system

Abstract

We provide the system. [Solution] An input method for users to enter their travel preferences, A collection means that collects relevant information via a network based on the travel preferences entered by the input means, A generation method that automatically generates travel plans using collected information, A display means for presenting the travel plan generated by the generation means to the user, A reservation method for making restaurant and activity reservations based on the aforementioned travel plan, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When a traveler visits a new place, there are problems such as being unable to efficiently use local transportation means or having a disrupted schedule due to insufficient information about the business hours and regular holidays of facilities. Therefore, it is not easy for travelers to efficiently plan their itinerary within limited time. There are also problems such as the reservation of activities and restaurants during the trip being troublesome and the difficulty of making real-time plan changes.

Means for Solving the Problems

[0005] This invention provides a system that, upon receiving travel preferences from a user, collects relevant information from a network based on those preferences, and uses the collected information to generate and present an optimal travel plan. Furthermore, by automatically executing necessary reservations based on the generated plan, users can enjoy their trip efficiently and comfortably. Through the above means, various problems during travel can be solved by providing a plan that takes into account transportation schedules and facility operating hours.

[0006] "User" refers to an individual or group that creates a travel plan using this system.

[0007] "Travel requests" refer to the user's requests, including conditions such as the places they want to visit, the activities they want to do, their budget, and the itinerary.

[0008] "Input method" refers to the device or interface that users use to input their travel preferences.

[0009] A "network" refers to a collection of communication paths that enable the transmission and reception of information.

[0010] "Related information" refers to a wide range of data related to travel, such as transportation options, facility opening hours, and event information.

[0011] "Collection means" refers to a component that has the function of acquiring relevant information from a network.

[0012] "Generative means" refers to an algorithm or process that creates a travel plan based on collected information.

[0013] "Display means" refers to devices or methods for presenting a generated travel plan to a user.

[0014] "Reservation method" refers to a system or process for making reservations for restaurants, activities, etc., based on a travel plan. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of 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 an emotion engine is combined. [[ID=4២]] [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. [[ID=4^]] [[ID=५6]]

BEST MODE FOR CARRYING OUT THE INVENTION

[0016] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] In embodiments of the present invention, the user first inputs their travel preferences using a terminal. The terminal transmits this input information to a server. Based on the received preferences, the server collects relevant information via the network. This relevant information includes transportation to the planned destination, the location and operating hours of facilities, and event information.

[0037] The server then uses an algorithm that generates a travel plan based on the collected information. The generated plan is constructed as a specific itinerary that reflects the schedules of transportation and the opening hours of facilities that need to be considered. At this stage, the aim is to make the user's itinerary more efficient and reduce waste and confusion, even if it is a place they are visiting for the first time.

[0038] The completed travel plan is sent from the server to the terminal and presented to the user. The user can review the plan and enter any necessary requests for modifications or additions. The server then receives this information, reconstructs the plan based on the new information, and presents it to the terminal again. This interactive process ensures that the plan best suits the user's needs is finalized.

[0039] In addition, the server makes reservations for necessary restaurants and activities according to the confirmed plan. This allows users to automatically check reservation details on their devices, significantly reducing the hassle of making reservations on-site.

[0040] For example, suppose a user wants to visit museums and popular restaurants when visiting Tokyo. Based on this request, the server collects the opening hours and locations of Tokyo museums, a list of accessible and highly-rated restaurants, and transportation options between them. As a result, a programmed itinerary is generated that includes visiting museum A in the morning and then having lunch at nearby restaurant B. This itinerary is optimized to maximize time efficiency and reservation certainty.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters their travel preferences into the terminal. This includes information such as places they want to visit, activities they are interested in, budget, and dates. The terminal verifies the entered information, formats it correctly, and sends it to the server.

[0044] Step 2:

[0045] The server collects relevant data based on the information received from the terminal. The server accesses various open data and APIs via the network to obtain information such as transportation timetables, facility opening hours, and event schedules.

[0046] Step 3:

[0047] The server uses the collected information to generate a travel plan. In this process, it programs an optimized plan tailored to the user's preferences, taking into account the order of visits, means of transportation, and the time required for each, based on the collected information.

[0048] Step 4:

[0049] The server sends the generated travel plan to the terminal. The terminal presents this plan to the user, allowing them to review its contents. The user enters any additional requests or modifications into the terminal as needed.

[0050] Step 5:

[0051] The device sends the user's correction requests collected to the server. The server recalculates the plan accordingly and regenerates a new travel plan that reflects the changes. The new plan is then sent back to the device.

[0052] Step 6:

[0053] Once the user confirms their travel plan, the server executes the booking based on that plan. The server makes restaurant reservations and activity arrangements as needed and sends booking confirmation information to the device. The device notifies the user and saves it as an itinerary.

[0054] (Example 1)

[0055] 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."

[0056] Currently, planning a trip involves a wide range of tasks, including gathering information about destinations, setting efficient routes, and making reservations. This often results in travelers spending a significant amount of time and effort, sometimes preventing them from creating an optimal travel plan. Furthermore, there is a need for flexible planning that can quickly adapt to changes in decision-making or information during the trip.

[0057] 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.

[0058] In this invention, the server includes a collection means for collecting relevant information via an information network, a generation means for automatically generating a travel plan, and a reservation means for making reservations for restaurants and activities based on the travel plan. This allows users to automatically obtain an efficient and flexible travel plan, and to respond quickly and accurately to changes in the plan.

[0059] "Users" refer to individuals who use this system to plan and adjust their travel itineraries.

[0060] "Input means" refers to devices or methods for users to input their travel preferences or requests for modifications to their travel plans.

[0061] "Information gathering means" refers to a mechanism or process for collecting necessary information related to travel planning via an information network.

[0062] "Generative means" refers to algorithms or systems that automatically create travel plans using collected information.

[0063] "Display means" refers to devices or methods that present the generated travel plan to the user visually or audibly.

[0064] "Reservation methods" refer to systems that allow you to make reservations in advance for necessary restaurants and activities based on your travel plans.

[0065] An "information network" refers to a medium for acquiring, transmitting, and receiving information, including the internet and other communication networks.

[0066] A "travel plan" refers to a schedule that integrates destinations, transportation, facility information, and reservation information based on the user's preferences.

[0067] This invention is a system that automatically generates travel plans and flexibly adjusts them according to the user's preferences. The specific implementation of this system is described below.

[0068] First, the user enters their travel preferences using a terminal. The terminal converts the data entered by the user into a digital format and sends it to the server. This process uses commonly used input devices (e.g., keyboards, touchscreens, etc.) and communication software (e.g., web browsers, mobile applications).

[0069] The server receives user requests and collects relevant information through its information network based on those requests. In this collection process, it uses publicly available online APIs (e.g., map service APIs, event information APIs) to obtain detailed information about transportation options and facilities at the destination.

[0070] Next, the server uses a generative AI model to create a travel plan based on the collected information. The AI ​​model constructs an efficient and feasible plan based on the user's prompt, such as "I want to plan a trip to Tokyo. I want to include museums and recommended restaurants, and make it an efficient itinerary." The specific algorithm takes into account transportation schedules and facility opening hours to minimize travel time while incorporating as many specified activities as possible.

[0071] The generated travel plan is sent from the server to the terminal and presented to the user. The user can review the presented plan and, if necessary, enter modification requests from the terminal. The terminal then sends this back to the server, which regenerates the plan based on the new conditions.

[0072] Furthermore, based on the finalized travel plan, the server automatically makes reservations for restaurants and activities through the reservation system. This process allows users to receive a consistent service from planning to booking, significantly reducing anxiety and the hassle of making arrangements during their trip.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user enters their travel preferences into the terminal. This data includes destinations, activities of interest, and desired dates. This information is converted into a digital format and sent to the server. Specifically, the terminal sends the information entered via the keyboard or touchscreen to the server as an HTTP request.

[0076] Step 2:

[0077] The server collects relevant information through its information network based on the received travel preference data. Specifically, it uses database queries and API calls to obtain information such as transportation options, facility locations and opening hours, and event information. This gathers the necessary data on the server. The server stores this data in an intermediate database so that it can be used for subsequent processing.

[0078] Step 3:

[0079] The server utilizes the collected information and generates travel plans using a generative AI model. Inputs include user preferences and collected relevant information. Based on this, the AI ​​model creates an efficient plan that takes into account transportation schedules and facility opening hours. The AI ​​model analyzes and optimizes the data to determine the optimal order of visits and formulate the travel plan.

[0080] Step 4:

[0081] The generated travel plan is sent from the server to the terminal and presented to the user. The terminal receives it and displays it on the screen in an easy-to-understand format. The user reviews the plan and decides whether they are satisfied with it. In this process, the terminal visually structures and displays the information, organizing the plan content in an easy-to-understand manner.

[0082] Step 5:

[0083] If a user wishes to modify or add to their travel plan, they enter their new requirements via their device. This information is then transmitted digitally to the server. The server receives this information and reconstructs the travel plan based on the requested changes.

[0084] Step 6:

[0085] The server generates a new travel plan in response to the modification request and sends it to the terminal. At this time, the server uses the AI ​​model again to create an efficient plan that reflects the changed conditions.

[0086] Step 7:

[0087] Based on the confirmed travel plan, the server automatically makes reservations for necessary facilities and activities. Specifically, it uses an online reservation system to secure reservations that match the date, time, and location. Then, it sends reservation confirmation details to the user's device. This allows the user to automatically check their reservation information, reducing the hassle during their trip.

[0088] (Application Example 1)

[0089] 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."

[0090] Modern consumers seek efficient purchasing experiences when shopping and using services in virtual spaces and online platforms. However, it is difficult for users to easily gather information on the products and services they desire and create optimal purchasing plans based on that information. Furthermore, there is a lack of efficient means to utilize promotional information from individual stores. Therefore, it is necessary to improve the user's purchasing experience and realize efficient shopping that eliminates waste.

[0091] 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.

[0092] In this invention, the server includes an input means for the user to input their purchase preferences, a collection means for collecting relevant information via a communication network, and a generation means for automatically generating a purchase plan for the user using the collected information. This enables the user to enjoy an efficient and customized purchasing experience.

[0093] A "user" is an individual or legal entity that uses the system to input purchase requests and review the presented purchase plan.

[0094] "Purchase request" refers to information that includes detailed requests and conditions regarding the product or service that the user wishes to purchase.

[0095] "Input means" refers to a device or interface for users to input their purchase requests into the system.

[0096] A "communication network" is a network infrastructure used to send and receive information, such as the internet.

[0097] "Related information" refers to data such as retail store operating hours and promotional information, which is obtained based on the user's purchasing preferences.

[0098] "Collection means" refers to a process or device for acquiring relevant information via a communication network.

[0099] The "generation method" refers to a part of a system that automatically creates a purchasing plan based on the collected relevant information.

[0100] A "purchase plan" is an optimized plan or schedule designed to help users efficiently purchase the goods and services they desire.

[0101] "Display means" refers to a screen or interface used to present the generated purchase plan to the user.

[0102] The "reservation method" refers to the part of the system used to manage necessary retail store reservations and promotional information based on a purchasing plan.

[0103] To realize this invention, the server provides an interface for users to input their purchase preferences. Users can use a device such as a smartphone or personal computer to input information about the products or services they wish to purchase. This input information is transmitted to the server, which then collects related information via the communication network.

[0104] The server uses a collection mechanism to acquire relevant data, such as retail store operating hours and promotional information, via the communication network. Based on the collected information, the generation mechanism automatically generates an optimal purchasing plan. The generated purchasing plan is displayed on the user's terminal for the user to review.

[0105] Users can review the generated purchase plan and modify it as needed. This interactive process helps users purchase goods and services efficiently and effectively. It also includes features for managing retail store reservations and promotional information based on the purchase plan.

[0106] In this system, the server performs real-time processing using the internet. For example, if a user wants to purchase a new product from a specific brand, the server uses data collection methods to gather inventory information and special sale information from the nearest store and presents the user with the optimal purchase plan.

[0107] Using a generative AI model, it's possible to suggest the optimal route and schedule based on user input. An example of a prompt would be:

[0108] "Based on the user's preferences, suggest the optimal route for efficient shopping within the virtual shopping center. Information to collect includes store opening hours, promotions, and inventory status."

[0109] In this way, the system can improve the user experience and support efficient purchasing activities.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user enters their purchase request using a terminal. This information includes the type and brand of the product they wish to buy, as well as their desired purchase conditions. This input is then sent from the terminal to the server.

[0113] Step 2:

[0114] Based on the user's purchase request, the server collects relevant information via the communication network. Specifically, it retrieves information on available retail store operating hours, inventory status, and promotions from a database using a network API. This information is then prepared as a set of data to satisfy the user's request.

[0115] Step 3:

[0116] Using the generation method, the server automatically generates a purchasing plan based on the collected relevant information. At this stage, the generation AI model is utilized to create an efficient and user-friendly optimal route and schedule. The output is a detailed purchasing plan.

[0117] Step 4:

[0118] The server sends the generated purchase plan to the user's terminal. This process involves layout processing to make the plan easy for the user to visually review, and the plan is presented via a display device.

[0119] Step 5:

[0120] The user reviews the presented purchase plan and, if necessary, enters revision requests via the terminal. These requests are sent to the server, which then regenerates the purchase plan based on the new information and user feedback.

[0121] Step 6:

[0122] The server manages retailer reservations and promotions based on the finalized purchase plan. This allows users to check necessary reservation information on their devices and enjoy the benefits.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] In an embodiment of the present invention, the user first inputs their travel preferences using a terminal. The terminal transmits this input information to a server and activates an emotion engine to recognize the user's emotions during the input process. The emotion engine analyzes the user's emotions in real time based on the content and manner of input and transmits this data to the server. It also learns from past emotion data to support the generation of plans that are better suited to the user's preferences.

[0125] Based on the received sentiment data and travel preferences, the server collects a wide range of relevant information through the network. This includes transportation information for planned destinations, facility opening hours, and event information. The server uses the collected information and the user's sentiment data to run a travel plan generation algorithm. The generated plan is adjusted according to the user's sentiment, ensuring an optimal experience.

[0126] The generated travel plan is sent from the server to the terminal and presented to the user. At this time, the order in which the plan is presented can be dynamically adjusted based on the analysis of the emotion engine. The user can review the plan and, if necessary, input any requests for modifications or additions into the terminal. The server then reconstructs the plan based on the new emotion data and presents it to the terminal in an interactive manner.

[0127] Furthermore, the server makes reservations for necessary restaurants and activities based on the finalized plan. This reservation process can also be made more personalized by taking the user's feelings into consideration. The server sends the reservation details to the device and notifies the user immediately.

[0128] For example, suppose a user desires to relax in Tokyo and requests to visit "quiet and beautiful places" through their device. The emotion engine determines from the user's input that relaxation is important, and the server reflects this emotional information to generate a travel plan that includes quiet museums and parks. This plan is designed to match the user's emotions and ensure a highly satisfying trip.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user enters their travel preferences through the terminal. The terminal transmits the input to the emotion engine in real time, which analyzes the user's emotions. The emotion engine evaluates the user's emotional state based on data including keystrokes and input speed during input.

[0132] Step 2:

[0133] The terminal combines the user's travel preferences with analyzed sentiment data and sends it to the server. Based on the received data, the server uses the network to collect relevant information. This includes destination traffic information, facility operating hours, and event details.

[0134] Step 3:

[0135] The server generates travel plans based on collected information and user sentiment data. The generation process incorporates the results of the sentiment engine; for example, if a desire to relax is detected, it prioritizes and includes comfortable, less crowded spots.

[0136] Step 4:

[0137] The server sends the generated travel plan to the device. The device displays this plan to the user and informs them that it is adjusting the order and content of the plan based on feedback from the emotion engine.

[0138] Step 5:

[0139] The user reviews the presented plan and enters any new requests or feedback as needed. The device sends this information to the server in real time, updating sentiment data along with it.

[0140] Step 6:

[0141] The server reconstructs the travel plan based on new requests and sentiment data. If the sentiment engine determines that a user's reaction will affect satisfaction, it adjusts the plan accordingly and presents it again.

[0142] Step 7:

[0143] The server processes bookings based on the finalized travel plan. It automatically makes reservations for restaurants, activities, and other services as much as possible, and sends the details to the device. The device notifies the user and verifies the appropriateness of the bookings based on sentiment data.

[0144] (Example 2)

[0145] 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".

[0146] Conventional travel plan generation systems have struggled to create plans that adequately consider users' emotions and preferences, and have failed to provide users with the optimal travel experience. In particular, the order and content of travel plan presentations were not optimized, and interactive adjustments to enhance user satisfaction were insufficient.

[0147] 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.

[0148] In this invention, the server includes an input means for users to input travel preferences and emotional data, a collection means for collecting information via a network based on the travel preferences and emotional data input by the input means, and a generation means for automatically generating travel plans using the collected information and emotional data, and generating travel plans optimized according to the user's emotions. This makes it possible to generate travel plans that are suited to the user's emotions and to dynamically adjust the presentation order.

[0149] A "user" refers to a person who uses the system to plan a trip and is the one who inputs their feelings and desires.

[0150] "Emotional data" refers to information that expresses a user's emotional state using numerical values ​​or categories, and is used for plan generation and optimization.

[0151] A "network" is a communication line used for sending and receiving information, and includes the internet and intranets.

[0152] "Collecting information" refers to the act of obtaining necessary travel-related information from internet databases, APIs, etc.

[0153] A "travel plan" refers to a travel itinerary and list of destinations generated based on the user's wishes and preferences.

[0154] "Adjusting the presentation order" is an operation that changes the order in which information and schedules within a travel plan are displayed, based on the user's emotional data.

[0155] "Reservation method" refers to the function that executes the process of securing participation slots for restaurants and activities based on the generated travel plan.

[0156] To implement this invention, a terminal is used in which the user first inputs their travel preferences and emotions. When the terminal transmits the input information to the server, it activates an emotion engine to analyze the user's emotions. A general-purpose emotion analysis module can be used as the emotion engine.

[0157] The terminal uses an emotion engine to analyze emotional data in real time from the user's input content and input method, and sends that data to the server. Furthermore, by learning from past emotional data, it enables more accurate emotional analysis. Based on the emotional data and travel preferences, the server collects relevant information about the planned destination via the internet. In this process, it utilizes APIs to obtain information such as traffic information, facility opening hours, and event information.

[0158] The server uses collected information and user sentiment data to apply an algorithm for generating travel plans. Using a generative AI model, it's possible to create an optimal travel plan tailored to the user's emotions. The order in which the generated plans are presented to the user is adjusted based on the sentiment engine's analysis.

[0159] For example, if a user enters a desire to "visit a quiet and beautiful place," the emotion engine detects the emotion of "wanting to relax," and the server generates a travel plan that includes quiet museums and parks based on that emotion information. Furthermore, the user can review this plan and interactively reconstruct it by entering additional requests or modifications on their device.

[0160] An example of a prompt might be, "Please recommend a relaxing place for my next trip. I'd like somewhere quiet and beautiful." Based on this prompt, the system will suggest a travel plan that suits the user.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user enters information about their travel preferences and emotions into the device. This input is specifically in text format and includes preferences such as "I want to visit a quiet and beautiful place." The entered preferences are processed as text data on the device and serve as the basis for analyzing the user's emotions.

[0164] Step 2:

[0165] The device activates an emotion engine based on the input information. The emotion engine analyzes the user's text data and past input patterns, and processes the data to obtain emotion data. For example, it extracts emotional states such as "I want to relax" from keywords, input speed, and context. This emotion data is then sent from the device to the server.

[0166] Step 3:

[0167] The server receives sentiment data and travel preferences sent from the terminal. Based on this data, the server collects relevant information via the internet. Specifically, it uses APIs to retrieve transportation information, facility opening hours, and event information for the destination, and prepares a structured set of information. This information becomes the input data for generating a travel plan.

[0168] Step 4:

[0169] The server uses collected information and sentiment data to run a generative AI model that generates travel plans. This model calculates and constructs the optimal travel plan based on the input sentiment data. The generated plan is dynamically optimized and adjusted according to the user's emotions.

[0170] Step 5:

[0171] The generated travel plan is sent from the server to the device and presented to the user on the device. Based on the analysis results of the emotion engine, the device adjusts the order in which events and activities within the plan are presented. This allows the user to view the travel plan in a way that aligns with their emotions.

[0172] Step 6:

[0173] Users can review the presented plan and re-enter any desired changes or additions into their device. For example, they might want to add a new tourist destination. This new information is sent from the device to the server, initiating the plan regeneration process.

[0174] Step 7:

[0175] The server reconstructs the travel plan based on new input information and sentiment data from the user. The generative AI model runs again to generate the updated plan. The regenerated plan is presented again on the device, awaiting the user's final confirmation.

[0176] Step 8:

[0177] After the user confirms their plan, the server executes the booking process based on that plan. For example, it confirms restaurant and activity reservations and sends the reservation details to the user's device. This notification allows the user to immediately confirm their reservation details and prepare to enjoy their trip with peace of mind.

[0178] (Application Example 2)

[0179] 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".

[0180] The travel experience in conventional autonomous vehicles has focused solely on its function as a means of transportation, and has rarely been flexibly customized based on the user's emotions or personal preferences. As a result, there has been a problem in that it has not been able to provide a comfortable travel experience that is in line with the user's feelings and mood.

[0181] 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.

[0182] In this invention, the server includes emotion analysis means that analyzes the user's emotions based on their input and reflects them in the travel plan, reservation means that makes reservations for facilities and activities based on the travel plan, and interior environment adjustment means that adjusts the environment inside the vehicle based on the emotion data acquired by the emotion analysis means. This makes it possible to present a travel plan based on the user's emotions and to automatically adjust the in-vehicle environment, thereby providing a highly personalized travel experience.

[0183] "Input means" refers to a device or interface for users to input their travel preferences.

[0184] "Collection means" refers to a system or module for collecting relevant information via a network.

[0185] "Generation method" refers to a process or algorithm for automatically generating travel plans using collected information.

[0186] "Display means" refers to a device or screen display function for presenting the generated travel plan to the user.

[0187] "Reservation method" refers to the procedure or system for making reservations for facilities and activities based on a travel plan.

[0188] "Emotional analysis means" refers to an engine or software that analyzes emotions based on user input and reflects them in the travel plan.

[0189] "Internal environment adjustment means" refers to a mechanism or control system for adjusting the environment inside a vehicle based on emotional data acquired by emotion analysis means.

[0190] In this embodiment of the invention, the terminal first receives the user's travel preferences. The user uses an input means on the terminal to input the destination and desired activities. The terminal transmits this input information to a server via the network and simultaneously activates an emotion analysis means to analyze the user's emotions at the time of input in real time. Emotion data is generated from the input content and input method.

[0191] The server collects relevant information via the network based on the received travel preference and sentiment data. This includes a wide range of data, such as transportation schedules, facility opening hours, and event information. Based on the collected information, the server automatically generates a travel plan using a generation tool, and this plan is adjusted according to the user's sentiment.

[0192] This process sends the generated travel plan to the device and displays it to the user. The user can review the travel plan and request modifications as needed. The server then re-analyzes the new sentiment data and provides a regenerated plan. This interactive process makes it possible to provide the user with the best possible travel experience.

[0193] Based on the finalized travel plan, the server then makes reservations for necessary facilities and activities. The reservation process also takes emotional data into consideration to provide personalized service and instantly notifies the user of reservation details on their device.

[0194] For example, if a user enters a travel preference for relaxation, the sentiment analysis tool will detect elements of relaxation and generate a plan that incorporates quiet museums and parks. It can also collect additional information through prompts such as "How are you feeling today?" or "What kind of music do you like?" based on the user's input.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user enters their travel preferences using a terminal. The user enters information such as destination, date and time, and desired activities, and the terminal sends this input to a sentiment analysis system. The input information is processed by the sentiment analysis system as text data.

[0198] Step 2:

[0199] The terminal activates the emotion analysis system and analyzes emotional data from the user's input content and input method. At this stage, a generative AI model is used to analyze the user's emotions as text data and generate emotion tags (e.g., relaxed, excited). The analysis results are sent to the server.

[0200] Step 3:

[0201] The server receives travel preference and sentiment data and uses collection methods to gather relevant information extensively over the network. This collection includes transportation information, facility opening hours, and event information. The server retrieves the information in HTML or JSON format and stores it in a database.

[0202] Step 4:

[0203] The server uses a generation method to automatically generate travel plans based on collected information and sentiment data. A generation AI model is used to select tourist destinations and events that are appropriate to the user's emotions. The generated plans are prepared as text or visual data.

[0204] Step 5:

[0205] The server sends the generated travel plan to the device, which then presents it to the user. The user can review the travel plan and request changes or modifications. The plan is displayed dynamically according to the UI.

[0206] Step 6:

[0207] When a user submits a request to modify a travel plan, the terminal sends the new sentiment data back to the server. The server restarts the generation mechanism to construct the regenerated plan and generates the new plan.

[0208] Step 7:

[0209] The server uses booking methods to reserve necessary facilities and activities based on the final plan and sentiment data. This booking process may involve integration with external services via APIs. Booking details are notified to the device.

[0210] 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.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] In embodiments of the present invention, the user first inputs their travel preferences using a terminal. The terminal transmits this input information to a server. Based on the received preferences, the server collects relevant information via the network. This relevant information includes transportation to the planned destination, the location and operating hours of facilities, and event information.

[0227] The server then uses an algorithm that generates a travel plan based on the collected information. The generated plan is constructed as a specific itinerary that reflects the schedules of transportation and the opening hours of facilities that need to be considered. At this stage, the aim is to make the user's itinerary more efficient and reduce waste and confusion, even if it is a place they are visiting for the first time.

[0228] The completed travel plan is sent from the server to the terminal and presented to the user. The user can review the plan and enter any necessary requests for modifications or additions. The server then receives this information, reconstructs the plan based on the new information, and presents it to the terminal again. This interactive process ensures that the plan best suits the user's needs is finalized.

[0229] In addition, the server makes reservations for necessary restaurants and activities according to the confirmed plan. This allows users to automatically check reservation details on their devices, significantly reducing the hassle of making reservations on-site.

[0230] For example, suppose a user wants to visit museums and popular restaurants when visiting Tokyo. Based on this request, the server collects the opening hours and locations of Tokyo museums, a list of accessible and highly-rated restaurants, and transportation options between them. As a result, a programmed itinerary is generated that includes visiting museum A in the morning and then having lunch at nearby restaurant B. This itinerary is optimized to maximize time efficiency and reservation certainty.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user enters their travel preferences into the terminal. This includes information such as places they want to visit, activities they are interested in, budget, and dates. The terminal verifies the entered information, formats it correctly, and sends it to the server.

[0234] Step 2:

[0235] The server collects relevant data based on the information received from the terminal. The server accesses various open data and APIs via the network to obtain information such as transportation timetables, facility opening hours, and event schedules.

[0236] Step 3:

[0237] The server uses the collected information to generate a travel plan. In this process, it programs an optimized plan tailored to the user's preferences, taking into account the order of visits, means of transportation, and the time required for each, based on the collected information.

[0238] Step 4:

[0239] The server sends the generated travel plan to the terminal. The terminal presents this plan to the user, allowing them to review its contents. The user enters any additional requests or modifications into the terminal as needed.

[0240] Step 5:

[0241] The device sends the user's correction requests collected to the server. The server recalculates the plan accordingly and regenerates a new travel plan that reflects the changes. The new plan is then sent back to the device.

[0242] Step 6:

[0243] Once the user confirms their travel plan, the server executes the booking based on that plan. The server makes restaurant reservations and activity arrangements as needed and sends booking confirmation information to the device. The device notifies the user and saves it as an itinerary.

[0244] (Example 1)

[0245] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0246] Currently, planning a trip involves a wide range of tasks, including gathering information about destinations, setting efficient routes, and making reservations. This often results in travelers spending a significant amount of time and effort, sometimes preventing them from creating an optimal travel plan. Furthermore, there is a need for flexible planning that can quickly adapt to changes in decision-making or information during the trip.

[0247] 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.

[0248] In this invention, the server includes a collection means for collecting relevant information via an information network, a generation means for automatically generating a travel plan, and a reservation means for making reservations for restaurants and activities based on the travel plan. This allows users to automatically obtain an efficient and flexible travel plan, and to respond quickly and accurately to changes in the plan.

[0249] "Users" refer to individuals who use this system to plan and adjust their travel itineraries.

[0250] "Input means" refers to devices or methods for users to input their travel preferences or requests for modifications to their travel plans.

[0251] "Information gathering means" refers to a mechanism or process for collecting necessary information related to travel planning via an information network.

[0252] "Generative means" refers to algorithms or systems that automatically create travel plans using collected information.

[0253] "Display means" refers to devices or methods that present the generated travel plan to the user visually or audibly.

[0254] "Reservation methods" refer to systems that allow you to make reservations in advance for necessary restaurants and activities based on your travel plans.

[0255] An "information network" refers to a medium for acquiring, transmitting, and receiving information, including the internet and other communication networks.

[0256] A "travel plan" refers to a schedule that integrates destinations, transportation, facility information, and reservation information based on the user's preferences.

[0257] This invention is a system that automatically generates travel plans and flexibly adjusts them according to the user's preferences. The specific implementation of this system is described below.

[0258] First, the user enters their travel preferences using a terminal. The terminal converts the data entered by the user into a digital format and sends it to the server. This process uses commonly used input devices (e.g., keyboards, touchscreens, etc.) and communication software (e.g., web browsers, mobile applications).

[0259] The server receives user requests and collects relevant information through its information network based on those requests. In this collection process, it uses publicly available online APIs (e.g., map service APIs, event information APIs) to obtain detailed information about transportation options and facilities at the destination.

[0260] Next, the server uses a generative AI model to create a travel plan based on the collected information. The AI ​​model constructs an efficient and feasible plan based on the user's prompt, such as "I want to plan a trip to Tokyo. I want to include museums and recommended restaurants, and make it an efficient itinerary." The specific algorithm takes into account transportation schedules and facility opening hours to minimize travel time while incorporating as many specified activities as possible.

[0261] The generated travel plan is sent from the server to the terminal and presented to the user. The user can review the presented plan and, if necessary, enter modification requests from the terminal. The terminal then sends this back to the server, which regenerates the plan based on the new conditions.

[0262] Furthermore, based on the finalized travel plan, the server automatically makes reservations for restaurants and activities through the reservation system. This process allows users to receive a consistent service from planning to booking, significantly reducing anxiety and the hassle of making arrangements during their trip.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The user enters their travel preferences into the terminal. This data includes destinations, activities of interest, and desired dates. This information is converted into a digital format and sent to the server. Specifically, the terminal sends the information entered via the keyboard or touchscreen to the server as an HTTP request.

[0266] Step 2:

[0267] The server collects relevant information through its information network based on the received travel preference data. Specifically, it uses database queries and API calls to obtain information such as transportation options, facility locations and opening hours, and event information. This gathers the necessary data on the server. The server then stores this data in an intermediate database for use in subsequent processing.

[0268] Step 3:

[0269] The server utilizes the collected information and generates travel plans using a generative AI model. Inputs include user preferences and collected relevant information. Based on this, the AI ​​model creates an efficient plan that takes into account transportation schedules and facility opening hours. The AI ​​model analyzes and optimizes the data to determine the optimal order of visits and formulate the travel plan.

[0270] Step 4:

[0271] The generated travel plan is sent from the server to the terminal and presented to the user. The terminal receives it and displays it on the screen in an easy-to-understand format. The user reviews the plan and decides whether they are satisfied with it. In this process, the terminal visually structures and displays the information, organizing the plan content in an easy-to-understand manner.

[0272] Step 5:

[0273] If a user wishes to modify or add to their travel plan, they enter their new requirements via their device. This information is then transmitted digitally to the server. The server receives this information and reconstructs the travel plan based on the requested changes.

[0274] Step 6:

[0275] The server generates a new travel plan in response to the modification request and sends it to the terminal. At this time, the server uses the AI ​​model again to create an efficient plan that reflects the changed conditions.

[0276] Step 7:

[0277] Based on the confirmed travel plan, the server automatically makes reservations for necessary facilities and activities. Specifically, it uses an online reservation system to secure reservations that match the date, time, and location. Then, it sends reservation confirmation details to the user's device. This allows the user to automatically check their reservation information, reducing the hassle during their trip.

[0278] (Application Example 1)

[0279] 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."

[0280] Modern users seek an efficient purchasing experience when shopping and using services in virtual spaces and online platforms. However, it is difficult for users to easily gather information on the products and services they desire and make an optimal purchasing plan based on it. Additionally, there is a lack of means to efficiently utilize the promotional information of individual stores. Therefore, it is necessary to improve the users' purchasing experience and achieve efficient shopping with less waste.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0282] In this invention, the server includes an input means for the user to input a purchase desire, a collection means for collecting relevant information via a communication network, and a generation means for automatically generating a user's purchase plan using the collected information. Thereby, it becomes possible for the user to enjoy an efficient and customized purchasing experience.

[0283] A "user" is an individual or a corporation that inputs a purchase desire using the system and confirms the presented purchase plan.

[0284] A "purchase desire" is information including detailed requests and conditions regarding the products or services that the user wishes to purchase.

[0285] An "input means" is a device or interface for the user to input a purchase desire into the system.

[0286] A "communication network" is a network infrastructure for transmitting and receiving information such as the Internet.

[0287] "Relevant information" is data such as the operating hours information and promotional information of retail stores, which is obtained based on the user's purchase desire.

[0288] A "collection means" is a process or device for obtaining relevant information via a communication network.

[0289] The "generation method" refers to a part of a system that automatically creates a purchasing plan based on the collected relevant information.

[0290] A "purchase plan" is an optimized plan or schedule designed to help users efficiently purchase the goods and services they desire.

[0291] "Display means" refers to a screen or interface used to present the generated purchase plan to the user.

[0292] The "reservation method" refers to the part of the system used to manage necessary retail store reservations and promotional information based on a purchasing plan.

[0293] To realize this invention, the server provides an interface for users to input their purchase preferences. Users can use a device such as a smartphone or personal computer to input information about the products or services they wish to purchase. This input information is transmitted to the server, which then collects related information via the communication network.

[0294] The server uses a collection mechanism to acquire relevant data, such as retail store operating hours and promotional information, via the communication network. Based on the collected information, the generation mechanism automatically generates an optimal purchasing plan. The generated purchasing plan is displayed on the user's terminal for the user to review.

[0295] Users can review the generated purchase plan and modify it as needed. This interactive process helps users purchase goods and services efficiently and effectively. It also includes features for managing retail store reservations and promotional information based on the purchase plan.

[0296] In this system, the server performs real-time processing using the internet. For example, if a user wants to purchase a new product from a specific brand, the server uses data collection methods to gather inventory information and special sale information from the nearest store and presents the user with the optimal purchase plan.

[0297] Using a generative AI model, it's possible to suggest the optimal route and schedule based on user input. An example of a prompt would be:

[0298] "Based on the user's preferences, suggest the optimal route for efficient shopping within the virtual shopping center. Information to collect includes store opening hours, promotions, and inventory status."

[0299] In this way, the system can improve the user experience and support efficient purchasing activities.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The user enters their purchase request using a terminal. This information includes the type and brand of the product they wish to buy, as well as their desired purchase conditions. This input is then sent from the terminal to the server.

[0303] Step 2:

[0304] Based on the user's purchase request, the server collects relevant information via the communication network. Specifically, it retrieves information on available retail store operating hours, inventory status, and promotions from a database using a network API. This information is then prepared as a set of data to satisfy the user's request.

[0305] Step 3:

[0306] Using the generation means, the server automatically generates a purchase plan based on the collected relevant information. At this stage, by leveraging the generation AI model, an optimal route and schedule that are efficient and in line with the user's wishes are created. As output, the details of the purchase plan are generated.

[0307] Step 4:

[0308] The server sends the generated purchase plan to the user terminal. In this process, layout processing is performed so that the user can visually confirm the plan easily, and the plan is presented via the display means.

[0309] Step 5:

[0310] The user checks the presented purchase plan and inputs a correction request from the terminal if necessary. This request is sent to the server, and the server regenerates the purchase plan based on the new information and the user's feedback.

[0311] Step 6:

[0312] Based on the purchase plan finally determined by the server, the reservation procedures and promotion management of the retail store are carried out. This enables the user to check the necessary reservation information on the terminal and enjoy the benefits.

[0313] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 29 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0314] In the form for implementing the present invention, first, the user uses the terminal to input travel wishes. The terminal sends this input information to the server and activates the emotion engine to recognize the user's emotion during the input process. The emotion engine analyzes the emotion in real time from the user's input content and input method and sends the data to the server. Also, by learning past emotion data, it supports the generation of a plan more in line with the user's preferences.

[0315] Based on the received sentiment data and travel preferences, the server collects a wide range of relevant information through the network. This includes transportation information for planned destinations, facility opening hours, and event information. The server uses the collected information and the user's sentiment data to run a travel plan generation algorithm. The generated plan is adjusted according to the user's sentiment, ensuring an optimal experience.

[0316] The generated travel plan is sent from the server to the terminal and presented to the user. At this time, the order in which the plan is presented can be dynamically adjusted based on the analysis of the emotion engine. The user can review the plan and, if necessary, input any requests for modifications or additions into the terminal. The server then reconstructs the plan based on the new emotion data and presents it to the terminal in an interactive manner.

[0317] Furthermore, the server makes reservations for necessary restaurants and activities based on the finalized plan. This reservation process can also be made more personalized by taking the user's feelings into consideration. The server sends the reservation details to the device and notifies the user immediately.

[0318] For example, suppose a user desires to relax in Tokyo and requests to visit "quiet and beautiful places" through their device. The emotion engine determines from the user's input that relaxation is important, and the server reflects this emotional information to generate a travel plan that includes quiet museums and parks. This plan is designed to match the user's emotions and ensure a highly satisfying trip.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user enters their travel preferences through the terminal. The terminal transmits the input to the emotion engine in real time, which analyzes the user's emotions. The emotion engine evaluates the user's emotional state based on data including keystrokes and input speed during input.

[0322] Step 2:

[0323] The terminal combines the user's travel preferences with analyzed sentiment data and sends it to the server. Based on the received data, the server uses the network to collect relevant information. This includes destination traffic information, facility operating hours, and event details.

[0324] Step 3:

[0325] The server generates travel plans based on collected information and user sentiment data. The generation process incorporates the results of the sentiment engine; for example, if a desire to relax is detected, it prioritizes and includes comfortable, less crowded spots.

[0326] Step 4:

[0327] The server sends the generated travel plan to the device. The device displays this plan to the user and informs them that it is adjusting the order and content of the plan based on feedback from the emotion engine.

[0328] Step 5:

[0329] The user reviews the presented plan and enters any new requests or feedback as needed. The device sends this information to the server in real time, updating sentiment data along with it.

[0330] Step 6:

[0331] The server reconstructs the travel plan based on new requests and sentiment data. If the sentiment engine determines that a user's reaction will affect satisfaction, it adjusts the plan accordingly and presents it again.

[0332] Step 7:

[0333] The server processes bookings based on the finalized travel plan. It automatically makes reservations for restaurants, activities, and other services as much as possible, and sends the details to the device. The device notifies the user and verifies the appropriateness of the bookings based on sentiment data.

[0334] (Example 2)

[0335] 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".

[0336] Conventional travel plan generation systems have struggled to create plans that adequately consider users' emotions and preferences, and have failed to provide users with the optimal travel experience. In particular, the order and content of travel plan presentations were not optimized, and interactive adjustments to enhance user satisfaction were insufficient.

[0337] 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.

[0338] In this invention, the server includes an input means for users to input travel preferences and emotional data, a collection means for collecting information via a network based on the travel preferences and emotional data input by the input means, and a generation means for automatically generating travel plans using the collected information and emotional data, and generating travel plans optimized according to the user's emotions. This makes it possible to generate travel plans that are suited to the user's emotions and to dynamically adjust the presentation order.

[0339] A "user" refers to a person who uses the system to plan a trip and is the one who inputs their feelings and desires.

[0340] "Emotional data" refers to information that expresses a user's emotional state using numerical values ​​or categories, and is used for plan generation and optimization.

[0341] A "network" is a communication line used for sending and receiving information, and includes the internet and intranets.

[0342] "Collecting information" refers to the act of obtaining necessary travel-related information from internet databases, APIs, etc.

[0343] A "travel plan" refers to a travel itinerary and list of destinations generated based on the user's wishes and preferences.

[0344] "Adjusting the presentation order" is an operation that changes the order in which information and schedules within a travel plan are displayed, based on the user's emotional data.

[0345] "Reservation method" refers to the function that executes the process of securing participation slots for restaurants and activities based on the generated travel plan.

[0346] To implement this invention, a terminal is used in which the user first inputs their travel preferences and emotions. When the terminal transmits the input information to the server, it activates an emotion engine to analyze the user's emotions. A general-purpose emotion analysis module can be used as the emotion engine.

[0347] The terminal uses an emotion engine to analyze emotional data in real time from the user's input content and input method, and sends that data to the server. Furthermore, by learning from past emotional data, it enables more accurate emotional analysis. Based on the emotional data and travel preferences, the server collects relevant information about the planned destination via the internet. In this process, it utilizes APIs to obtain information such as traffic information, facility opening hours, and event information.

[0348] The server uses collected information and user sentiment data to apply an algorithm for generating travel plans. Using a generative AI model, it's possible to create an optimal travel plan tailored to the user's emotions. The order in which the generated plans are presented to the user is adjusted based on the sentiment engine's analysis.

[0349] For example, if a user enters a desire to "visit a quiet and beautiful place," the emotion engine detects the emotion of "wanting to relax," and the server generates a travel plan that includes quiet museums and parks based on that emotion information. Furthermore, the user can review this plan and interactively reconstruct it by entering additional requests or modifications on their device.

[0350] An example of a prompt might be, "Please recommend a relaxing place for my next trip. I'd like somewhere quiet and beautiful." Based on this prompt, the system will suggest a travel plan that suits the user.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The user enters information about their travel preferences and emotions into the device. This input is specifically in text format and includes preferences such as "I want to visit a quiet and beautiful place." The entered preferences are processed as text data on the device and serve as the basis for analyzing the user's emotions.

[0354] Step 2:

[0355] The device activates an emotion engine based on the input information. The emotion engine analyzes the user's text data and past input patterns, and processes the data to obtain emotion data. For example, it extracts emotional states such as "I want to relax" from keywords, input speed, and context. This emotion data is then sent from the device to the server.

[0356] Step 3:

[0357] The server receives sentiment data and travel preferences sent from the terminal. Based on this data, the server collects relevant information via the internet. Specifically, it uses APIs to retrieve transportation information, facility opening hours, and event information for the destination, and prepares a structured set of information. This information becomes the input data for generating a travel plan.

[0358] Step 4:

[0359] The server uses collected information and sentiment data to run a generative AI model that generates travel plans. This model calculates and constructs the optimal travel plan based on the input sentiment data. The generated plan is dynamically optimized and adjusted according to the user's emotions.

[0360] Step 5:

[0361] The generated travel plan is sent from the server to the device and presented to the user on the device. Based on the analysis results of the emotion engine, the device adjusts the order in which events and activities within the plan are presented. This allows the user to view the travel plan in a way that aligns with their emotions.

[0362] Step 6:

[0363] Users can review the presented plan and re-enter any desired changes or additions into their device. For example, they might want to add a new tourist destination. This new information is sent from the device to the server, initiating the plan regeneration process.

[0364] Step 7:

[0365] The server reconstructs the travel plan based on new input information and sentiment data from the user. The generative AI model runs again to generate the updated plan. The regenerated plan is presented again on the device, awaiting the user's final confirmation.

[0366] Step 8:

[0367] After the user confirms their plan, the server executes the booking process based on that plan. For example, it confirms restaurant and activity reservations and sends the reservation details to the user's device. This notification allows the user to immediately confirm their reservation details and prepare to enjoy their trip with peace of mind.

[0368] (Application Example 2)

[0369] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0370] The travel experience in conventional autonomous vehicles has focused solely on its function as a means of transportation, and has rarely been flexibly customized based on the user's emotions or personal preferences. As a result, there has been a problem in that it has not been able to provide a comfortable travel experience that is in line with the user's feelings and mood.

[0371] 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.

[0372] In this invention, the server includes emotion analysis means that analyzes the user's emotions based on their input and reflects them in the travel plan, reservation means that makes reservations for facilities and activities based on the travel plan, and interior environment adjustment means that adjusts the environment inside the vehicle based on the emotion data acquired by the emotion analysis means. This makes it possible to present a travel plan based on the user's emotions and to automatically adjust the in-vehicle environment, thereby providing a highly personalized travel experience.

[0373] "Input means" refers to a device or interface for users to input their travel preferences.

[0374] "Collection means" refers to a system or module for collecting relevant information via a network.

[0375] "Generation method" refers to a process or algorithm for automatically generating travel plans using collected information.

[0376] "Display means" refers to a device or screen display function for presenting the generated travel plan to the user.

[0377] "Reservation method" refers to the procedure or system for making reservations for facilities and activities based on a travel plan.

[0378] "Emotional analysis means" refers to an engine or software that analyzes emotions based on user input and reflects them in the travel plan.

[0379] "Internal environment adjustment means" refers to a mechanism or control system for adjusting the environment inside a vehicle based on emotional data acquired by emotion analysis means.

[0380] In this embodiment of the invention, the terminal first receives the user's travel preferences. The user uses an input means on the terminal to input the destination and desired activities. The terminal transmits this input information to a server via the network and simultaneously activates an emotion analysis means to analyze the user's emotions at the time of input in real time. Emotion data is generated from the input content and input method.

[0381] The server collects relevant information via the network based on the received travel preference and sentiment data. This includes a wide range of data, such as transportation schedules, facility opening hours, and event information. Based on the collected information, the server automatically generates a travel plan using a generation tool, and this plan is adjusted according to the user's sentiment.

[0382] This process sends the generated travel plan to the device and displays it to the user. The user can review the travel plan and request modifications as needed. The server then re-analyzes the new sentiment data and provides a regenerated plan. This interactive process makes it possible to provide the user with the best possible travel experience.

[0383] Based on the finalized travel plan, the server then makes reservations for necessary facilities and activities. The reservation process also takes emotional data into consideration to provide personalized service and instantly notifies the user of reservation details on their device.

[0384] For example, if a user enters a travel preference for relaxation, the sentiment analysis tool will detect elements of relaxation and generate a plan that incorporates quiet museums and parks. It can also collect additional information through prompts such as "How are you feeling today?" or "What kind of music do you like?" based on the user's input.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The user enters their travel preferences using a terminal. The user enters information such as destination, date and time, and desired activities, and the terminal sends this input to a sentiment analysis system. The input information is processed by the sentiment analysis system as text data.

[0388] Step 2:

[0389] The terminal activates the emotion analysis system and analyzes emotional data from the user's input content and input method. At this stage, a generative AI model is used to analyze the user's emotions as text data and generate emotion tags (e.g., relaxed, excited). The analysis results are sent to the server.

[0390] Step 3:

[0391] The server receives travel preference and sentiment data and uses collection methods to gather relevant information extensively over the network. This collection includes transportation information, facility opening hours, and event information. The server retrieves the information in HTML or JSON format and stores it in a database.

[0392] Step 4:

[0393] The server uses a generation method to automatically generate travel plans based on collected information and sentiment data. A generation AI model is used to select tourist destinations and events that are appropriate to the user's emotions. The generated plans are prepared as text or visual data.

[0394] Step 5:

[0395] The server sends the generated travel plan to the device, which then presents it to the user. The user can review the travel plan and request changes or modifications. The plan is displayed dynamically according to the UI.

[0396] Step 6:

[0397] When a user submits a request to modify a travel plan, the terminal sends the new sentiment data back to the server. The server restarts the generation mechanism to construct the regenerated plan and generates the new plan.

[0398] Step 7:

[0399] The server uses booking methods to reserve necessary facilities and activities based on the final plan and sentiment data. This booking process may involve integration with external services via APIs. Booking details are notified to the device.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] In embodiments of the present invention, the user first inputs their travel preferences using a terminal. The terminal transmits this input information to a server. Based on the received preferences, the server collects relevant information via the network. This relevant information includes transportation to the planned destination, the location and operating hours of facilities, and event information.

[0417] The server then uses an algorithm that generates a travel plan based on the collected information. The generated plan is constructed as a specific itinerary that reflects the schedules of transportation and the opening hours of facilities that need to be considered. At this stage, the aim is to make the user's itinerary more efficient and reduce waste and confusion, even if it is a place they are visiting for the first time.

[0418] The completed travel plan is sent from the server to the terminal and presented to the user. The user can review the plan and enter any necessary requests for modifications or additions. The server then receives this information, reconstructs the plan based on the new information, and presents it to the terminal again. This interactive process ensures that the plan best suits the user's needs is finalized.

[0419] In addition, the server makes reservations for necessary restaurants and activities according to the confirmed plan. This allows users to automatically check reservation details on their devices, significantly reducing the hassle of making reservations on-site.

[0420] For example, suppose a user wants to visit museums and popular restaurants when visiting Tokyo. Based on this request, the server collects the opening hours and locations of Tokyo museums, a list of accessible and highly-rated restaurants, and transportation options between them. As a result, a programmed itinerary is generated that includes visiting museum A in the morning and then having lunch at nearby restaurant B. This itinerary is optimized to maximize time efficiency and reservation certainty.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The user enters their travel preferences into the terminal. This includes information such as places they want to visit, activities they are interested in, budget, and dates. The terminal verifies the entered information, formats it correctly, and sends it to the server.

[0424] Step 2:

[0425] The server collects relevant data based on the information received from the terminal. The server accesses various open data and APIs via the network to obtain information such as transportation timetables, facility opening hours, and event schedules.

[0426] Step 3:

[0427] The server uses the collected information to generate a travel plan. In this process, it programs an optimized plan tailored to the user's preferences, taking into account the order of visits, means of transportation, and the time required for each, based on the collected information.

[0428] Step 4:

[0429] The server sends the generated travel plan to the terminal. The terminal presents this plan to the user, allowing them to review its contents. The user enters any additional requests or modifications into the terminal as needed.

[0430] Step 5:

[0431] The device sends the user's correction requests collected to the server. The server recalculates the plan accordingly and regenerates a new travel plan that reflects the changes. The new plan is then sent back to the device.

[0432] Step 6:

[0433] Once the user confirms their travel plan, the server executes the booking based on that plan. The server makes restaurant reservations and activity arrangements as needed and sends booking confirmation information to the device. The device notifies the user and saves it as an itinerary.

[0434] (Example 1)

[0435] 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."

[0436] Currently, planning a trip involves a wide range of tasks, including gathering information about destinations, setting efficient routes, and making reservations. This often results in travelers spending a significant amount of time and effort, sometimes preventing them from creating an optimal travel plan. Furthermore, there is a need for flexible planning that can quickly adapt to changes in decision-making or information during the trip.

[0437] 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.

[0438] In this invention, the server includes a collection means for collecting relevant information via an information network, a generation means for automatically generating a travel plan, and a reservation means for making reservations for restaurants and activities based on the travel plan. This allows users to automatically obtain an efficient and flexible travel plan, and to respond quickly and accurately to changes in the plan.

[0439] "Users" refer to individuals who use this system to plan and adjust their travel itineraries.

[0440] "Input means" refers to devices or methods for users to input their travel preferences or requests for modifications to their travel plans.

[0441] "Information gathering means" refers to a mechanism or process for collecting necessary information related to travel planning via an information network.

[0442] "Generative means" refers to algorithms or systems that automatically create travel plans using collected information.

[0443] "Display means" refers to devices or methods that present the generated travel plan to the user visually or audibly.

[0444] "Reservation methods" refer to systems that allow you to make reservations in advance for necessary restaurants and activities based on your travel plans.

[0445] An "information network" refers to a medium for acquiring, transmitting, and receiving information, including the internet and other communication networks.

[0446] A "travel plan" refers to a schedule that integrates destinations, transportation, facility information, and reservation information based on the user's preferences.

[0447] This invention is a system that automatically generates travel plans and flexibly adjusts them according to the user's preferences. The specific implementation of this system is described below.

[0448] First, the user enters their travel preferences using a terminal. The terminal converts the data entered by the user into a digital format and sends it to the server. This process uses commonly used input devices (e.g., keyboards, touchscreens, etc.) and communication software (e.g., web browsers, mobile applications).

[0449] The server receives user requests and collects relevant information through its information network based on those requests. In this collection process, it uses publicly available online APIs (e.g., map service APIs, event information APIs) to obtain detailed information about transportation options and facilities at the destination.

[0450] Next, the server uses a generative AI model to create a travel plan based on the collected information. The AI ​​model constructs an efficient and feasible plan based on the user's prompt, such as "I want to plan a trip to Tokyo. I want to include museums and recommended restaurants, and make it an efficient itinerary." The specific algorithm takes into account transportation schedules and facility opening hours to minimize travel time while incorporating as many specified activities as possible.

[0451] The generated travel plan is sent from the server to the terminal and presented to the user. The user can review the presented plan and, if necessary, enter modification requests from the terminal. The terminal then sends this back to the server, which regenerates the plan based on the new conditions.

[0452] Furthermore, based on the finalized travel plan, the server automatically makes reservations for restaurants and activities through the reservation system. This process allows users to receive a consistent service from planning to booking, significantly reducing anxiety and the hassle of making arrangements during their trip.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The user enters their travel preferences into the terminal. This data includes destinations, activities of interest, and desired dates. This information is converted into a digital format and sent to the server. Specifically, the terminal sends the information entered via the keyboard or touchscreen to the server as an HTTP request.

[0456] Step 2:

[0457] The server collects relevant information through its information network based on the received travel preference data. Specifically, it uses database queries and API calls to obtain information such as transportation options, facility locations and opening hours, and event information. This gathers the necessary data on the server. The server stores this data in an intermediate database so that it can be used for subsequent processing.

[0458] Step 3:

[0459] The server utilizes the collected information and generates travel plans using a generative AI model. Inputs include user preferences and collected relevant information. Based on this, the AI ​​model creates an efficient plan that takes into account transportation schedules and facility opening hours. The AI ​​model analyzes and optimizes the data to determine the optimal order of visits and formulate the travel plan.

[0460] Step 4:

[0461] The generated travel plan is sent from the server to the terminal and presented to the user. The terminal receives it and displays it on the screen in an easy-to-understand format. The user reviews the plan and decides whether they are satisfied with it. In this process, the terminal visually structures and displays the information, organizing the plan content in an easy-to-understand manner.

[0462] Step 5:

[0463] If a user wishes to modify or add to their travel plan, they enter their new requirements via their device. This information is then transmitted digitally to the server. The server receives this information and reconstructs the travel plan based on the requested changes.

[0464] Step 6:

[0465] The server generates a new travel plan in response to the modification request and sends it to the terminal. At this time, the server uses the AI ​​model again to create an efficient plan that reflects the changed conditions.

[0466] Step 7:

[0467] Based on the confirmed travel plan, the server automatically makes reservations for necessary facilities and activities. Specifically, it uses an online reservation system to secure reservations that match the date, time, and location. Then, it sends reservation confirmation details to the user's device. This allows the user to automatically check their reservation information, reducing the hassle during their trip.

[0468] (Application Example 1)

[0469] 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."

[0470] Modern consumers seek efficient purchasing experiences when shopping and using services in virtual spaces and online platforms. However, it is difficult for users to easily gather information on the products and services they desire and create optimal purchasing plans based on that information. Furthermore, there is a lack of efficient means to utilize promotional information from individual stores. Therefore, it is necessary to improve the user's purchasing experience and realize efficient shopping that eliminates waste.

[0471] 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.

[0472] In this invention, the server includes an input means for the user to input their purchase preferences, a collection means for collecting relevant information via a communication network, and a generation means for automatically generating a purchase plan for the user using the collected information. This enables the user to enjoy an efficient and customized purchasing experience.

[0473] A "user" is an individual or legal entity that uses the system to input purchase requests and review the presented purchase plan.

[0474] "Purchase request" refers to information that includes detailed requests and conditions regarding the product or service that the user wishes to purchase.

[0475] "Input means" refers to a device or interface for users to input their purchase requests into the system.

[0476] A "communication network" is a network infrastructure used to send and receive information, such as the internet.

[0477] "Related information" refers to data such as retail store operating hours and promotional information, which is obtained based on the user's purchasing preferences.

[0478] "Collection means" refers to a process or device for acquiring relevant information via a communication network.

[0479] The "generation method" refers to a part of a system that automatically creates a purchasing plan based on the collected relevant information.

[0480] A "purchase plan" is an optimized plan or schedule designed to help users efficiently purchase the goods and services they desire.

[0481] "Display means" refers to a screen or interface used to present the generated purchase plan to the user.

[0482] The "reservation method" refers to the part of the system used to manage necessary retail store reservations and promotional information based on a purchasing plan.

[0483] To realize this invention, the server provides an interface for users to input their purchase preferences. Users can use a device such as a smartphone or personal computer to input information about the products or services they wish to purchase. This input information is transmitted to the server, which then collects related information via the communication network.

[0484] The server uses a collection mechanism to acquire relevant data, such as retail store operating hours and promotional information, via the communication network. Based on the collected information, the generation mechanism automatically generates an optimal purchasing plan. The generated purchasing plan is displayed on the user's terminal for the user to review.

[0485] Users can review the generated purchase plan and modify it as needed. This interactive process helps users purchase goods and services efficiently and effectively. It also includes features for managing retail store reservations and promotional information based on the purchase plan.

[0486] In this system, the server performs real-time processing using the internet. For example, if a user wants to purchase a new product from a specific brand, the server uses data collection methods to gather inventory information and special sale information from the nearest store and presents the user with the optimal purchase plan.

[0487] Using a generative AI model, it's possible to suggest the optimal route and schedule based on user input. An example of a prompt would be:

[0488] "Based on the user's preferences, suggest the optimal route for efficient shopping within the virtual shopping center. Information to collect includes store opening hours, promotions, and inventory status."

[0489] In this way, the system can improve the user experience and support efficient purchasing activities.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] The user enters their purchase request using a terminal. This information includes the type and brand of the product they wish to buy, as well as their desired purchase conditions. This input is then sent from the terminal to the server.

[0493] Step 2:

[0494] Based on the user's purchase request, the server collects relevant information via the communication network. Specifically, it retrieves information on available retail store operating hours, inventory status, and promotions from a database using a network API. This information is then prepared as a set of data to satisfy the user's request.

[0495] Step 3:

[0496] Using the generation method, the server automatically generates a purchasing plan based on the collected relevant information. At this stage, the generation AI model is utilized to create an efficient and user-friendly optimal route and schedule. The output is a detailed purchasing plan.

[0497] Step 4:

[0498] The server sends the generated purchase plan to the user's terminal. This process involves layout processing to make the plan easy for the user to visually review, and the plan is presented via a display device.

[0499] Step 5:

[0500] The user reviews the presented purchase plan and, if necessary, enters revision requests via the terminal. These requests are sent to the server, which then regenerates the purchase plan based on the new information and user feedback.

[0501] Step 6:

[0502] The server manages retailer reservations and promotions based on the finalized purchase plan. This allows users to check necessary reservation information on their devices and enjoy the benefits.

[0503] 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.

[0504] In an embodiment of the present invention, the user first inputs their travel preferences using a terminal. The terminal transmits this input information to a server and activates an emotion engine to recognize the user's emotions during the input process. The emotion engine analyzes the user's emotions in real time based on the content and manner of input and transmits this data to the server. It also learns from past emotion data to support the generation of plans that are better suited to the user's preferences.

[0505] Based on the received sentiment data and travel preferences, the server collects a wide range of relevant information through the network. This includes transportation information for planned destinations, facility opening hours, and event information. The server uses the collected information and the user's sentiment data to run a travel plan generation algorithm. The generated plan is adjusted according to the user's sentiment, ensuring an optimal experience.

[0506] The generated travel plan is sent from the server to the terminal and presented to the user. At this time, the order in which the plan is presented can be dynamically adjusted based on the analysis of the emotion engine. The user can review the plan and, if necessary, input any requests for modifications or additions into the terminal. The server then reconstructs the plan based on the new emotion data and presents it to the terminal in an interactive manner.

[0507] Furthermore, the server makes reservations for necessary restaurants and activities based on the finalized plan. This reservation process can also be made more personalized by taking the user's feelings into consideration. The server sends the reservation details to the device and notifies the user immediately.

[0508] For example, suppose a user desires to relax in Tokyo and requests to visit "quiet and beautiful places" through their device. The emotion engine determines from the user's input that relaxation is important, and the server reflects this emotional information to generate a travel plan that includes quiet museums and parks. This plan is designed to match the user's emotions and ensure a highly satisfying trip.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The user enters their travel preferences through the terminal. The terminal transmits the input to the emotion engine in real time, which analyzes the user's emotions. The emotion engine evaluates the user's emotional state based on data including keystrokes and input speed during input.

[0512] Step 2:

[0513] The terminal combines the user's travel preferences with analyzed sentiment data and sends it to the server. Based on the received data, the server uses the network to collect relevant information. This includes destination traffic information, facility operating hours, and event details.

[0514] Step 3:

[0515] The server generates travel plans based on collected information and user sentiment data. The generation process incorporates the results of the sentiment engine; for example, if a desire to relax is detected, it prioritizes and includes comfortable, less crowded spots.

[0516] Step 4:

[0517] The server sends the generated travel plan to the device. The device displays this plan to the user and informs them that it is adjusting the order and content of the plan based on feedback from the emotion engine.

[0518] Step 5:

[0519] The user reviews the presented plan and enters any new requests or feedback as needed. The device sends this information to the server in real time, updating sentiment data along with it.

[0520] Step 6:

[0521] The server reconstructs the travel plan based on new requests and sentiment data. If the sentiment engine determines that a user's reaction will affect satisfaction, it adjusts the plan accordingly and presents it again.

[0522] Step 7:

[0523] The server processes bookings based on the finalized travel plan. It automatically makes reservations for restaurants, activities, and other services as much as possible, and sends the details to the device. The device notifies the user and verifies the appropriateness of the bookings based on sentiment data.

[0524] (Example 2)

[0525] 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."

[0526] Conventional travel plan generation systems have struggled to create plans that adequately consider users' emotions and preferences, and have failed to provide users with the optimal travel experience. In particular, the order and content of travel plan presentations were not optimized, and interactive adjustments to enhance user satisfaction were insufficient.

[0527] 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.

[0528] In this invention, the server includes an input means for users to input travel preferences and emotional data, a collection means for collecting information via a network based on the travel preferences and emotional data input by the input means, and a generation means for automatically generating travel plans using the collected information and emotional data, and generating travel plans optimized according to the user's emotions. This makes it possible to generate travel plans that are suited to the user's emotions and to dynamically adjust the presentation order.

[0529] A "user" refers to a person who uses the system to plan a trip and is the one who inputs their feelings and desires.

[0530] "Emotional data" refers to information that expresses a user's emotional state using numerical values ​​or categories, and is used for plan generation and optimization.

[0531] A "network" is a communication line used for sending and receiving information, and includes the internet and intranets.

[0532] "Collecting information" refers to the act of obtaining necessary travel-related information from internet databases, APIs, etc.

[0533] A "travel plan" refers to a travel itinerary and list of destinations generated based on the user's wishes and preferences.

[0534] "Adjusting the presentation order" is an operation that changes the order in which information and schedules within a travel plan are displayed, based on the user's emotional data.

[0535] "Reservation method" refers to the function that executes the process of securing participation slots for restaurants and activities based on the generated travel plan.

[0536] To implement this invention, a terminal is used in which the user first inputs their travel preferences and emotions. When the terminal transmits the input information to the server, it activates an emotion engine to analyze the user's emotions. A general-purpose emotion analysis module can be used as the emotion engine.

[0537] The terminal uses an emotion engine to analyze emotional data in real time from the user's input content and input method, and sends that data to the server. Furthermore, by learning from past emotional data, it enables more accurate emotional analysis. Based on the emotional data and travel preferences, the server collects relevant information about the planned destination via the internet. In this process, it utilizes APIs to obtain information such as traffic information, facility opening hours, and event information.

[0538] The server uses collected information and user sentiment data to apply an algorithm for generating travel plans. Using a generative AI model, it's possible to create an optimal travel plan tailored to the user's emotions. The order in which the generated plans are presented to the user is adjusted based on the sentiment engine's analysis.

[0539] For example, if a user enters a desire to "visit a quiet and beautiful place," the emotion engine detects the emotion of "wanting to relax," and the server generates a travel plan that includes quiet museums and parks based on that emotion information. Furthermore, the user can review this plan and interactively reconstruct it by entering additional requests or modifications on their device.

[0540] An example of a prompt might be, "Please recommend a relaxing place for my next trip. I'd like somewhere quiet and beautiful." Based on this prompt, the system will suggest a travel plan that suits the user.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The user enters information about their travel preferences and emotions into the device. This input is specifically in text format and includes preferences such as "I want to visit a quiet and beautiful place." The entered preferences are processed as text data on the device and serve as the basis for analyzing the user's emotions.

[0544] Step 2:

[0545] The device activates an emotion engine based on the input information. The emotion engine analyzes the user's text data and past input patterns, and processes the data to obtain emotion data. For example, it extracts emotional states such as "I want to relax" from keywords, input speed, and context. This emotion data is then sent from the device to the server.

[0546] Step 3:

[0547] The server receives sentiment data and travel preferences sent from the terminal. Based on this data, the server collects relevant information via the internet. Specifically, it uses APIs to retrieve transportation information, facility opening hours, and event information for the destination, and prepares a structured set of information. This information becomes the input data for generating a travel plan.

[0548] Step 4:

[0549] The server uses collected information and sentiment data to run a generative AI model that generates travel plans. This model calculates and constructs the optimal travel plan based on the input sentiment data. The generated plan is dynamically optimized and adjusted according to the user's emotions.

[0550] Step 5:

[0551] The generated travel plan is sent from the server to the device and presented to the user on the device. Based on the analysis results of the emotion engine, the device adjusts the order in which events and activities within the plan are presented. This allows the user to view the travel plan in a way that aligns with their emotions.

[0552] Step 6:

[0553] Users can review the presented plan and re-enter any desired changes or additions into their device. For example, they might want to add a new tourist destination. This new information is sent from the device to the server, initiating the plan regeneration process.

[0554] Step 7:

[0555] The server reconstructs the travel plan based on new input information and sentiment data from the user. The generative AI model runs again to generate the updated plan. The regenerated plan is presented again on the device, awaiting the user's final confirmation.

[0556] Step 8:

[0557] After the user confirms their plan, the server executes the booking process based on that plan. For example, it confirms restaurant and activity reservations and sends the reservation details to the user's device. This notification allows the user to immediately confirm their reservation details and prepare to enjoy their trip with peace of mind.

[0558] (Application Example 2)

[0559] 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."

[0560] The travel experience in conventional autonomous vehicles has focused solely on its function as a means of transportation, and has rarely been flexibly customized based on the user's emotions or personal preferences. As a result, there has been a problem in that it has not been able to provide a comfortable travel experience that is in line with the user's feelings and mood.

[0561] 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.

[0562] In this invention, the server includes emotion analysis means that analyzes the user's emotions based on their input and reflects them in the travel plan, reservation means that makes reservations for facilities and activities based on the travel plan, and interior environment adjustment means that adjusts the environment inside the vehicle based on the emotion data acquired by the emotion analysis means. This makes it possible to present a travel plan based on the user's emotions and to automatically adjust the in-vehicle environment, thereby providing a highly personalized travel experience.

[0563] "Input means" refers to a device or interface for users to input their travel preferences.

[0564] "Collection means" refers to a system or module for collecting relevant information via a network.

[0565] "Generation method" refers to a process or algorithm for automatically generating travel plans using collected information.

[0566] "Display means" refers to a device or screen display function for presenting the generated travel plan to the user.

[0567] "Reservation method" refers to the procedure or system for making reservations for facilities and activities based on a travel plan.

[0568] "Emotional analysis means" refers to an engine or software that analyzes emotions based on user input and reflects them in the travel plan.

[0569] "Internal environment adjustment means" refers to a mechanism or control system for adjusting the environment inside a vehicle based on emotional data acquired by emotion analysis means.

[0570] In this embodiment of the invention, the terminal first receives the user's travel preferences. The user uses an input means on the terminal to input the destination and desired activities. The terminal transmits this input information to a server via the network and simultaneously activates an emotion analysis means to analyze the user's emotions at the time of input in real time. Emotion data is generated from the input content and input method.

[0571] The server collects relevant information via the network based on the received travel preference and sentiment data. This includes a wide range of data, such as transportation schedules, facility opening hours, and event information. Based on the collected information, the server automatically generates a travel plan using a generation tool, and this plan is adjusted according to the user's sentiment.

[0572] This process sends the generated travel plan to the device and displays it to the user. The user can review the travel plan and request modifications as needed. The server then re-analyzes the new sentiment data and provides a regenerated plan. This interactive process makes it possible to provide the user with the best possible travel experience.

[0573] Based on the finalized travel plan, the server then makes reservations for necessary facilities and activities. The reservation process also takes emotional data into consideration to provide personalized service and instantly notifies the user of reservation details on their device.

[0574] For example, if a user enters a travel preference for relaxation, the sentiment analysis tool will detect elements of relaxation and generate a plan that incorporates quiet museums and parks. It can also collect additional information through prompts such as "How are you feeling today?" or "What kind of music do you like?" based on the user's input.

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The user enters their travel preferences using a terminal. The user enters information such as destination, date and time, and desired activities, and the terminal sends this input to a sentiment analysis system. The input information is processed by the sentiment analysis system as text data.

[0578] Step 2:

[0579] The terminal activates the emotion analysis system and analyzes emotional data from the user's input content and input method. At this stage, a generative AI model is used to analyze the user's emotions as text data and generate emotion tags (e.g., relaxed, excited). The analysis results are sent to the server.

[0580] Step 3:

[0581] The server receives travel preference and sentiment data and uses collection methods to gather relevant information extensively over the network. This collection includes transportation information, facility opening hours, and event information. The server retrieves the information in HTML or JSON format and stores it in a database.

[0582] Step 4:

[0583] The server uses a generation method to automatically generate travel plans based on collected information and sentiment data. A generation AI model is used to select tourist destinations and events that are appropriate to the user's emotions. The generated plans are prepared as text or visual data.

[0584] Step 5:

[0585] The server sends the generated travel plan to the device, which then presents it to the user. The user can review the travel plan and request changes or modifications. The plan is displayed dynamically according to the UI.

[0586] Step 6:

[0587] When a user submits a request to modify a travel plan, the terminal sends the new sentiment data back to the server. The server restarts the generation mechanism to construct the regenerated plan and generates the new plan.

[0588] Step 7:

[0589] The server uses booking methods to reserve necessary facilities and activities based on the final plan and sentiment data. This booking process may involve integration with external services via APIs. Booking details are notified to the device.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] [Fourth Embodiment]

[0594] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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".

[0607] In embodiments of the present invention, the user first inputs their travel preferences using a terminal. The terminal transmits this input information to a server. Based on the received preferences, the server collects relevant information via the network. This relevant information includes transportation to the planned destination, the location and operating hours of facilities, and event information.

[0608] The server then uses an algorithm that generates a travel plan based on the collected information. The generated plan is constructed as a specific itinerary that reflects the schedules of transportation and the opening hours of facilities that need to be considered. At this stage, the aim is to make the user's itinerary more efficient and reduce waste and confusion, even if it is a place they are visiting for the first time.

[0609] The completed travel plan is sent from the server to the terminal and presented to the user. The user can review the plan and enter any necessary requests for modifications or additions. The server then receives this information, reconstructs the plan based on the new information, and presents it to the terminal again. This interactive process ensures that the plan best suits the user's needs is finalized.

[0610] In addition, the server makes reservations for necessary restaurants and activities according to the confirmed plan. This allows users to automatically check reservation details on their devices, significantly reducing the hassle of making reservations on-site.

[0611] For example, suppose a user wants to visit museums and popular restaurants when visiting Tokyo. Based on this request, the server collects the opening hours and locations of Tokyo museums, a list of accessible and highly-rated restaurants, and transportation options between them. As a result, a programmed itinerary is generated that includes visiting museum A in the morning and then having lunch at nearby restaurant B. This itinerary is optimized to maximize time efficiency and reservation certainty.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] The user enters their travel preferences into the terminal. This includes information such as places they want to visit, activities they are interested in, budget, and dates. The terminal verifies the entered information, formats it correctly, and sends it to the server.

[0615] Step 2:

[0616] The server collects relevant data based on the information received from the terminal. The server accesses various open data and APIs via the network to obtain information such as transportation timetables, facility opening hours, and event schedules.

[0617] Step 3:

[0618] The server uses the collected information to generate a travel plan. In this process, it programs an optimized plan tailored to the user's preferences, taking into account the order of visits, means of transportation, and the time required for each, based on the collected information.

[0619] Step 4:

[0620] The server sends the generated travel plan to the terminal. The terminal presents this plan to the user, allowing them to review its contents. The user enters any additional requests or modifications into the terminal as needed.

[0621] Step 5:

[0622] The device sends the user's correction requests collected to the server. The server recalculates the plan accordingly and regenerates a new travel plan that reflects the changes. The new plan is then sent back to the device.

[0623] Step 6:

[0624] Once the user confirms their travel plan, the server executes the booking based on that plan. The server makes restaurant reservations and activity arrangements as needed and sends booking confirmation information to the device. The device notifies the user and saves it as an itinerary.

[0625] (Example 1)

[0626] 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".

[0627] Currently, planning a trip involves a wide range of tasks, including gathering information about destinations, setting efficient routes, and making reservations. This often results in travelers spending a significant amount of time and effort, sometimes preventing them from creating an optimal travel plan. Furthermore, there is a need for flexible planning that can quickly adapt to changes in decision-making or information during the trip.

[0628] 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.

[0629] In this invention, the server includes a collection means for collecting relevant information via an information network, a generation means for automatically generating a travel plan, and a reservation means for making reservations for restaurants and activities based on the travel plan. This allows users to automatically obtain an efficient and flexible travel plan, and to respond quickly and accurately to changes in the plan.

[0630] "Users" refer to individuals who use this system to plan and adjust their travel itineraries.

[0631] "Input means" refers to devices or methods for users to input their travel preferences or requests for modifications to their travel plans.

[0632] "Information gathering means" refers to a mechanism or process for collecting necessary information related to travel planning via an information network.

[0633] "Generative means" refers to algorithms or systems that automatically create travel plans using collected information.

[0634] "Display means" refers to devices or methods that present the generated travel plan to the user visually or audibly.

[0635] "Reservation methods" refer to systems that allow you to make reservations in advance for necessary restaurants and activities based on your travel plans.

[0636] An "information network" refers to a medium for acquiring, transmitting, and receiving information, including the internet and other communication networks.

[0637] A "travel plan" refers to a schedule that integrates destinations, transportation, facility information, and reservation information based on the user's preferences.

[0638] This invention is a system that automatically generates travel plans and flexibly adjusts them according to the user's preferences. The specific implementation of this system is described below.

[0639] First, the user enters their travel preferences using a terminal. The terminal converts the data entered by the user into a digital format and sends it to the server. This process uses commonly used input devices (e.g., keyboards, touchscreens, etc.) and communication software (e.g., web browsers, mobile applications).

[0640] The server receives user requests and collects relevant information through its information network based on those requests. In this collection process, it uses publicly available online APIs (e.g., map service APIs, event information APIs) to obtain detailed information about transportation options and facilities at the destination.

[0641] Next, the server uses a generative AI model to create a travel plan based on the collected information. The AI ​​model constructs an efficient and feasible plan based on the user's prompt, such as "I want to plan a trip to Tokyo. I want to include museums and recommended restaurants, and make it an efficient itinerary." The specific algorithm takes into account transportation schedules and facility opening hours to minimize travel time while incorporating as many specified activities as possible.

[0642] The generated travel plan is sent from the server to the terminal and presented to the user. The user can review the presented plan and, if necessary, enter modification requests from the terminal. The terminal then sends this back to the server, which regenerates the plan based on the new conditions.

[0643] Furthermore, based on the finalized travel plan, the server automatically makes reservations for restaurants and activities through the reservation system. This process allows users to receive a consistent service from planning to booking, significantly reducing anxiety and the hassle of making arrangements during their trip.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] The user enters their travel preferences into the terminal. This data includes destinations, activities of interest, and desired dates. This information is converted into a digital format and sent to the server. Specifically, the terminal sends the information entered via the keyboard or touchscreen to the server as an HTTP request.

[0647] Step 2:

[0648] The server collects relevant information through its information network based on the received travel preference data. Specifically, it uses database queries and API calls to obtain information such as transportation options, facility locations and opening hours, and event information. This gathers the necessary data on the server. The server then stores this data in an intermediate database for use in subsequent processing.

[0649] Step 3:

[0650] The server utilizes the collected information and generates travel plans using a generative AI model. Inputs include user preferences and collected relevant information. Based on this, the AI ​​model creates an efficient plan that takes into account transportation schedules and facility opening hours. The AI ​​model analyzes and optimizes the data to determine the optimal order of visits and formulate the travel plan.

[0651] Step 4:

[0652] The generated travel plan is sent from the server to the terminal and presented to the user. The terminal receives it and displays it on the screen in an easy-to-understand format. The user reviews the plan and decides whether they are satisfied with it. In this process, the terminal visually structures and displays the information, organizing the plan content in an easy-to-understand manner.

[0653] Step 5:

[0654] If a user wishes to modify or add to their travel plan, they enter their new requirements via their device. This information is then transmitted digitally to the server. The server receives this information and reconstructs the travel plan based on the requested changes.

[0655] Step 6:

[0656] The server generates a new travel plan in response to the modification request and sends it to the terminal. At this time, the server uses the AI ​​model again to create an efficient plan that reflects the changed conditions.

[0657] Step 7:

[0658] Based on the confirmed travel plan, the server automatically makes reservations for necessary facilities and activities. Specifically, it uses an online reservation system to secure reservations that match the date, time, and location. Then, it sends reservation confirmation details to the user's device. This allows the user to automatically check their reservation information, reducing the hassle during their trip.

[0659] (Application Example 1)

[0660] 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".

[0661] Modern consumers seek efficient purchasing experiences when shopping and using services in virtual spaces and online platforms. However, it is difficult for users to easily gather information on the products and services they desire and create optimal purchasing plans based on that information. Furthermore, there is a lack of efficient means to utilize promotional information from individual stores. Therefore, it is necessary to improve the user's purchasing experience and realize efficient shopping that eliminates waste.

[0662] 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.

[0663] In this invention, the server includes an input means for the user to input their purchase preferences, a collection means for collecting relevant information via a communication network, and a generation means for automatically generating a purchase plan for the user using the collected information. This enables the user to enjoy an efficient and customized purchasing experience.

[0664] A "user" is an individual or legal entity that uses the system to input purchase requests and review the presented purchase plan.

[0665] "Purchase request" refers to information that includes detailed requests and conditions regarding the product or service that the user wishes to purchase.

[0666] "Input means" refers to a device or interface for users to input their purchase requests into the system.

[0667] A "communication network" is a network infrastructure used to send and receive information, such as the internet.

[0668] "Related information" refers to data such as retail store operating hours and promotional information, which is obtained based on the user's purchasing preferences.

[0669] "Collection means" refers to a process or device for acquiring relevant information via a communication network.

[0670] The "generation method" refers to a part of a system that automatically creates a purchasing plan based on the collected relevant information.

[0671] A "purchase plan" is an optimized plan or schedule designed to help users efficiently purchase the goods and services they desire.

[0672] "Display means" refers to a screen or interface used to present the generated purchase plan to the user.

[0673] The "reservation method" refers to the part of the system used to manage necessary retail store reservations and promotional information based on a purchasing plan.

[0674] To realize this invention, the server provides an interface for users to input their purchase preferences. Users can use a device such as a smartphone or personal computer to input information about the products or services they wish to purchase. This input information is transmitted to the server, which then collects related information via the communication network.

[0675] The server uses a collection mechanism to acquire relevant data, such as retail store operating hours and promotional information, via the communication network. Based on the collected information, the generation mechanism automatically generates an optimal purchasing plan. The generated purchasing plan is displayed on the user's terminal for the user to review.

[0676] Users can review the generated purchase plan and modify it as needed. This interactive process helps users purchase goods and services efficiently and effectively. It also includes features for managing retail store reservations and promotional information based on the purchase plan.

[0677] In this system, the server performs real-time processing using the internet. For example, if a user wants to purchase a new product from a specific brand, the server uses data collection methods to gather inventory information and special sale information from the nearest store and presents the user with the optimal purchase plan.

[0678] Using a generative AI model, it's possible to suggest the optimal route and schedule based on user input. An example of a prompt would be:

[0679] "Based on the user's preferences, suggest the optimal route for efficient shopping within the virtual shopping center. Information to collect includes store opening hours, promotions, and inventory status."

[0680] In this way, the system can improve the user experience and support efficient purchasing activities.

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The user enters their purchase request using a terminal. This information includes the type and brand of the product they wish to buy, as well as their desired purchase conditions. This input is then sent from the terminal to the server.

[0684] Step 2:

[0685] Based on the user's purchase request, the server collects relevant information via the communication network. Specifically, it retrieves information on available retail store operating hours, inventory status, and promotions from a database using a network API. This information is then prepared as a set of data to satisfy the user's request.

[0686] Step 3:

[0687] Using the generation method, the server automatically generates a purchasing plan based on the collected relevant information. At this stage, the generation AI model is utilized to create an efficient and user-friendly optimal route and schedule. The output is a detailed purchasing plan.

[0688] Step 4:

[0689] The server sends the generated purchase plan to the user's terminal. This process involves layout processing to make the plan easy for the user to visually review, and the plan is presented via a display device.

[0690] Step 5:

[0691] The user reviews the presented purchase plan and, if necessary, enters revision requests via the terminal. These requests are sent to the server, which then regenerates the purchase plan based on the new information and user feedback.

[0692] Step 6:

[0693] The server manages retailer reservations and promotions based on the finalized purchase plan. This allows users to check necessary reservation information on their devices and enjoy the benefits.

[0694] 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.

[0695] In an embodiment of the present invention, the user first inputs their travel preferences using a terminal. The terminal transmits this input information to a server and activates an emotion engine to recognize the user's emotions during the input process. The emotion engine analyzes the user's emotions in real time based on the content and manner of input and transmits this data to the server. It also learns from past emotion data to support the generation of plans that are better suited to the user's preferences.

[0696] Based on the received sentiment data and travel preferences, the server collects a wide range of relevant information through the network. This includes transportation information for planned destinations, facility opening hours, and event information. The server uses the collected information and the user's sentiment data to run a travel plan generation algorithm. The generated plan is adjusted according to the user's sentiment, ensuring an optimal experience.

[0697] The generated travel plan is sent from the server to the terminal and presented to the user. At this time, the order in which the plan is presented can be dynamically adjusted based on the analysis of the emotion engine. The user can review the plan and, if necessary, input any requests for modifications or additions into the terminal. The server then reconstructs the plan based on the new emotion data and presents it to the terminal in an interactive manner.

[0698] Furthermore, the server makes reservations for necessary restaurants and activities based on the finalized plan. This reservation process can also be made more personalized by taking the user's feelings into consideration. The server sends the reservation details to the device and notifies the user immediately.

[0699] For example, suppose a user desires to relax in Tokyo and requests to visit "quiet and beautiful places" through their device. The emotion engine determines from the user's input that relaxation is important, and the server reflects this emotional information to generate a travel plan that includes quiet museums and parks. This plan is designed to match the user's emotions and ensure a highly satisfying trip.

[0700] The following describes the processing flow.

[0701] Step 1:

[0702] The user enters their travel preferences through the terminal. The terminal transmits the input to the emotion engine in real time, which analyzes the user's emotions. The emotion engine evaluates the user's emotional state based on data including keystrokes and input speed during input.

[0703] Step 2:

[0704] The terminal combines the user's travel preferences with analyzed sentiment data and sends it to the server. Based on the received data, the server uses the network to collect relevant information. This includes destination traffic information, facility operating hours, and event details.

[0705] Step 3:

[0706] The server generates travel plans based on collected information and user sentiment data. The generation process incorporates the results of the sentiment engine; for example, if a desire to relax is detected, it prioritizes and includes comfortable, less crowded spots.

[0707] Step 4:

[0708] The server sends the generated travel plan to the device. The device displays this plan to the user and informs them that it is adjusting the order and content of the plan based on feedback from the emotion engine.

[0709] Step 5:

[0710] The user reviews the presented plan and enters any new requests or feedback as needed. The device sends this information to the server in real time, updating sentiment data along with it.

[0711] Step 6:

[0712] The server reconstructs the travel plan based on new requests and sentiment data. If the sentiment engine determines that a user's reaction will affect satisfaction, it adjusts the plan accordingly and presents it again.

[0713] Step 7:

[0714] The server processes bookings based on the finalized travel plan. It automatically makes reservations for restaurants, activities, and other services as much as possible, and sends the details to the device. The device notifies the user and verifies the appropriateness of the bookings based on sentiment data.

[0715] (Example 2)

[0716] 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".

[0717] Conventional travel plan generation systems have struggled to create plans that adequately consider users' emotions and preferences, and have failed to provide users with the optimal travel experience. In particular, the order and content of travel plan presentations were not optimized, and interactive adjustments to enhance user satisfaction were insufficient.

[0718] 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.

[0719] In this invention, the server includes an input means for users to input travel preferences and emotional data, a collection means for collecting information via a network based on the travel preferences and emotional data input by the input means, and a generation means for automatically generating travel plans using the collected information and emotional data, and generating travel plans optimized according to the user's emotions. This makes it possible to generate travel plans that are suited to the user's emotions and to dynamically adjust the presentation order.

[0720] A "user" refers to a person who uses the system to plan a trip and is the one who inputs their feelings and desires.

[0721] "Emotional data" refers to information that expresses a user's emotional state using numerical values ​​or categories, and is used for plan generation and optimization.

[0722] A "network" is a communication line used for sending and receiving information, and includes the internet and intranets.

[0723] "Collecting information" refers to the act of obtaining necessary travel-related information from internet databases, APIs, etc.

[0724] A "travel plan" refers to a travel itinerary and list of destinations generated based on the user's wishes and preferences.

[0725] "Adjusting the presentation order" is an operation that changes the order in which information and schedules within a travel plan are displayed, based on the user's emotional data.

[0726] "Reservation method" refers to the function that executes the process of securing participation slots for restaurants and activities based on the generated travel plan.

[0727] To implement this invention, a terminal is used in which the user first inputs their travel preferences and emotions. When the terminal transmits the input information to the server, it activates an emotion engine to analyze the user's emotions. A general-purpose emotion analysis module can be used as the emotion engine.

[0728] The terminal uses an emotion engine to analyze emotional data in real time from the user's input content and input method, and sends that data to the server. Furthermore, by learning from past emotional data, it enables more accurate emotional analysis. Based on the emotional data and travel preferences, the server collects relevant information about the planned destination via the internet. In this process, it utilizes APIs to obtain information such as traffic information, facility opening hours, and event information.

[0729] The server uses collected information and user sentiment data to apply an algorithm for generating travel plans. Using a generative AI model, it's possible to create an optimal travel plan tailored to the user's emotions. The order in which the generated plans are presented to the user is adjusted based on the sentiment engine's analysis.

[0730] For example, if a user enters a desire to "visit a quiet and beautiful place," the emotion engine detects the emotion of "wanting to relax," and the server generates a travel plan that includes quiet museums and parks based on that emotion information. Furthermore, the user can review this plan and interactively reconstruct it by entering additional requests or modifications on their device.

[0731] An example of a prompt might be, "Please recommend a relaxing place for my next trip. I'd like somewhere quiet and beautiful." Based on this prompt, the system will suggest a travel plan that suits the user.

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] The user enters information about their travel preferences and emotions into the device. This input is specifically in text format and includes preferences such as "I want to visit a quiet and beautiful place." The entered preferences are processed as text data on the device and serve as the basis for analyzing the user's emotions.

[0735] Step 2:

[0736] The device activates an emotion engine based on the input information. The emotion engine analyzes the user's text data and past input patterns, and processes the data to obtain emotion data. For example, it extracts emotional states such as "I want to relax" from keywords, input speed, and context. This emotion data is then sent from the device to the server.

[0737] Step 3:

[0738] The server receives sentiment data and travel preferences sent from the terminal. Based on this data, the server collects relevant information via the internet. Specifically, it uses APIs to retrieve transportation information, facility opening hours, and event information for the destination, and prepares a structured set of information. This information becomes the input data for generating a travel plan.

[0739] Step 4:

[0740] The server uses collected information and sentiment data to run a generative AI model that generates travel plans. This model calculates and constructs the optimal travel plan based on the input sentiment data. The generated plan is dynamically optimized and adjusted according to the user's emotions.

[0741] Step 5:

[0742] The generated travel plan is sent from the server to the device and presented to the user on the device. Based on the analysis results of the emotion engine, the device adjusts the order in which events and activities within the plan are presented. This allows the user to view the travel plan in a way that aligns with their emotions.

[0743] Step 6:

[0744] Users can review the presented plan and re-enter any desired changes or additions into their device. For example, they might want to add a new tourist destination. This new information is sent from the device to the server, initiating the plan regeneration process.

[0745] Step 7:

[0746] The server reconstructs the travel plan based on new input information and sentiment data from the user. The generative AI model runs again to generate the updated plan. The regenerated plan is presented again on the device, awaiting the user's final confirmation.

[0747] Step 8:

[0748] After the user confirms their plan, the server executes the booking process based on that plan. For example, it confirms restaurant and activity reservations and sends the reservation details to the user's device. This notification allows the user to immediately confirm their reservation details and prepare to enjoy their trip with peace of mind.

[0749] (Application Example 2)

[0750] 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".

[0751] The travel experience in conventional autonomous vehicles has focused solely on its function as a means of transportation, and has rarely been flexibly customized based on the user's emotions or personal preferences. As a result, there has been a problem in that it has not been able to provide a comfortable travel experience that is in line with the user's feelings and mood.

[0752] 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.

[0753] In this invention, the server includes emotion analysis means that analyzes the user's emotions based on their input and reflects them in the travel plan, reservation means that makes reservations for facilities and activities based on the travel plan, and interior environment adjustment means that adjusts the environment inside the vehicle based on the emotion data acquired by the emotion analysis means. This makes it possible to present a travel plan based on the user's emotions and to automatically adjust the in-vehicle environment, thereby providing a highly personalized travel experience.

[0754] "Input means" refers to a device or interface for users to input their travel preferences.

[0755] "Collection means" refers to a system or module for collecting relevant information via a network.

[0756] "Generation method" refers to a process or algorithm for automatically generating travel plans using collected information.

[0757] "Display means" refers to a device or screen display function for presenting the generated travel plan to the user.

[0758] "Reservation method" refers to the procedure or system for making reservations for facilities and activities based on a travel plan.

[0759] "Emotional analysis means" refers to an engine or software that analyzes emotions based on user input and reflects them in the travel plan.

[0760] "Internal environment adjustment means" refers to a mechanism or control system for adjusting the environment inside a vehicle based on emotional data acquired by emotion analysis means.

[0761] In this embodiment of the invention, the terminal first receives the user's travel preferences. The user uses an input means on the terminal to input the destination and desired activities. The terminal transmits this input information to a server via the network and simultaneously activates an emotion analysis means to analyze the user's emotions at the time of input in real time. Emotion data is generated from the input content and input method.

[0762] The server collects relevant information via the network based on the received travel preference and sentiment data. This includes a wide range of data, such as transportation schedules, facility opening hours, and event information. Based on the collected information, the server automatically generates a travel plan using a generation tool, and this plan is adjusted according to the user's sentiment.

[0763] This process sends the generated travel plan to the device and displays it to the user. The user can review the travel plan and request modifications as needed. The server then re-analyzes the new sentiment data and provides a regenerated plan. This interactive process makes it possible to provide the user with the best possible travel experience.

[0764] Based on the finalized travel plan, the server then makes reservations for necessary facilities and activities. The reservation process also takes emotional data into consideration to provide personalized service and instantly notifies the user of reservation details on their device.

[0765] For example, if a user enters a travel preference for relaxation, the sentiment analysis tool will detect elements of relaxation and generate a plan that incorporates quiet museums and parks. It can also collect additional information through prompts such as "How are you feeling today?" or "What kind of music do you like?" based on the user's input.

[0766] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0767] Step 1:

[0768] The user enters their travel preferences using a terminal. The user enters information such as destination, date and time, and desired activities, and the terminal sends this input to a sentiment analysis system. The input information is processed by the sentiment analysis system as text data.

[0769] Step 2:

[0770] The terminal activates the emotion analysis system and analyzes emotional data from the user's input content and input method. At this stage, a generative AI model is used to analyze the user's emotions as text data and generate emotion tags (e.g., relaxed, excited). The analysis results are sent to the server.

[0771] Step 3:

[0772] The server receives travel preference and sentiment data and uses collection methods to gather relevant information extensively over the network. This collection includes transportation information, facility opening hours, and event information. The server retrieves the information in HTML or JSON format and stores it in a database.

[0773] Step 4:

[0774] The server uses a generation method to automatically generate travel plans based on collected information and sentiment data. A generation AI model is used to select tourist destinations and events that are appropriate to the user's emotions. The generated plans are prepared as text or visual data.

[0775] Step 5:

[0776] The server sends the generated travel plan to the device, which then presents it to the user. The user can review the travel plan and request changes or modifications. The plan is displayed dynamically according to the UI.

[0777] Step 6:

[0778] When a user submits a request to modify a travel plan, the terminal sends the new sentiment data back to the server. The server restarts the generation mechanism to construct the regenerated plan and generates the new plan.

[0779] Step 7:

[0780] The server uses booking methods to reserve necessary facilities and activities based on the final plan and sentiment data. This booking process may involve integration with external services via APIs. Booking details are notified to the device.

[0781] 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.

[0782] 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.

[0783] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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."

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0802] The following is further disclosed regarding the embodiments described above.

[0803] (Claim 1)

[0804] An input method for users to enter their travel preferences,

[0805] A collection means that collects relevant information via a network based on the travel preferences entered by the input means,

[0806] A generation method that automatically generates travel plans using collected information,

[0807] A display means for presenting the travel plan generated by the generation means to the user,

[0808] A reservation method for making restaurant and activity reservations based on the aforementioned travel plan,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, characterized in that it takes into account transportation schedules and facility operating hours when generating the aforementioned travel plan.

[0812] (Claim 3)

[0813] The system according to claim 1, characterized in that the display means receives a request to modify the travel plan presented, and the generation means presents a regenerated travel plan.

[0814] "Example 1"

[0815] (Claim 1)

[0816] An input method for users to enter their travel preferences,

[0817] A collection means that collects relevant information via an information network based on the travel preferences entered by the input means,

[0818] A generation method for automatically generating travel plans using collected information,

[0819] A display means for presenting the travel plan generated by the generation means to the user,

[0820] A reservation method for making reservations for restaurants and activities based on the aforementioned travel plan,

[0821] An input means for the user to input a request for modification to the presented travel plan,

[0822] A generation means for regenerating the travel plan based on the aforementioned modification request,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, characterized in that, when generating the aforementioned travel plan, it takes into account public transportation timetables and facility operating information to optimize an efficient order of visits.

[0826] (Claim 3)

[0827] The system according to claim 1, characterized in that the reservation means automatically makes online reservations through the system in accordance with the user's travel plan and provides details of the reservations.

[0828] "Application Example 1"

[0829] (Claim 1)

[0830] An input method for users to enter their purchase requests,

[0831] A collection means that collects relevant information via a communication network based on the purchase request entered by the input means,

[0832] A generation method that automatically generates a user's purchasing plan using collected information,

[0833] A display means for presenting the purchase plan generated by the generation means to the user,

[0834] A reservation means for managing retail store reservations and promotional information based on the aforementioned purchasing plan,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, characterized in that it takes into account the operating hours and promotional information of retail stores when generating the aforementioned purchasing plan.

[0838] (Claim 3)

[0839] The system according to claim 1, characterized in that the display means receives a request to modify the purchase plan presented, and the generation means presents a regenerated purchase plan.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] An input method in which users enter their travel preferences and emotional data,

[0843] A collection means that collects information via a network based on travel preferences and emotional data entered by the aforementioned input means,

[0844] A means for automatically generating travel plans using collected information and emotion data, and generating travel plans optimized according to emotions,

[0845] A display means that dynamically adjusts the presentation order of the travel plans generated by the generation means based on the user's emotional data and presents them accordingly.

[0846] A reservation method that makes a reservation based on the aforementioned travel plan and notifies the user of the details thereof,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, characterized in that it takes into account transportation time data and facility operating time data when generating the aforementioned travel plan.

[0850] (Claim 3)

[0851] The system according to claim 1, characterized in that the display means receives a request to modify the travel plan presented, and the generation means presents a travel plan regenerated using emotion data.

[0852] "Application example 2 when combining with an emotional engine"

[0853] (Claim 1)

[0854] An input method for users to enter their travel preferences,

[0855] A collection means that collects relevant information via a network based on the travel preferences entered by the input means,

[0856] A generation method that automatically generates travel plans using collected information,

[0857] A display means for presenting the travel plan generated by the generation means to the user,

[0858] A reservation method for booking facilities and activities based on the aforementioned travel plan,

[0859] A sentiment analysis method that analyzes emotions based on user input and reflects them in the travel plan,

[0860] An internal environment adjustment means that adjusts the environment inside the automobile based on the emotional data acquired by the aforementioned emotional analysis means,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, characterized in that it takes into account transportation schedules and facility operating hours when generating the aforementioned travel plan.

[0864] (Claim 3)

[0865] The system according to claim 1, characterized in that the display means receives a request to modify the travel plan presented, and the generation means presents a regenerated travel plan. [Explanation of symbols]

[0866] 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. An input method for users to enter their travel preferences, A collection means that collects relevant information via a network based on the travel preferences entered by the input means, A generation method that automatically generates travel plans using collected information, A display means for presenting the travel plan generated by the generation means to the user, A reservation method for making restaurant and activity reservations based on the aforementioned travel plan, A system that includes this.

2. The system according to claim 1, characterized in that it takes into account transportation schedules and facility operating hours when generating the aforementioned travel plan.

3. The system according to claim 1, characterized in that the display means receives a request to modify the travel plan presented, and the generation means presents the regenerated travel plan.

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