system

Abstract

We provide the system. [Solution] A means of receiving multiple travel-related requests from users, A means for analyzing information regarding the destination and target activity based on the aforementioned preference information, A means for calculating the shortest travel path based on the analyzed information, A means of automatically generating a travel schedule based on calculation results, A means for providing the automatically generated schedule to the user, 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] There is a problem that individual travelers and novice travelers cannot efficiently plan a schedule based on their travel purposes and interests. Conventional travel plans require travelers to collect various information and manually create a schedule while considering means of transportation and destinations, which is time-consuming and laborious. Also, when changes occur in the itinerary at the destination, it is difficult to quickly reconstruct the schedule.

Means for Solving the Problems

[0005] This invention provides a system that receives multiple travel-related preference information from users, analyzes that information, and automatically generates an optimal travel schedule. Specifically, it includes a means to analyze destination and activity information based on the user's preference information and calculate the shortest travel route, thereby presenting travelers with an efficient and purpose-oriented schedule and reducing the burden of planning. Furthermore, the schedule is dynamically adjusted according to the user's customization requests, allowing for flexible responses to changes in plans while traveling.

[0006] "User" refers to an individual or group that uses the system to input their travel preferences and receive travel schedule suggestions.

[0007] "Multiple travel preferences" include information necessary for planning a trip, such as the destination, tourist attractions to visit, food preferences, preferred mode of transportation, and specific activities or experiences.

[0008] "Means" refers to the tools, methods, or processes used to achieve a particular purpose or perform a function.

[0009] "Analyzing" refers to the act of analyzing and interpreting collected data to extract information relevant to a specific purpose.

[0010] "Calculating the shortest travel path" means determining the route that minimizes the distance from a specified starting point to a destination, based on time or efficiency.

[0011] A "travel schedule" refers to a plan that organizes the destinations, planned activities, means of transportation, and meal plans for the duration of a trip on a timeline.

[0012] "Automatic generation" refers to a process where a system creates results using a mechanism based on given data and conditions, without requiring human input or operation.

[0013] A "customization request" refers to a request from a user to change or adjust the standard suggestions according to their own needs and preferences.

[0014] A "database" refers to an organized collection of data built to efficiently store, retrieve, and manage information. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

[0017] First, the language 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), etc.

[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 and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[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] This invention is a system that efficiently supports users in planning their trips. Based on the user's input preferences, it calculates the shortest travel route and automatically generates a travel schedule.

[0037] First, the user accesses the system through their device and enters information related to their travel plan. This information includes destination, places to visit, activities of interest, types of meals, and preferred modes of transportation.

[0038] Next, the terminal sends this input information as data to the server. The server analyzes the received information to identify travel destinations and activity details. In this process, natural language processing technology is used to understand the user's preferences in detail.

[0039] Based on the analyzed information, the server retrieves the latest geographical and traffic information from its database. Furthermore, it calculates the shortest travel route that best suits the user's specified conditions based on the collected data. This uses a routing algorithm that takes into account travel time between destinations and the efficiency of the means of transport.

[0040] Based on the calculated travel route, the server generates a travel schedule according to the user's preferences. For example, it might include visiting tourist attractions in the morning, suggesting a preferred type of restaurant for lunch, and visiting another tourist spot in the afternoon. This allows users to make the most of their time and enjoy their trip.

[0041] If a user wishes to change their schedule, they can make a request using the chatbot function on their device. The server receives this request and recalculates the schedule and responds accordingly. In this way, the system can accommodate user customization requests and flexibly adjust the itinerary.

[0042] For example, if a user enters a request such as "Visit a historical shrine in the morning, enjoy sushi at lunchtime, and visit an art museum in the afternoon," the system will provide an optimal schedule tailored to this request. This allows travelers to have more flexibility in their planning at their destination, resulting in a stress-free travel experience.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses a terminal to enter travel request information. This includes the destination, planned tourist attractions, desired activities, dietary preferences, and mode of transportation.

[0046] Step 2:

[0047] The terminal sends the information entered by the user to the server. The data is structured in a format such as JSON to ensure consistency.

[0048] Step 3:

[0049] The server uses the received data to analyze the input information using natural language processing techniques. It identifies destination and activity categories and extracts the necessary information.

[0050] Step 4:

[0051] Based on the analyzed data, the server accesses an internal database to retrieve detailed information about destinations and activities. Furthermore, it obtains the latest traffic and event information through third-party APIs.

[0052] Step 5:

[0053] The server calculates the shortest travel route, taking into account the user's preferred mode of transportation and traffic conditions. It uses digital map information and routing algorithms to derive the most efficient route.

[0054] Step 6:

[0055] The server creates a travel schedule based on the travel route and user requests. The schedule is designed to maximize time by taking into account the time spent at each planned location and travel time.

[0056] Step 7:

[0057] The server sends the generated schedule to the terminal, where the user reviews it. The schedule is presented in an interactive format, allowing for responses to user inquiries and requests for changes.

[0058] Step 8:

[0059] Users can ask questions about or change their schedules through the chatbot function. The server then generates an updated schedule with the necessary changes and provides it to the user.

[0060] (Example 1)

[0061] 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."

[0062] Conventional travel planning support systems have difficulty automatically generating travel plans that accurately reflect the diverse preferences of users, and they are also insufficient in responding to user requests for customization. As a result, users often spend a lot of time and effort on planning, leading to a stressful travel experience.

[0063] 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.

[0064] In this invention, the server includes means for receiving multiple travel-related preference information from a user, means for analyzing the preference information using natural language processing technology to identify information regarding destinations and activities, means for acquiring geographical and traffic information from an external information management device based on the analyzed information, means for calculating the shortest travel route, means for generating a travel plan, and means for dynamically modifying the plan based on the user's modification requests. This enables the automatic generation and modification of efficient and flexible travel plans tailored to the user's individual requests and conditions.

[0065] "Preference information" refers to information about the user's specific travel destinations, places they wish to visit, activities they are interested in, and preferred types of meals and modes of transportation.

[0066] "Natural language processing technology" refers to techniques that enable computers to understand and analyze human language, and includes methods for extracting meaning from text data.

[0067] "External information management device" refers to other systems or services that provide data such as geographic information and traffic information.

[0068] "Geographic information" refers to various location information and map data related to travel planning.

[0069] "Traffic information" refers to information such as the operating status, travel time, and route information for various modes of transportation.

[0070] The "shortest travel route" refers to the route that minimizes the time and distance required when traveling between multiple destinations.

[0071] A "travel plan" refers to the itinerary and itinerary of a trip, which are constructed based on the user's preferences.

[0072] A "recording device" refers to data storage used to save user input information and analysis results, thereby improving the user's future convenience.

[0073] The embodiment of this invention is configured as a system that streamlines the user's travel planning process and provides customization capabilities. This system mainly consists of a server, a terminal, and a user interface.

[0074] First, the user uses a terminal to input desired information for their travel plan into the system. This includes information such as places they want to visit, activities they are interested in, food preferences, and preferred modes of transportation. The terminal then transmits the user's input information to the server via a secure communication protocol. Protocols such as HTTPS are often used for this communication.

[0075] Next, the server analyzes the received information. Natural language processing (NLP) techniques are used for the analysis, extracting important keywords and phrases from the user's desired information. This makes it possible to understand in detail what the user specifically wants.

[0076] Subsequently, the server acquires geographical and traffic information from an external information management device based on the analysis results. This process utilizes map data provision services and traffic operation information APIs. Based on this information, the server calculates the shortest travel route best suited to the user's conditions.

[0077] Based on these calculation results, the server generates a travel plan. Possible algorithms used include Dijkstra's algorithm and A-STAR algorithm. The plan incorporates elements such as schedules for each destination and recommended dining locations. This plan is designed to optimize the user experience using a generative AI model.

[0078] The generated travel plan is presented to the user via a terminal. The user reviews this plan and, if necessary, sends a change request from the terminal. The server recalculates the plan based on this request and provides the revised schedule.

[0079] A concrete example of a prompt message would be, "Please input the places the user wants to visit in the morning and the type of lunch they want to have, and suggest activities for the afternoon." This input can be used to generate flexible plans for the AI ​​model. Overall, this system helps to create a stress-free travel planning experience for the user.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uses a device to input desired information for their travel plan. This data includes cities and places they wish to visit, activities of interest, types of food they want to eat, and preferred modes of transportation. The device formats this information and sends it to the server as data packets. During this process, an input form is displayed on the user interface, and data is collected as the user enters their desired information.

[0083] Step 2:

[0084] The server analyzes data packets received from the terminal. In this process, natural language processing techniques are used to extract keywords included in the user's preferences. The input data is in text format, and after analysis, it is output as structured data. Specifically, the server applies an NLP model and performs entity recognition to identify each preference category (e.g., location, activity).

[0085] Step 3:

[0086] The server retrieves relevant geographic and traffic information from an external information management device based on the analysis results. It uses the analyzed data as input and receives JSON-formatted information obtained from an API as output. Specifically, the server sends requests to external map data services and traffic data services and stores the information received in the response in a database.

[0087] Step 4:

[0088] The server calculates the shortest travel route using the acquired information. At this stage, it executes an algorithm to find the most efficient travel route based on the acquired geographical and traffic information. The input data is already acquired information, and the output is optimal route information including the order of visits, travel distance, and time. A routing algorithm (e.g., optimal route algorithm) runs on the server and the calculation is performed.

[0089] Step 5:

[0090] The server generates a travel plan based on the calculated travel route. A generation AI model is used to output a schedule that best reflects the user's preferences. Input includes calculated data and user preference information, and output is a detailed schedule broken down by time slot. The generated plan is optimized to ensure the user can enjoy their trip without stress.

[0091] Step 6:

[0092] The terminal displays the generated travel plan to the user. The schedule details are presented in a list format on the on-screen interface, allowing the user to see the overall flow. At this stage, the user is provided with the ability to review the schedule and enter adjustment requests as needed.

[0093] Step 7:

[0094] If a user wishes to change their schedule, they re-enter their desired information via their terminal and send a change request to the server. The server receives this request and recalculates the schedule based on the new conditions entered. In this process, steps 3 through 5 are repeated, and an updated travel plan is created as output.

[0095] Step 8:

[0096] The server sends the final travel plan to the terminal, allowing the user to review the optimized schedule. This prepares the user to execute their trip based on the new schedule. The generated plan is provided in digital format and is accessible to the user at any time.

[0097] (Application Example 1)

[0098] 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."

[0099] In recent years, the demand for food delivery has increased, making efficient delivery routes and precise delivery times particularly important. However, many delivery systems struggle to cope with congested traffic and the need for time-specific deliveries, leading to decreased customer satisfaction. Another challenge is the lack of flexible adjustment mechanisms to optimize deliveries.

[0100] 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.

[0101] In this invention, the server includes means for receiving multiple preference data regarding food delivery from a user, means for analyzing information regarding the delivery destination and selected food items based on the preference data, and means for calculating the shortest travel route based on the analyzed information. This enables the provision of efficient and flexible food delivery.

[0102] A "user" is an individual or group that uses a system or service and presents specific requests or desires.

[0103] "Food delivery" refers to the process and series of activities involved in transporting selected food items to a designated location.

[0104] "Desired data" refers to a collection of specific delivery conditions and food-related information that users provide to the system.

[0105] "Delivery destination" refers to the geographical location where the food product is ultimately delivered.

[0106] "Selected food items" refer to food or beverage items chosen by users based on specific criteria.

[0107] "Analysis" is the process of understanding details based on received information and revealing relationships and characteristics.

[0108] The "shortest travel route" is the route calculated to minimize travel time and distance between the starting point and the destination.

[0109] "Calculation" refers to performing necessary calculations to derive a specific result or value.

[0110] A "schedule" is a plan of time and sequence, indicating a predetermined flow of activities.

[0111] This invention relates to a system for efficiently delivering food, providing an optimal delivery plan for "desired data" by processing and exchanging data among three parties: the user, the terminal, and the server. First, the user inputs information about a specific food item and the desired delivery destination via a smartphone or similar device. This input information is sent to a server built on the cloud. The server analyzes the information and calculates the "shortest travel route" that is most suitable for the desired data. This calculation uses services that can obtain geographical information, such as the Google® Maps API, and various software libraries, including the Python request library. Based on the analysis results, the server constructs an optimal delivery schedule and returns that schedule to the user's terminal. At this point, if the user wishes to adjust the delivery time or route, the server provides flexible support using a chatbot function. For example, if the user inputs a prompt such as "deliver the pizza within 20 minutes," the system uses a traffic optimization algorithm to present the fastest possible delivery schedule.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user uses a terminal to enter detailed delivery requests. This information includes the type of food, delivery address, and desired delivery time. The resulting input data is in JSON format, containing details about the delivery request.

[0115] Step 2:

[0116] The terminal sends the user's requested data to a cloud-based server. This data transmission is performed using an HTTP POST request, and the input data is passed to the server as delivery request information.

[0117] Step 3:

[0118] The server analyzes the received data and extracts information about the delivery address and food items. This analysis process utilizes Python's natural language processing library to format the information as structured data. The output will include specific addresses and data on selected food items.

[0119] Step 4:

[0120] The server uses the Google Maps API to calculate the shortest travel route to the delivery destination. The input is structured address data held by the server, and the output is geographical route information and associated estimated travel time information.

[0121] Step 5:

[0122] The server uses the calculated travel route information to create the optimal delivery schedule. This is real-time operational optimization within the dispatch system, and the generated schedule information is output. This schedule includes the delivery person's movements and estimated arrival times.

[0123] Step 6:

[0124] The server returns the configured delivery schedule to the terminal, providing it to the user. The data is displayed on a visualized interface, allowing users to verify contact information and delivery details.

[0125] Step 7:

[0126] If a user wishes to adjust delivery details or schedule, they send a request to the server via their device. The server then uses a chatbot function to interactively reconfigure the schedule. In this case, the input is the user's new request, and the output is the optimized schedule.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention combines an emotion engine with a system designed to support travel planning, providing efficient and personalized travel suggestions. Users first input their travel preferences via a terminal. This input includes destinations, places to visit, activities to experience, preferred cuisine, and desired modes of transportation.

[0129] Next, the terminal sends the user's input data to the server. This data, including emotional nuances, is structured in text format. The server analyzes the received information to identify destinations and activity options. At this time, natural language processing techniques and sentiment analysis algorithms are combined to recognize emotions from the wording and context entered by the user.

[0130] The server uses an emotion engine to identify the user's emotional state, such as joy, excitement, calmness, or anxiety, and can then tailor travel and activity options to the user's mood. For example, if the user indicates they want to relax, the system will suggest travel plans that include quiet beaches or hot springs.

[0131] Furthermore, the server calculates the shortest travel route based on the analyzed sentiment data and arranges tourist destinations and activities in an order that is optimal for the user's mood. This allows the system to automatically generate an efficient travel schedule. The generated schedule is sent to the terminal and displayed in an interface accessible to the user.

[0132] Users can use the chatbot function on their device to ask questions about or request adjustments to their travel schedule. For example, if a user requests "more active activities," the server will immediately restructure the schedule, taking sentiment data into consideration, and update the suggestions.

[0133] As a concrete example, if a user inputs "I want a memorable experience," the emotion engine would analyze this request as "an experience of inspiration and discovery," and design a plan that includes visits to historical sites and scenic spots. In this way, the introduction of the emotion engine makes travel planning more personalized and more satisfying for the user.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user uses a device to input travel preferences, including destination, places to visit, desired activities, type of food, mode of transportation, and current emotional state. During input, the user is presented with options regarding their emotions, allowing them to select an option or enter their emotions in a free-form field.

[0137] Step 2:

[0138] The terminal sends the entered data to the server. Because the data is structured and includes sentiment information, it is sent in a standardized format (e.g., JSON).

[0139] Step 3:

[0140] The server analyzes the received data. It utilizes natural language processing technology to extract user-requested information and uses an emotion engine to detect and interpret the input emotional information.

[0141] Step 4:

[0142] The server considers the user's emotional state and extracts appropriate destinations, activities, and dining options from the database. Furthermore, it retrieves the latest traffic information and calculates the most efficient travel route. Based on the user's emotional information, it suggests calm environments to users seeking relaxation, for example.

[0143] Step 5:

[0144] The server automatically generates the most suitable travel schedule for the user based on collected and calculated data. The schedule is customized based on the user's emotional state, and the order and content of activities are adjusted accordingly.

[0145] Step 6:

[0146] The server sends the generated schedule to the terminal. The terminal then provides this to the user, displaying the schedule in a visually easy-to-understand format.

[0147] Step 7:

[0148] Users can view the provided schedule on their device. If necessary, they can request schedule changes using the chatbot function, and in doing so, they can also express their desire for further customization based on their emotions.

[0149] Step 8:

[0150] The server receives user feedback and change requests, analyzes new data as needed, and dynamically adjusts the schedule. The adjusted schedule is then sent back to the terminal and presented to the user.

[0151] (Example 2)

[0152] 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".

[0153] Conventional travel planning support systems have struggled to provide suggestions that fully consider users' emotions and individual preferences. As a result, users often fail to achieve the experiences they desire, leading to decreased satisfaction. Furthermore, there were problems with efficiently optimizing and flexibly adjusting travel schedules.

[0154] 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.

[0155] In this invention, the server includes means for receiving multiple pieces of user preference information, means for analyzing the user's emotions using natural language processing technology and making suggestions based on that emotional state, and means for calculating the optimal travel route. This makes it possible to generate and adjust personalized travel suggestions and efficient schedules based on the user's emotions and preferences.

[0156] "Users" refer to individuals who create travel plans and receive suggestions through the system.

[0157] "Desired information" refers to information that includes the user's specific requests regarding travel destinations, activities, transportation, etc.

[0158] "Means of analysis" refers to algorithms and programs that process input information and extract useful insights from it.

[0159] "Sentiment analysis" refers to a technical process that identifies and pinpoints emotional nuances from information entered by the user.

[0160] "Means of identifying options" refers to a function that determines suitable travel destinations and activities based on the user's emotional state.

[0161] "Means for calculating travel routes" refers to a function that calculates an efficient travel route based on geographical information and the user's preferences.

[0162] "Automatic generation method" refers to a function where the system autonomously creates a travel schedule based on input data.

[0163] One embodiment of this invention is a system that personalizes and optimizes travel plans based on the user's emotions. The system mainly consists of a server and terminals and aims to improve the user's travel experience.

[0164] The terminal provides an interface for users to input travel preferences. The interface includes multiple input fields containing information such as destination, places to visit, activities to experience, and preferred meals and transportation. This information is structured in text format and then sent to the server.

[0165] The server receives this input information and performs data analysis using natural language processing (NLP) techniques and sentiment analysis algorithms. NLP techniques understand the user's intentions and emotions from the input request information, while the sentiment analysis algorithm identifies the user's emotional state. Based on this analysis, the system automatically generates an optimal travel plan tailored to the user's emotions.

[0166] For example, if a user inputs "I want to relax in a quiet place," the system can suggest a travel plan that prioritizes options such as beach resorts and hot springs based on the emotion of "relaxation." Furthermore, an example of a prompt using a generative AI model is "User input: I want to relax. How can we generate a travel plan based on this and perform emotion analysis?"

[0167] This invention allows for more personalized travel planning than ever before, enabling suggestions that respond to the user's emotions and natural language expression. As a result, users can enjoy a more satisfying travel experience.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The user enters their travel preferences into the terminal. This input includes information such as destination, places to visit, activities to experience, food preferences, and preferred modes of transportation, all in text format. The terminal receives this information and converts it into a structured data format. Text data is used as input, and the output is generated in a format that the server can receive.

[0171] Step 2:

[0172] The terminal sends structured request information to the server. During this process, the data is encrypted and transmitted over the internet. The input is structured data, and the output is the data transferred to the server.

[0173] Step 3:

[0174] The server performs analysis based on the received data. Using natural language processing technology, it identifies the user's intentions and emotions from the input information. Next, it uses an emotion analysis algorithm to identify the user's emotional state. The input is information about the user's wishes, and the output is data indicating their emotional state.

[0175] Step 4:

[0176] The server generates a suitable travel plan based on emotional states. It automatically generates a travel schedule by selecting and combining activities and destinations suggested by the emotion engine. The input is analyzed emotional data, and the output is a prototype travel plan.

[0177] Step 5:

[0178] The server calculates the shortest travel route and optimizes the travel schedule. This process uses a route calculation algorithm and fine-tunes the plan while considering the user's emotional state. Inputs include the travel plan and geographical information, and the output is the final travel schedule.

[0179] Step 6:

[0180] The completed travel schedule is sent to the device and displayed to the user. The user can review this schedule and request changes as needed through the chatbot function. The input is the final schedule, and the output is the user's feedback.

[0181] Step 7:

[0182] If users provide feedback or request changes, the server readjusts the travel plan, again taking sentiment data into consideration. This ensures that the travel experience best suits the user.

[0183] In this series of steps, computational processing and data analysis are efficiently performed using generative AI models. An example of a prompt is, "If the user indicates a desire to relax, how should an appropriate travel plan be generated?"

[0184] (Application Example 2)

[0185] 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".

[0186] Traditionally, travel planning proposals have been based on users' basic preferences, often without considering their emotions or specific needs. As a result, users have not been provided with the most suitable travel plans, making it difficult to enhance their satisfaction with their travel experience. This invention aims to enable the proposal of travel plans that incorporate emotional information, thereby providing personalized travel plans that match the diverse needs and emotions of users.

[0187] 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.

[0188] In this invention, the server includes means for receiving multiple travel preference information from the user, means for analyzing information about the destination and target activities based on the preference information, and emotion analysis means for determining the suggested experience based on the analyzed information and the user's emotional state. This makes it possible to automatically generate an optimal travel plan that is in line with the user's emotional state.

[0189] A "user" is an individual or legal entity that intends to use this system to plan a trip.

[0190] "Desired information" refers to the specific requests and preferences that users have regarding their travel, such as destinations, activities, meals, and transportation.

[0191] A "destination" refers to a specific geographical location that the user wishes to visit.

[0192] "Target activities" refer to the activities and events that users wish to experience during their trip.

[0193] "Emotion analysis methods" refer to technical techniques for analyzing the type and state of emotions from user input information.

[0194] A "virtual reality device" is a device that provides users with a realistic travel experience through computer-generated simulations.

[0195] A "travel schedule" is the overall plan and timetable for a trip, constructed based on the analysis results.

[0196] A "database" is an information aggregation system used to store and analyze user preferences and emotional state data.

[0197] To implement this invention, a system configuration using a server, terminals, and a virtual reality device is required. The system performs the following processes.

[0198] First, the user uses their device to enter several travel preferences. These preferences include destination, places to visit, activities to participate in, preferred meals, and preferred modes of transportation. This data is then sent to the server as text input.

[0199] Based on the received information, the server analyzes the text using natural language processing technologies (e.g., SpaCy, Google Natural Language API) and further utilizes sentiment analysis models (e.g., Hugging Face) as a means of sentiment analysis. This allows the server to understand the user's emotional state and adjust the suggested travel plan accordingly.

[0200] Based on the analysis results, the server calculates the shortest travel route and automatically generates a travel schedule that takes sentiment analysis into account. The generated schedule is then sent to the user's device.

[0201] Furthermore, using virtual reality equipment, users can virtually experience the suggested travel plan. This feature allows users to experience virtual sightseeing before actually visiting the destination, thereby increasing their anticipation for the trip.

[0202] To give a concrete example, if a user enters "I want an adventure," sentiment analysis interprets this desire as "excitement," and a virtual tour plan of an area with abundant activities is suggested.

[0203] An example of a prompt message would be: "Analyze the user's emotions and suggest the best virtual travel plan. User input: 'I want an adventure.'"

[0204] By implementing this system, users can receive more personalized travel plans based on their emotions and experience them virtually, offering a new travel experience.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] Users enter their travel preferences via their device. This information includes destinations, places to visit, activities to experience, preferred cuisine, and desired modes of transportation. This data is sent to the server in text format.

[0208] Step 2:

[0209] The server analyzes the received request information using natural language processing technology. It receives user text data as input and extracts keywords such as nouns and verbs. This process identifies the destination and target activity.

[0210] Step 3:

[0211] The server uses emotion analysis tools to extract emotional states from the user's desired information. Specifically, it uses an emotion analysis model to analyze the nuances of emotions such as "I want to relax" or "I want to have an adventure." Based on these results, the proposed travel plan is adjusted.

[0212] Step 4:

[0213] The server calculates the shortest travel path, taking into account the analyzed sentiment information. Using destination and activity information as input, it generates efficient path data as output. This process utilizes graph theory algorithms.

[0214] Step 5:

[0215] The server integrates the shortest travel route and sentiment analysis results to automatically generate a travel schedule. Here, tourist destinations and activities are arranged in an order that suits the user's emotions. This schedule will later be used as the basic data for the virtual travel experience.

[0216] Step 6:

[0217] The generated travel schedule is sent to the device. The user can then review the suggested itinerary upon receiving this data.

[0218] Step 7:

[0219] The device or user virtually experiences the travel plan using a virtual reality device. This experience allows the user to visually check tourist attractions and activities at the travel destination in advance.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention is a system that efficiently supports users in planning their trips. Based on the user's input preferences, it calculates the shortest travel route and automatically generates a travel schedule.

[0237] First, the user accesses the system through their device and enters information related to their travel plan. This information includes destination, places to visit, activities of interest, types of meals, and preferred modes of transportation.

[0238] Next, the terminal sends this input information as data to the server. The server analyzes the received information to identify travel destinations and activity details. In this process, natural language processing technology is used to understand the user's preferences in detail.

[0239] Based on the analyzed information, the server retrieves the latest geographical and traffic information from its database. Furthermore, it calculates the shortest travel route that best suits the user's specified conditions based on the collected data. This uses a routing algorithm that takes into account travel time between destinations and the efficiency of the means of transport.

[0240] Based on the calculated travel route, the server generates a travel schedule according to the user's preferences. For example, it might include visiting tourist attractions in the morning, suggesting a preferred type of restaurant for lunch, and visiting another tourist spot in the afternoon. This allows users to make the most of their time and enjoy their trip.

[0241] If a user wishes to change their schedule, they can make a request using the chatbot function on their device. The server receives this request and recalculates the schedule and responds accordingly. In this way, the system can accommodate user customization requests and flexibly adjust the itinerary.

[0242] For example, if a user enters a request such as "Visit a historical shrine in the morning, enjoy sushi at lunchtime, and visit an art museum in the afternoon," the system will provide an optimal schedule tailored to this request. This allows travelers to have more flexibility in their planning at their destination, resulting in a stress-free travel experience.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user uses a terminal to enter travel request information. This includes the destination, planned tourist attractions, desired activities, dietary preferences, and mode of transportation.

[0246] Step 2:

[0247] The terminal sends the information entered by the user to the server. The data is structured in a format such as JSON to ensure consistency.

[0248] Step 3:

[0249] The server uses the received data to analyze the input information using natural language processing techniques. It identifies destination and activity categories and extracts the necessary information.

[0250] Step 4:

[0251] Based on the analyzed data, the server accesses an internal database to retrieve detailed information about destinations and activities. Furthermore, it obtains the latest traffic and event information through third-party APIs.

[0252] Step 5:

[0253] The server calculates the shortest travel route, taking into account the user's preferred mode of transportation and traffic conditions. It uses digital map information and routing algorithms to derive the most efficient route.

[0254] Step 6:

[0255] The server creates a travel schedule based on the travel route and user requests. The schedule is designed to maximize time by taking into account the time spent at each planned location and travel time.

[0256] Step 7:

[0257] The server sends the generated schedule to the terminal, where the user reviews it. The schedule is presented in an interactive format, allowing for responses to user inquiries and requests for changes.

[0258] Step 8:

[0259] Users can ask questions about or change their schedules through the chatbot function. The server then generates an updated schedule with the necessary changes and provides it to the user.

[0260] (Example 1)

[0261] 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."

[0262] Conventional travel planning support systems have difficulty automatically generating travel plans that accurately reflect the diverse preferences of users, and they are also insufficient in responding to user requests for customization. As a result, users often spend a lot of time and effort on planning, leading to a stressful travel experience.

[0263] 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.

[0264] In this invention, the server includes means for receiving multiple travel-related preference information from a user, means for analyzing the preference information using natural language processing technology to identify information regarding destinations and activities, means for acquiring geographical and traffic information from an external information management device based on the analyzed information, means for calculating the shortest travel route, means for generating a travel plan, and means for dynamically modifying the plan based on the user's modification requests. This enables the automatic generation and modification of efficient and flexible travel plans tailored to the user's individual requests and conditions.

[0265] "Preference information" refers to information about the user's specific travel destinations, places they wish to visit, activities they are interested in, and preferred types of meals and modes of transportation.

[0266] "Natural language processing technology" refers to techniques that enable computers to understand and analyze human language, and includes methods for extracting meaning from text data.

[0267] "External information management device" refers to other systems or services that provide data such as geographic information and traffic information.

[0268] "Geographic information" refers to various location information and map data related to travel planning.

[0269] "Traffic information" refers to information such as the operating status, travel time, and route information for various modes of transportation.

[0270] The "shortest travel route" refers to the route that minimizes the time and distance required when traveling between multiple destinations.

[0271] A "travel plan" refers to the itinerary and itinerary of a trip, which are constructed based on the user's preferences.

[0272] A "recording device" refers to data storage used to save user input information and analysis results, thereby improving the user's future convenience.

[0273] The embodiment of this invention is configured as a system that streamlines the user's travel planning process and provides customization capabilities. This system mainly consists of a server, a terminal, and a user interface.

[0274] First, the user uses a terminal to input desired information for their travel plan into the system. This includes information such as places they want to visit, activities they are interested in, food preferences, and preferred modes of transportation. The terminal then transmits the user's input information to the server via a secure communication protocol. Protocols such as HTTPS are often used for this communication.

[0275] Next, the server analyzes the received information. Natural language processing (NLP) techniques are used for the analysis, extracting important keywords and phrases from the user's desired information. This makes it possible to understand in detail what the user specifically wants.

[0276] Subsequently, the server acquires geographical and traffic information from an external information management device based on the analysis results. This process utilizes map data provision services and traffic operation information APIs. Based on this information, the server calculates the shortest travel route best suited to the user's conditions.

[0277] Based on these calculation results, the server generates a travel plan. Possible algorithms used include Dijkstra's algorithm and A-STAR algorithm. The plan incorporates elements such as schedules for each destination and recommended dining locations. This plan is designed to optimize the user experience using a generative AI model.

[0278] The generated travel plan is presented to the user via a terminal. The user reviews this plan and, if necessary, sends a change request from the terminal. The server recalculates the plan based on this request and provides the revised schedule.

[0279] A concrete example of a prompt message would be, "Please input the places the user wants to visit in the morning and the type of lunch they want to have, and suggest activities for the afternoon." This input can be used to generate flexible plans for the AI ​​model. Overall, this system helps to create a stress-free travel planning experience for the user.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user uses the terminal to input the desired information necessary for the travel plan. The data to be input includes the cities or spots to visit, activities of interest, types of food, and means of transportation to be used. The terminal formats this information and sends it to the server as data packets. In this process, an input form is displayed on the user interface, and data is collected by inputting the desired information.

[0283] Step 2:

[0284] The server analyzes the data packets received from the terminal. In this process, keywords included in the user's desires are extracted by natural language processing technology. The input data is in text format and is output as structured data after analysis. As a specific operation, the server applies an NLP model and performs entity recognition to identify each desired category (e.g., location, activity).

[0285] Step 3:

[0286] The server obtains relevant geographical information and traffic information from the external information management device based on the analysis results. Using the analyzed data as input, it receives information in JSON format obtained from the API as output. As a specific operation, the server sends requests to external map data services and traffic data services, and stores the information received in the response in the database.

[0287] Step 4:

[0288] The server calculates the shortest travel route using the acquired information. At this stage, it executes an algorithm to find the most efficient travel route based on the acquired geographical and traffic information. The input data is already acquired information, and the output is optimal route information including the order of visits, travel distance, and time. A routing algorithm (e.g., optimal route algorithm) runs on the server and the calculation is performed.

[0289] Step 5:

[0290] The server generates a travel plan based on the calculated travel route. A generation AI model is used to output a schedule that best reflects the user's preferences. Input includes calculated data and user preference information, and output is a detailed schedule broken down by time slot. The generated plan is optimized to ensure the user can enjoy their trip without stress.

[0291] Step 6:

[0292] The terminal displays the generated travel plan to the user. The schedule details are presented in a list format on the on-screen interface, allowing the user to see the overall flow. At this stage, the user is provided with the ability to review the schedule and enter adjustment requests as needed.

[0293] Step 7:

[0294] If a user wishes to change their schedule, they re-enter their desired information via their terminal and send a change request to the server. The server receives this request and recalculates the schedule based on the new conditions entered. In this process, steps 3 through 5 are repeated, and an updated travel plan is created as output.

[0295] Step 8:

[0296] The server sends the final travel plan to the terminal, allowing the user to review the optimized schedule. This prepares the user to execute their trip based on the new schedule. The generated plan is provided in digital format and is accessible to the user at any time.

[0297] (Application Example 1)

[0298] 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."

[0299] In recent years, the demand for food delivery has increased, making efficient delivery routes and precise delivery times particularly important. However, many delivery systems struggle to cope with congested traffic and the need for time-specific deliveries, leading to decreased customer satisfaction. Another challenge is the lack of flexible adjustment mechanisms to optimize deliveries.

[0300] 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.

[0301] In this invention, the server includes means for receiving multiple preference data regarding food delivery from a user, means for analyzing information regarding the delivery destination and selected food items based on the preference data, and means for calculating the shortest travel route based on the analyzed information. This enables the provision of efficient and flexible food delivery.

[0302] A "user" is an individual or group that uses a system or service and presents specific requests or desires.

[0303] "Food delivery" refers to the process and series of activities involved in transporting selected food items to a designated location.

[0304] "Desired data" refers to a collection of specific delivery conditions and food-related information that users provide to the system.

[0305] "Delivery destination" refers to the geographical location where the food should ultimately be delivered.

[0306] "Selected food" is an item of food or drink selected by the user based on specific conditions.

[0307] "Analysis" is a process of understanding details based on the received information and clarifying relevance and characteristics.

[0308] "Shortest travel route" is a route calculated to minimize travel time and distance between the starting point and the destination.

[0309] "Calculation" means performing necessary calculations to derive specific results or values.

[0310] "Schedule" represents a plan of time and order, indicating the predetermined flow of activities.

[0311] This invention relates to a system for efficiently delivering food, providing an optimal delivery plan for "desired data" through data processing and exchange among three parties: the user, the terminal, and the server. First, the user inputs information about specific food items and the desired delivery destination via a smartphone or similar device. This input information is sent to a server built on the cloud. The server analyzes the information and calculates the "shortest travel route" most suitable for the desired data. This calculation utilizes services capable of obtaining geographical information, such as the Google Maps API, and various software libraries, including Python's request library. Based on the analysis results, the server constructs an optimal delivery schedule and returns it to the user's terminal. At this point, if the user wishes to adjust the delivery time or route, the server provides flexible support using a chatbot function. For example, if the user inputs a prompt such as "deliver the pizza within 20 minutes," the system uses a traffic optimization algorithm to present the fastest possible delivery schedule.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The user uses a terminal to enter detailed delivery requests. This information includes the type of food, delivery address, and desired delivery time. The resulting input data is in JSON format, containing details about the delivery request.

[0315] Step 2:

[0316] The terminal sends the user's requested data to a cloud-based server. This data transmission is performed using an HTTP POST request, and the input data is passed to the server as delivery request information.

[0317] Step 3:

[0318] The server analyzes the received data and extracts information about the delivery address and food items. This analysis process utilizes Python's natural language processing library to format the information as structured data. The output will include specific addresses and data on selected food items.

[0319] Step 4:

[0320] The server uses the Google Maps API to calculate the shortest travel route to the delivery destination. The input is structured address data held by the server, and the output is geographical route information and associated estimated travel time information.

[0321] Step 5:

[0322] The server uses the calculated travel route information to create the optimal delivery schedule. This is real-time operational optimization within the dispatch system, and the generated schedule information is output. This schedule includes the delivery person's movements and estimated arrival times.

[0323] Step 6:

[0324] The server returns the configured delivery schedule to the terminal, providing it to the user. The data is displayed on a visualized interface, allowing users to verify contact information and delivery details.

[0325] Step 7:

[0326] If a user wishes to adjust delivery details or schedule, they send a request to the server via their device. The server then uses a chatbot function to interactively reconfigure the schedule. In this case, the input is the user's new request, and the output is the optimized schedule.

[0327] 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.

[0328] This invention combines an emotion engine with a system designed to support travel planning, providing efficient and personalized travel suggestions. Users first input their travel preferences via a terminal. This input includes destinations, places to visit, activities to experience, preferred cuisine, and desired modes of transportation.

[0329] Next, the terminal sends the user's input data to the server. This data, including emotional nuances, is structured in text format. The server analyzes the received information to identify destinations and activity options. At this time, natural language processing techniques and sentiment analysis algorithms are combined to recognize emotions from the wording and context entered by the user.

[0330] The server uses an emotion engine to identify the user's emotional state, such as joy, excitement, calmness, or anxiety, and can then tailor travel and activity options to the user's mood. For example, if the user indicates they want to relax, the system will suggest travel plans that include quiet beaches or hot springs.

[0331] Furthermore, the server calculates the shortest travel route based on the analyzed sentiment data and arranges tourist destinations and activities in an order that is optimal for the user's mood. This allows the system to automatically generate an efficient travel schedule. The generated schedule is sent to the terminal and displayed in an interface accessible to the user.

[0332] Users can use the chatbot function on their device to ask questions about or request adjustments to their travel schedule. For example, if a user requests "more active activities," the server will immediately restructure the schedule, taking sentiment data into consideration, and update the suggestions.

[0333] As a concrete example, if a user inputs "I want a memorable experience," the emotion engine would analyze this request as "an experience of inspiration and discovery," and design a plan that includes visits to historical sites and scenic spots. In this way, the introduction of the emotion engine makes travel planning more personalized and more satisfying for the user.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The user uses a device to input travel preferences, including destination, places to visit, desired activities, type of food, mode of transportation, and current emotional state. During input, the user is presented with options regarding their emotions, allowing them to select an option or enter their emotions in a free-form field.

[0337] Step 2:

[0338] The terminal sends the entered data to the server. Because the data is structured and includes sentiment information, it is sent in a standardized format (e.g., JSON).

[0339] Step 3:

[0340] The server analyzes the received data. It utilizes natural language processing technology to extract user-requested information and uses an emotion engine to detect and interpret the input emotional information.

[0341] Step 4:

[0342] The server considers the user's emotional state and extracts appropriate destinations, activities, and dining options from the database. Furthermore, it retrieves the latest traffic information and calculates the most efficient travel route. Based on the user's emotional information, it suggests calm environments to users seeking relaxation, for example.

[0343] Step 5:

[0344] The server automatically generates the most suitable travel schedule for the user based on collected and calculated data. The schedule is customized based on the user's emotional state, and the order and content of activities are adjusted accordingly.

[0345] Step 6:

[0346] The server sends the generated schedule to the terminal. The terminal then provides this to the user, displaying the schedule in a visually easy-to-understand format.

[0347] Step 7:

[0348] Users can view the provided schedule on their device. If necessary, they can request schedule changes using the chatbot function, and in doing so, they can also express their desire for further customization based on their emotions.

[0349] Step 8:

[0350] The server receives user feedback and change requests, analyzes new data as needed, and dynamically adjusts the schedule. The adjusted schedule is then sent back to the terminal and presented to the user.

[0351] (Example 2)

[0352] 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".

[0353] Conventional travel planning support systems have struggled to provide suggestions that fully consider users' emotions and individual preferences. As a result, users often fail to achieve the experiences they desire, leading to decreased satisfaction. Furthermore, there were problems with efficiently optimizing and flexibly adjusting travel schedules.

[0354] 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.

[0355] In this invention, the server includes means for receiving multiple pieces of user preference information, means for analyzing the user's emotions using natural language processing technology and making suggestions based on that emotional state, and means for calculating the optimal travel route. This makes it possible to generate and adjust personalized travel suggestions and efficient schedules based on the user's emotions and preferences.

[0356] "Users" refer to individuals who create travel plans and receive suggestions through the system.

[0357] "Desired information" refers to information that includes the user's specific requests regarding travel destinations, activities, transportation, etc.

[0358] "Means of analysis" refers to algorithms and programs that process input information and extract useful insights from it.

[0359] "Sentiment analysis" refers to a technical process that identifies and pinpoints emotional nuances from information entered by the user.

[0360] "Means of identifying options" refers to a function that determines suitable travel destinations and activities based on the user's emotional state.

[0361] "Means for calculating travel routes" refers to a function that calculates an efficient travel route based on geographical information and the user's preferences.

[0362] "Automatic generation method" refers to a function where the system autonomously creates a travel schedule based on input data.

[0363] One embodiment of this invention is a system that personalizes and optimizes travel plans based on the user's emotions. The system mainly consists of a server and terminals and aims to improve the user's travel experience.

[0364] The terminal provides an interface for users to input travel preferences. The interface includes multiple input fields containing information such as destination, places to visit, activities to experience, and preferred meals and transportation. This information is structured in text format and then sent to the server.

[0365] The server receives this input information and performs data analysis using natural language processing (NLP) techniques and sentiment analysis algorithms. NLP techniques understand the user's intentions and emotions from the input request information, while the sentiment analysis algorithm identifies the user's emotional state. Based on this analysis, the system automatically generates an optimal travel plan tailored to the user's emotions.

[0366] For example, if a user inputs "I want to relax in a quiet place," the system can suggest a travel plan that prioritizes options such as beach resorts and hot springs based on the emotion of "relaxation." Furthermore, an example of a prompt using a generative AI model is "User input: I want to relax. How can we generate a travel plan based on this and perform emotion analysis?"

[0367] This invention allows for more personalized travel planning than ever before, enabling suggestions that respond to the user's emotions and natural language expression. As a result, users can enjoy a more satisfying travel experience.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The user enters their travel preferences into the terminal. This input includes information such as destination, places to visit, activities to experience, food preferences, and preferred modes of transportation, all in text format. The terminal receives this information and converts it into a structured data format. Text data is used as input, and the output is generated in a format that the server can receive.

[0371] Step 2:

[0372] The terminal sends structured request information to the server. During this process, the data is encrypted and transmitted over the internet. The input is structured data, and the output is the data transferred to the server.

[0373] Step 3:

[0374] The server performs analysis based on the received data. Using natural language processing technology, it identifies the user's intentions and emotions from the input information. Next, it uses an emotion analysis algorithm to identify the user's emotional state. The input is information about the user's wishes, and the output is data indicating their emotional state.

[0375] Step 4:

[0376] The server generates a suitable travel plan based on emotional states. It automatically generates a travel schedule by selecting and combining activities and destinations suggested by the emotion engine. The input is analyzed emotional data, and the output is a prototype travel plan.

[0377] Step 5:

[0378] The server calculates the shortest travel route and optimizes the travel schedule. This process uses a route calculation algorithm and fine-tunes the plan while considering the user's emotional state. Inputs include the travel plan and geographical information, and the output is the final travel schedule.

[0379] Step 6:

[0380] The completed travel schedule is sent to the device and displayed to the user. The user can review this schedule and request changes as needed through the chatbot function. The input is the final schedule, and the output is the user's feedback.

[0381] Step 7:

[0382] If users provide feedback or request changes, the server readjusts the travel plan, again taking sentiment data into consideration. This ensures that the travel experience best suits the user.

[0383] In this series of steps, computational processing and data analysis are efficiently performed using generative AI models. An example of a prompt is, "If the user indicates a desire to relax, how should an appropriate travel plan be generated?"

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0386] Traditionally, travel planning proposals have been based on users' basic preferences, often without considering their emotions or specific needs. As a result, users have not been provided with the most suitable travel plans, making it difficult to enhance their satisfaction with their travel experience. This invention aims to enable the proposal of travel plans that incorporate emotional information, thereby providing personalized travel plans that match the diverse needs and emotions of users.

[0387] 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.

[0388] In this invention, the server includes means for receiving multiple travel preference information from the user, means for analyzing information about the destination and target activities based on the preference information, and emotion analysis means for determining the suggested experience based on the analyzed information and the user's emotional state. This makes it possible to automatically generate an optimal travel plan that is in line with the user's emotional state.

[0389] A "user" is an individual or legal entity that intends to use this system to plan a trip.

[0390] "Desired information" refers to the specific requests and preferences that users have regarding their travel, such as destinations, activities, meals, and transportation.

[0391] A "destination" refers to a specific geographical location that the user wishes to visit.

[0392] "Target activities" refer to the activities and events that users wish to experience during their trip.

[0393] "Emotion analysis methods" refer to technical techniques for analyzing the type and state of emotions from user input information.

[0394] A "virtual reality device" is a device that provides users with a realistic travel experience through computer-generated simulations.

[0395] A "travel schedule" is the overall plan and timetable for a trip, constructed based on the analysis results.

[0396] A "database" is an information aggregation system used to store and analyze user preferences and emotional state data.

[0397] To implement this invention, a system configuration using a server, terminals, and a virtual reality device is required. The system performs the following processes.

[0398] First, the user uses their device to enter several travel preferences. These preferences include destination, places to visit, activities to participate in, preferred meals, and preferred modes of transportation. This data is then sent to the server as text input.

[0399] Based on the received information, the server analyzes the text using natural language processing technologies (e.g., SpaCy, Google Natural Language API) and further utilizes sentiment analysis models (e.g., Hugging Face) as a means of sentiment analysis. This allows the server to understand the user's emotional state and adjust the suggested travel plan accordingly.

[0400] Based on the analysis results, the server calculates the shortest travel route and automatically generates a travel schedule that takes sentiment analysis into account. The generated schedule is then sent to the user's device.

[0401] Furthermore, using virtual reality equipment, users can virtually experience the suggested travel plan. This feature allows users to experience virtual sightseeing before actually visiting the destination, thereby increasing their anticipation for the trip.

[0402] To give a concrete example, if a user enters "I want an adventure," sentiment analysis interprets this desire as "excitement," and a virtual tour plan of an area with abundant activities is suggested.

[0403] An example of a prompt message would be: "Analyze the user's emotions and suggest the best virtual travel plan. User input: 'I want an adventure.'"

[0404] By implementing this system, users can receive more personalized travel plans based on their emotions and experience them virtually, offering a new travel experience.

[0405] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0406] Step 1:

[0407] Users enter their travel preferences via their device. This information includes destinations, places to visit, activities to experience, preferred cuisine, and desired modes of transportation. This data is sent to the server in text format.

[0408] Step 2:

[0409] The server analyzes the received request information using natural language processing technology. It receives user text data as input and extracts keywords such as nouns and verbs. This process identifies the destination and target activity.

[0410] Step 3:

[0411] The server uses emotion analysis tools to extract emotional states from the user's desired information. Specifically, it uses an emotion analysis model to analyze the nuances of emotions such as "I want to relax" or "I want to have an adventure." Based on these results, the proposed travel plan is adjusted.

[0412] Step 4:

[0413] The server calculates the shortest travel path, taking into account the analyzed sentiment information. Using destination and activity information as input, it generates efficient path data as output. This process utilizes graph theory algorithms.

[0414] Step 5:

[0415] The server integrates the shortest travel route and sentiment analysis results to automatically generate a travel schedule. Here, tourist destinations and activities are arranged in an order that suits the user's emotions. This schedule will later be used as the basic data for the virtual travel experience.

[0416] Step 6:

[0417] The generated travel schedule is sent to the device. The user can then review the suggested itinerary upon receiving this data.

[0418] Step 7:

[0419] The device or user virtually experiences the travel plan using a virtual reality device. This experience allows the user to visually check tourist attractions and activities at the travel destination in advance.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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".

[0436] This invention is a system that efficiently supports users in planning their trips. Based on the user's input preferences, it calculates the shortest travel route and automatically generates a travel schedule.

[0437] First, the user accesses the system through their device and enters information related to their travel plan. This information includes destination, places to visit, activities of interest, types of meals, and preferred modes of transportation.

[0438] Next, the terminal sends this input information as data to the server. The server analyzes the received information to identify travel destinations and activity details. In this process, natural language processing technology is used to understand the user's preferences in detail.

[0439] Based on the analyzed information, the server retrieves the latest geographical and traffic information from its database. Furthermore, it calculates the shortest travel route that best suits the user's specified conditions based on the collected data. This uses a routing algorithm that takes into account travel time between destinations and the efficiency of the means of transport.

[0440] Based on the calculated travel route, the server generates a travel schedule according to the user's preferences. For example, it might include visiting tourist attractions in the morning, suggesting a preferred type of restaurant for lunch, and visiting another tourist spot in the afternoon. This allows users to make the most of their time and enjoy their trip.

[0441] If a user wishes to change their schedule, they can make a request using the chatbot function on their device. The server receives this request and recalculates the schedule and responds accordingly. In this way, the system can accommodate user customization requests and flexibly adjust the itinerary.

[0442] For example, if a user enters a request such as "Visit a historical shrine in the morning, enjoy sushi at lunchtime, and visit an art museum in the afternoon," the system will provide an optimal schedule tailored to this request. This allows travelers to have more flexibility in their planning at their destination, resulting in a stress-free travel experience.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The user uses a terminal to enter travel request information. This includes the destination, planned tourist attractions, desired activities, dietary preferences, and mode of transportation.

[0446] Step 2:

[0447] The terminal sends the information entered by the user to the server. The data is structured in a format such as JSON to ensure consistency.

[0448] Step 3:

[0449] The server uses the received data to analyze the input information using natural language processing techniques. It identifies destination and activity categories and extracts the necessary information.

[0450] Step 4:

[0451] Based on the analyzed data, the server accesses an internal database to retrieve detailed information about destinations and activities. Furthermore, it obtains the latest traffic and event information through third-party APIs.

[0452] Step 5:

[0453] The server calculates the shortest travel route, taking into account the user's preferred mode of transportation and traffic conditions. It uses digital map information and routing algorithms to derive the most efficient route.

[0454] Step 6:

[0455] The server creates a travel schedule based on the travel route and user requests. The schedule is designed to maximize time by taking into account the time spent at each planned location and travel time.

[0456] Step 7:

[0457] The server sends the generated schedule to the terminal, where the user reviews it. The schedule is presented in an interactive format, allowing for responses to user inquiries and requests for changes.

[0458] Step 8:

[0459] Users can ask questions about or change their schedules through the chatbot function. The server then generates an updated schedule with the necessary changes and provides it to the user.

[0460] (Example 1)

[0461] 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."

[0462] Conventional travel planning support systems have difficulty automatically generating travel plans that accurately reflect the diverse preferences of users, and they are also insufficient in responding to user requests for customization. As a result, users often spend a lot of time and effort on planning, leading to a stressful travel experience.

[0463] 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.

[0464] In this invention, the server includes means for receiving multiple travel-related preference information from a user, means for analyzing the preference information using natural language processing technology to identify information regarding destinations and activities, means for acquiring geographical and traffic information from an external information management device based on the analyzed information, means for calculating the shortest travel route, means for generating a travel plan, and means for dynamically modifying the plan based on the user's modification requests. This enables the automatic generation and modification of efficient and flexible travel plans tailored to the user's individual requests and conditions.

[0465] "Preference information" refers to information about the user's specific travel destinations, places they wish to visit, activities they are interested in, and preferred types of meals and modes of transportation.

[0466] "Natural language processing technology" refers to techniques that enable computers to understand and analyze human language, and includes methods for extracting meaning from text data.

[0467] "External information management device" refers to other systems or services that provide data such as geographic information and traffic information.

[0468] "Geographic information" refers to various location information and map data related to travel planning.

[0469] "Traffic information" refers to information such as the operating status, travel time, and route information for various modes of transportation.

[0470] The "shortest travel route" refers to the route that minimizes the time and distance required when traveling between multiple destinations.

[0471] A "travel plan" refers to the itinerary and itinerary of a trip, which are constructed based on the user's preferences.

[0472] A "recording device" refers to data storage used to save user input information and analysis results, thereby improving the user's future convenience.

[0473] The embodiment of this invention is configured as a system that streamlines the user's travel planning process and provides customization capabilities. This system mainly consists of a server, a terminal, and a user interface.

[0474] First, the user uses a terminal to input desired information for their travel plan into the system. This includes information such as places they want to visit, activities they are interested in, food preferences, and preferred modes of transportation. The terminal then transmits the user's input information to the server via a secure communication protocol. Protocols such as HTTPS are often used for this communication.

[0475] Next, the server analyzes the received information. Natural language processing (NLP) techniques are used for the analysis, extracting important keywords and phrases from the user's desired information. This makes it possible to understand in detail what the user specifically wants.

[0476] Subsequently, the server acquires geographical and traffic information from an external information management device based on the analysis results. This process utilizes map data provision services and traffic operation information APIs. Based on this information, the server calculates the shortest travel route best suited to the user's conditions.

[0477] Based on these calculation results, the server generates a travel plan. Possible algorithms used include Dijkstra's algorithm and A-STAR algorithm. The plan incorporates elements such as schedules for each destination and recommended dining locations. This plan is designed to optimize the user experience using a generative AI model.

[0478] The generated travel plan is presented to the user via a terminal. The user reviews this plan and, if necessary, sends a change request from the terminal. The server recalculates the plan based on this request and provides the revised schedule.

[0479] A concrete example of a prompt message would be, "Please input the places the user wants to visit in the morning and the type of lunch they want to have, and suggest activities for the afternoon." This input can be used to generate flexible plans for the AI ​​model. Overall, this system helps to create a stress-free travel planning experience for the user.

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] The user uses a device to input desired information for their travel plan. This data includes cities and places they wish to visit, activities of interest, types of food they want to eat, and preferred modes of transportation. The device formats this information and sends it to the server as data packets. During this process, an input form is displayed on the user interface, and data is collected as the user enters their desired information.

[0483] Step 2:

[0484] The server analyzes data packets received from the terminal. In this process, natural language processing techniques are used to extract keywords included in the user's preferences. The input data is in text format, and after analysis, it is output as structured data. Specifically, the server applies an NLP model and performs entity recognition to identify each preference category (e.g., location, activity).

[0485] Step 3:

[0486] The server retrieves relevant geographic and traffic information from an external information management device based on the analysis results. It uses the analyzed data as input and receives JSON-formatted information obtained from an API as output. Specifically, the server sends requests to external map data services and traffic data services and stores the information received in the response in a database.

[0487] Step 4:

[0488] The server calculates the shortest travel route using the acquired information. At this stage, it executes an algorithm to find the most efficient travel route based on the acquired geographical and traffic information. The input data is already acquired information, and the output is optimal route information including the order of visits, travel distance, and time. A routing algorithm (e.g., optimal route algorithm) runs on the server and the calculation is performed.

[0489] Step 5:

[0490] The server generates a travel plan based on the calculated travel route. A generation AI model is used to output a schedule that best reflects the user's preferences. Input includes calculated data and user preference information, and output is a detailed schedule broken down by time slot. The generated plan is optimized to ensure the user can enjoy their trip without stress.

[0491] Step 6:

[0492] The terminal displays the generated travel plan to the user. The schedule details are presented in a list format on the on-screen interface, allowing the user to see the overall flow. At this stage, the user is provided with the ability to review the schedule and enter adjustment requests as needed.

[0493] Step 7:

[0494] If a user wishes to change their schedule, they re-enter their desired information via their terminal and send a change request to the server. The server receives this request and recalculates the schedule based on the new conditions entered. In this process, steps 3 through 5 are repeated, and an updated travel plan is created as output.

[0495] Step 8:

[0496] The server sends the final travel plan to the terminal, allowing the user to review the optimized schedule. This prepares the user to execute their trip based on the new schedule. The generated plan is provided in digital format and is accessible to the user at any time.

[0497] (Application Example 1)

[0498] 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."

[0499] In recent years, the demand for food delivery has increased, making efficient delivery routes and precise delivery times particularly important. However, many delivery systems struggle to cope with congested traffic and the need for time-specific deliveries, leading to decreased customer satisfaction. Another challenge is the lack of flexible adjustment mechanisms to optimize deliveries.

[0500] 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.

[0501] In this invention, the server includes means for receiving multiple preference data regarding food delivery from a user, means for analyzing information regarding the delivery destination and selected food items based on the preference data, and means for calculating the shortest travel route based on the analyzed information. This enables the provision of efficient and flexible food delivery.

[0502] A "user" is an individual or group that uses a system or service and presents specific requests or desires.

[0503] "Food delivery" refers to the process and series of activities involved in transporting selected food items to a designated location.

[0504] "Desired data" refers to a collection of specific delivery conditions and food-related information that users provide to the system.

[0505] "Delivery destination" refers to the geographical location where the food product is ultimately delivered.

[0506] "Selected food items" refer to food or beverage items chosen by users based on specific criteria.

[0507] "Analysis" is the process of understanding details based on received information and revealing relationships and characteristics.

[0508] The "shortest travel route" is the route calculated to minimize travel time and distance between the starting point and the destination.

[0509] "Calculation" refers to performing necessary calculations to derive a specific result or value.

[0510] A "schedule" is a plan of time and sequence, indicating a predetermined flow of activities.

[0511] This invention relates to a system for efficiently delivering food, providing an optimal delivery plan for "desired data" through data processing and exchange among three parties: the user, the terminal, and the server. First, the user inputs information about specific food items and the desired delivery destination via a smartphone or similar device. This input information is sent to a server built on the cloud. The server analyzes the information and calculates the "shortest travel route" most suitable for the desired data. This calculation utilizes services capable of obtaining geographical information, such as the Google Maps API, and various software libraries, including Python's request library. Based on the analysis results, the server constructs an optimal delivery schedule and returns it to the user's terminal. At this point, if the user wishes to adjust the delivery time or route, the server provides flexible support using a chatbot function. For example, if the user inputs a prompt such as "deliver the pizza within 20 minutes," the system uses a traffic optimization algorithm to present the fastest possible delivery schedule.

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The user uses a terminal to enter detailed delivery requests. This information includes the type of food, delivery address, and desired delivery time. The resulting input data is in JSON format, containing details about the delivery request.

[0515] Step 2:

[0516] The terminal sends the user's requested data to a cloud-based server. This data transmission is performed using an HTTP POST request, and the input data is passed to the server as delivery request information.

[0517] Step 3:

[0518] The server analyzes the received data and extracts information about the delivery address and food items. This analysis process utilizes Python's natural language processing library to format the information as structured data. The output will include specific addresses and data on selected food items.

[0519] Step 4:

[0520] The server uses the Google Maps API to calculate the shortest travel route to the delivery destination. The input is structured address data held by the server, and the output is geographical route information and associated estimated travel time information.

[0521] Step 5:

[0522] The server uses the calculated travel route information to create the optimal delivery schedule. This is real-time operational optimization within the dispatch system, and the generated schedule information is output. This schedule includes the delivery person's movements and estimated arrival times.

[0523] Step 6:

[0524] The server returns the configured delivery schedule to the terminal, providing it to the user. The data is displayed on a visualized interface, allowing users to verify contact information and delivery details.

[0525] Step 7:

[0526] If a user wishes to adjust delivery details or schedule, they send a request to the server via their device. The server then uses a chatbot function to interactively reconfigure the schedule. In this case, the input is the user's new request, and the output is the optimized schedule.

[0527] 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.

[0528] This invention combines an emotion engine with a system designed to support travel planning, providing efficient and personalized travel suggestions. Users first input their travel preferences via a terminal. This input includes destinations, places to visit, activities to experience, preferred cuisine, and desired modes of transportation.

[0529] Next, the terminal sends the user's input data to the server. This data, including emotional nuances, is structured in text format. The server analyzes the received information to identify destinations and activity options. At this time, natural language processing techniques and sentiment analysis algorithms are combined to recognize emotions from the wording and context entered by the user.

[0530] The server uses an emotion engine to identify the user's emotional state, such as joy, excitement, calmness, or anxiety, and can then tailor travel and activity options to the user's mood. For example, if the user indicates they want to relax, the system will suggest travel plans that include quiet beaches or hot springs.

[0531] Furthermore, the server calculates the shortest travel route based on the analyzed sentiment data and arranges tourist destinations and activities in an order that is optimal for the user's mood. This allows the system to automatically generate an efficient travel schedule. The generated schedule is sent to the terminal and displayed in an interface accessible to the user.

[0532] Users can use the chatbot function on their device to ask questions about or request adjustments to their travel schedule. For example, if a user requests "more active activities," the server will immediately restructure the schedule, taking sentiment data into consideration, and update the suggestions.

[0533] As a concrete example, if a user inputs "I want a memorable experience," the emotion engine would analyze this request as "an experience of inspiration and discovery," and design a plan that includes visits to historical sites and scenic spots. In this way, the introduction of the emotion engine makes travel planning more personalized and more satisfying for the user.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The user uses a device to input travel preferences, including destination, places to visit, desired activities, type of food, mode of transportation, and current emotional state. During input, the user is presented with options regarding their emotions, allowing them to select an option or enter their emotions in a free-form field.

[0537] Step 2:

[0538] The terminal sends the entered data to the server. Because the data is structured and includes sentiment information, it is sent in a standardized format (e.g., JSON).

[0539] Step 3:

[0540] The server analyzes the received data. It utilizes natural language processing technology to extract user-requested information and uses an emotion engine to detect and interpret the input emotional information.

[0541] Step 4:

[0542] The server considers the user's emotional state and extracts appropriate destinations, activities, and dining options from the database. Furthermore, it retrieves the latest traffic information and calculates the most efficient travel route. Based on the user's emotional information, it suggests calm environments to users seeking relaxation, for example.

[0543] Step 5:

[0544] The server automatically generates the most suitable travel schedule for the user based on collected and calculated data. The schedule is customized based on the user's emotional state, and the order and content of activities are adjusted accordingly.

[0545] Step 6:

[0546] The server sends the generated schedule to the terminal. The terminal then provides this to the user, displaying the schedule in a visually easy-to-understand format.

[0547] Step 7:

[0548] Users can view the provided schedule on their device. If necessary, they can request schedule changes using the chatbot function, and in doing so, they can also express their desire for further customization based on their emotions.

[0549] Step 8:

[0550] The server receives user feedback and change requests, analyzes new data as needed, and dynamically adjusts the schedule. The adjusted schedule is then sent back to the terminal and presented to the user.

[0551] (Example 2)

[0552] 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."

[0553] Conventional travel planning support systems have struggled to provide suggestions that fully consider users' emotions and individual preferences. As a result, users often fail to achieve the experiences they desire, leading to decreased satisfaction. Furthermore, there were problems with efficiently optimizing and flexibly adjusting travel schedules.

[0554] 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.

[0555] In this invention, the server includes means for receiving multiple pieces of user preference information, means for analyzing the user's emotions using natural language processing technology and making suggestions based on that emotional state, and means for calculating the optimal travel route. This makes it possible to generate and adjust personalized travel suggestions and efficient schedules based on the user's emotions and preferences.

[0556] "Users" refer to individuals who create travel plans and receive suggestions through the system.

[0557] "Desired information" refers to information that includes the user's specific requests regarding travel destinations, activities, transportation, etc.

[0558] "Means of analysis" refers to algorithms and programs that process input information and extract useful insights from it.

[0559] "Sentiment analysis" refers to a technical process that identifies and pinpoints emotional nuances from information entered by the user.

[0560] "Means of identifying options" refers to a function that determines suitable travel destinations and activities based on the user's emotional state.

[0561] "Means for calculating travel routes" refers to a function that calculates an efficient travel route based on geographical information and the user's preferences.

[0562] "Automatic generation method" refers to a function where the system autonomously creates a travel schedule based on input data.

[0563] One embodiment of this invention is a system that personalizes and optimizes travel plans based on the user's emotions. The system mainly consists of a server and terminals and aims to improve the user's travel experience.

[0564] The terminal provides an interface for users to input travel preferences. The interface includes multiple input fields containing information such as destination, places to visit, activities to experience, and preferred meals and transportation. This information is structured in text format and then sent to the server.

[0565] The server receives this input information and performs data analysis using natural language processing (NLP) techniques and sentiment analysis algorithms. NLP techniques understand the user's intentions and emotions from the input request information, while the sentiment analysis algorithm identifies the user's emotional state. Based on this analysis, the system automatically generates an optimal travel plan tailored to the user's emotions.

[0566] For example, if a user inputs "I want to relax in a quiet place," the system can suggest a travel plan that prioritizes options such as beach resorts and hot springs based on the emotion of "relaxation." Furthermore, an example of a prompt using a generative AI model is "User input: I want to relax. How can we generate a travel plan based on this and perform emotion analysis?"

[0567] This invention allows for more personalized travel planning than ever before, enabling suggestions that respond to the user's emotions and natural language expression. As a result, users can enjoy a more satisfying travel experience.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The user enters their travel preferences into the terminal. This input includes information such as destination, places to visit, activities to experience, food preferences, and preferred modes of transportation, all in text format. The terminal receives this information and converts it into a structured data format. Text data is used as input, and the output is generated in a format that the server can receive.

[0571] Step 2:

[0572] The terminal sends structured request information to the server. During this process, the data is encrypted and transmitted over the internet. The input is structured data, and the output is the data transferred to the server.

[0573] Step 3:

[0574] The server performs analysis based on the received data. Using natural language processing technology, it identifies the user's intentions and emotions from the input information. Next, it uses an emotion analysis algorithm to identify the user's emotional state. The input is information about the user's wishes, and the output is data indicating their emotional state.

[0575] Step 4:

[0576] The server generates a suitable travel plan based on emotional states. It automatically generates a travel schedule by selecting and combining activities and destinations suggested by the emotion engine. The input is analyzed emotional data, and the output is a prototype travel plan.

[0577] Step 5:

[0578] The server calculates the shortest travel route and optimizes the travel schedule. This process uses a route calculation algorithm and fine-tunes the plan while considering the user's emotional state. Inputs include the travel plan and geographical information, and the output is the final travel schedule.

[0579] Step 6:

[0580] The completed travel schedule is sent to the device and displayed to the user. The user can review this schedule and request changes as needed through the chatbot function. The input is the final schedule, and the output is the user's feedback.

[0581] Step 7:

[0582] If users provide feedback or request changes, the server readjusts the travel plan, again taking sentiment data into consideration. This ensures that the travel experience best suits the user.

[0583] In this series of steps, computational processing and data analysis are efficiently performed using generative AI models. An example of a prompt is, "If the user indicates a desire to relax, how should an appropriate travel plan be generated?"

[0584] (Application Example 2)

[0585] 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."

[0586] Traditionally, travel planning proposals have been based on users' basic preferences, often without considering their emotions or specific needs. As a result, users have not been provided with the most suitable travel plans, making it difficult to enhance their satisfaction with their travel experience. This invention aims to enable the proposal of travel plans that incorporate emotional information, thereby providing personalized travel plans that match the diverse needs and emotions of users.

[0587] 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.

[0588] In this invention, the server includes means for receiving multiple travel preference information from the user, means for analyzing information about the destination and target activities based on the preference information, and emotion analysis means for determining the suggested experience based on the analyzed information and the user's emotional state. This makes it possible to automatically generate an optimal travel plan that is in line with the user's emotional state.

[0589] A "user" is an individual or legal entity that intends to use this system to plan a trip.

[0590] "Desired information" refers to the specific requests and preferences that users have regarding their travel, such as destinations, activities, meals, and transportation.

[0591] A "destination" refers to a specific geographical location that the user wishes to visit.

[0592] "Target activities" refer to the activities and events that users wish to experience during their trip.

[0593] "Emotion analysis methods" refer to technical techniques for analyzing the type and state of emotions from user input information.

[0594] A "virtual reality device" is a device that provides users with a realistic travel experience through computer-generated simulations.

[0595] A "travel schedule" is the overall plan and timetable for a trip, constructed based on the analysis results.

[0596] A "database" is an information aggregation system used to store and analyze user preferences and emotional state data.

[0597] To implement this invention, a system configuration using a server, terminals, and a virtual reality device is required. The system performs the following processes.

[0598] First, the user uses their device to enter several travel preferences. These preferences include destination, places to visit, activities to participate in, preferred meals, and preferred modes of transportation. This data is then sent to the server as text input.

[0599] Based on the received information, the server analyzes the text using natural language processing technologies (e.g., SpaCy, Google Natural Language API) and further utilizes sentiment analysis models (e.g., Hugging Face) as a means of sentiment analysis. This allows the server to understand the user's emotional state and adjust the suggested travel plan accordingly.

[0600] Based on the analysis results, the server calculates the shortest travel route and automatically generates a travel schedule that takes sentiment analysis into account. The generated schedule is then sent to the user's device.

[0601] Furthermore, using virtual reality equipment, users can virtually experience the suggested travel plan. This feature allows users to experience virtual sightseeing before actually visiting the destination, thereby increasing their anticipation for the trip.

[0602] To give a concrete example, if a user enters "I want an adventure," sentiment analysis interprets this desire as "excitement," and a virtual tour plan of an area with abundant activities is suggested.

[0603] An example of a prompt message would be: "Analyze the user's emotions and suggest the best virtual travel plan. User input: 'I want an adventure.'"

[0604] By implementing this system, users can receive more personalized travel plans based on their emotions and experience them virtually, offering a new travel experience.

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] Users enter their travel preferences via their device. This information includes destinations, places to visit, activities to experience, preferred cuisine, and desired modes of transportation. This data is sent to the server in text format.

[0608] Step 2:

[0609] The server analyzes the received request information using natural language processing technology. It receives user text data as input and extracts keywords such as nouns and verbs. This process identifies the destination and target activity.

[0610] Step 3:

[0611] The server uses emotion analysis tools to extract emotional states from the user's desired information. Specifically, it uses an emotion analysis model to analyze the nuances of emotions such as "I want to relax" or "I want to have an adventure." Based on these results, the proposed travel plan is adjusted.

[0612] Step 4:

[0613] The server calculates the shortest travel path, taking into account the analyzed sentiment information. Using destination and activity information as input, it generates efficient path data as output. This process utilizes graph theory algorithms.

[0614] Step 5:

[0615] The server integrates the shortest travel route and sentiment analysis results to automatically generate a travel schedule. Here, tourist destinations and activities are arranged in an order that suits the user's emotions. This schedule will later be used as the basic data for the virtual travel experience.

[0616] Step 6:

[0617] The generated travel schedule is sent to the device. The user can then review the suggested itinerary upon receiving this data.

[0618] Step 7:

[0619] The device or user virtually experiences the travel plan using a virtual reality device. This experience allows the user to visually check tourist attractions and activities at the travel destination in advance.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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".

[0637] This invention is a system that efficiently supports users in planning their trips. Based on the user's input preferences, it calculates the shortest travel route and automatically generates a travel schedule.

[0638] First, the user accesses the system through their device and enters information related to their travel plan. This information includes destination, places to visit, activities of interest, types of meals, and preferred modes of transportation.

[0639] Next, the terminal sends this input information as data to the server. The server analyzes the received information to identify travel destinations and activity details. In this process, natural language processing technology is used to understand the user's preferences in detail.

[0640] Based on the analyzed information, the server retrieves the latest geographical and traffic information from its database. Furthermore, it calculates the shortest travel route that best suits the user's specified conditions based on the collected data. This uses a routing algorithm that takes into account travel time between destinations and the efficiency of the means of transport.

[0641] Based on the calculated travel route, the server generates a travel schedule according to the user's preferences. For example, it might include visiting tourist attractions in the morning, suggesting a preferred type of restaurant for lunch, and visiting another tourist spot in the afternoon. This allows users to make the most of their time and enjoy their trip.

[0642] If a user wishes to change their schedule, they can make a request using the chatbot function on their device. The server receives this request and recalculates the schedule and responds accordingly. In this way, the system can accommodate user customization requests and flexibly adjust the itinerary.

[0643] For example, if a user enters a request such as "Visit a historical shrine in the morning, enjoy sushi at lunchtime, and visit an art museum in the afternoon," the system will provide an optimal schedule tailored to this request. This allows travelers to have more flexibility in their planning at their destination, resulting in a stress-free travel experience.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The user uses a terminal to enter travel request information. This includes the destination, planned tourist attractions, desired activities, dietary preferences, and mode of transportation.

[0647] Step 2:

[0648] The terminal sends the information entered by the user to the server. The data is structured in a format such as JSON to ensure consistency.

[0649] Step 3:

[0650] The server uses the received data to analyze the input information using natural language processing techniques. It identifies destination and activity categories and extracts the necessary information.

[0651] Step 4:

[0652] Based on the analyzed data, the server accesses an internal database to retrieve detailed information about destinations and activities. Furthermore, it obtains the latest traffic and event information through third-party APIs.

[0653] Step 5:

[0654] The server calculates the shortest travel route, taking into account the user's preferred mode of transportation and traffic conditions. It uses digital map information and routing algorithms to derive the most efficient route.

[0655] Step 6:

[0656] The server creates a travel schedule based on the travel route and user requests. The schedule is designed to maximize time by taking into account the time spent at each planned location and travel time.

[0657] Step 7:

[0658] The server sends the generated schedule to the terminal, where the user reviews it. The schedule is presented in an interactive format, allowing for responses to user inquiries and requests for changes.

[0659] Step 8:

[0660] Users can ask questions about or change their schedules through the chatbot function. The server then generates an updated schedule with the necessary changes and provides it to the user.

[0661] (Example 1)

[0662] 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".

[0663] Conventional travel planning support systems have difficulty automatically generating travel plans that accurately reflect the diverse preferences of users, and they are also insufficient in responding to user requests for customization. As a result, users often spend a lot of time and effort on planning, leading to a stressful travel experience.

[0664] 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.

[0665] In this invention, the server includes means for receiving multiple travel-related preference information from a user, means for analyzing the preference information using natural language processing technology to identify information regarding destinations and activities, means for acquiring geographical and traffic information from an external information management device based on the analyzed information, means for calculating the shortest travel route, means for generating a travel plan, and means for dynamically modifying the plan based on the user's modification requests. This enables the automatic generation and modification of efficient and flexible travel plans tailored to the user's individual requests and conditions.

[0666] "Preference information" refers to information about the user's specific travel destinations, places they wish to visit, activities they are interested in, and preferred types of meals and modes of transportation.

[0667] "Natural language processing technology" refers to techniques that enable computers to understand and analyze human language, and includes methods for extracting meaning from text data.

[0668] "External information management device" refers to other systems or services that provide data such as geographic information and traffic information.

[0669] "Geographic information" refers to various location information and map data related to travel planning.

[0670] "Traffic information" refers to information such as the operating status, travel time, and route information for various modes of transportation.

[0671] The "shortest travel route" refers to the route that minimizes the time and distance required when traveling between multiple destinations.

[0672] A "travel plan" refers to the itinerary and itinerary of a trip, which are constructed based on the user's preferences.

[0673] A "recording device" refers to data storage used to save user input information and analysis results, thereby improving the user's future convenience.

[0674] The embodiment of this invention is configured as a system that streamlines the user's travel planning process and provides customization capabilities. This system mainly consists of a server, a terminal, and a user interface.

[0675] First, the user uses a terminal to input desired information for their travel plan into the system. This includes information such as places they want to visit, activities they are interested in, food preferences, and preferred modes of transportation. The terminal then transmits the user's input information to the server via a secure communication protocol. Protocols such as HTTPS are often used for this communication.

[0676] Next, the server analyzes the received information. Natural language processing (NLP) techniques are used for the analysis, extracting important keywords and phrases from the user's desired information. This makes it possible to understand in detail what the user specifically wants.

[0677] Subsequently, the server acquires geographical and traffic information from an external information management device based on the analysis results. This process utilizes map data provision services and traffic operation information APIs. Based on this information, the server calculates the shortest travel route best suited to the user's conditions.

[0678] Based on these calculation results, the server generates a travel plan. Possible algorithms used include Dijkstra's algorithm and A-STAR algorithm. The plan incorporates elements such as schedules for each destination and recommended dining locations. This plan is designed to optimize the user experience using a generative AI model.

[0679] The generated travel plan is presented to the user via a terminal. The user reviews this plan and, if necessary, sends a change request from the terminal. The server recalculates the plan based on this request and provides the revised schedule.

[0680] A concrete example of a prompt message would be, "Please input the places the user wants to visit in the morning and the type of lunch they want to have, and suggest activities for the afternoon." This input can be used to generate flexible plans for the AI ​​model. Overall, this system helps to create a stress-free travel planning experience for the user.

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1:

[0683] The user uses a device to input desired information for their travel plan. This data includes cities and places they wish to visit, activities of interest, types of food they want to eat, and preferred modes of transportation. The device formats this information and sends it to the server as data packets. During this process, an input form is displayed on the user interface, and data is collected as the user enters their desired information.

[0684] Step 2:

[0685] The server analyzes data packets received from the terminal. In this process, natural language processing techniques are used to extract keywords included in the user's preferences. The input data is in text format, and after analysis, it is output as structured data. Specifically, the server applies an NLP model and performs entity recognition to identify each preference category (e.g., location, activity).

[0686] Step 3:

[0687] The server retrieves relevant geographic and traffic information from an external information management device based on the analysis results. It uses the analyzed data as input and receives JSON-formatted information obtained from an API as output. Specifically, the server sends requests to external map data services and traffic data services and stores the information received in the response in a database.

[0688] Step 4:

[0689] The server calculates the shortest travel route using the acquired information. At this stage, it executes an algorithm to find the most efficient travel route based on the acquired geographical and traffic information. The input data is already acquired information, and the output is optimal route information including the order of visits, travel distance, and time. A routing algorithm (e.g., optimal route algorithm) runs on the server and the calculation is performed.

[0690] Step 5:

[0691] The server generates a travel plan based on the calculated travel route. A generation AI model is used to output a schedule that best reflects the user's preferences. Input includes calculated data and user preference information, and output is a detailed schedule broken down by time slot. The generated plan is optimized to ensure the user can enjoy their trip without stress.

[0692] Step 6:

[0693] The terminal displays the generated travel plan to the user. The schedule details are presented in a list format on the on-screen interface, allowing the user to see the overall flow. At this stage, the user is provided with the ability to review the schedule and enter adjustment requests as needed.

[0694] Step 7:

[0695] If a user wishes to change their schedule, they re-enter their desired information via their terminal and send a change request to the server. The server receives this request and recalculates the schedule based on the new conditions entered. In this process, steps 3 through 5 are repeated, and an updated travel plan is created as output.

[0696] Step 8:

[0697] The server sends the final travel plan to the terminal, allowing the user to review the optimized schedule. This prepares the user to execute their trip based on the new schedule. The generated plan is provided in digital format and is accessible to the user at any time.

[0698] (Application Example 1)

[0699] 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".

[0700] In recent years, the demand for food delivery has increased, making efficient delivery routes and precise delivery times particularly important. However, many delivery systems struggle to cope with congested traffic and the need for time-specific deliveries, leading to decreased customer satisfaction. Another challenge is the lack of flexible adjustment mechanisms to optimize deliveries.

[0701] 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.

[0702] In this invention, the server includes means for receiving multiple preference data regarding food delivery from a user, means for analyzing information regarding the delivery destination and selected food items based on the preference data, and means for calculating the shortest travel route based on the analyzed information. This enables the provision of efficient and flexible food delivery.

[0703] A "user" is an individual or group that uses a system or service and presents specific requests or desires.

[0704] "Food delivery" refers to the process and series of activities involved in transporting selected food items to a designated location.

[0705] "Desired data" refers to a collection of specific delivery conditions and food-related information that users provide to the system.

[0706] "Delivery destination" refers to the geographical location where the food product is ultimately delivered.

[0707] "Selected food items" refer to food or beverage items chosen by users based on specific criteria.

[0708] "Analysis" is the process of understanding details based on received information and revealing relationships and characteristics.

[0709] The "shortest travel route" is the route calculated to minimize travel time and distance between the starting point and the destination.

[0710] "Calculation" refers to performing necessary calculations to derive a specific result or value.

[0711] A "schedule" is a plan of time and sequence, indicating a predetermined flow of activities.

[0712] This invention relates to a system for efficiently delivering food, providing an optimal delivery plan for "desired data" through data processing and exchange among three parties: the user, the terminal, and the server. First, the user inputs information about specific food items and the desired delivery destination via a smartphone or similar device. This input information is sent to a server built on the cloud. The server analyzes the information and calculates the "shortest travel route" most suitable for the desired data. This calculation utilizes services capable of obtaining geographical information, such as the Google Maps API, and various software libraries, including Python's request library. Based on the analysis results, the server constructs an optimal delivery schedule and returns it to the user's terminal. At this point, if the user wishes to adjust the delivery time or route, the server provides flexible support using a chatbot function. For example, if the user inputs a prompt such as "deliver the pizza within 20 minutes," the system uses a traffic optimization algorithm to present the fastest possible delivery schedule.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The user uses a terminal to enter detailed delivery requests. This information includes the type of food, delivery address, and desired delivery time. The resulting input data is in JSON format, containing details about the delivery request.

[0716] Step 2:

[0717] The terminal sends the user's requested data to a cloud-based server. This data transmission is performed using an HTTP POST request, and the input data is passed to the server as delivery request information.

[0718] Step 3:

[0719] The server analyzes the received data and extracts information about the delivery address and food items. This analysis process utilizes Python's natural language processing library to format the information as structured data. The output will include specific addresses and data on selected food items.

[0720] Step 4:

[0721] The server uses the Google Maps API to calculate the shortest travel route to the delivery destination. The input is structured address data held by the server, and the output is geographical route information and associated estimated travel time information.

[0722] Step 5:

[0723] The server uses the calculated travel route information to create the optimal delivery schedule. This is real-time operational optimization within the dispatch system, and the generated schedule information is output. This schedule includes the delivery person's movements and estimated arrival times.

[0724] Step 6:

[0725] The server returns the configured delivery schedule to the terminal, providing it to the user. The data is displayed on a visualized interface, allowing users to verify contact information and delivery details.

[0726] Step 7:

[0727] If a user wishes to adjust delivery details or schedule, they send a request to the server via their device. The server then uses a chatbot function to interactively reconfigure the schedule. In this case, the input is the user's new request, and the output is the optimized schedule.

[0728] 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.

[0729] This invention combines an emotion engine with a system designed to support travel planning, providing efficient and personalized travel suggestions. Users first input their travel preferences via a terminal. This input includes destinations, places to visit, activities to experience, preferred cuisine, and desired modes of transportation.

[0730] Next, the terminal sends the user's input data to the server. This data, including emotional nuances, is structured in text format. The server analyzes the received information to identify destinations and activity options. At this time, natural language processing techniques and sentiment analysis algorithms are combined to recognize emotions from the wording and context entered by the user.

[0731] The server uses an emotion engine to identify the user's emotional state, such as joy, excitement, calmness, or anxiety, and can then tailor travel and activity options to the user's mood. For example, if the user indicates they want to relax, the system will suggest travel plans that include quiet beaches or hot springs.

[0732] Furthermore, the server calculates the shortest travel route based on the analyzed sentiment data and arranges tourist destinations and activities in an order that is optimal for the user's mood. This allows the system to automatically generate an efficient travel schedule. The generated schedule is sent to the terminal and displayed in an interface accessible to the user.

[0733] Users can use the chatbot function on their device to ask questions about or request adjustments to their travel schedule. For example, if a user requests "more active activities," the server will immediately restructure the schedule, taking sentiment data into consideration, and update the suggestions.

[0734] As a concrete example, if a user inputs "I want a memorable experience," the emotion engine would analyze this request as "an experience of inspiration and discovery," and design a plan that includes visits to historical sites and scenic spots. In this way, the introduction of the emotion engine makes travel planning more personalized and more satisfying for the user.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The user uses a device to input travel preferences, including destination, places to visit, desired activities, type of food, mode of transportation, and current emotional state. During input, the user is presented with options regarding their emotions, allowing them to select an option or enter their emotions in a free-form field.

[0738] Step 2:

[0739] The terminal sends the entered data to the server. Because the data is structured and includes sentiment information, it is sent in a standardized format (e.g., JSON).

[0740] Step 3:

[0741] The server analyzes the received data. It utilizes natural language processing technology to extract user-requested information and uses an emotion engine to detect and interpret the input emotional information.

[0742] Step 4:

[0743] The server considers the user's emotional state and extracts appropriate destinations, activities, and dining options from the database. Furthermore, it retrieves the latest traffic information and calculates the most efficient travel route. Based on the user's emotional information, it suggests calm environments to users seeking relaxation, for example.

[0744] Step 5:

[0745] The server automatically generates the most suitable travel schedule for the user based on collected and calculated data. The schedule is customized based on the user's emotional state, and the order and content of activities are adjusted accordingly.

[0746] Step 6:

[0747] The server sends the generated schedule to the terminal. The terminal then provides this to the user, displaying the schedule in a visually easy-to-understand format.

[0748] Step 7:

[0749] Users can view the provided schedule on their device. If necessary, they can request schedule changes using the chatbot function, and in doing so, they can also express their desire for further customization based on their emotions.

[0750] Step 8:

[0751] The server receives user feedback and change requests, analyzes new data as needed, and dynamically adjusts the schedule. The adjusted schedule is then sent back to the terminal and presented to the user.

[0752] (Example 2)

[0753] 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".

[0754] Conventional travel planning support systems have struggled to provide suggestions that fully consider users' emotions and individual preferences. As a result, users often fail to achieve the experiences they desire, leading to decreased satisfaction. Furthermore, there were problems with efficiently optimizing and flexibly adjusting travel schedules.

[0755] 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.

[0756] In this invention, the server includes means for receiving multiple pieces of user preference information, means for analyzing the user's emotions using natural language processing technology and making suggestions based on that emotional state, and means for calculating the optimal travel route. This makes it possible to generate and adjust personalized travel suggestions and efficient schedules based on the user's emotions and preferences.

[0757] "Users" refer to individuals who create travel plans and receive suggestions through the system.

[0758] "Desired information" refers to information that includes the user's specific requests regarding travel destinations, activities, transportation, etc.

[0759] "Means of analysis" refers to algorithms and programs that process input information and extract useful insights from it.

[0760] "Sentiment analysis" refers to a technical process that identifies and pinpoints emotional nuances from information entered by the user.

[0761] "Means of identifying options" refers to a function that determines suitable travel destinations and activities based on the user's emotional state.

[0762] "Means for calculating travel routes" refers to a function that calculates an efficient travel route based on geographical information and the user's preferences.

[0763] "Automatic generation method" refers to a function where the system autonomously creates a travel schedule based on input data.

[0764] One embodiment of this invention is a system that personalizes and optimizes travel plans based on the user's emotions. The system mainly consists of a server and terminals and aims to improve the user's travel experience.

[0765] The terminal provides an interface for users to input travel preferences. The interface includes multiple input fields containing information such as destination, places to visit, activities to experience, and preferred meals and transportation. This information is structured in text format and then sent to the server.

[0766] The server receives this input information and performs data analysis using natural language processing (NLP) techniques and sentiment analysis algorithms. NLP techniques understand the user's intentions and emotions from the input request information, while the sentiment analysis algorithm identifies the user's emotional state. Based on this analysis, the system automatically generates an optimal travel plan tailored to the user's emotions.

[0767] For example, if a user inputs "I want to relax in a quiet place," the system can suggest a travel plan that prioritizes options such as beach resorts and hot springs based on the emotion of "relaxation." Furthermore, an example of a prompt using a generative AI model is "User input: I want to relax. How can we generate a travel plan based on this and perform emotion analysis?"

[0768] This invention allows for more personalized travel planning than ever before, enabling suggestions that respond to the user's emotions and natural language expression. As a result, users can enjoy a more satisfying travel experience.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The user enters their travel preferences into the terminal. This input includes information such as destination, places to visit, activities to experience, food preferences, and preferred modes of transportation, all in text format. The terminal receives this information and converts it into a structured data format. Text data is used as input, and the output is generated in a format that the server can receive.

[0772] Step 2:

[0773] The terminal sends structured request information to the server. During this process, the data is encrypted and transmitted over the internet. The input is structured data, and the output is the data transferred to the server.

[0774] Step 3:

[0775] The server performs analysis based on the received data. Using natural language processing technology, it identifies the user's intentions and emotions from the input information. Next, it uses an emotion analysis algorithm to identify the user's emotional state. The input is information about the user's wishes, and the output is data indicating their emotional state.

[0776] Step 4:

[0777] The server generates a suitable travel plan based on emotional states. It automatically generates a travel schedule by selecting and combining activities and destinations suggested by the emotion engine. The input is analyzed emotional data, and the output is a prototype travel plan.

[0778] Step 5:

[0779] The server calculates the shortest travel route and optimizes the travel schedule. This process uses a route calculation algorithm and fine-tunes the plan while considering the user's emotional state. Inputs include the travel plan and geographical information, and the output is the final travel schedule.

[0780] Step 6:

[0781] The completed travel schedule is sent to the device and displayed to the user. The user can review this schedule and request changes as needed through the chatbot function. The input is the final schedule, and the output is the user's feedback.

[0782] Step 7:

[0783] If users provide feedback or request changes, the server readjusts the travel plan, again taking sentiment data into consideration. This ensures that the travel experience best suits the user.

[0784] In this series of steps, computational processing and data analysis are efficiently performed using generative AI models. An example of a prompt is, "If the user indicates a desire to relax, how should an appropriate travel plan be generated?"

[0785] (Application Example 2)

[0786] 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".

[0787] Traditionally, travel planning proposals have been based on users' basic preferences, often without considering their emotions or specific needs. As a result, users have not been provided with the most suitable travel plans, making it difficult to enhance their satisfaction with their travel experience. This invention aims to enable the proposal of travel plans that incorporate emotional information, thereby providing personalized travel plans that match the diverse needs and emotions of users.

[0788] 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.

[0789] In this invention, the server includes means for receiving multiple travel preference information from the user, means for analyzing information about the destination and target activities based on the preference information, and emotion analysis means for determining the suggested experience based on the analyzed information and the user's emotional state. This makes it possible to automatically generate an optimal travel plan that is in line with the user's emotional state.

[0790] A "user" is an individual or legal entity that intends to use this system to plan a trip.

[0791] "Desired information" refers to the specific requests and preferences that users have regarding their travel, such as destinations, activities, meals, and transportation.

[0792] A "destination" refers to a specific geographical location that the user wishes to visit.

[0793] "Target activities" refer to the activities and events that users wish to experience during their trip.

[0794] "Emotion analysis methods" refer to technical techniques for analyzing the type and state of emotions from user input information.

[0795] A "virtual reality device" is a device that provides users with a realistic travel experience through computer-generated simulations.

[0796] A "travel schedule" is the overall plan and timetable for a trip, constructed based on the analysis results.

[0797] A "database" is an information aggregation system used to store and analyze user preferences and emotional state data.

[0798] To implement this invention, a system configuration using a server, terminals, and a virtual reality device is required. The system performs the following processes.

[0799] First, the user uses their device to enter several travel preferences. These preferences include destination, places to visit, activities to participate in, preferred meals, and preferred modes of transportation. This data is then sent to the server as text input.

[0800] Based on the received information, the server analyzes the text using natural language processing technologies (e.g., SpaCy, Google Natural Language API) and further utilizes sentiment analysis models (e.g., Hugging Face) as a means of sentiment analysis. This allows the server to understand the user's emotional state and adjust the suggested travel plan accordingly.

[0801] Based on the analysis results, the server calculates the shortest travel route and automatically generates a travel schedule that takes sentiment analysis into account. The generated schedule is then sent to the user's device.

[0802] Furthermore, using virtual reality equipment, users can virtually experience the suggested travel plan. This feature allows users to experience virtual sightseeing before actually visiting the destination, thereby increasing their anticipation for the trip.

[0803] To give a concrete example, if a user enters "I want an adventure," sentiment analysis interprets this desire as "excitement," and a virtual tour plan of an area with abundant activities is suggested.

[0804] An example of a prompt message would be: "Analyze the user's emotions and suggest the best virtual travel plan. User input: 'I want an adventure.'"

[0805] By implementing this system, users can receive more personalized travel plans based on their emotions and experience them virtually, offering a new travel experience.

[0806] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0807] Step 1:

[0808] Users enter their travel preferences via their device. This information includes destinations, places to visit, activities to experience, preferred cuisine, and desired modes of transportation. This data is sent to the server in text format.

[0809] Step 2:

[0810] The server analyzes the received request information using natural language processing technology. It receives user text data as input and extracts keywords such as nouns and verbs. This process identifies the destination and target activity.

[0811] Step 3:

[0812] The server uses emotion analysis tools to extract emotional states from the user's desired information. Specifically, it uses an emotion analysis model to analyze the nuances of emotions such as "I want to relax" or "I want to have an adventure." Based on these results, the proposed travel plan is adjusted.

[0813] Step 4:

[0814] The server calculates the shortest travel path, taking into account the analyzed sentiment information. Using destination and activity information as input, it generates efficient path data as output. This process utilizes graph theory algorithms.

[0815] Step 5:

[0816] The server integrates the shortest travel route and sentiment analysis results to automatically generate a travel schedule. Here, tourist destinations and activities are arranged in an order that suits the user's emotions. This schedule will later be used as the basic data for the virtual travel experience.

[0817] Step 6:

[0818] The generated travel schedule is sent to the device. The user can then review the suggested itinerary upon receiving this data.

[0819] Step 7:

[0820] The device or user virtually experiences the travel plan using a virtual reality device. This experience allows the user to visually check tourist attractions and activities at the travel destination in advance.

[0821] 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.

[0822] 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.

[0823] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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."

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0842] The following is further disclosed regarding the embodiments described above.

[0843] (Claim 1)

[0844] A means of receiving multiple travel-related requests from users,

[0845] A means for analyzing information regarding the destination and target activity based on the aforementioned preference information,

[0846] A means for calculating the shortest travel path based on the analyzed information,

[0847] A means of automatically generating a travel schedule based on calculation results,

[0848] A means for providing the automatically generated schedule to the user,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, further comprising means for dynamically adjusting the aforementioned travel schedule based on the user's customization requests.

[0852] (Claim 3)

[0853] The system according to claim 1, comprising a database for storing and analyzing travel preference information and using it for future recommendations.

[0854] "Example 1"

[0855] (Claim 1)

[0856] A means of receiving multiple travel-related requests from users,

[0857] A means for analyzing the aforementioned desired information using natural language processing technology to identify information regarding destinations and activities,

[0858] A means for acquiring geographic information and traffic information from an external information management device based on the analyzed information,

[0859] A means of calculating the shortest travel path based on the acquired information,

[0860] A means for generating a travel plan based on calculation results and user preference information,

[0861] Means for providing the generated travel plan to the user,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising means for dynamically changing the travel plan based on a user's request for modification.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising a recording device that stores desired travel information and analytical information related to the aforementioned travel, and uses this information to make future recommendations.

[0867] "Application Example 1"

[0868] (Claim 1)

[0869] A means of receiving multiple preference data regarding food delivery from users,

[0870] A means for analyzing information regarding the delivery destination and selected food items based on the aforementioned desired data,

[0871] A means for calculating the shortest travel path based on the analyzed information,

[0872] A means of automatically configuring a delivery schedule based on the calculation results,

[0873] Means for providing the automatically configured schedule to the user,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means for dynamically optimizing the delivery schedule based on adjustment requests from users.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising an information storage facility for storing and analyzing the aforementioned food delivery preference data and utilizing it for future recommendations.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] A means of receiving multiple travel-related requests from users,

[0882] A means for analyzing information regarding the destination and target activity based on the aforementioned preference information,

[0883] A means to analyze the user's emotions and identify activity options that correspond to their emotional state,

[0884] A means for calculating the shortest travel path based on the analyzed information,

[0885] A means for automatically generating a travel schedule based on calculation results and the user's emotional state,

[0886] A means for providing the automatically generated schedule to the user,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, further comprising means for dynamically adjusting the aforementioned travel schedule based on the user's customization requests while taking emotional data into consideration.

[0890] (Claim 3)

[0891] The system according to claim 1, comprising a database for storing and analyzing travel preference information and sentiment analysis results, and for use in future recommendations.

[0892] "Application example 2 when combining with an emotional engine"

[0893] (Claim 1)

[0894] A means of receiving multiple travel-related requests from users,

[0895] A means for analyzing information regarding the destination and target activity based on the aforementioned preference information,

[0896] An emotion analysis method that determines the proposed experience based on the analyzed information and the user's emotional state,

[0897] A means for calculating the shortest travel path, taking into account the analyzed emotional information,

[0898] A means of automatically generating a travel schedule based on calculation results,

[0899] A means of providing the user with the automatically generated schedule and providing a virtual tourism experience using a virtual reality device,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, further comprising means for dynamically adjusting the travel schedule based on the user's customization requests and updating the travel plan based on sentiment analysis.

[0903] (Claim 3)

[0904] The system according to claim 1, comprising a database that stores and analyzes travel preference information and user emotional state data for use in future recommendations. [Explanation of symbols]

[0905] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving multiple travel-related requests from users, A means for analyzing information regarding the destination and target activity based on the aforementioned preference information, A means for calculating the shortest travel path based on the analyzed information, A means of automatically generating a travel schedule based on calculation results, A means for providing the automatically generated schedule to the user, A system that includes this.

2. The system according to claim 1, further comprising means for dynamically adjusting the aforementioned travel schedule based on the user's customization requests.

3. The system according to claim 1, comprising a database for storing and analyzing travel preference information and utilizing it for future recommendations.

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