tracking the places and routes visited by an individual during a specific period.

[0017] "Usage information" refers to relevant data and records obtained during the process of using services or facilities.

Brief Explanation of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in 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.

Modes for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0022] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] 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.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention provides a system that analyzes a user's clothing images and creates a travel plan best suited to their individual preferences. The invention is implemented below based on the roles of the server, terminal, and user.

[0040] 1. Obtain images of the user's clothing.

[0041] The user takes a picture of their everyday clothes using their device's camera function. The application prepares to automatically send this image to the server. The user can check if the captured image is suitable and retake it if necessary.

[0042] 2. Image Analysis

[0043] Images sent from the device are analyzed on the server using a deep learning model. The server extracts clothing features—such as color, style, and accessories—and compares them to similar fashion styles stored in a database. This comparison estimates the user's preferences, and preference data is generated.

[0044] 3. Creating a travel plan

[0045] The server generates travel plans based on user preference data and pre-registered constraints (such as allergy information and acrophobia). These plans include recommended tourist destinations, local restaurants, and accommodations. This information is compiled into a visually easy-to-understand "Preference Travel Map" and provided to the user.

[0046] 4. Presenting the plan and obtaining feedback

[0047] The device displays a travel map, and the user reviews the provided travel plan. The user can provide feedback on the plan through the device. This feedback includes ratings and requests, which will be considered when creating future travel plans.

[0048] 5. Collection and utilization of usage data

[0049] During your trip, the device uses GPS location data to record your travel history. It also collects data on visited tourist spots and restaurants. This data is transferred to a server and used to personalize your next travel plan.

[0050] In this way, the present invention provides travel plans tailored to the user's specific preferences and constraints, optimizing the travel experience. The specific processing flow achieves a high level of usability throughout the system, based on the technical details of each step. For example, a user who prefers casual attire can be offered a plan centered around theme parks and outdoor activities.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] The user launches an application on their device and takes a picture of their outfit for the day using the camera. The captured image is reviewed by the device and compressed before being sent to the server.

[0054] Step 2:

[0055] The terminal sends compressed image data to the server. The transmission is conducted via a secure protocol to protect the data.

[0056] Step 3:

[0057] The server uses an AI model to analyze the received images. It performs facial recognition to identify clothing and extracts features such as color, style, and accessories.

[0058] Step 4:

[0059] The server compares clothing feature data with a database of fashion styles to estimate the user's fashion preferences. This preference data is then stored in the user profile.

[0060] Step 5:

[0061] The server generates appropriate travel plans based on preference data and user-registered constraints. It collects information on tourist destinations, restaurants, and accommodations and visualizes it as a "Preference Travel Map."

[0062] Step 6:

[0063] The server sends the completed travel map to the device.

[0064] Step 7:

[0065] The device displays a personalized travel map to the user. The user reviews their travel plan and provides feedback through the device as needed.

[0066] Step 8:

[0067] During the user's trip, the device uses GPS to record their movement history. It also collects information on visited tourist attractions, facilities, and restaurants.

[0068] Step 9:

[0069] After the trip ends, the device sends the collected data to the server. The server stores this data in a database to help create future travel plans.

[0070] (Example 1)

[0071] 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."

[0072] Traditional travel planning systems have a challenge in that they struggle to provide suggestions that fully consider the user's individual preferences and constraints. Furthermore, they lack sufficient mechanisms for incorporating feedback from past travel experiences into future plans, resulting in planning that doesn't address individual needs.

[0073] 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.

[0074] In this invention, the server includes information processing means for analyzing images of an individual's clothing obtained from a terminal and estimating the individual's preferences; data generation means for automatically generating a travel plan based on the preferences and pre-registered constraint information; and display means for visually displaying the generated travel plan. This makes it possible to provide travel plans based on the user's individual preferences and constraints.

[0075] A "terminal" refers to an information processing device used by an individual, which has a camera and communication functions and can take pictures and send and receive data.

[0076] "Clothing images" are digital data of clothing worn by an individual, and are used to analyze the characteristics of the clothing.

[0077] "Analysis" refers to the process of extracting specific attributes or features from image data and generating information.

[0078] "Preferences" refer to data that shows an individual's tastes and preferences, particularly trends in fashion and travel.

[0079] "Information processing means" refers to a component of a system that has the technical and software mechanisms necessary for collecting, analyzing, storing, or using data.

[0080] "Travel planning" refers to an itinerary that combines tourist destinations, accommodations, restaurants, etc., selected based on the user's preferences and constraints.

[0081] "Data generation means" refers to the components of a system that perform processing to construct new data and plans based on collected information.

[0082] "Visually displaying" refers to outputting digital data to a display device in a form that humans can understand, making it possible to check the content.

[0083] "Ratings" refer to feedback information provided by users, particularly their satisfaction with the provided plan and areas for improvement.

[0084] "Location history" refers to a record of a user's movements, recorded based on the device's location information service.

[0085] "Usage data" refers to data about how a particular service or facility was used by users.

[0086] This invention relates to a system that provides users with personalized travel plans. This system functions through the cooperation of three parties: a terminal, a server, and the user.

[0087] The device is a typical information processing device such as a smartphone or tablet, and it uses its camera function to photograph the user's clothing. The captured image is then transmitted to a server via the network. The transmitted image is securely sent using an encryption protocol.

[0088] The server uses a deep learning model for image analysis, which is implemented using TENSORFLOW®, a standard machine learning framework. The server identifies the user's fashion preferences from the analysis results. This preference data is obtained by comparing the user's fashion style with similar fashion styles stored in the server's database.

[0089] The server then uses this preference data to plan a trip. Utilizing a generative AI model, it automatically generates a travel plan that takes into account not only the user's preferences but also pre-registered constraints such as allergies and acrophobia. This plan includes tourist attractions, accommodations, and local restaurants.

[0090] Users can provide feedback on their travel plans to the server via their devices. The server incorporates this feedback into its algorithms to further improve the accuracy of future travel plans.

[0091] During your trip, the device uses GPS to record your location history and collects information about the tourist attractions and restaurants you visit. This information is sent to a server and used to personalize future travel plans.

[0092] For example, if a user prefers casual clothing, the server can suggest travel plans that primarily include theme parks and outdoor activities. A possible prompt for the generating AI model might be: "Based on recent clothing images of the user, suggest a travel plan that prefers a casual and relaxed atmosphere. The user has allergies and a fear of heights."

[0093] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0094] Step 1:

[0095] The user takes a picture of their everyday clothing using the device's camera function. The captured image is saved to the device's temporary storage. The user can review the image and retake it if it is unsuitable. The input is the image data captured by the user, and the output is the reviewed image file.

[0096] Step 2:

[0097] The terminal sends the verified image to the server using an encryption protocol. After receiving the image data, the server inputs it into a deep learning model. The input is encrypted image data, and the output is data indicating the characteristics of the clothing. In this step, the server analyzes the image and extracts the clothing's color, style, accessories, etc.

[0098] Step 3:

[0099] The server compares the extracted clothing feature data with style information stored in the database. The input is clothing feature data, and the output is data indicating the user's preferences. Through this process, the server identifies similar fashion styles and determines the user's preferences.

[0100] Step 4:

[0101] The server generates travel plans considering the user's preference data and pre-registered personal constraint information. Utilizing a generation AI model, the input is preference data and constraint information, and the output is a recommended travel plan. The travel plan includes tourist destinations, accommodations, and dining options.

[0102] Step 5:

[0103] The terminal visually displays the travel plan sent from the server in a user interface. The user can review the details of this plan and provide feedback as needed. The input is the travel plan data, and the output is the user's feedback data.

[0104] Step 6:

[0105] During travel, the device collects the user's location information using GPS and records a log of places visited. The server receives location history data periodically sent from the device. The input is location information and visit logs, and the output is data for improving future travel plans. This information is reflected in future plans, improving personalization.

[0106] (Application Example 1)

[0107] 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."

[0108] The problem that this invention aims to solve is to efficiently generate travel plans and shopping plans within commercial facilities that are tailored to the individual preferences of users, thereby improving the user experience. Another problem is to optimize the shopping experience in physical stores by suggesting product selections within commercial facilities based on each user's style and past purchase history.

[0109] 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.

[0110] In this invention, the server includes means for analyzing images of an individual's clothing obtained from a terminal to estimate their preferences, means for automatically generating a travel itinerary based on those preferences, and means for recommending items within a commercial facility based on the user's attributes. This makes it possible to provide travel plans and optimal purchasing plans within commercial facilities that are tailored to the user's individual preferences.

[0111] A "terminal" is an information processing device used by a user, equipped with communication and camera functions, and responsible for acquiring images and transmitting them to a server.

[0112] "Images of personal clothing" refers to digital data containing visual information of clothing taken by a user using their device.

[0113] "Preferences" refer to a user's tendency to show specific preferences, and include information such as clothing style, colors, and characteristics related to accessories.

[0114] A "travel itinerary" is a plan that includes tourist destinations and services to be visited, automatically generated based on the user's preferences and constraints.

[0115] A "commercial facility" refers to a building or place used for the sale of goods or the provision of services, and is a concept that includes physical stores.

[0116] "Recommending products" means selecting and presenting products or services that are suitable for a user based on their individual preferences.

[0117] "Location information" refers to data that indicates a specific location using technologies such as GPS, and is used to identify a user's current location and travel route.

[0118] The system for implementing this invention consists primarily of a user terminal, a server, and a commercial facility. The terminal consists of a smartphone or smart glasses owned by the user, and uses its camera function to acquire images of the user's clothing. The acquired images are transmitted to the server via a communication function. The server uses TensorFlow, a deep learning model, to perform image analysis and estimate the user's preferences. Through this analysis, features such as clothing style, color, and accessories are extracted to generate user preference information.

[0119] The generated preference information is used to automatically create itineraries for travel destinations the user plans to visit. It also provides personalized product recommendations to help users enjoy shopping at specific commercial facilities. Specifically, a list of recommended products is sent from the server to the user's device, and a map showing the optimal order of visits within the store is displayed. By using OpenStreetMap data, users can easily find their way to specific product sections without getting lost.

[0120] Furthermore, location information within the commercial facility is acquired via GPS to determine the user's current location. This data is sent to a server and used to provide the optimal route by comparing it with the location of recommended products. This improves the user's shopping experience and enables more efficient browsing within the commercial facility.

[0121] For example, if a user prefers casual fashion, they might receive information about discounted outdoor gear that suits their taste, and the location of a specific product section would be shown on a map. This allows the user to efficiently find and purchase items they are interested in. An example of a prompt for the generative AI model might be something specific like, "Please recommend items that match casual, outdoor-appropriate clothing."

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The user uses the device to take a picture of their clothing. The camera function is used to photograph the user's clothing as input, acquiring digital image data. This data is stored on the device for further processing. The user can review the results and retake the picture if necessary.

[0125] Step 2:

[0126] The terminal sends the captured image to the server. The input is the digital image data acquired in step 1, which is sent to the server via HTTP communication. The output is the image data transferred to the server, which is ready for analysis. The terminal confirms that the data transmission is complete and proceeds to the next step.

[0127] Step 3:

[0128] The server analyzes the received image data using a deep learning model. The input is image data sent from the terminal, and a generative AI model using TensorFlow is used to estimate the user's preferences. Specifically, it extracts features such as color, style, and accessories from the image and generates preference data based on this. As output, the preference data is stored on the server.

[0129] Step 4:

[0130] The server calculates travel itineraries and recommended products within commercial facilities based on the generated preference data. The input consists of preference data and constraints previously registered by the user. Using these, it automatically constructs a list of travel destinations and products that match the user's preferences. The output is then ready to send the calculation results to the terminal.

[0131] Step 5:

[0132] The terminal receives information from the server and displays it visually. The input is the travel itinerary and recommended product list sent from the server in step 4. Based on this, it is displayed in a user-friendly format as a travel map or store map. As output, the user is ready to review the travel plan and product information on the screen and enter feedback.

[0133] Step 6:

[0134] The user reviews the presented information and enters feedback into the terminal as needed. This feedback includes user ratings and requests regarding travel plans and product lists, which are sent to the server via the terminal. The feedback data is stored on the server as output and used to improve the accuracy of future recommendations.

[0135] Step 7:

[0136] The server records location information during travel and visit history within commercial facilities. Inputs include GPS data sent from the device and activity logs within stores. Based on this, the server analyzes the user's movement patterns and purchasing tendencies, and incorporates this into future plans. The output is the analysis results, which are used to improve services going forward.

[0137] 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.

[0138] This invention is a travel planning system that incorporates an emotion engine that recognizes the user's emotions. By taking into account the user's emotions in addition to their preferences and constraints, this system provides a more appropriate and satisfying travel experience.

[0139] The following processes are performed to implement this system.

[0140] 1. Collection of user clothing images and sentiment data

[0141] The user takes a picture of their outfit for the day via their device, and simultaneously collects emotional data from their facial expressions and voice using the device's sensors and camera. This data is then transmitted from the device to a server.

[0142] 2. Data Analysis

[0143] The server first analyzes clothing images to estimate the user's preferences. In addition, it uses an emotion engine to analyze emotional data and identify the user's current emotional state. For example, emotions such as happy, calm, or anxious may be recognized.

[0144] 3. Creating and adapting travel plans

[0145] The server considers user preference data and emotional state to create a travel plan. This plan suggests tourist destinations and dining spots that reflect the user's preferences and is adjusted to enhance their emotions. The generated "preferred travel map" dynamically changes according to the user's real-time emotions.

[0146] 4. Presentation of the plan and user impact

[0147] The device visually presents the generated travel plan to the user. The user can then review the plan and provide feedback based on their satisfaction level and expectations. This feedback will be actively used to improve future plan generation.

[0148] 5. Emotional monitoring during travel

[0149] During the trip, the device monitors the user's emotions in real time. If emotions change, the server quickly replans the itinerary and suggests it to the user. This allows the user to enjoy the trip even more.

[0150] Specific example

[0151] For example, if a user is in a calm mood and their preference for nature experiences is recognized, the system will suggest a travel plan that includes gentle hiking trails and nature parks. Furthermore, if the user's emotions are heightened during the trip, more exciting attractions or activities can be added.

[0152] In this way, the present invention continues to provide highly customizable travel experiences that meet the individual emotions and preferences of users.

[0153] The following describes the processing flow.

[0154] Step 1:

[0155] The user launches an application on their device and takes a picture of their outfit for the day with the camera. At the same time, the device uses the camera and microphone to record the user's facial expressions and voice, collecting emotional data.

[0156] Step 2:

[0157] The device sends captured images of clothing and emotional data to the server. A secure data transmission protocol is used for transmission.

[0158] Step 3:

[0159] The server applies an AI model to analyze the received clothing images. It extracts features such as color, texture, and style from the images to estimate the user's fashion preferences.

[0160] Step 4:

[0161] The server uses an emotion engine to analyze the received emotional data. It recognizes the user's current emotional state (e.g., joy, calmness, stress) from facial expressions and voice tone.

[0162] Step 5:

[0163] The server generates a travel plan based on estimated user preferences and current emotions. The plan includes tourist destinations and activities that suit the user's preferences and circumstances, and also takes into account the user's constraints.

[0164] Step 6:

[0165] The server formats the generated travel plan as a "Preferenced Travel Map" and sends it to the terminal.

[0166] Step 7:

[0167] The device visually displays a personalized travel map to the user. Users can review the plan and provide feedback on their satisfaction level and expectations.

[0168] Step 8:

[0169] During travel, the device continuously monitors the user's emotions in real time using sensors and cameras. If the user's emotions change, the device sends this information to a server.

[0170] Step 9:

[0171] The server receives real-time sentiment data and adjusts the travel plan as needed. It replans the suggested itinerary and activities to better match the user's emotions.

[0172] Step 10:

[0173] The server sends the replanned itinerary to the terminal and presents the user with new suggestions. This data, along with feedback to improve the user experience, is recorded and used for future planning.

[0174] (Example 2)

[0175] 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".

[0176] Traditional travel planning systems, while considering user preferences, often lacked the flexibility and individualization to respond to users' changing emotional states. This could lead to frustration for users when their emotions shifted during their trip. Furthermore, feedback was often not adequately utilized for future planning improvements, hindering the accuracy of the planning process.

[0177] 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.

[0178] In this invention, the server includes means for analyzing images of an individual's body adornment acquired from a terminal and estimating the individual's preferences; means for collecting emotional data from facial expressions and voice using the terminal; and means for analyzing the emotional data using an emotion engine and identifying the individual's emotional state. This enables flexible and personalized travel planning that is tailored to the user's preferences and current emotional state.

[0179] A "terminal" is a portable information and communication device used by a user, and is a device that has image capture and data collection functions.

[0180] "Body adornment" refers to information about a user's personal appearance, such as their clothing and accessories.

[0181] "Analysis" is the process of interpreting information based on acquired data and deriving specific conclusions or meanings.

[0182] "Preference" refers to an individual's likes and tendencies in choosing specific things or events.

[0183] "Emotional data" refers to numerical or string-based information that indicates a user's emotional state.

[0184] An "emotion engine" is a combination of software or hardware used to analyze emotional data and identify and evaluate a user's emotional state.

[0185] "Travel planning" refers to a list of itineraries and destinations suggested based on the user's preferences and emotional state.

[0186] "Constraints" are time, geographical, or other limitations that must be incorporated into the travel plan.

[0187] "Visual display" refers to presenting information in a format that can be visually confirmed through a user interface.

[0188] A "feedback function" is a means or system for users to provide evaluations and opinions based on their usage experience.

[0189] A "generative AI model" is a predictive model that uses artificial intelligence algorithms to more effectively personalize travel plans.

[0190] This invention relates to a travel planning system that incorporates an emotion engine that recognizes user emotions. This system provides the optimal travel plan for the user by taking into account the user's emotions in addition to their preferences and constraints.

[0191] The user takes a picture of their outfit for the day using the device, and simultaneously collects emotional data through facial expressions and voice. The device has a built-in camera, microphone, and various sensors, and uses this hardware to acquire the necessary data. The acquired data is transmitted to a server via an internet connection.

[0192] The server first uses image analysis software to analyze images of the user's clothing and estimate their personal preferences. Next, it uses an emotion engine to analyze facial expressions and voice data to identify emotional states such as happiness, calmness, and anxiety. This emotion engine is software that incorporates machine learning algorithms, enabling it to identify emotions with high accuracy.

[0193] The server then uses the analyzed preference and emotional state data to generate personalized travel plans. This process employs a generative AI model and predictive algorithms to provide personalized suggestions. The generated plans also take into account constraints such as safety and time of day.

[0194] For example, if a user is in a calm mood and enjoys nature experiences, the server will suggest a plan to visit a nature park. Furthermore, if the user's emotions become more heightened during the trip, the server can suggest additional active attractions.

[0195] An example of a prompt message would be, "The user is in a calm mood and enjoys nature experiences. What kind of travel plan can you suggest?" This message is then input into the generative AI model to create the optimal plan.

[0196] The terminal ultimately presents the travel plan received from the server to the user visually and collects user feedback. This feedback becomes important data for further improving the accuracy of future travel plans.

[0197] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0198] Step 1:

[0199] The user takes an image of their outfit for the day using the device. The device's sensors also collect emotional data, including facial expressions and voice. This data is temporarily stored by the device's application. The input consists of the outfit image and emotional data, and the output is a dataset ready for transmission to the server.

[0200] Step 2:

[0201] The device transmits collected clothing images and emotion data to the server via the network. The server receives this data and prepares it for the next data analysis step. The input is the dataset from the device, and the output is the information stored in the server's database.

[0202] Step 3:

[0203] The server uses image analysis software to analyze clothing images. Specifically, it activates an image recognition algorithm to estimate the user's preferences based on the style and color of the clothing. The input is clothing images stored on the server, and the output is the user's preference profile.

[0204] Step 4:

[0205] The server analyzes emotional data using an emotion engine. Specifically, it identifies the user's emotional state from facial expressions and voice through a machine learning model. For example, it can detect emotions such as excitement, calmness, or anxiety from the data. The input is emotional data stored on the server, and the output is the user's emotional state.

[0206] Step 5:

[0207] The server combines the user's preference profile and emotional state and generates a travel plan based on them. Here, a generative AI model is used to automatically generate a personalized travel plan based on the user's input prompts. The input is the preference profile and emotional state, and the output is the travel plan.

[0208] Step 6:

[0209] The server applies constraints to the generated travel plan, such as specific time and budget limits, and selection of places that can be visited. This creates a plan that is feasible for the user. The input is the basic travel plan, and the output is the final travel plan with the constraints reflected.

[0210] Step 7:

[0211] The terminal receives the final travel plan sent from the server and displays it visually to the user. The user can review the plan and provide feedback through the terminal. The input is the travel plan data from the server, and the output is the travel plan interface that the user sees.

[0212] (Application Example 2)

[0213] 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".

[0214] In travel planning, there is a challenge in improving satisfaction to an extent previously unattainable by taking into account not only individual preferences but also the emotional state at the time. However, technologies for real-time emotion recognition and dynamic itinerary updates based on that recognition are not yet readily available. In this situation, there is a need to develop a system that provides a travel experience optimized for each individual user.

[0215] 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.

[0216] In this invention, the server includes means for analyzing images of an individual's clothing obtained from a terminal and estimating the individual's preferences; means for recognizing and analyzing the individual's emotional state in real time; means for automatically generating a travel itinerary based on the preferences and emotional state; means for reflecting the individual's constraints in the itinerary; means for dynamically updating the generated itinerary according to the individual's emotional state; and means for visually displaying the generated itinerary. This makes it possible to provide a dynamic and personalized travel itinerary that takes the individual's emotional state into consideration in real time.

[0217] "Images of personal clothing obtained from a device" refers to visual representations of clothing worn by an individual, captured using a mobile communication device.

[0218] "Means for estimating individual preferences" refers to technological elements that infer a person's preferred style and tastes from analyzed image data of their clothing.

[0219] "A means of recognizing and analyzing an individual's emotional state in real time" refers to a process that instantly determines an individual's psychological state at a given moment through facial expressions and voice data collected from a device.

[0220] "Methods for automatically generating travel itineraries" refer to technologies that schedule a trip to a destination based on acquired data.

[0221] A "dynamically updating mechanism" is a function that technically changes pre-configured plans in response to changes in the user's emotions.

[0222] "Methods for dynamically updating according to emotional state" refers to technology that instantly adjusts travel plans based on user emotional data acquired in real time.

[0223] "Means of visual display" refers to technologies that visually represent generated information or process results through a user interface.

[0224] In implementing this invention, a system is used that links a mobile information terminal with a server. In this system, the user first takes an image of their clothing using the terminal, and the image is sent to the server. The server executes an image recognition algorithm using Python to estimate the user's preferences from the style and color of their clothing. Furthermore, it analyzes facial expressions and voice tone in real time through the terminal's camera and microphone, and identifies the emotional state using an emotion recognition model such as TensorFlow.

[0225] By combining this preference data and emotional data, an optimal travel itinerary is automatically generated. This itinerary is visually displayed to the user through the device's user interface. If the user's emotional state changes in real time, the server instantly updates the itinerary to optimize the travel experience.

[0226] For example, if a user is in a calm mood while sightseeing, the system will suggest an itinerary centered around parks and museums with a peaceful atmosphere. On the other hand, if the user is emotionally aroused, the plan will automatically adjust to include events and activities that will provide excitement.

[0227] When using a generative AI model, an example of a prompt message is: "Please suggest the best sightseeing spots and activities for a user who is currently in a calm emotional state and wants to enjoy sightseeing. Please include nature experiences and cultural elements." In this way, a personalized travel experience tailored to the user's emotions becomes possible.

[0228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0229] Step 1:

[0230] The user takes a picture of their outfit for the day using the device's camera. This image becomes the input data, and the device sends the image to the server.

[0231] Step 2:

[0232] The server analyzes the received images of clothing using Python. Specifically, it extracts color and style features using image recognition algorithms and estimates the user's preferences based on these. Preference data is then generated as output.

[0233] Step 3:

[0234] The device captures the user's facial expressions with its camera and collects audio through its microphone. This data becomes input, and the device sends the emotional data to the server.

[0235] Step 4:

[0236] The server uses emotion recognition models such as TensorFlow to analyze the received facial expressions and audio data. This identifies the user's emotional state (e.g., calm, excited, relaxed) and outputs emotion data.

[0237] Step 5:

[0238] The server combines acquired preference and sentiment data to automatically generate travel itineraries. This includes data processing to select tourist destinations and activities optimized for the user's current state.

[0239] Step 6:

[0240] The generated travel itinerary is visually displayed on the device's screen. Users can review the itinerary and make selections or changes.

[0241] Step 7:

[0242] During the trip, the device monitors the user's emotions in real time and continuously sends this data to the server, enabling dynamic updates of the itinerary based on the user's emotional state. Each time a new itinerary is generated, the updated information is output and presented to the user.

[0243] 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.

[0244] Data generation model 58 is a type of 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.

[0245] 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.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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".

[0259] This invention provides a system that analyzes a user's clothing images and creates a travel plan best suited to their individual preferences. The invention is implemented below based on the roles of the server, terminal, and user.

[0260] 1. Obtain images of the user's clothing.

[0261] The user takes a picture of their everyday clothes using their device's camera function. The application prepares to automatically send this image to the server. The user can check if the captured image is suitable and retake it if necessary.

[0262] 2. Image Analysis

[0263] Images sent from the device are analyzed on the server using a deep learning model. The server extracts clothing features—such as color, style, and accessories—and compares them to similar fashion styles stored in a database. This comparison estimates the user's preferences, and preference data is generated.

[0264] 3. Creating a travel plan

[0265] The server generates travel plans based on user preference data and pre-registered constraints (such as allergy information and acrophobia). These plans include recommended tourist destinations, local restaurants, and accommodations. This information is compiled into a visually easy-to-understand "Preference Travel Map" and provided to the user.

[0266] 4. Presenting the plan and obtaining feedback

[0267] The device displays a travel map, and the user reviews the provided travel plan. The user can provide feedback on the plan through the device. This feedback includes ratings and requests, which will be considered when creating future travel plans.

[0268] 5. Collection and utilization of usage data

[0269] During your trip, the device uses GPS location data to record your travel history. It also collects data on visited tourist spots and restaurants. This data is transferred to a server and used to personalize your next travel plan.

[0270] In this way, the present invention provides travel plans tailored to the user's specific preferences and constraints, optimizing the travel experience. The specific processing flow achieves a high level of usability throughout the system, based on the technical details of each step. For example, a user who prefers casual attire can be offered a plan centered around theme parks and outdoor activities.

[0271] The following describes the processing flow.

[0272] Step 1:

[0273] The user launches an application on their device and takes a picture of their outfit for the day using the camera. The captured image is reviewed by the device and compressed before being sent to the server.

[0274] Step 2:

[0275] The terminal sends compressed image data to the server. The transmission is conducted via a secure protocol to protect the data.

[0276] Step 3:

[0277] The server uses an AI model to analyze the received images. It performs facial recognition to identify clothing and extracts features such as color, style, and accessories.

[0278] Step 4:

[0279] The server compares clothing feature data with a database of fashion styles to estimate the user's fashion preferences. This preference data is then stored in the user profile.

[0280] Step 5:

[0281] The server generates appropriate travel plans based on preference data and user-registered constraints. It collects information on tourist destinations, restaurants, and accommodations and visualizes it as a "Preference Travel Map."

[0282] Step 6:

[0283] The server transmits the completed preferred travel map to the terminal.

[0284] Step 7:

[0285] The terminal displays the preferred travel map to the user. The user checks the travel plan and provides feedback through the terminal if necessary.

[0286] Step 8:

[0287] During the user's trip, the terminal uses GPS to record the movement history. Also, information on visited tourist attractions, facilities, and restaurants is collected.

[0288] Step 9:

[0289] After the trip ends, the terminal transmits the collected data to the server. The server saves this in a database for use in creating the next plan.

[0290] (Example 1)

[0291] 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".

[0292] The conventional travel planning system had the problem that it was difficult to make proposals that fully considered the user's personal preferences and constraint information. Also, the mechanism for reflecting the feedback of the travel experience in the next plan was insufficient, and planning that met individual needs was not carried out.

[0293] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0294] In this invention, the server includes information processing means for analyzing images of an individual's clothing obtained from a terminal and estimating the individual's preferences; data generation means for automatically generating a travel plan based on the preferences and pre-registered constraint information; and display means for visually displaying the generated travel plan. This makes it possible to provide travel plans based on the user's individual preferences and constraints.

[0295] A "terminal" refers to an information processing device used by an individual, which has a camera and communication functions and can take pictures and send and receive data.

[0296] "Clothing images" are digital data of clothing worn by an individual, and are used to analyze the characteristics of the clothing.

[0297] "Analysis" refers to the process of extracting specific attributes or features from image data and generating information.

[0298] "Preferences" refer to data that shows an individual's tastes and preferences, particularly trends in fashion and travel.

[0299] "Information processing means" refers to a component of a system that has the technical and software mechanisms necessary for collecting, analyzing, storing, or using data.

[0300] "Travel planning" refers to an itinerary that combines tourist destinations, accommodations, restaurants, etc., selected based on the user's preferences and constraints.

[0301] "Data generation means" refers to the components of a system that perform processing to construct new data and plans based on collected information.

[0302] "Visually displaying" refers to outputting digital data to a display device in a form that humans can understand, making it possible to check the content.

[0303] "Evaluation" refers to the feedback information provided by the user, and particularly indicates the satisfaction and improvement points regarding the provided plan.

[0304] "Location history" refers to the record of the user's movements recorded based on the terminal's location information service.

[0305] "Usage situation" refers to the data regarding how a specific service or facility is used by the user.

[0306] The present invention relates to a system for providing an individualized travel plan for a user. This system functions through the cooperation of a terminal, a server, and the user.

[0307] The terminal is an information processing device such as a general smartphone or tablet, and uses its camera function to photograph the user's clothing. The image captured here is transmitted to the server via a network. The transmitted image is securely transmitted using an encryption protocol.

[0308] The server uses a deep learning model for image analysis, and this model is implemented using TensorFlow, a standard machine learning framework. The server identifies the user's fashion preferences from the analysis results. This preference data is obtained by comparing it with similar fashion styles stored in the server's database.

[0309] After that, the server formulates a travel plan based on this preference data. By leveraging a generative AI model and taking into account not only the user's preferences but also constraints such as pre-registered allergy information and fear of heights, etc., it automatically generates a travel plan. This plan includes tourist attractions, accommodation facilities, and local dining spots.

[0310] Users can provide feedback on their travel plans to the server via their devices. The server incorporates this feedback into its algorithms to further improve the accuracy of future travel plans.

[0311] During your trip, the device uses GPS to record your location history and collects information about the tourist attractions and restaurants you visit. This information is sent to a server and used to personalize future travel plans.

[0312] For example, if a user prefers casual clothing, the server can suggest travel plans that primarily include theme parks and outdoor activities. A possible prompt for the generating AI model might be: "Based on recent clothing images of the user, suggest a travel plan that prefers a casual and relaxed atmosphere. The user has allergies and a fear of heights."

[0313] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0314] Step 1:

[0315] The user takes a picture of their everyday clothing using the device's camera function. The captured image is saved to the device's temporary storage. The user can review the image and retake it if it is unsuitable. The input is the image data captured by the user, and the output is the reviewed image file.

[0316] Step 2:

[0317] The terminal sends the verified image to the server using an encryption protocol. After receiving the image data, the server inputs it into a deep learning model. The input is encrypted image data, and the output is data indicating the characteristics of the clothing. In this step, the server analyzes the image and extracts the clothing's color, style, accessories, etc.

[0318] Step 3:

[0319] The server compares the extracted clothing feature data with style information stored in the database. The input is clothing feature data, and the output is data indicating the user's preferences. Through this process, the server identifies similar fashion styles and determines the user's preferences.

[0320] Step 4:

[0321] The server generates travel plans considering the user's preference data and pre-registered personal constraint information. Utilizing a generation AI model, the input is preference data and constraint information, and the output is a recommended travel plan. The travel plan includes tourist destinations, accommodations, and dining options.

[0322] Step 5:

[0323] The terminal visually displays the travel plan sent from the server in a user interface. The user can review the details of this plan and provide feedback as needed. The input is the travel plan data, and the output is the user's feedback data.

[0324] Step 6:

[0325] During travel, the device collects the user's location information using GPS and records a log of places visited. The server receives location history data periodically sent from the device. The input is location information and visit logs, and the output is data for improving future travel plans. This information is reflected in future plans, improving personalization.

[0326] (Application Example 1)

[0327] 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."

[0328] The problem that this invention aims to solve is to efficiently generate travel plans and shopping plans within commercial facilities that are tailored to the individual preferences of users, thereby improving the user experience. Another problem is to optimize the shopping experience in physical stores by suggesting product selections within commercial facilities based on each user's style and past purchase history.

[0329] 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.

[0330] In this invention, the server includes means for analyzing images of an individual's clothing obtained from a terminal to estimate their preferences, means for automatically generating a travel itinerary based on those preferences, and means for recommending items within a commercial facility based on the user's attributes. This makes it possible to provide travel plans and optimal purchasing plans within commercial facilities that are tailored to the user's individual preferences.

[0331] A "terminal" is an information processing device used by a user, equipped with communication and camera functions, and responsible for acquiring images and transmitting them to a server.

[0332] "Images of personal clothing" refers to digital data containing visual information of clothing taken by a user using their device.

[0333] "Preferences" refer to a user's tendency to show specific preferences, and include information such as clothing style, colors, and characteristics related to accessories.

[0334] A "travel itinerary" is a plan that includes tourist destinations and services to be visited, automatically generated based on the user's preferences and constraints.

[0335] A "commercial facility" refers to a building or place used for the sale of goods or the provision of services, and is a concept that includes physical stores.

[0336] "Recommending products" means selecting and presenting products or services that are suitable for a user based on their individual preferences.

[0337] "Location information" refers to data that indicates a specific location using technologies such as GPS, and is used to identify a user's current location and travel route.

[0338] The system for implementing this invention consists primarily of a user terminal, a server, and a commercial facility. The terminal consists of a smartphone or smart glasses owned by the user, and uses its camera function to acquire images of the user's clothing. The acquired images are transmitted to the server via a communication function. The server uses TensorFlow, a deep learning model, to perform image analysis and estimate the user's preferences. Through this analysis, features such as clothing style, color, and accessories are extracted to generate user preference information.

[0339] The generated preference information is used to automatically create itineraries for travel destinations the user plans to visit. It also provides personalized product recommendations to help users enjoy shopping at specific commercial facilities. Specifically, a list of recommended products is sent from the server to the user's device, and a map showing the optimal order of visits within the store is displayed. By using OpenStreetMap data, users can easily find their way to specific product sections without getting lost.

[0340] Furthermore, location information within the commercial facility is acquired via GPS to determine the user's current location. This data is sent to a server and used to provide the optimal route by comparing it with the location of recommended products. This improves the user's shopping experience and enables more efficient browsing within the commercial facility.

[0341] For example, if a user prefers casual fashion, they might receive information about discounted outdoor gear that suits their taste, and the location of a specific product section would be shown on a map. This allows the user to efficiently find and purchase items they are interested in. An example of a prompt for the generative AI model might be something specific like, "Please recommend items that match casual, outdoor-appropriate clothing."

[0342] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0343] Step 1:

[0344] The user uses the device to take a picture of their clothing. The camera function is used to photograph the user's clothing as input, acquiring digital image data. This data is stored on the device for further processing. The user can review the results and retake the picture if necessary.

[0345] Step 2:

[0346] The terminal sends the captured image to the server. The input is the digital image data acquired in step 1, which is sent to the server via HTTP communication. The output is the image data transferred to the server, which is ready for analysis. The terminal confirms that the data transmission is complete and proceeds to the next step.

[0347] Step 3:

[0348] The server analyzes the received image data using a deep learning model. The input is image data sent from the terminal, and a generative AI model using TensorFlow is used to estimate the user's preferences. Specifically, it extracts features such as color, style, and accessories from the image and generates preference data based on this. As output, the preference data is stored on the server.

[0349] Step 4:

[0350] The server calculates travel itineraries and recommended products within commercial facilities based on the generated preference data. The input consists of preference data and constraints previously registered by the user. Using these, it automatically constructs a list of travel destinations and products that match the user's preferences. The output is then ready to send the calculation results to the terminal.

[0351] Step 5:

[0352] The terminal receives information from the server and displays it visually. The input is the travel itinerary and recommended product list sent from the server in step 4. Based on this, it is displayed in a user-friendly format as a travel map or store map. As output, the user is ready to review the travel plan and product information on the screen and enter feedback.

[0353] Step 6:

[0354] The user reviews the presented information and enters feedback into the terminal as needed. This feedback includes user ratings and requests regarding travel plans and product lists, which are sent to the server via the terminal. The feedback data is stored on the server as output and used to improve the accuracy of future recommendations.

[0355] Step 7:

[0356] The server records location information during travel and visit history within commercial facilities. Inputs include GPS data sent from the device and activity logs within stores. Based on this, the server analyzes the user's movement patterns and purchasing tendencies, and incorporates this into future plans. The output is the analysis results, which are used to improve services going forward.

[0357] 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.

[0358] This invention is a travel planning system that incorporates an emotion engine that recognizes the user's emotions. By taking into account the user's emotions in addition to their preferences and constraints, this system provides a more appropriate and satisfying travel experience.

[0359] The following processes are performed to implement this system.

[0360] 1. Collection of user clothing images and sentiment data

[0361] The user takes a picture of their outfit for the day via their device, and simultaneously collects emotional data from their facial expressions and voice using the device's sensors and camera. This data is then transmitted from the device to a server.

[0362] 2. Data Analysis

[0363] The server first analyzes clothing images to estimate the user's preferences. In addition, it uses an emotion engine to analyze emotional data and identify the user's current emotional state. For example, emotions such as happy, calm, or anxious may be recognized.

[0364] 3. Creating and adapting travel plans

[0365] The server considers user preference data and emotional state to create a travel plan. This plan suggests tourist destinations and dining spots that reflect the user's preferences and is adjusted to enhance their emotions. The generated "preferred travel map" dynamically changes according to the user's real-time emotions.

[0366] 4. Presentation of the plan and user impact

[0367] The device visually presents the generated travel plan to the user. The user can then review the plan and provide feedback based on their satisfaction level and expectations. This feedback will be actively used to improve future plan generation.

[0368] 5. Emotional monitoring during travel

[0369] During the trip, the device monitors the user's emotions in real time. If emotions change, the server quickly replans the itinerary and suggests it to the user. This allows the user to enjoy the trip even more.

[0370] Specific example

[0371] For example, if a user is in a calm mood and their preference for nature experiences is recognized, the system will suggest a travel plan that includes gentle hiking trails and nature parks. Furthermore, if the user's emotions are heightened during the trip, more exciting attractions or activities can be added.

[0372] In this way, the present invention continues to provide highly customizable travel experiences that meet the individual emotions and preferences of users.

[0373] The following describes the processing flow.

[0374] Step 1:

[0375] The user launches an application on their device and takes a picture of their outfit for the day with the camera. At the same time, the device uses the camera and microphone to record the user's facial expressions and voice, collecting emotional data.

[0376] Step 2:

[0377] The device sends captured images of clothing and emotional data to the server. A secure data transmission protocol is used for transmission.

[0378] Step 3:

[0379] The server applies an AI model to analyze the received clothing images. It extracts features such as color, texture, and style from the images to estimate the user's fashion preferences.

[0380] Step 4:

[0381] The server uses an emotion engine to analyze the received emotional data. It recognizes the user's current emotional state (e.g., joy, calmness, stress) from facial expressions and voice tone.

[0382] Step 5:

[0383] The server generates a travel plan based on estimated user preferences and current emotions. The plan includes tourist destinations and activities that suit the user's preferences and circumstances, and also takes into account the user's constraints.

[0384] Step 6:

[0385] The server formats the generated travel plan as a "Preferenced Travel Map" and sends it to the terminal.

[0386] Step 7:

[0387] The device visually displays a personalized travel map to the user. Users can review the plan and provide feedback on their satisfaction level and expectations.

[0388] Step 8:

[0389] During travel, the device continuously monitors the user's emotions in real time using sensors and cameras. If the user's emotions change, the device sends this information to a server.

[0390] Step 9:

[0391] The server receives real-time sentiment data and adjusts the travel plan as needed. It replans the suggested itinerary and activities to better match the user's emotions.

[0392] Step 10:

[0393] The server sends the replanned itinerary to the terminal and presents the user with new suggestions. This data, along with feedback to improve the user experience, is recorded and used for future planning.

[0394] (Example 2)

[0395] 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".

[0396] Traditional travel planning systems, while considering user preferences, often lacked the flexibility and individualization to respond to users' changing emotional states. This could lead to frustration for users when their emotions shifted during their trip. Furthermore, feedback was often not adequately utilized for future planning improvements, hindering the accuracy of the planning process.

[0397] 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.

[0398] In this invention, the server includes means for analyzing images of an individual's body adornment acquired from a terminal and estimating the individual's preferences; means for collecting emotional data from facial expressions and voice using the terminal; and means for analyzing the emotional data using an emotion engine and identifying the individual's emotional state. This enables flexible and personalized travel planning that is tailored to the user's preferences and current emotional state.

[0399] A "terminal" is a portable information and communication device used by a user, and is a device that has image capture and data collection functions.

[0400] "Body adornment" refers to information about a user's personal appearance, such as their clothing and accessories.

[0401] "Analysis" is the process of interpreting information based on acquired data and deriving specific conclusions or meanings.

[0402] "Preference" refers to an individual's likes and tendencies in choosing specific things or events.

[0403] "Emotional data" refers to numerical or string-based information that indicates a user's emotional state.

[0404] An "emotion engine" is a combination of software or hardware used to analyze emotional data and identify and evaluate a user's emotional state.

[0405] "Travel planning" refers to a list of itineraries and destinations suggested based on the user's preferences and emotional state.

[0406] "Constraints" are time, geographical, or other limitations that must be incorporated into the travel plan.

[0407] "Visual display" refers to presenting information in a format that can be visually confirmed through a user interface.

[0408] A "feedback function" is a means or system for users to provide evaluations and opinions based on their usage experience.

[0409] A "generative AI model" is a predictive model that uses artificial intelligence algorithms to more effectively personalize travel plans.

[0410] This invention relates to a travel planning system that incorporates an emotion engine that recognizes user emotions. This system provides the optimal travel plan for the user by taking into account the user's emotions in addition to their preferences and constraints.

[0411] The user takes a picture of their outfit for the day using the device, and simultaneously collects emotional data through facial expressions and voice. The device has a built-in camera, microphone, and various sensors, and uses this hardware to acquire the necessary data. The acquired data is transmitted to a server via an internet connection.

[0412] The server first uses image analysis software to analyze images of the user's clothing and estimate their personal preferences. Next, it uses an emotion engine to analyze facial expressions and voice data to identify emotional states such as happiness, calmness, and anxiety. This emotion engine is software that incorporates machine learning algorithms, enabling it to identify emotions with high accuracy.

[0413] The server then uses the analyzed preference and emotional state data to generate personalized travel plans. This process employs a generative AI model and predictive algorithms to provide personalized suggestions. The generated plans also take into account constraints such as safety and time of day.

[0414] For example, if a user is in a calm mood and enjoys nature experiences, the server will suggest a plan to visit a nature park. Furthermore, if the user's emotions become more heightened during the trip, the server can suggest additional active attractions.

[0415] An example of a prompt message would be, "The user is in a calm mood and enjoys nature experiences. What kind of travel plan can you suggest?" This message is then input into the generative AI model to create the optimal plan.

[0416] The terminal ultimately presents the travel plan received from the server to the user visually and collects user feedback. This feedback becomes important data for further improving the accuracy of future travel plans.

[0417] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0418] Step 1:

[0419] The user takes an image of their outfit for the day using the device. The device's sensors also collect emotional data, including facial expressions and voice. This data is temporarily stored by the device's application. The input consists of the outfit image and emotional data, and the output is a dataset ready for transmission to the server.

[0420] Step 2:

[0421] The device transmits collected clothing images and emotion data to the server via the network. The server receives this data and prepares it for the next data analysis step. The input is the dataset from the device, and the output is the information stored in the server's database.

[0422] Step 3:

[0423] The server uses image analysis software to analyze clothing images. Specifically, it activates an image recognition algorithm to estimate the user's preferences based on the style and color of the clothing. The input is clothing images stored on the server, and the output is the user's preference profile.

[0424] Step 4:

[0425] The server analyzes emotional data using an emotion engine. Specifically, it identifies the user's emotional state from facial expressions and voice through a machine learning model. For example, it can detect emotions such as excitement, calmness, or anxiety from the data. The input is emotional data stored on the server, and the output is the user's emotional state.

[0426] Step 5:

[0427] The server combines the user's preference profile and emotional state and generates a travel plan based on them. Here, a generative AI model is used to automatically generate a personalized travel plan based on the user's input prompts. The input is the preference profile and emotional state, and the output is the travel plan.

[0428] Step 6:

[0429] The server applies constraints to the generated travel plan, such as specific time and budget limits, and selection of places that can be visited. This creates a plan that is feasible for the user. The input is the basic travel plan, and the output is the final travel plan with the constraints reflected.

[0430] Step 7:

[0431] The terminal receives the final travel plan sent from the server and displays it visually to the user. The user can review the plan and provide feedback through the terminal. The input is the travel plan data from the server, and the output is the travel plan interface that the user sees.

[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] In travel planning, there is a challenge in improving satisfaction to an extent previously unattainable by taking into account not only individual preferences but also the emotional state at the time. However, technologies for real-time emotion recognition and dynamic itinerary updates based on that recognition are not yet readily available. In this situation, there is a need to develop a system that provides a travel experience optimized for each individual user.

[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 analyzing images of an individual's clothing obtained from a terminal and estimating the individual's preferences; means for recognizing and analyzing the individual's emotional state in real time; means for automatically generating a travel itinerary based on the preferences and emotional state; means for reflecting the individual's constraints in the itinerary; means for dynamically updating the generated itinerary according to the individual's emotional state; and means for visually displaying the generated itinerary. This makes it possible to provide a dynamic and personalized travel itinerary that takes the individual's emotional state into consideration in real time.

[0437] "Images of personal clothing obtained from a device" refers to visual representations of clothing worn by an individual, captured using a mobile communication device.

[0438] "Means for estimating individual preferences" refers to technological elements that infer a person's preferred style and tastes from analyzed image data of their clothing.

[0439] "A means of recognizing and analyzing an individual's emotional state in real time" refers to a process that instantly determines an individual's psychological state at a given moment through facial expressions and voice data collected from a device.

[0440] "Methods for automatically generating travel itineraries" refer to technologies that schedule a trip to a destination based on acquired data.

[0441] A "dynamically updating mechanism" is a function that technically changes pre-configured plans in response to changes in the user's emotions.

[0442] "Methods for dynamically updating according to emotional state" refers to technology that instantly adjusts travel plans based on user emotional data acquired in real time.

[0443] "Means of visual display" refers to technologies that visually represent generated information or process results through a user interface.

[0444] In implementing this invention, a system is used that links a mobile information terminal with a server. In this system, the user first takes an image of their clothing using the terminal, and the image is sent to the server. The server executes an image recognition algorithm using Python to estimate the user's preferences from the style and color of their clothing. Furthermore, it analyzes facial expressions and voice tone in real time through the terminal's camera and microphone, and identifies the emotional state using an emotion recognition model such as TensorFlow.

[0445] By combining this preference data and emotional data, an optimal travel itinerary is automatically generated. This itinerary is visually displayed to the user through the device's user interface. If the user's emotional state changes in real time, the server instantly updates the itinerary to optimize the travel experience.

[0446] For example, if a user is in a calm mood while sightseeing, the system will suggest an itinerary centered around parks and museums with a peaceful atmosphere. On the other hand, if the user is emotionally aroused, the plan will automatically adjust to include events and activities that will provide excitement.

[0447] When using a generative AI model, an example of a prompt message is: "Please suggest the best sightseeing spots and activities for a user who is currently in a calm emotional state and wants to enjoy sightseeing. Please include nature experiences and cultural elements." In this way, a personalized travel experience tailored to the user's emotions becomes possible.

[0448] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0449] Step 1:

[0450] The user takes a picture of their outfit for the day using the device's camera. This image becomes the input data, and the device sends the image to the server.

[0451] Step 2:

[0452] The server analyzes the received images of clothing using Python. Specifically, it extracts color and style features using image recognition algorithms and estimates the user's preferences based on these. Preference data is then generated as output.

[0453] Step 3:

[0454] The device captures the user's facial expressions with its camera and collects audio through its microphone. This data becomes input, and the device sends the emotional data to the server.

[0455] Step 4:

[0456] The server uses emotion recognition models such as TensorFlow to analyze the received facial expressions and audio data. This identifies the user's emotional state (e.g., calm, excited, relaxed) and outputs emotion data.

[0457] Step 5:

[0458] The server combines acquired preference and sentiment data to automatically generate travel itineraries. This includes data processing to select tourist destinations and activities optimized for the user's current state.

[0459] Step 6:

[0460] The generated travel itinerary is visually displayed on the device's screen. Users can review the itinerary and make selections or changes.

[0461] Step 7:

[0462] During the trip, the device monitors the user's emotions in real time and continuously sends this data to the server, enabling dynamic updates of the itinerary based on the user's emotional state. Each time a new itinerary is generated, the updated information is output and presented to the user.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] [Third Embodiment]

[0467] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0468] 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.

[0469] 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).

[0470] 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.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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".

[0479] This invention provides a system that analyzes a user's clothing images and creates a travel plan best suited to their individual preferences. The invention is implemented below based on the roles of the server, terminal, and user.

[0480] 1. Obtain images of the user's clothing.

[0481] The user takes a picture of their everyday clothes using their device's camera function. The application prepares to automatically send this image to the server. The user can check if the captured image is suitable and retake it if necessary.

[0482] 2. Image Analysis

[0483] Images sent from the device are analyzed on the server using a deep learning model. The server extracts clothing features—such as color, style, and accessories—and compares them to similar fashion styles stored in a database. This comparison estimates the user's preferences, and preference data is generated.

[0484] 3. Creating a travel plan

[0485] The server generates travel plans based on user preference data and pre-registered constraints (such as allergy information and acrophobia). These plans include recommended tourist destinations, local restaurants, and accommodations. This information is compiled into a visually easy-to-understand "Preference Travel Map" and provided to the user.

[0486] 4. Presenting the plan and obtaining feedback

[0487] The device displays a travel map, and the user reviews the provided travel plan. The user can provide feedback on the plan through the device. This feedback includes ratings and requests, which will be considered when creating future travel plans.

[0488] 5. Collection and utilization of usage data

[0489] During your trip, the device uses GPS location data to record your travel history. It also collects data on visited tourist spots and restaurants. This data is transferred to a server and used to personalize your next travel plan.

[0490] In this way, the present invention provides travel plans tailored to the user's specific preferences and constraints, optimizing the travel experience. The specific processing flow achieves a high level of usability throughout the system, based on the technical details of each step. For example, a user who prefers casual attire can be offered a plan centered around theme parks and outdoor activities.

[0491] The following describes the processing flow.

[0492] Step 1:

[0493] The user launches an application on their device and takes a picture of their outfit for the day using the camera. The captured image is reviewed by the device and compressed before being sent to the server.

[0494] Step 2:

[0495] The terminal sends compressed image data to the server. The transmission is conducted via a secure protocol to protect the data.

[0496] Step 3:

[0497] The server uses an AI model to analyze the received images. It performs facial recognition to identify clothing and extracts features such as color, style, and accessories.

[0498] Step 4:

[0499] The server compares clothing feature data with a database of fashion styles to estimate the user's fashion preferences. This preference data is then stored in the user profile.

[0500] Step 5:

[0501] The server generates appropriate travel plans based on preference data and user-registered constraints. It collects information on tourist destinations, restaurants, and accommodations and visualizes it as a "Preference Travel Map."

[0502] Step 6:

[0503] The server sends the completed travel map to the device.

[0504] Step 7:

[0505] The device displays a personalized travel map to the user. The user reviews their travel plan and provides feedback through the device as needed.

[0506] Step 8:

[0507] During the user's trip, the device uses GPS to record their movement history. It also collects information on visited tourist attractions, facilities, and restaurants.

[0508] Step 9:

[0509] After the trip ends, the device sends the collected data to the server. The server stores this data in a database to help create future travel plans.

[0510] (Example 1)

[0511] 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."

[0512] Traditional travel planning systems have a challenge in that they struggle to provide suggestions that fully consider the user's individual preferences and constraints. Furthermore, they lack sufficient mechanisms for incorporating feedback from past travel experiences into future plans, resulting in planning that doesn't address individual needs.

[0513] 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.

[0514] In this invention, the server includes information processing means for analyzing images of an individual's clothing obtained from a terminal and estimating the individual's preferences; data generation means for automatically generating a travel plan based on the preferences and pre-registered constraint information; and display means for visually displaying the generated travel plan. This makes it possible to provide travel plans based on the user's individual preferences and constraints.

[0515] A "terminal" refers to an information processing device used by an individual, which has a camera and communication functions and can take pictures and send and receive data.

[0516] "Clothing images" are digital data of clothing worn by an individual, and are used to analyze the characteristics of the clothing.

[0517] "Analysis" refers to the process of extracting specific attributes or features from image data and generating information.

[0518] "Preferences" refer to data that shows an individual's tastes and preferences, particularly trends in fashion and travel.

[0519] "Information processing means" refers to a component of a system that has the technical and software mechanisms necessary for collecting, analyzing, storing, or using data.

[0520] "Travel planning" refers to an itinerary that combines tourist destinations, accommodations, restaurants, etc., selected based on the user's preferences and constraints.

[0521] "Data generation means" refers to the components of a system that perform processing to construct new data and plans based on collected information.

[0522] "Visually displaying" refers to outputting digital data to a display device in a form that humans can understand, making it possible to check the content.

[0523] "Ratings" refer to feedback information provided by users, particularly their satisfaction with the provided plan and areas for improvement.

[0524] "Location history" refers to a record of a user's movements, recorded based on the device's location information service.

[0525] "Usage data" refers to data about how a particular service or facility was used by users.

[0526] This invention relates to a system that provides users with personalized travel plans. This system functions through the cooperation of three parties: a terminal, a server, and the user.

[0527] The device is a typical information processing device such as a smartphone or tablet, and it uses its camera function to photograph the user's clothing. The captured image is then transmitted to a server via the network. The transmitted image is securely sent using an encryption protocol.

[0528] The server uses a deep learning model for image analysis, which is implemented using TensorFlow, a standard machine learning framework. From the analysis results, the server identifies the user's fashion preferences. This preference data is obtained by comparing the user's fashion style with similar fashion styles stored in the server's database.

[0529] The server then uses this preference data to plan a trip. Utilizing a generative AI model, it automatically generates a travel plan that takes into account not only the user's preferences but also pre-registered constraints such as allergies and acrophobia. This plan includes tourist attractions, accommodations, and local restaurants.

[0530] Users can provide feedback on their travel plans to the server via their devices. The server incorporates this feedback into its algorithms to further improve the accuracy of future travel plans.

[0531] During your trip, the device uses GPS to record your location history and collects information about the tourist attractions and restaurants you visit. This information is sent to a server and used to personalize future travel plans.

[0532] For example, if a user prefers casual clothing, the server can suggest travel plans that primarily include theme parks and outdoor activities. A possible prompt for the generating AI model might be: "Based on recent clothing images of the user, suggest a travel plan that prefers a casual and relaxed atmosphere. The user has allergies and a fear of heights."

[0533] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0534] Step 1:

[0535] The user takes a picture of their everyday clothing using the device's camera function. The captured image is saved to the device's temporary storage. The user can review the image and retake it if it is unsuitable. The input is the image data captured by the user, and the output is the reviewed image file.

[0536] Step 2:

[0537] The terminal sends the verified image to the server using an encryption protocol. After receiving the image data, the server inputs it into a deep learning model. The input is encrypted image data, and the output is data indicating the characteristics of the clothing. In this step, the server analyzes the image and extracts the clothing's color, style, accessories, etc.

[0538] Step 3:

[0539] The server compares the extracted clothing feature data with style information stored in the database. The input is clothing feature data, and the output is data indicating the user's preferences. Through this process, the server identifies similar fashion styles and determines the user's preferences.

[0540] Step 4:

[0541] The server generates travel plans considering the user's preference data and pre-registered personal constraint information. Utilizing a generation AI model, the input is preference data and constraint information, and the output is a recommended travel plan. The travel plan includes tourist destinations, accommodations, and dining options.

[0542] Step 5:

[0543] The terminal visually displays the travel plan sent from the server in a user interface. The user can review the details of this plan and provide feedback as needed. The input is the travel plan data, and the output is the user's feedback data.

[0544] Step 6:

[0545] During travel, the device collects the user's location information using GPS and records a log of places visited. The server receives location history data periodically sent from the device. The input is location information and visit logs, and the output is data for improving future travel plans. This information is reflected in future plans, improving personalization.

[0546] (Application Example 1)

[0547] 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."

[0548] The problem that this invention aims to solve is to efficiently generate travel plans and shopping plans within commercial facilities that are tailored to the individual preferences of users, thereby improving the user experience. Another problem is to optimize the shopping experience in physical stores by suggesting product selections within commercial facilities based on each user's style and past purchase history.

[0549] 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.

[0550] In this invention, the server includes means for analyzing images of an individual's clothing obtained from a terminal to estimate their preferences, means for automatically generating a travel itinerary based on those preferences, and means for recommending items within a commercial facility based on the user's attributes. This makes it possible to provide travel plans and optimal purchasing plans within commercial facilities that are tailored to the user's individual preferences.

[0551] A "terminal" is an information processing device used by a user, equipped with communication and camera functions, and responsible for acquiring images and transmitting them to a server.

[0552] "Images of personal clothing" refers to digital data containing visual information of clothing taken by a user using their device.

[0553] "Preferences" refer to a user's tendency to show specific preferences, and include information such as clothing style, colors, and characteristics related to accessories.

[0554] A "travel itinerary" is a plan that includes tourist destinations and services to be visited, automatically generated based on the user's preferences and constraints.

[0555] A "commercial facility" refers to a building or place used for the sale of goods or the provision of services, and is a concept that includes physical stores.

[0556] "Recommending products" means selecting and presenting products or services that are suitable for a user based on their individual preferences.

[0557] "Location information" refers to data that indicates a specific location using technologies such as GPS, and is used to identify a user's current location and travel route.

[0558] The system for implementing this invention consists primarily of a user terminal, a server, and a commercial facility. The terminal consists of a smartphone or smart glasses owned by the user, and uses its camera function to acquire images of the user's clothing. The acquired images are transmitted to the server via a communication function. The server uses TensorFlow, a deep learning model, to perform image analysis and estimate the user's preferences. Through this analysis, features such as clothing style, color, and accessories are extracted to generate user preference information.

[0559] The generated preference information is used to automatically create itineraries for travel destinations the user plans to visit. It also provides personalized product recommendations to help users enjoy shopping at specific commercial facilities. Specifically, a list of recommended products is sent from the server to the user's device, and a map showing the optimal order of visits within the store is displayed. By using OpenStreetMap data, users can easily find their way to specific product sections without getting lost.

[0560] Furthermore, location information within the commercial facility is acquired via GPS to determine the user's current location. This data is sent to a server and used to provide the optimal route by comparing it with the location of recommended products. This improves the user's shopping experience and enables more efficient browsing within the commercial facility.

[0561] For example, if a user prefers casual fashion, they might receive information about discounted outdoor gear that suits their taste, and the location of a specific product section would be shown on a map. This allows the user to efficiently find and purchase items they are interested in. An example of a prompt for the generative AI model might be something specific like, "Please recommend items that match casual, outdoor-appropriate clothing."

[0562] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0563] Step 1:

[0564] The user uses the device to take a picture of their clothing. The camera function is used to photograph the user's clothing as input, acquiring digital image data. This data is stored on the device for further processing. The user can review the results and retake the picture if necessary.

[0565] Step 2:

[0566] The terminal sends the captured image to the server. The input is the digital image data acquired in step 1, which is sent to the server via HTTP communication. The output is the image data transferred to the server, which is ready for analysis. The terminal confirms that the data transmission is complete and proceeds to the next step.

[0567] Step 3:

[0568] The server analyzes the received image data using a deep learning model. The input is image data sent from the terminal, and a generative AI model using TensorFlow is used to estimate the user's preferences. Specifically, it extracts features such as color, style, and accessories from the image and generates preference data based on this. As output, the preference data is stored on the server.

[0569] Step 4:

[0570] The server calculates travel itineraries and recommended products within commercial facilities based on the generated preference data. The input consists of preference data and constraints previously registered by the user. Using these, it automatically constructs a list of travel destinations and products that match the user's preferences. The output is then ready to send the calculation results to the terminal.

[0571] Step 5:

[0572] The terminal receives information from the server and displays it visually. The input is the travel itinerary and recommended product list sent from the server in step 4. Based on this, it is displayed in a user-friendly format as a travel map or store map. As output, the user is ready to review the travel plan and product information on the screen and enter feedback.

[0573] Step 6:

[0574] The user reviews the presented information and enters feedback into the terminal as needed. This feedback includes user ratings and requests regarding travel plans and product lists, which are sent to the server via the terminal. The feedback data is stored on the server as output and used to improve the accuracy of future recommendations.

[0575] Step 7:

[0576] The server records location information during travel and visit history within commercial facilities. Inputs include GPS data sent from the device and activity logs within stores. Based on this, the server analyzes the user's movement patterns and purchasing tendencies, and incorporates this into future plans. The output is the analysis results, which are used to improve services going forward.

[0577] 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.

[0578] This invention is a travel planning system that incorporates an emotion engine that recognizes the user's emotions. By taking into account the user's emotions in addition to their preferences and constraints, this system provides a more appropriate and satisfying travel experience.

[0579] The following processes are performed to implement this system.

[0580] 1. Collection of user clothing images and sentiment data

[0581] The user takes a picture of their outfit for the day via their device, and simultaneously collects emotional data from their facial expressions and voice using the device's sensors and camera. This data is then transmitted from the device to a server.

[0582] 2. Data Analysis

[0583] The server first analyzes clothing images to estimate the user's preferences. In addition, it uses an emotion engine to analyze emotional data and identify the user's current emotional state. For example, emotions such as happy, calm, or anxious may be recognized.

[0584] 3. Creating and adapting travel plans

[0585] The server considers user preference data and emotional state to create a travel plan. This plan suggests tourist destinations and dining spots that reflect the user's preferences and is adjusted to enhance their emotions. The generated "preferred travel map" dynamically changes according to the user's real-time emotions.

[0586] 4. Presentation of the plan and user impact

[0587] The device visually presents the generated travel plan to the user. The user can then review the plan and provide feedback based on their satisfaction level and expectations. This feedback will be actively used to improve future plan generation.

[0588] 5. Emotional monitoring during travel

[0589] During the trip, the device monitors the user's emotions in real time. If emotions change, the server quickly replans the itinerary and suggests it to the user. This allows the user to enjoy the trip even more.

[0590] Specific example

[0591] For example, if a user is in a calm mood and their preference for nature experiences is recognized, the system will suggest a travel plan that includes gentle hiking trails and nature parks. Furthermore, if the user's emotions are heightened during the trip, more exciting attractions or activities can be added.

[0592] In this way, the present invention continues to provide highly customizable travel experiences that meet the individual emotions and preferences of users.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] The user launches an application on their device and takes a picture of their outfit for the day with the camera. At the same time, the device uses the camera and microphone to record the user's facial expressions and voice, collecting emotional data.

[0596] Step 2:

[0597] The device sends captured images of clothing and emotional data to the server. A secure data transmission protocol is used for transmission.

[0598] Step 3:

[0599] The server applies an AI model to analyze the received clothing images. It extracts features such as color, texture, and style from the images to estimate the user's fashion preferences.

[0600] Step 4:

[0601] The server uses an emotion engine to analyze the received emotional data. It recognizes the user's current emotional state (e.g., joy, calmness, stress) from facial expressions and voice tone.

[0602] Step 5:

[0603] The server generates a travel plan based on estimated user preferences and current emotions. The plan includes tourist destinations and activities that suit the user's preferences and circumstances, and also takes into account the user's constraints.

[0604] Step 6:

[0605] The server formats the generated travel plan as a "Preferenced Travel Map" and sends it to the terminal.

[0606] Step 7:

[0607] The device visually displays a personalized travel map to the user. Users can review the plan and provide feedback on their satisfaction level and expectations.

[0608] Step 8:

[0609] During travel, the device continuously monitors the user's emotions in real time using sensors and cameras. If the user's emotions change, the device sends this information to a server.

[0610] Step 9:

[0611] The server receives real-time sentiment data and adjusts the travel plan as needed. It replans the suggested itinerary and activities to better match the user's emotions.

[0612] Step 10:

[0613] The server sends the replanned itinerary to the terminal and presents the user with new suggestions. This data, along with feedback to improve the user experience, is recorded and used for future planning.

[0614] (Example 2)

[0615] 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."

[0616] Traditional travel planning systems, while considering user preferences, often lacked the flexibility and individualization to respond to users' changing emotional states. This could lead to frustration for users when their emotions shifted during their trip. Furthermore, feedback was often not adequately utilized for future planning improvements, hindering the accuracy of the planning process.

[0617] 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.

[0618] In this invention, the server includes means for analyzing images of an individual's body adornment acquired from a terminal and estimating the individual's preferences; means for collecting emotional data from facial expressions and voice using the terminal; and means for analyzing the emotional data using an emotion engine and identifying the individual's emotional state. This enables flexible and personalized travel planning that is tailored to the user's preferences and current emotional state.

[0619] A "terminal" is a portable information and communication device used by a user, and is a device that has image capture and data collection functions.

[0620] "Body adornment" refers to information about a user's personal appearance, such as their clothing and accessories.

[0621] "Analysis" is the process of interpreting information based on acquired data and deriving specific conclusions or meanings.

[0622] "Preference" refers to an individual's likes and tendencies in choosing specific things or events.

[0623] "Emotional data" refers to numerical or string-based information that indicates a user's emotional state.

[0624] An "emotion engine" is a combination of software or hardware used to analyze emotional data and identify and evaluate a user's emotional state.

[0625] "Travel planning" refers to a list of itineraries and destinations suggested based on the user's preferences and emotional state.

[0626] "Constraints" are time, geographical, or other limitations that must be incorporated into the travel plan.

[0627] "Visual display" refers to presenting information in a format that can be visually confirmed through a user interface.

[0628] A "feedback function" is a means or system for users to provide evaluations and opinions based on their usage experience.

[0629] A "generative AI model" is a predictive model that uses artificial intelligence algorithms to more effectively personalize travel plans.

[0630] This invention relates to a travel planning system that incorporates an emotion engine that recognizes user emotions. This system provides the optimal travel plan for the user by taking into account the user's emotions in addition to their preferences and constraints.

[0631] The user takes a picture of their outfit for the day using the device, and simultaneously collects emotional data through facial expressions and voice. The device has a built-in camera, microphone, and various sensors, and uses this hardware to acquire the necessary data. The acquired data is transmitted to a server via an internet connection.

[0632] The server first uses image analysis software to analyze images of the user's clothing and estimate their personal preferences. Next, it uses an emotion engine to analyze facial expressions and voice data to identify emotional states such as happiness, calmness, and anxiety. This emotion engine is software that incorporates machine learning algorithms, enabling it to identify emotions with high accuracy.

[0633] The server then uses the analyzed preference and emotional state data to generate personalized travel plans. This process employs a generative AI model and predictive algorithms to provide personalized suggestions. The generated plans also take into account constraints such as safety and time of day.

[0634] For example, if a user is in a calm mood and enjoys nature experiences, the server will suggest a plan to visit a nature park. Furthermore, if the user's emotions become more heightened during the trip, the server can suggest additional active attractions.

[0635] An example of a prompt message would be, "The user is in a calm mood and enjoys nature experiences. What kind of travel plan can you suggest?" This message is then input into the generative AI model to create the optimal plan.

[0636] The terminal ultimately presents the travel plan received from the server to the user visually and collects user feedback. This feedback becomes important data for further improving the accuracy of future travel plans.

[0637] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0638] Step 1:

[0639] The user takes an image of their outfit for the day using the device. The device's sensors also collect emotional data, including facial expressions and voice. This data is temporarily stored by the device's application. The input consists of the outfit image and emotional data, and the output is a dataset ready for transmission to the server.

[0640] Step 2:

[0641] The device transmits collected clothing images and emotion data to the server via the network. The server receives this data and prepares it for the next data analysis step. The input is the dataset from the device, and the output is the information stored in the server's database.

[0642] Step 3:

[0643] The server uses image analysis software to analyze clothing images. Specifically, it activates an image recognition algorithm to estimate the user's preferences based on the style and color of the clothing. The input is clothing images stored on the server, and the output is the user's preference profile.

[0644] Step 4:

[0645] The server analyzes emotional data using an emotion engine. Specifically, it identifies the user's emotional state from facial expressions and voice through a machine learning model. For example, it can detect emotions such as excitement, calmness, or anxiety from the data. The input is emotional data stored on the server, and the output is the user's emotional state.

[0646] Step 5:

[0647] The server combines the user's preference profile and emotional state and generates a travel plan based on them. Here, a generative AI model is used to automatically generate a personalized travel plan based on the user's input prompts. The input is the preference profile and emotional state, and the output is the travel plan.

[0648] Step 6:

[0649] The server applies constraints to the generated travel plan, such as specific time and budget limits, and selection of places that can be visited. This creates a plan that is feasible for the user. The input is the basic travel plan, and the output is the final travel plan with the constraints reflected.

[0650] Step 7:

[0651] The terminal receives the final travel plan sent from the server and displays it visually to the user. The user can review the plan and provide feedback through the terminal. The input is the travel plan data from the server, and the output is the travel plan interface that the user sees.

[0652] (Application Example 2)

[0653] 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."

[0654] In travel planning, there is a challenge in improving satisfaction to an extent previously unattainable by taking into account not only individual preferences but also the emotional state at the time. However, technologies for real-time emotion recognition and dynamic itinerary updates based on that recognition are not yet readily available. In this situation, there is a need to develop a system that provides a travel experience optimized for each individual user.

[0655] 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.

[0656] In this invention, the server includes means for analyzing images of an individual's clothing obtained from a terminal and estimating the individual's preferences; means for recognizing and analyzing the individual's emotional state in real time; means for automatically generating a travel itinerary based on the preferences and emotional state; means for reflecting the individual's constraints in the itinerary; means for dynamically updating the generated itinerary according to the individual's emotional state; and means for visually displaying the generated itinerary. This makes it possible to provide a dynamic and personalized travel itinerary that takes the individual's emotional state into consideration in real time.

[0657] "Images of personal clothing obtained from a device" refers to visual representations of clothing worn by an individual, captured using a mobile communication device.

[0658] "Means for estimating individual preferences" refers to technological elements that infer a person's preferred style and tastes from analyzed image data of their clothing.

[0659] "A means of recognizing and analyzing an individual's emotional state in real time" refers to a process that instantly determines an individual's psychological state at a given moment through facial expressions and voice data collected from a device.

[0660] "Methods for automatically generating travel itineraries" refer to technologies that schedule a trip to a destination based on acquired data.

[0661] A "dynamically updating mechanism" is a function that technically changes pre-configured plans in response to changes in the user's emotions.

[0662] "Methods for dynamically updating according to emotional state" refers to technology that instantly adjusts travel plans based on user emotional data acquired in real time.

[0663] "Means of visual display" refers to technologies that visually represent generated information or process results through a user interface.

[0664] In implementing this invention, a system is used that links a mobile information terminal with a server. In this system, the user first takes an image of their clothing using the terminal, and the image is sent to the server. The server executes an image recognition algorithm using Python to estimate the user's preferences from the style and color of their clothing. Furthermore, it analyzes facial expressions and voice tone in real time through the terminal's camera and microphone, and identifies the emotional state using an emotion recognition model such as TensorFlow.

[0665] By combining this preference data and emotional data, an optimal travel itinerary is automatically generated. This itinerary is visually displayed to the user through the device's user interface. If the user's emotional state changes in real time, the server instantly updates the itinerary to optimize the travel experience.

[0666] For example, if a user is in a calm mood while sightseeing, the system will suggest an itinerary centered around parks and museums with a peaceful atmosphere. On the other hand, if the user is emotionally aroused, the plan will automatically adjust to include events and activities that will provide excitement.

[0667] When using a generative AI model, an example of a prompt message is: "Please suggest the best sightseeing spots and activities for a user who is currently in a calm emotional state and wants to enjoy sightseeing. Please include nature experiences and cultural elements." In this way, a personalized travel experience tailored to the user's emotions becomes possible.

[0668] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0669] Step 1:

[0670] The user takes a picture of their outfit for the day using the device's camera. This image becomes the input data, and the device sends the image to the server.

[0671] Step 2:

[0672] The server analyzes the received images of clothing using Python. Specifically, it extracts color and style features using image recognition algorithms and estimates the user's preferences based on these. Preference data is then generated as output.

[0673] Step 3:

[0674] The device captures the user's facial expressions with its camera and collects audio through its microphone. This data becomes input, and the device sends the emotional data to the server.

[0675] Step 4:

[0676] The server uses emotion recognition models such as TensorFlow to analyze the received facial expressions and audio data. This identifies the user's emotional state (e.g., calm, excited, relaxed) and outputs emotion data.

[0677] Step 5:

[0678] The server combines acquired preference and sentiment data to automatically generate travel itineraries. This includes data processing to select tourist destinations and activities optimized for the user's current state.

[0679] Step 6:

[0680] The generated travel itinerary is visually displayed on the device's screen. Users can review the itinerary and make selections or changes.

[0681] Step 7:

[0682] During the trip, the device monitors the user's emotions in real time and continuously sends this data to the server, enabling dynamic updates of the itinerary based on the user's emotional state. Each time a new itinerary is generated, the updated information is output and presented to the user.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] [Fourth Embodiment]

[0687] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0688] 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.

[0689] 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).

[0690] 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.

[0691] 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.

[0692] 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).

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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".

[0700] This invention provides a system that analyzes a user's clothing images and creates a travel plan best suited to their individual preferences. The invention is implemented below based on the roles of the server, terminal, and user.

[0701] 1. Obtain images of the user's clothing.

[0702] The user takes a picture of their everyday clothes using their device's camera function. The application prepares to automatically send this image to the server. The user can check if the captured image is suitable and retake it if necessary.

[0703] 2. Image Analysis

[0704] Images sent from the device are analyzed on the server using a deep learning model. The server extracts clothing features—such as color, style, and accessories—and compares them to similar fashion styles stored in a database. This comparison estimates the user's preferences, and preference data is generated.

[0705] 3. Creating a travel plan

[0706] The server generates travel plans based on user preference data and pre-registered constraints (such as allergy information and acrophobia). These plans include recommended tourist destinations, local restaurants, and accommodations. This information is compiled into a visually easy-to-understand "Preference Travel Map" and provided to the user.

[0707] 4. Presenting the plan and obtaining feedback

[0708] The device displays a travel map, and the user reviews the provided travel plan. The user can provide feedback on the plan through the device. This feedback includes ratings and requests, which will be considered when creating future travel plans.

[0709] 5. Collection and utilization of usage data

[0710] During your trip, the device uses GPS location data to record your travel history. It also collects data on visited tourist spots and restaurants. This data is transferred to a server and used to personalize your next travel plan.

[0711] In this way, the present invention provides travel plans tailored to the user's specific preferences and constraints, optimizing the travel experience. The specific processing flow achieves a high level of usability throughout the system, based on the technical details of each step. For example, a user who prefers casual attire can be offered a plan centered around theme parks and outdoor activities.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The user launches an application on their device and takes a picture of their outfit for the day using the camera. The captured image is reviewed by the device and compressed before being sent to the server.

[0715] Step 2:

[0716] The terminal sends compressed image data to the server. The transmission is conducted via a secure protocol to protect the data.

[0717] Step 3:

[0718] The server uses an AI model to analyze the received images. It performs facial recognition to identify clothing and extracts features such as color, style, and accessories.

[0719] Step 4:

[0720] The server compares clothing feature data with a database of fashion styles to estimate the user's fashion preferences. This preference data is then stored in the user profile.

[0721] Step 5:

[0722] The server generates appropriate travel plans based on preference data and user-registered constraints. It collects information on tourist destinations, restaurants, and accommodations and visualizes it as a "Preference Travel Map."

[0723] Step 6:

[0724] The server sends the completed travel map to the device.

[0725] Step 7:

[0726] The device displays a personalized travel map to the user. The user reviews their travel plan and provides feedback through the device as needed.

[0727] Step 8:

[0728] During the user's trip, the device uses GPS to record their movement history. It also collects information on visited tourist attractions, facilities, and restaurants.

[0729] Step 9:

[0730] After the trip ends, the device sends the collected data to the server. The server stores this data in a database to help create future travel plans.

[0731] (Example 1)

[0732] 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".

[0733] Traditional travel planning systems have a challenge in that they struggle to provide suggestions that fully consider the user's individual preferences and constraints. Furthermore, they lack sufficient mechanisms for incorporating feedback from past travel experiences into future plans, resulting in planning that doesn't address individual needs.

[0734] 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.

[0735] In this invention, the server includes information processing means for analyzing images of an individual's clothing obtained from a terminal and estimating the individual's preferences; data generation means for automatically generating a travel plan based on the preferences and pre-registered constraint information; and display means for visually displaying the generated travel plan. This makes it possible to provide travel plans based on the user's individual preferences and constraints.

[0736] A "terminal" refers to an information processing device used by an individual, which has a camera and communication functions and can take pictures and send and receive data.

[0737] "Clothing images" are digital data of clothing worn by an individual, and are used to analyze the characteristics of the clothing.

[0738] "Analysis" refers to the process of extracting specific attributes or features from image data and generating information.

[0739] "Preferences" refer to data that shows an individual's tastes and preferences, particularly trends in fashion and travel.

[0740] "Information processing means" refers to a component of a system that has the technical and software mechanisms necessary for collecting, analyzing, storing, or using data.

[0741] "Travel planning" refers to an itinerary that combines tourist destinations, accommodations, restaurants, etc., selected based on the user's preferences and constraints.

[0742] "Data generation means" refers to the components of a system that perform processing to construct new data and plans based on collected information.

[0743] "Visually displaying" refers to outputting digital data to a display device in a form that humans can understand, making it possible to check the content.

[0744] "Ratings" refer to feedback information provided by users, particularly their satisfaction with the provided plan and areas for improvement.

[0745] "Location history" refers to a record of a user's movements, recorded based on the device's location information service.

[0746] "Usage data" refers to data about how a particular service or facility was used by users.

[0747] This invention relates to a system that provides users with personalized travel plans. This system functions through the cooperation of three parties: a terminal, a server, and the user.

[0748] The device is a typical information processing device such as a smartphone or tablet, and it uses its camera function to photograph the user's clothing. The captured image is then transmitted to a server via the network. The transmitted image is securely sent using an encryption protocol.

[0749] The server uses a deep learning model for image analysis, which is implemented using TensorFlow, a standard machine learning framework. From the analysis results, the server identifies the user's fashion preferences. This preference data is obtained by comparing the user's fashion style with similar fashion styles stored in the server's database.

[0750] The server then uses this preference data to plan a trip. Utilizing a generative AI model, it automatically generates a travel plan that takes into account not only the user's preferences but also pre-registered constraints such as allergies and acrophobia. This plan includes tourist attractions, accommodations, and local restaurants.

[0751] Users can provide feedback on their travel plans to the server via their devices. The server incorporates this feedback into its algorithms to further improve the accuracy of future travel plans.

[0752] During your trip, the device uses GPS to record your location history and collects information about the tourist attractions and restaurants you visit. This information is sent to a server and used to personalize future travel plans.

[0753] For example, if a user prefers casual clothing, the server can suggest travel plans that primarily include theme parks and outdoor activities. A possible prompt for the generating AI model might be: "Based on recent clothing images of the user, suggest a travel plan that prefers a casual and relaxed atmosphere. The user has allergies and a fear of heights."

[0754] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0755] Step 1:

[0756] The user takes a picture of their everyday clothing using the device's camera function. The captured image is saved to the device's temporary storage. The user can review the image and retake it if it is unsuitable. The input is the image data captured by the user, and the output is the reviewed image file.

[0757] Step 2:

[0758] The terminal sends the verified image to the server using an encryption protocol. After receiving the image data, the server inputs it into a deep learning model. The input is encrypted image data, and the output is data indicating the characteristics of the clothing. In this step, the server analyzes the image and extracts the clothing's color, style, accessories, etc.

[0759] Step 3:

[0760] The server compares the extracted clothing feature data with style information stored in the database. The input is clothing feature data, and the output is data indicating the user's preferences. Through this process, the server identifies similar fashion styles and determines the user's preferences.

[0761] Step 4:

[0762] The server generates travel plans considering the user's preference data and pre-registered personal constraint information. Utilizing a generation AI model, the input is preference data and constraint information, and the output is a recommended travel plan. The travel plan includes tourist destinations, accommodations, and dining options.

[0763] Step 5:

[0764] The terminal visually displays the travel plan sent from the server in a user interface. The user can review the details of this plan and provide feedback as needed. The input is the travel plan data, and the output is the user's feedback data.

[0765] Step 6:

[0766] During travel, the device collects the user's location information using GPS and records a log of places visited. The server receives location history data periodically sent from the device. The input is location information and visit logs, and the output is data for improving future travel plans. This information is reflected in future plans, improving personalization.

[0767] (Application Example 1)

[0768] 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".

[0769] The problem that this invention aims to solve is to efficiently generate travel plans and shopping plans within commercial facilities that are tailored to the individual preferences of users, thereby improving the user experience. Another problem is to optimize the shopping experience in physical stores by suggesting product selections within commercial facilities based on each user's style and past purchase history.

[0770] 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.

[0771] In this invention, the server includes means for analyzing images of an individual's clothing obtained from a terminal to estimate their preferences, means for automatically generating a travel itinerary based on those preferences, and means for recommending items within a commercial facility based on the user's attributes. This makes it possible to provide travel plans and optimal purchasing plans within commercial facilities that are tailored to the user's individual preferences.

[0772] A "terminal" is an information processing device used by a user, equipped with communication and camera functions, and responsible for acquiring images and transmitting them to a server.

[0773] "Images of personal clothing" refers to digital data containing visual information of clothing taken by a user using their device.

[0774] "Preferences" refer to a user's tendency to show specific preferences, and include information such as clothing style, colors, and characteristics related to accessories.

[0775] A "travel itinerary" is a plan that includes tourist destinations and services to be visited, automatically generated based on the user's preferences and constraints.

[0776] A "commercial facility" refers to a building or place used for the sale of goods or the provision of services, and is a concept that includes physical stores.

[0777] "Recommending products" means selecting and presenting products or services that are suitable for a user based on their individual preferences.

[0778] "Location information" refers to data that indicates a specific location using technologies such as GPS, and is used to identify a user's current location and travel route.

[0779] The system for implementing this invention consists primarily of a user terminal, a server, and a commercial facility. The terminal consists of a smartphone or smart glasses owned by the user, and uses its camera function to acquire images of the user's clothing. The acquired images are transmitted to the server via a communication function. The server uses TensorFlow, a deep learning model, to perform image analysis and estimate the user's preferences. Through this analysis, features such as clothing style, color, and accessories are extracted to generate user preference information.

[0780] The generated preference information is used to automatically create itineraries for travel destinations the user plans to visit. It also provides personalized product recommendations to help users enjoy shopping at specific commercial facilities. Specifically, a list of recommended products is sent from the server to the user's device, and a map showing the optimal order of visits within the store is displayed. By using OpenStreetMap data, users can easily find their way to specific product sections without getting lost.

[0781] Furthermore, location information within the commercial facility is acquired via GPS to determine the user's current location. This data is sent to a server and used to provide the optimal route by comparing it with the location of recommended products. This improves the user's shopping experience and enables more efficient browsing within the commercial facility.

[0782] For example, if a user prefers casual fashion, they might receive information about discounted outdoor gear that suits their taste, and the location of a specific product section would be shown on a map. This allows the user to efficiently find and purchase items they are interested in. An example of a prompt for the generative AI model might be something specific like, "Please recommend items that match casual, outdoor-appropriate clothing."

[0783] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0784] Step 1:

[0785] The user uses the device to take a picture of their clothing. The camera function is used to photograph the user's clothing as input, acquiring digital image data. This data is stored on the device for further processing. The user can review the results and retake the picture if necessary.

[0786] Step 2:

[0787] The terminal sends the captured image to the server. The input is the digital image data acquired in step 1, which is sent to the server via HTTP communication. The output is the image data transferred to the server, which is ready for analysis. The terminal confirms that the data transmission is complete and proceeds to the next step.

[0788] Step 3:

[0789] The server analyzes the received image data using a deep learning model. The input is image data sent from the terminal, and a generative AI model using TensorFlow is used to estimate the user's preferences. Specifically, it extracts features such as color, style, and accessories from the image and generates preference data based on this. As output, the preference data is stored on the server.

[0790] Step 4:

[0791] The server calculates travel itineraries and recommended products within commercial facilities based on the generated preference data. The input consists of preference data and constraints previously registered by the user. Using these, it automatically constructs a list of travel destinations and products that match the user's preferences. The output is then ready to send the calculation results to the terminal.

[0792] Step 5:

[0793] The terminal receives information from the server and displays it visually. The input is the travel itinerary and recommended product list sent from the server in step 4. Based on this, it is displayed in a user-friendly format as a travel map or store map. As output, the user is ready to review the travel plan and product information on the screen and enter feedback.

[0794] Step 6:

[0795] The user reviews the presented information and enters feedback into the terminal as needed. This feedback includes user ratings and requests regarding travel plans and product lists, which are sent to the server via the terminal. The feedback data is stored on the server as output and used to improve the accuracy of future recommendations.

[0796] Step 7:

[0797] The server records location information during travel and visit history within commercial facilities. Inputs include GPS data sent from the device and activity logs within stores. Based on this, the server analyzes the user's movement patterns and purchasing tendencies, and incorporates this into future plans. The output is the analysis results, which are used to improve services going forward.

[0798] 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.

[0799] This invention is a travel planning system that incorporates an emotion engine that recognizes the user's emotions. By taking into account the user's emotions in addition to their preferences and constraints, this system provides a more appropriate and satisfying travel experience.

[0800] The following processes are performed to implement this system.

[0801] 1. Collection of user clothing images and sentiment data

[0802] The user takes a picture of their outfit for the day via their device, and simultaneously collects emotional data from their facial expressions and voice using the device's sensors and camera. This data is then transmitted from the device to a server.

[0803] 2. Data Analysis

[0804] The server first analyzes clothing images to estimate the user's preferences. In addition, it uses an emotion engine to analyze emotional data and identify the user's current emotional state. For example, emotions such as happy, calm, or anxious may be recognized.

[0805] 3. Creating and adapting travel plans

[0806] The server considers user preference data and emotional state to create a travel plan. This plan suggests tourist destinations and dining spots that reflect the user's preferences and is adjusted to enhance their emotions. The generated "preferred travel map" dynamically changes according to the user's real-time emotions.

[0807] 4. Presentation of the plan and user impact

[0808] The device visually presents the generated travel plan to the user. The user can then review the plan and provide feedback based on their satisfaction level and expectations. This feedback will be actively used to improve future plan generation.

[0809] 5. Emotional monitoring during travel

[0810] During the trip, the device monitors the user's emotions in real time. If emotions change, the server quickly replans the itinerary and suggests it to the user. This allows the user to enjoy the trip even more.

[0811] Specific example

[0812] For example, if a user is in a calm mood and their preference for nature experiences is recognized, the system will suggest a travel plan that includes gentle hiking trails and nature parks. Furthermore, if the user's emotions are heightened during the trip, more exciting attractions or activities can be added.

[0813] In this way, the present invention continues to provide highly customizable travel experiences that meet the individual emotions and preferences of users.

[0814] The following describes the processing flow.

[0815] Step 1:

[0816] The user launches an application on their device and takes a picture of their outfit for the day with the camera. At the same time, the device uses the camera and microphone to record the user's facial expressions and voice, collecting emotional data.

[0817] Step 2:

[0818] The device sends captured images of clothing and emotional data to the server. A secure data transmission protocol is used for transmission.

[0819] Step 3:

[0820] The server applies an AI model to analyze the received clothing images. It extracts features such as color, texture, and style from the images to estimate the user's fashion preferences.

[0821] Step 4:

[0822] The server uses an emotion engine to analyze the received emotional data. It recognizes the user's current emotional state (e.g., joy, calmness, stress) from facial expressions and voice tone.

[0823] Step 5:

[0824] The server generates a travel plan based on estimated user preferences and current emotions. The plan includes tourist destinations and activities that suit the user's preferences and circumstances, and also takes into account the user's constraints.

[0825] Step 6:

[0826] The server formats the generated travel plan as a "Preferenced Travel Map" and sends it to the terminal.

[0827] Step 7:

[0828] The device visually displays a personalized travel map to the user. Users can review the plan and provide feedback on their satisfaction level and expectations.

[0829] Step 8:

[0830] During travel, the device continuously monitors the user's emotions in real time using sensors and cameras. If the user's emotions change, the device sends this information to a server.

[0831] Step 9:

[0832] The server receives real-time sentiment data and adjusts the travel plan as needed. It replans the suggested itinerary and activities to better match the user's emotions.

[0833] Step 10:

[0834] The server sends the replanned itinerary to the terminal and presents the user with new suggestions. This data, along with feedback to improve the user experience, is recorded and used for future planning.

[0835] (Example 2)

[0836] 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".

[0837] Traditional travel planning systems, while considering user preferences, often lacked the flexibility and individualization to respond to users' changing emotional states. This could lead to frustration for users when their emotions shifted during their trip. Furthermore, feedback was often not adequately utilized for future planning improvements, hindering the accuracy of the planning process.

[0838] 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.

[0839] In this invention, the server includes means for analyzing images of an individual's body adornment acquired from a terminal and estimating the individual's preferences; means for collecting emotional data from facial expressions and voice using the terminal; and means for analyzing the emotional data using an emotion engine and identifying the individual's emotional state. This enables flexible and personalized travel planning that is tailored to the user's preferences and current emotional state.

[0840] A "terminal" is a portable information and communication device used by a user, and is a device that has image capture and data collection functions.

[0841] "Body adornment" refers to information about a user's personal appearance, such as their clothing and accessories.

[0842] "Analysis" is the process of interpreting information based on acquired data and deriving specific conclusions or meanings.

[0843] "Preference" refers to an individual's likes and tendencies in choosing specific things or events.

[0844] "Emotional data" refers to numerical or string-based information that indicates a user's emotional state.

[0845] An "emotion engine" is a combination of software or hardware used to analyze emotional data and identify and evaluate a user's emotional state.

[0846] "Travel planning" refers to a list of itineraries and destinations suggested based on the user's preferences and emotional state.

[0847] "Constraints" are time, geographical, or other limitations that must be incorporated into the travel plan.

[0848] "Visual display" refers to presenting information in a format that can be visually confirmed through a user interface.

[0849] A "feedback function" is a means or system for users to provide evaluations and opinions based on their usage experience.

[0850] A "generative AI model" is a predictive model that uses artificial intelligence algorithms to more effectively personalize travel plans.

[0851] This invention relates to a travel planning system that incorporates an emotion engine that recognizes user emotions. This system provides the optimal travel plan for the user by taking into account the user's emotions in addition to their preferences and constraints.

[0852] The user takes a picture of their outfit for the day using the device, and simultaneously collects emotional data through facial expressions and voice. The device has a built-in camera, microphone, and various sensors, and uses this hardware to acquire the necessary data. The acquired data is transmitted to a server via an internet connection.

[0853] The server first uses image analysis software to analyze images of the user's clothing and estimate their personal preferences. Next, it uses an emotion engine to analyze facial expressions and voice data to identify emotional states such as happiness, calmness, and anxiety. This emotion engine is software that incorporates machine learning algorithms, enabling it to identify emotions with high accuracy.

[0854] The server then uses the analyzed preference and emotional state data to generate personalized travel plans. This process employs a generative AI model and predictive algorithms to provide personalized suggestions. The generated plans also take into account constraints such as safety and time of day.

[0855] For example, if a user is in a calm mood and enjoys nature experiences, the server will suggest a plan to visit a nature park. Furthermore, if the user's emotions become more heightened during the trip, the server can suggest additional active attractions.

[0856] An example of a prompt message would be, "The user is in a calm mood and enjoys nature experiences. What kind of travel plan can you suggest?" This message is then input into the generative AI model to create the optimal plan.

[0857] The terminal ultimately presents the travel plan received from the server to the user visually and collects user feedback. This feedback becomes important data for further improving the accuracy of future travel plans.

[0858] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0859] Step 1:

[0860] The user takes an image of their outfit for the day using the device. The device's sensors also collect emotional data, including facial expressions and voice. This data is temporarily stored by the device's application. The input consists of the outfit image and emotional data, and the output is a dataset ready for transmission to the server.

[0861] Step 2:

[0862] The device transmits collected clothing images and emotion data to the server via the network. The server receives this data and prepares it for the next data analysis step. The input is the dataset from the device, and the output is the information stored in the server's database.

[0863] Step 3:

[0864] The server uses image analysis software to analyze clothing images. Specifically, it activates an image recognition algorithm to estimate the user's preferences based on the style and color of the clothing. The input is clothing images stored on the server, and the output is the user's preference profile.

[0865] Step 4:

[0866] The server analyzes emotional data using an emotion engine. Specifically, it identifies the user's emotional state from facial expressions and voice through a machine learning model. For example, it can detect emotions such as excitement, calmness, or anxiety from the data. The input is emotional data stored on the server, and the output is the user's emotional state.

[0867] Step 5:

[0868] The server combines the user's preference profile and emotional state and generates a travel plan based on them. Here, a generative AI model is used to automatically generate a personalized travel plan based on the user's input prompts. The input is the preference profile and emotional state, and the output is the travel plan.

[0869] Step 6:

[0870] The server applies constraints to the generated travel plan, such as specific time and budget limits, and selection of places that can be visited. This creates a plan that is feasible for the user. The input is the basic travel plan, and the output is the final travel plan with the constraints reflected.

[0871] Step 7:

[0872] The terminal receives the final travel plan sent from the server and displays it visually to the user. The user can review the plan and provide feedback through the terminal. The input is the travel plan data from the server, and the output is the travel plan interface that the user sees.

[0873] (Application Example 2)

[0874] 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".

[0875] In travel planning, there is a challenge in improving satisfaction to an extent previously unattainable by taking into account not only individual preferences but also the emotional state at the time. However, technologies for real-time emotion recognition and dynamic itinerary updates based on that recognition are not yet readily available. In this situation, there is a need to develop a system that provides a travel experience optimized for each individual user.

[0876] 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.

[0877] In this invention, the server includes means for analyzing images of an individual's clothing obtained from a terminal and estimating the individual's preferences; means for recognizing and analyzing the individual's emotional state in real time; means for automatically generating a travel itinerary based on the preferences and emotional state; means for reflecting the individual's constraints in the itinerary; means for dynamically updating the generated itinerary according to the individual's emotional state; and means for visually displaying the generated itinerary. This makes it possible to provide a dynamic and personalized travel itinerary that takes the individual's emotional state into consideration in real time.

[0878] "Images of personal clothing obtained from a device" refers to visual representations of clothing worn by an individual, captured using a mobile communication device.

[0879] "Means for estimating individual preferences" refers to technological elements that infer a person's preferred style and tastes from analyzed image data of their clothing.

[0880] "A means of recognizing and analyzing an individual's emotional state in real time" refers to a process that instantly determines an individual's psychological state at a given moment through facial expressions and voice data collected from a device.

[0881] "Methods for automatically generating travel itineraries" refer to technologies that schedule a trip to a destination based on acquired data.

[0882] A "dynamically updating mechanism" is a function that technically changes pre-configured plans in response to changes in the user's emotions.

[0883] "Methods for dynamically updating according to emotional state" refers to technology that instantly adjusts travel plans based on user emotional data acquired in real time.

[0884] "Means of visual display" refers to technologies that visually represent generated information or process results through a user interface.

[0885] In implementing this invention, a system is used that links a mobile information terminal with a server. In this system, the user first takes an image of their clothing using the terminal, and the image is sent to the server. The server executes an image recognition algorithm using Python to estimate the user's preferences from the style and color of their clothing. Furthermore, it analyzes facial expressions and voice tone in real time through the terminal's camera and microphone, and identifies the emotional state using an emotion recognition model such as TensorFlow.

[0886] By combining this preference data and emotional data, an optimal travel itinerary is automatically generated. This itinerary is visually displayed to the user through the device's user interface. If the user's emotional state changes in real time, the server instantly updates the itinerary to optimize the travel experience.

[0887] For example, if a user is in a calm mood while sightseeing, the system will suggest an itinerary centered around parks and museums with a peaceful atmosphere. On the other hand, if the user is emotionally aroused, the plan will automatically adjust to include events and activities that will provide excitement.

[0888] When using a generative AI model, an example of a prompt message is: "Please suggest the best sightseeing spots and activities for a user who is currently in a calm emotional state and wants to enjoy sightseeing. Please include nature experiences and cultural elements." In this way, a personalized travel experience tailored to the user's emotions becomes possible.

[0889] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0890] Step 1:

[0891] The user takes a picture of their outfit for the day using the device's camera. This image becomes the input data, and the device sends the image to the server.

[0892] Step 2:

[0893] The server analyzes the received images of clothing using Python. Specifically, it extracts color and style features using image recognition algorithms and estimates the user's preferences based on these. Preference data is then generated as output.

[0894] Step 3:

[0895] The device captures the user's facial expressions with its camera and collects audio through its microphone. This data becomes input, and the device sends the emotional data to the server.

[0896] Step 4:

[0897] The server uses emotion recognition models such as TensorFlow to analyze the received facial expressions and audio data. This identifies the user's emotional state (e.g., calm, excited, relaxed) and outputs emotion data.

[0898] Step 5:

[0899] The server combines acquired preference and sentiment data to automatically generate travel itineraries. This includes data processing to select tourist destinations and activities optimized for the user's current state.

[0900] Step 6:

[0901] The generated travel itinerary is visually displayed on the device's screen. Users can review the itinerary and make selections or changes.

[0902] Step 7:

[0903] During the trip, the device monitors the user's emotions in real time and continuously sends this data to the server, enabling dynamic updates of the itinerary based on the user's emotional state. Each time a new itinerary is generated, the updated information is output and presented to the user.

[0904] 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.

[0905] 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.

[0906] 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 robot 414.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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."

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0925] The following is further disclosed regarding the embodiments described above.

[0926] (Claim 1)

[0927] By analyzing images of personal clothing obtained from the device,

[0928] A means for estimating an individual's preferences based on the aforementioned image,

[0929] A means for automatically generating a travel itinerary based on the aforementioned preferences,

[0930] Means for reflecting individual constraints in the aforementioned process,

[0931] Means for visually displaying the generated process,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] Provide a feedback function to the device.

[0935] This includes means for improving the accuracy of subsequent processes based on the aforementioned feedback,

[0936] The system according to claim 1.

[0937] (Claim 3)

[0938] Movement history and usage information collected through the device,

[0939] Including means to be used to improve future travel itineraries,

[0940] The system according to claim 1.

[0941] "Example 1"

[0942] (Claim 1)

[0943] By analyzing images of personal clothing obtained from the device,

[0944] Information processing means for estimating an individual's preferences based on the aforementioned image,

[0945] A data generation means that automatically generates a travel plan based on the aforementioned preferences and pre-registered constraint information,

[0946] A display means for visually displaying the generated travel plan,

[0947] A system that includes this.

[0948] (Claim 2)

[0949] The device is provided with a function to accept evaluations.

[0950] Includes information processing means to improve the accuracy of subsequent plans based on the aforementioned evaluation,

[0951] The system according to claim 1.

[0952] (Claim 3)

[0953] Location history and usage information collected through the device,

[0954] Includes information processing tools to be used to improve future travel plans,

[0955] The system according to claim 1.

[0956] "Application Example 1"

[0957] (Claim 1)

[0958] By analyzing images of personal clothing obtained from the device,

[0959] A means for estimating an individual's preferences based on the aforementioned image,

[0960] A means for automatically generating a travel itinerary based on the aforementioned preferences,

[0961] Means for reflecting individual constraints in the aforementioned process,

[0962] Means for visually displaying the generated process,

[0963] A method for recommending attribute-based items within a commercial facility based on images of clothing obtained from a terminal,

[0964] Means for providing an optimal route using location information within the commercial facility,

[0965] A system that includes this.

[0966] (Claim 2)

[0967] Provide a feedback function to the device.

[0968] This includes means for improving the accuracy of subsequent processes based on the aforementioned feedback,

[0969] This includes means for collecting product evaluation data within the aforementioned commercial facility and improving the accuracy of recommendations.

[0970] The system according to claim 1.

[0971] (Claim 3)

[0972] Movement history and usage information collected through the device,

[0973] Including means to be used to improve future travel itineraries,

[0974] This includes means of using visit routes and purchase history within the aforementioned commercial facility to improve recommended products for future visits,

[0975] The system according to claim 1.

[0976] "Example 2 of combining an emotion engine"

[0977] (Claim 1)

[0978] By analyzing images of personal body adornments obtained from the device,

[0979] A means for estimating an individual's preferences based on the aforementioned image,

[0980] A means of collecting emotional data from facial expressions and voice using a terminal,

[0981] A means for analyzing the aforementioned emotional data using an emotion engine and identifying an individual's emotional state,

[0982] A means of automatically generating travel plans based on individual preferences and emotional states,

[0983] Means to reflect individual constraints in the aforementioned plan,

[0984] Means for visually displaying the generated plan,

[0985] A system that includes this.

[0986] (Claim 2)

[0987] Provide a feedback function to the device.

[0988] The means include, based on the aforementioned feedback and the generated AI model, to improve the accuracy of subsequent plans,

[0989] The system according to claim 1.

[0990] (Claim 3)

[0991] Movement history and usage information collected through the device,

[0992] The system according to claim 1.

[0993] "Application example 2 when combining with an emotional engine"

[0994] (Claim 1)

[0995] By analyzing images of personal clothing obtained from the device,

[0996] A means for estimating an individual's preferences based on the aforementioned image,

[0997] A means of recognizing and analyzing an individual's emotional state in real time,

[0998] A means for automatically generating a travel itinerary based on the aforementioned preferences and emotional state,

[0999] Means for reflecting individual constraints in the aforementioned process,

[1000] Means for dynamically updating the generated process according to the individual's emotional state,

[1001] Means for visually displaying the generated process,

[1002] A system that includes this.

[1003] (Claim 2)

[1004] Provide a feedback function to the device.

[1005] The means include, based on the aforementioned feedback and changes in emotional state, to improve the accuracy of subsequent steps,

[1006] The system according to claim 1.

[1007] (Claim 3)

[1008] Movement history and usage information collected through the device, as well as emotional data.

[1009] Including means to be used to improve future travel itineraries,

[1010] The system according to claim 1. [Explanation of Symbols]

[1011] 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. By analyzing images of personal clothing obtained from the device, A means for estimating an individual's preferences based on the aforementioned image, A means for automatically generating a travel itinerary based on the aforementioned preferences, Means for reflecting individual constraints in the aforementioned process, Means for visually displaying the generated process, A method for recommending attribute-based items within a commercial facility based on images of clothing obtained from a terminal, Means for providing an optimal route using location information within the commercial facility, A system that includes this.

2. Provide a feedback function to the device. This includes means for improving the accuracy of subsequent processes based on the aforementioned feedback, This includes means for collecting product evaluation data within the aforementioned commercial facility and improving the accuracy of recommendations. The system according to claim 1.

3. Movement history and usage information collected through the device, Including means to be used to improve future travel itineraries, This includes means of using visit routes and purchase history within the aforementioned commercial facility to improve recommended products for future visits, The system according to claim 1.

Images