System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting the mood, what the user wants to see, what the user wants to feel, the required time, and the cost, a means for receiving and analyzing the inputted data, a means for generating the optimum plan based on the inputted data, a means for automatically arranging the reservation and the moving means necessary for the generated plan, and a means for presenting the optimum plan to the user and receiving the confirmation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Traditional activity planning is often done manually, which not only requires a lot of effort from the user, but also makes it difficult to design an optimal plan that reflects the user's mood and preferences. In particular, transportation and reservations must be arranged separately for each service, which wastes time and effort. Furthermore, as the user's mood and preferences fluctuate, real-time adaptation is required. This invention aims to efficiently solve these problems and maximize the user experience. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for users to input their mood, what they want to see and experience, the required time, and the cost; a means for receiving and analyzing the input data; a means for generating an optimal plan based on the input data; a means for automatically arranging reservations and transportation required for the generated plan; and a means for presenting the optimal plan to the user and receiving confirmation. This allows users to easily obtain the optimal plan that suits their mood and requests. Furthermore, by generating a plan that takes into account the user's past choices and current trends, it is possible to provide an even more satisfying experience. Furthermore, by linking with APIs of various services to automatically arrange reservations, users can complete all procedures on a single platform.

[0006] The "user input form" is an interface that allows users to enter information such as their mood, what they want to see, what they want to experience, the required time, and the cost.

[0007] "Data analysis" is a process for generating a plan that meets the user's needs based on input data received from the user.

[0008] "Optimal plan generation" is the process of creating a plan that best suits the user's wishes and conditions based on the results of data analysis.

[0009] "Reservation arrangement" is a process of automatically making reservations and arrangements with related service providers based on the generated plan.

[0010] "Transportation arrangement" is a process that automatically arranges the transportation required for the user to carry out the plan.

[0011] "Plan presentation" is the process of displaying the generated plan to the user and obtaining the user's confirmation and approval.

[0012] "Past selections" refers to the user's previous selections and activity history.

[0013] "Trends" refer to current fashions and general trends, and are information that is used as a reference when generating plans based on these trends.

[0014] "API" is an abbreviation for Application Program Interface, a set of rules and procedures that allow different software systems to communicate and exchange data.

[0015] "Integration" means that different systems and services communicate with each other and work together. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] MODE FOR CARRYING OUT THE INVENTION

[0038] The present invention provides a specific embodiment of a system that automatically generates an optimal plan taking into consideration the user's mood, what they want to see and do, the required time, and costs, and even handles reservations and transportation arrangements. This system is composed of terminals, a server, and multiple service provider systems in cooperation with each other.

[0039] User Interface Design

[0040] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time it will take, the cost, etc. This form allows users to easily input and submit their wishes and requirements.

[0041] Data Acquisition and Analysis Module

[0042] The data entered by the user into the form is sent from the device to the server, which then analyzes the data and pulls out multiple candidates from a database that match the user's preferences and criteria. This analysis is done using machine learning algorithms and rule-based filters.

[0043] Optimal plan generation module

[0044] The server then generates the optimal plan based on the analysis results, taking into account the user's preferences and conditions. This plan generation process also references the user's past choices and trend information. For example, if a user inputs "I want to visit an art gallery," the server will evaluate the information on multiple galleries and suggest the most suitable one.

[0045] Reservations and transportation arrangements

[0046] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[0047] Presenting and confirming the plan

[0048] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0049] Specific scenario example

[0050] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[0051] 1. User Input

[0052] The device displays an input form, and the user enters "relaxation," "art gallery," "3 hours," and "under 5,000 yen," then clicks the submit button.

[0053] 2. Data Analysis

[0054] The server receives the data sent from the device and analyzes it for "relaxation," "art gallery," "3 hours," and "under 5,000 yen."

[0055] 3. Generating the optimal plan

[0056] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, and determines that this best suits the user's requirements.

[0057] 4. Reservations and transportation arrangements

[0058] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[0059] 5. Presenting the plan

[0060] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[0061] This specific scenario allows users to easily enjoy the optimal plan according to their mood and needs without having to perform cumbersome manual work.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The device displays a form for the user to input their mood, what they want to see, what they want to experience, the time required, and the cost. The user enters these conditions into the form and clicks the submit button.

[0065] Step 2:

[0066] The device sends the input data, including the user's mood, what they want to see, what they want to experience, the time required, and the cost, to the server.

[0067] Step 3:

[0068] The server receives the data sent from the device, analyzes the received data, and generates a list of the best candidates based on the user's input.

[0069] Step 4:

[0070] The server uses machine learning algorithms and rule-based filters to narrow down the list of candidates, taking into account the user's past choices and current trends to identify the most suitable candidates.

[0071] Step 5:

[0072] The server designs an optimal plan, which includes information on candidate spots, travel time, costs, and access methods.

[0073] Step 6:

[0074] The server calls the APIs of various service provider systems to make the necessary reservations, such as reserving tickets to a museum, reserving a table at a cafe, or arranging transportation.

[0075] Step 7:

[0076] The server receives reservation confirmation information from each service provider and reflects it in the optimal plan. Once all reservations and arrangements are complete, the server generates the final plan data.

[0077] Step 8:

[0078] The server sends the final plan to the device, which displays it to the user, providing details and buttons to confirm or change it.

[0079] Step 9:

[0080] The user confirms the plan and clicks the approve button. The contents of the confirmed plan are reflected.

[0081] Step 10:

[0082] The server receives the user's approval and finalizes all bookings, sending the final confirmation information and tickets to the terminal and notifying the user.

[0083] This processing step allows users to automatically obtain the best plan based on their mood and needs without any hassle.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] In traditional travel and leisure planning, users have to research multiple information sources and manually make reservations and arrangements based on the information. This process is time-consuming and laborious, making it cumbersome for many users. Furthermore, when there are many options, it can be difficult to select the optimal plan. There is a need for a system that can resolve these issues and enable users to efficiently and easily obtain the optimal plan.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes means for a user to input their mood, what they want to see and experience, the required time, and the cost, means for receiving the input data and analyzing it using a machine learning algorithm or a rule-based filter, means for generating an optimal plan based on the analysis results and taking into account the user's past choices and trend information, means for automatically arranging reservations and transportation required for the generated plan via the API of the linked service provider system, and means for presenting the optimal plan to the user and receiving confirmation. This enables users to efficiently generate, confirm, and approve the optimal plan that best suits their preferences without performing cumbersome manual work.

[0089] A "user" is an individual or group that uses this system and is the entity that inputs conditions such as mood, what they want to see, what they want to experience, required time, and cost.

[0090] "Mood" refers to the emotion or experience the user desires to feel, including states such as relaxation or excitement.

[0091] "What you want to see" refers to places, events, or activities you want to experience.

[0092] "Experiences" refers to activities or events that users would like to experience in real life, such as visiting an art gallery or taking a tea break at a cafe.

[0093] "Duration" refers to the period of time that a User intends to spend on a Plan.

[0094] "Cost" refers to the amount that a User has set as a budget for a Plan.

[0095] "Terminal" refers to the device through which a user accesses the system and makes necessary inputs, including computers, smartphones, tablets, etc.

[0096] "Server" refers to a computer system that receives and analyzes data sent from the terminal, generates the optimal plan based on the conditions, and makes the necessary reservations and arrangements.

[0097] "Database" refers to a system for storing and managing various data, including users' past choices and trend information.

[0098] "Machine learning algorithms" refers to statistical techniques and models used in analyzing data, including methods to derive optimal candidates based on user criteria.

[0099] A "rule-based filter" is a method of analyzing data based on predefined rules and selecting information that meets the user's criteria.

[0100] A "plan" refers to a specific proposal that includes places to visit, experiences, travel time, costs, etc., generated based on user input.

[0101] "API" refers to an interface that allows different systems and services to work together, and includes, for example, a reservation system or a means for arranging transportation.

[0102] "Trend information" refers to current trends and data that may interest users, including information that can be used as a reference for generating plans.

[0103] "Service provider system" refers to a system that provides various services (e.g., ticket reservations, transportation, restaurant reservations) to users.

[0104] MODE FOR CARRYING OUT THE INVENTION

[0105] The present invention provides a specific embodiment of a system that automatically generates an optimal plan taking into consideration the user's mood, what they want to see and do, the required time, and costs, and even handles reservations and transportation arrangements. This system is composed of terminals, a server, and multiple service provider systems in cooperation with each other.

[0106] System Configuration

[0107] The system consists of the following main components:

[0108] Device: A device that allows users to input conditions such as mood, what they want to see, what they want to experience, required time, cost, etc. This includes PCs, smartphones, tablets, etc.

[0109] Server: Receives data sent from devices and analyzes and processes the data using machine learning algorithms and rule-based filters, utilizing libraries such as Python's scikit-learn and TENSORFLOW (registered trademark).

[0110] Database: Stores and manages data including users' past choices and trend information.

[0111] Service provider systems: External systems such as ticket reservation systems, transportation systems, and restaurant reservation systems. They are connected via API.

[0112] Processing flow

[0113] 1. Providing a user interface

[0114] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time required, the cost, etc. This form allows users to easily input and submit their wishes and requirements.

[0115] 2. Data collection and analysis

[0116] The data entered by the user into the form is sent from the device to the server, which then analyzes the data and retrieves multiple candidates from a database that match the user's preferences and criteria. This analysis is performed using machine learning algorithms (e.g., scikit-learn) and rule-based filters.

[0117] 3. Generating the optimal plan

[0118] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes and conditions. For example, if a user inputs "I want to visit an art gallery," the server will evaluate information on multiple galleries and suggest the most suitable one.

[0119] 4. Reservations and transportation arrangements

[0120] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems, such as reserving museum tickets, reserving a seat at a cafe, and arranging a taxi.

[0121] 5. Present and confirm the plan

[0122] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0123] Examples of prompt statements

[0124] Below are some examples of specific prompt sentences.

[0125] ---

[0126] User Input

[0127] The device displays a prompt form, and the user enters "relaxation," "art gallery," "3 hours," and "under 5,000 yen," then clicks the submit button.

[0128] Data analysis

[0129] The server receives the data sent from the device and analyzes the options: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[0130] Generating an optimal plan

[0131] The server generates a plan that includes a "special exhibition at the museum" and a "tea break at a nearby cafe."

[0132] Reservations and transportation arrangements

[0133] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[0134] Presenting the plan

[0135] The server sends the generated plan to the device, and the device displays the final plan to the user, who then checks and approves the plan.

[0136] ---

[0137] This specific operation allows the user to easily enjoy the optimal plan according to their mood and desires without having to perform cumbersome manual work.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Step 1: The user enters conditions such as mood, what they want to see, what they want to experience, required time, and cost.

[0140] Input: The user enters conditions such as "relaxation," "art gallery," "3 hours," and "under 5,000 yen" into the input form on their device.

[0141] Processing operations: The terminal receives user input and stores it as internal data.

[0142] Output: The device generates the user input data and is ready to send it to the server.

[0143] Step 2: The device sends the user's input data to the server.

[0144] Input: User input data stored on the device.

[0145] Processing operation: The terminal sends the input data to the server.

[0146] Output: The server receives the user's input data.

[0147] Step 3: The server parses the user's input data.

[0148] Input: User input data received by the server, such as "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[0149] Processing behavior: The server analyzes the data using machine learning algorithms (e.g., scikit-learn) and rule-based filters. This analysis pulls potential matches from the database.

[0150] Output: The analysis results in a list of candidates that match the user's criteria.

[0151] Step 4: The server generates an optimal plan.

[0152] Input: A list of candidates obtained as a result of the analysis.

[0153] Processing operation: The server generates the optimal plan by taking into account the user's past selection history and current trend information. For example, a special exhibition at the National Art Center, Tokyo is selected from among several art galleries, and a tea break at a nearby cafe is included in the plan.

[0154] Output: The generated optimal plan data.

[0155] Step 5: The server automatically arranges any necessary reservations and transportation.

[0156] Input: Generated optimal plan data.

[0157] Processing operation: The server automatically makes necessary reservations (e.g., museum ticket reservations, cafe seat reservations) and transportation (e.g., taxi arrangements) through the APIs of various linked service provider systems.

[0158] Output: Data on completed reservation and transportation arrangements.

[0159] Step 6: The server sends the final plan to the device.

[0160] Input: Data for completed reservations and transportation arrangements.

[0161] Processing operation: The server sends the final plan data to the terminal.

[0162] Output: The device receives the final plan.

[0163] Step 7: The device presents the final plan to the user for confirmation.

[0164] Input: The final plan data sent to the device.

[0165] Processing behavior: The terminal displays the final plan to the user, who reviews the plan and approves it if satisfied.

[0166] Output: If user approval is obtained, the final plan is confirmed.

[0167] (Application example 1)

[0168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0169] With traditional food delivery services, users had to manually search for the food and restaurant that best suited their mood and preferences, and then make individual reservations and arrangements, which was time-consuming and tedious. It was also difficult to find the optimal plan that perfectly matched a specific mood or budget. This made it difficult for users to enjoy the ideal food delivery experience without going through complicated procedures.

[0170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0171] In this invention, the server includes means for a user to input their mood, what they want to see and experience, the required time, and the cost, means for receiving and analyzing the input data, means for generating an optimal plan based on the input data, means for automatically arranging reservations and transportation required for the generated plan, means for generating an optimal food delivery plan based on a specific mood, type of food, the required time, and the budget, and automatically making the required reservations and delivery arrangements, and means for presenting the optimal plan to the user and receiving confirmation. This allows users to easily enjoy the optimal food delivery plan that suits their mood and conditions at the time without having to perform cumbersome manual work.

[0172] "User" refers to any individual who uses the System.

[0173] "Mood" refers to the emotions and psychological state that a user is feeling at that time.

[0174] "What you want to see" refers to objects or places that users want to enjoy visually.

[0175] "What you want to experience" refers to the activities or actions that users actually want to experience.

[0176] "Duration" refers to the amount of time a user wants to spend on a particular activity or project.

[0177] "Cost" means the amount you plan to pay for a particular activity or plan.

[0178] "Means" refers to the methods or methods used to achieve a particular purpose.

[0179] "Input Data" refers to information provided by a user to a system.

[0180] "Analyzing" refers to evaluating and processing input data using machine learning algorithms and rule-based filters.

[0181] The "optimal plan" refers to the proposal that best suits the user's mood and conditions.

[0182] "Automatically arranging reservations and transportation" refers to the process by which the system automatically arranges the necessary reservations and transportation for the user.

[0183] "Generated Plan" refers to the specific proposal created by the system based on the user's input data.

[0184] "Food Delivery Plan" refers to a meal delivery plan generated by a user based on their mood, type of food, time required, and budget.

[0185] "Automatically making reservations and delivery arrangements" refers to the process by which the system automatically makes the reservations and delivery arrangements necessary for food delivery services.

[0186] "Presenting the optimal plan to the user" refers to the process of showing the system-generated proposal to the user and asking for their confirmation.

[0187] "Receiving confirmation" refers to the act of the user approving the proposed plan.

[0188] The present invention is a system that automatically generates an optimal food delivery plan taking into consideration the user's mood, desired experience, required time, cost, etc., and makes the necessary reservations and arrangements. The present invention is configured in cooperation with a terminal, a server, and multiple service provider systems. Specific embodiments are described below.

[0189] User Interface Design

[0190] The terminal provides a form for users to input their mood, desired cuisine, required time, budget, etc. This form allows users to easily input and submit their wishes and requirements.

[0191] Data Acquisition and Analysis Module

[0192] The data entered by the user into the form is sent from the device to a server, which then analyzes the data and pulls out multiple candidates from a database that match the user's preferences and criteria. This analysis is done using machine learning algorithms, such as generative AI models, and rule-based filters.

[0193] Optimal plan generation module

[0194] The server then generates the optimal food delivery plan based on the analysis results, taking into account the user's preferences and conditions. This plan generation process also takes into account the user's past choices and current trend information.

[0195] Reservations and Arrangements

[0196] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., food delivery service APIs), including ordering from restaurants and arranging delivery.

[0197] Presenting and confirming the plan

[0198] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0199] Specific scenario example

[0200] Below is a specific example of a user creating a plan with the conditions "relaxed atmosphere," "Italian food," "30 minutes," and "under 2,000 yen."

[0201] 1. User Input

[0202] The device displays an input form, and the user enters "relaxation," "Italian food," "30 minutes," and "under 2,000 yen," and clicks the submit button.

[0203] 2. Data Analysis

[0204] The server receives the data sent from the device and analyzes the criteria: "relaxation," "Italian food," "30 minutes," and "under 2,000 yen."

[0205] 3. Generating the optimal plan

[0206] The server then uses the analysis results to suggest the most suitable restaurant and menu, and generates a plan for it. For example, it generates a plan for "Pasta Carbonara" that takes into account the delivery time, cost, and other factors.

[0207] 4. Reservations and Arrangements

[0208] The server uses the API of the affiliated food delivery service to send the order to the restaurant and arrange for delivery.

[0209] 5. Presenting the plan

[0210] The server sends the generated plan to the terminal, and the terminal displays the final plan to the user, who then checks and approves the plan.

[0211] An example of a prompt is as follows:

[0212] Mood: Relaxed

[0213] Preferred cuisine: Italian

[0214] Duration: 30 minutes

[0215] Budget: 2000 yen

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] The terminal displays a form for the user to enter their mood, desired cuisine, required time, budget, etc. The user fills in each item and clicks the submit button.

[0219] (Input: User's mood, desired dish, time required, budget. Output: Submitted user data)

[0220] Step 2:

[0221] User data is sent from the device to the server, which then analyzes the received user data.

[0222] (Input: User data sent. Output: Analysis results)

[0223] Step 3:

[0224] The server analyzes the received user data and pulls out candidate restaurants and menus from a database, processing the data using generative AI models and rule-based filters.

[0225] (Input: Analysis requirements (mood, cuisine, time, budget). Output: Candidate list)

[0226] Step 4:

[0227] Based on the analysis results, the server generates a food delivery plan that best suits the user's mood, desired cuisine, required time, and budget. This plan generation process also takes into account the user's past selections and trend information.

[0228] (Input: candidate list. Output: optimal plan)

[0229] Step 5:

[0230] Based on the generated plan, the server automatically makes the necessary reservations and delivery arrangements through the APIs of the various service provider systems it is connected to. Specifically, it uses the API of the food delivery service to send the order to the restaurant and arrange for delivery.

[0231] (Input: Optimal plan. Output: Reservation completion notification)

[0232] Step 6:

[0233] After the necessary reservations and arrangements are completed, the server sends the generated optimal plan to the terminal, which then displays the plan to the user.

[0234] (Input: Reservation completion notification. Output: Plan display)

[0235] Step 7:

[0236] The terminal presents the final plan to the user for review and approval. The user reviews the presented plan and approves it if satisfied.

[0237] (Input: Displayed plan. Output: User approval)

[0238] Step 8:

[0239] Once the user approves the plan, the server will receive the confirmation and begin implementing the plan. Finally, the user can receive food delivery that meets their requirements.

[0240] (Input: User approval. Output: Food delivery)

[0241] The above is a detailed description of the specific operations and processing steps in an actual system.

[0242] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0243] MODE FOR CARRYING OUT THE INVENTION

[0244] This invention is a system that automatically generates an optimal plan by considering the user's mood, what they want to see and experience, the required time, cost, etc., and provides a more accurate plan by combining it with an emotion engine that recognizes the user's emotions. This system is composed of terminals, a server, an emotion engine, and cooperation with multiple service provider systems.

[0245] User Interface Design

[0246] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time it will take, the cost, etc. In this form, users can easily input and submit their wishes and requirements.

[0247] Emotion engine integration

[0248] The emotion engine has the ability to recognize the user's emotions by collecting and analyzing their facial expressions and voice data in real time, in addition to the data input by the user. This allows the emotional data obtained from the user's facial expressions and voice to be reflected in the system and used to generate plans.

[0249] Data Acquisition and Analysis Module

[0250] The data entered by the user into the form and the emotional data recognized by the emotion engine are sent from the device to the server. The server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions. This analysis is performed using machine learning algorithms and rule-based filters.

[0251] Optimal plan generation module

[0252] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. This plan generation process also references the user's past choices and trend information. For example, if a user enters "I want to visit an art gallery" and the emotion engine analyzes that the user is relaxing, the server will suggest a plan centered around relaxing art galleries.

[0253] Reservations and transportation arrangements

[0254] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[0255] Presenting and confirming the plan

[0256] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0257] Specific scenario example

[0258] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[0259] 1. User Input and Emotion Recognition

[0260] The device displays an input form, and the user enters "relax," "art gallery," "3 hours," and "under 5,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "relax."

[0261] 2. Data Analysis

[0262] The server receives the input data and emotion recognition data sent from the device and analyzes the results: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[0263] 3. Generating the optimal plan

[0264] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, determining that this is most suitable for the user's conditions and emotions.

[0265] 4. Reservations and transportation arrangements

[0266] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[0267] 5. Presenting the plan

[0268] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[0269] This specific scenario allows users to easily enjoy the optimal plan according to their mood and desires without having to perform tedious manual tasks. The introduction of the emotion engine makes it possible to provide plans that are more suited to the user's emotions.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The device displays a form for the user to input their mood, what they want to see, what they want to experience, the required time, and the cost. The user enters these conditions into the form and clicks the submit button. The emotion engine also collects and analyzes the user's facial expressions and voice in real time.

[0273] Step 2:

[0274] The device sends the input data and emotional data recognized by the emotion engine to the server. The data includes the user's mood, what they want to see and experience, the required time, the cost, and emotions recognized from facial expressions and voice.

[0275] Step 3:

[0276] The server receives the data sent from the device, analyzes the received data, and generates a list of optimal candidates based on the user's input and emotions.

[0277] Step 4:

[0278] The server uses machine learning algorithms and rule-based filters to narrow down the list of candidates, taking into account the user's past choices and current trend information to identify the most suitable candidates.

[0279] Step 5:

[0280] The server then designs an optimal itinerary, including information on potential spots, travel time, costs, and access methods. For example, it suggests a special exhibition at the National Art Center, Tokyo, taking into account the criteria of "art gallery" and "relaxation."

[0281] Step 6:

[0282] The server calls the APIs of various service provider systems to make the reservations required for the generated plan, such as automatically reserving museum tickets, reserving cafe seats, and arranging transportation.

[0283] Step 7:

[0284] The server receives reservation confirmation information from each service provider and reflects it in the optimal plan. Once all reservations and arrangements are complete, the server generates the final plan data.

[0285] Step 8:

[0286] The server sends the final plan to the device, which displays the plan details to the user and provides confirmation and change buttons.

[0287] Step 9:

[0288] The user checks the plan and clicks the approve button. The confirmed plan contents are sent to the server and are updated accordingly.

[0289] Step 10:

[0290] The server receives the user's approval and finalizes all bookings, sending the final confirmation information and tickets to the terminal and notifying the user.

[0291] This processing step allows users to automatically obtain the optimal plan based on their emotions, wishes, and conditions without any effort. The introduction of an emotion engine makes it possible to provide services that are more adapted to the user's emotions.

[0292] Example 2

[0293] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] There is a demand for a system that allows users to easily generate plans that match their moods and desires, and automatically arranges reservations and transportation based on those plans. However, conventional systems generate plans based only on user input data and are unable to take the user's emotions into account, making it difficult to provide plans that match the user's true desires and circumstances.

[0295] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0296] In this invention, the server includes a means for the user to input their mood, what they want to see and experience, the required time, and the cost, a means for collecting and analyzing the user's facial expression and voice data to recognize their emotions, and a means for receiving and analyzing the input data and the recognized emotion data. This makes it possible to generate a plan that takes into account the emotion data in addition to the user's mood and wishes.

[0297] "User" refers to a person who uses the system to generate plans and make reservations.

[0298] "Mood" refers to information that expresses the emotions and psychological state that a user is feeling.

[0299] "What to see" refers to information that describes the places users want to visit or the activities they want to experience.

[0300] "Things you want to experience" refers to information that describes activities or events that users would actually like to experience.

[0301] "Time required" refers to information that indicates the range of time required for the user to execute the plan.

[0302] "Cost" refers to information that represents the range of amounts a user can pay for a plan.

[0303] "Form" refers to the input screen where users can enter their mood, what they want to see, what they want to experience, the time required, and the cost.

[0304] An "emotion engine" refers to a system that has the ability to collect and analyze a user's facial expressions and voice data to recognize their emotions.

[0305] "Database" refers to a collection of information that stores data needed to generate a plan.

[0306] A "machine learning algorithm" refers to a method that uses data to generate a model for making predictions or classifications.

[0307] "Integration" refers to different systems and services exchanging information with each other through APIs.

[0308] "API (Application Program Interface)" refers to the definitions and protocols that allow different software systems to communicate with each other and utilize their functions.

[0309] A "plan" refers to a specific action plan generated based on the user's wishes, conditions, and emotions.

[0310] "Reservation arrangement" refers to the process of securing the necessary reservations and transportation based on the generated plan.

[0311] "Terminal" refers to an electronic device that a user operates and uses to input their moods and preferences.

[0312] "Server" refers to the computer system that receives data from users, analyzes it, generates plans, and arranges reservations.

[0313] MODE FOR CARRYING OUT THE INVENTION

[0314] This invention is a system that automatically generates an optimal plan by considering the user's mood, what they want to see and experience, the required time, cost, etc., and provides a more accurate plan by combining it with an emotion engine that recognizes the user's emotions. This system is composed of terminals, a server, an emotion engine, and cooperation with multiple service provider systems.

[0315] User Interface Design

[0316] The device provides a form for users to input their mood, what they want to see, what they want to experience, the time required, and the cost. This form allows users to easily input and submit their wishes and requirements. For example, users can communicate their specific wishes and conditions by entering "relaxation," "art gallery," "3 hours," and "under 5,000 yen."

[0317] Emotion engine integration

[0318] The emotion engine has the ability to recognize the user's emotions by collecting and analyzing the user's facial expressions and voice data in real time, in addition to the user's input data. This allows the emotional data obtained from the user's facial expressions and voice to be reflected in the system and used to generate plans. For example, if a user makes input with a relaxed expression, the emotion engine will recognize the emotion as "relaxed."

[0319] Data Acquisition and Analysis Module

[0320] The data entered by the user into the form and the emotional data recognized by the emotion engine are sent from the device to the server. The server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions. This analysis is performed using machine learning algorithms and rule-based filters.

[0321] Optimal plan generation module

[0322] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. This plan generation process also references the user's past choices and trend information. For example, if a user enters "I want to visit an art gallery" and the emotion engine analyzes that the user is relaxing, the server will suggest a plan centered around relaxing art galleries.

[0323] Reservations and transportation arrangements

[0324] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[0325] Presenting and confirming the plan

[0326] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0327] Specific scenario example

[0328] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[0329] Input prompt statement:

[0330] I feel relaxed. I want to visit an art gallery. The total time is less than 3 hours. I would like to plan the cost to be less than 5,000 yen.

[0331] 1. User Input and Emotion Recognition:

[0332] The device displays an input form, and the user enters "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "Relax."

[0333] 2. Data Analysis:

[0334] The server receives the input data and emotion recognition data sent from the device and analyzes the results: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[0335] 3. Generate the optimal plan:

[0336] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, and determines that this best suits the user's requirements.

[0337] 4. Booking and Transportation Arrangements:

[0338] The server automatically reserves museum tickets, cafe reservations, and arranges necessary transportation.

[0339] 5. Present the plan:

[0340] The server sends the generated plan to the terminal, which displays the final plan to the user, who then reviews it and approves it if satisfied.

[0341] This specific scenario allows users to easily enjoy the optimal plan according to their mood and desires without having to perform tedious manual tasks. The introduction of the emotion engine makes it possible to provide plans that are more suited to the user's emotions.

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1: Getting User Input

[0344] What it means: The device displays a form where the user can input their mood, what they want to see and do, how long it will take, and how much it will cost.

[0345] Input: The user enters "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen" into the input form.

[0346] Data processing / calculation: Collects input information and converts it into a data format for sending to the server.

[0347] Output: A data packet containing the input information.

[0348] Specific actions: The user operates the device, enters the required information into the form, and clicks the submit button.

[0349] Step 2: Collecting emotion data

[0350] Specifics: The device uses the built-in camera and microphone to collect the user's facial expression and voice data, which is then analyzed by the emotion engine.

[0351] Input: Facial image captured by a camera and audio data recorded by a microphone.

[0352] Data processing / calculation: The emotion engine analyzes the collected image and audio data and evaluates the user's emotional state.

[0353] Output: Parsed emotion data (e.g. "Relaxed").

[0354] Specific operation: The device captures the user's facial expressions with a camera, records audio with a microphone, and sends this data to the emotion engine.

[0355] Step 3: Sending data

[0356] Specific explanation: The terminal transmits the user's input data and the emotion data recognized by the emotion engine to the server.

[0357] Input: User input data and sentiment data.

[0358] Data processing / calculation: The data is compiled into data packets and sent to the server via the network.

[0359] Output: Data packets sent to the server.

[0360] Specific operation: After the user clicks the send button, the device sends the data to the server.

[0361] Step 4: Data analysis

[0362] Specifically, the server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions.

[0363] Input: User input data and emotion data sent from the device.

[0364] Data processing / calculation: Analyzes data using machine learning algorithms and rule-based filters to extract candidates that match the user's criteria.

[0365] Output: A list of plan candidates.

[0366] Specific operation: The server extracts plan candidates that match the conditions from the database and lists them as analysis results.

[0367] Step 5: Generate an optimal plan

[0368] Specific explanation: Based on the analysis results, the server generates the optimal plan taking into account the user's wishes, conditions, and emotions.

[0369] Input: Parsed user data and plan candidate list.

[0370] Data processing / calculation: The optimal plan is selected by taking into account the user's past choices and trend information.

[0371] Output: Optimal plan.

[0372] Specific operation: The server generates the optimal plan and stores its details in a data format.

[0373] Step 6: Make reservations and arrange transportation

[0374] Specific explanation: Based on the generated plan, the server makes the necessary reservations and arrangements using the APIs of various service provider systems.

[0375] Input: Details of the best plan.

[0376] Data processing / calculation: Sending reservation and arrangement requests through the APIs of various services.

[0377] Output: Reservation confirmation and transportation arrangement information.

[0378] Specific operation: The server uses, for example, the API of a ticket reservation system to reserve tickets for a museum.

[0379] Step 7: Present the generated plan

[0380] Specific explanation: The server sends the final plan and reservation information to the terminal, which displays it to the user.

[0381] Input: Reservation confirmation information and transportation arrangement information.

[0382] Data processing / calculation: Convert the plan into a format that is easy for users to view.

[0383] Output: Final plan information for display.

[0384] Specific operation: The terminal displays the plan information received from the server and provides an interface for the user to review and approve.

[0385] The above is a description of the specific processing steps of the system.

[0386] (Application example 2)

[0387] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0388] Conventional plan generation systems rely on user input data and do not fully consider the user's emotions or momentary mood. Furthermore, the automation of the reservation and arrangement process is limited, placing a heavy burden on the user. Furthermore, in the case of food delivery, the system is insufficient in proposing plans that suit the user's current emotions.

[0389] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input information about their mood, what they want to see and experience, the required time, and the cost; means for analyzing the user's facial expressions and voice to recognize their emotions; means for generating an optimal plan based on the analyzed data and emotional data; means for automatically arranging necessary reservations and transportation based on the generated plan; and means for presenting the optimal plan to the user and receiving confirmation. This makes it possible to provide an optimal plan that takes the user's emotions into consideration and reduce the burden on the user by automating reservation arrangements.

[0390] "User input means" refers to the interface that allows users to input information such as their mood, what they want to see, what they want to experience, the time required, and the cost.

[0391] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions and voice data in real time to recognize the user's current emotions.

[0392] The "means for generating a plan" is an algorithm or system that automatically creates an optimal plan based on the user's input data and emotional data.

[0393] "Means for automatically arranging reservations and transportation" refers to a system that automatically arranges necessary reservations and transportation based on the generated plan. This includes ticket reservations, transportation arrangements, restaurant reservations, etc.

[0394] The "means for presenting a plan to the user and receiving confirmation" refers to an interface that presents the optimal plan to the user visually or audibly, and allows the user to confirm and approve the plan.

[0395] "Means of linking with APIs of various services" refers to an integrated system that uses APIs to exchange information with external services and automatically makes reservations and arrangements.

[0396] "Generative model" refers to a machine learning model or AI algorithm that proposes the optimal plan for the user.

[0397] A "means for generating prompt sentences" is a program or algorithm that automatically generates prompt sentences to be input into a generative AI model.

[0398] This invention is a system that automatically generates an optimal plan by taking into consideration the user's mood, what they want to see and experience, the required time, and costs. Furthermore, by combining it with the functionality of an emotion engine that recognizes the user's emotions, it provides a more accurate plan. This system includes a terminal, a server, an emotion engine, and links with various service provider systems.

[0399] Hardware and Software Configuration

[0400] 1. Device:

[0401] Camera: Used to capture the user's facial expressions and analyze their emotions.

[0402] Microphone: Used to collect your voice data and analyze your voice.

[0403] User interface: Provide a form to input your mood, what you want to see and experience, time required, budget, etc.

[0404] 2. Server:

[0405] Data analysis module: Receives data entered by the user and emotional data recognized by the emotion engine, and analyzes it using machine learning algorithms and rule-based filters.

[0406] Optimal plan generation module: Generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. It also references the user's past choices and trend information.

[0407] Reservation arrangement module: Automatically makes necessary reservations and arrangements through the APIs of various service provider systems.

[0408] 3. Emotion Engine:

[0409] Facial Expression Analysis: Use libraries such as OpenCV to analyze the user's facial expressions and recognize emotions.

[0410] Speech analysis: Use the speech_recognition library to convert speech data into text and analyze user sentiment.

[0411] Program processing overview

[0412] The server receives user input data and emotional data sent from the device in real time and analyzes it in the data analysis module. This analysis uses machine learning algorithms and rule-based filters. Based on the analysis results, the optimal plan generation module generates the optimal plan that suits the user's wishes, conditions, and emotions.

[0413] The reservation arrangement module then automatically makes the necessary reservations and arrangements through the APIs of various service provider systems (e.g., food delivery services). After the optimal plan is generated and all necessary reservations and arrangements are completed, the information is sent to the terminal and the final plan is displayed to the user.

[0414] Specific Examples

[0415] For example, if a user creates a plan with the conditions "I want to eat curry in a relaxed mood," "My budget is under 3,000 yen," and "I want to eat it right away," it will look like this:

[0416] Example prompt sentence:

[0417] A user says they want to eat curry, their budget is under 3000 yen, and they want to eat it immediately. Please suggest the best food delivery plan.

[0418] 1. User Input and Emotion Recognition:

[0419] The device displays an input form, and the user enters "relax," "curry," "immediately," and "under 3,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "relax."

[0420] 2. Data Analysis:

[0421] The server receives the input data and emotion recognition data sent from the device and analyzes the results as "relaxed," "curry," "immediately," and "under 3,000 yen."

[0422] 3. Generate the optimal plan:

[0423] The server selects the optimal delivery menu for the meal plan and determines which is most suitable for the user's conditions and emotions.

[0424] 4. Reservations and Arrangements:

[0425] The server reserves the corresponding menu (for example, a curry set) via the delivery service's API and arranges delivery.

[0426] 5. Plan presentation and confirmation:

[0427] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[0428] This system allows users to easily enjoy the food delivery plan that best suits their mood and conditions. The introduction of an emotion engine makes it possible to provide plans that are even more suited to the user's emotions.

[0429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0430] Step 1:

[0431] User Input and Emotion Recognition

[0432] Users use the device's input form to enter information such as their mood, what they want to see and experience, the travel time, and the cost. At the same time, the device's camera and microphone collect the user's facial and voice data. The emotion engine analyzes this data and recognizes the user's current emotions.

[0433] Input: User input data (e.g., "Relax," "Curry," "Right away," "Under 3,000 yen"), user facial expression data, voice data

[0434] Output: User's emotion data (e.g. "Relaxed")

[0435] Step 2:

[0436] Data transmission

[0437] The terminal transmits the user's input data and the emotion data analyzed by the emotion engine to the server.

[0438] Input: User input data, user emotion data

[0439] Output: Consolidated data sent to the server

[0440] Step 3:

[0441] Data analysis

[0442] The server analyzes the received data using machine learning algorithms and rule-based filters, filtering and ranking the plans in the database based on the user's preferences, conditions, and emotions.

[0443] Input: User input data, user sentiment data, plan candidates in the database

[0444] Output: A filtered list of plan candidates

[0445] Step 4:

[0446] Generating an optimal plan

[0447] The server then generates the optimal plan for the user based on the data analysis results. This process takes into account the user's past choices and current trends. The generated plan includes a meal plan, such as the optimal curry delivery menu.

[0448] Input: Filtered plan candidate list, historical selection data, trend information

[0449] Output: Optimal plan (e.g. curry delivery menu)

[0450] Step 5:

[0451] Reservations and Arrangements

[0452] Based on the generated optimal plan, the server automatically makes the necessary reservations and arrangements through the APIs of the various related service provider systems. For example, it reserves delivery of a curry set through the API of a food delivery service.

[0453] Input: Optimal plan, API of service provider system

[0454] Output: Booking confirmation data and booking information

[0455] Step 6:

[0456] Plan presentation and confirmation

[0457] The server sends the information of the optimal plan with all reservations and arrangements completed to the terminal, and the terminal displays the final plan to the user and asks for their confirmation and approval.

[0458] Input: Reservation confirmation data, arrangement information, optimal plan

[0459] Output: Plan presented to user, user approval

[0460] Step 7:

[0461] Gathering feedback

[0462] After the user uses the service, the device collects feedback from the user, and the user's satisfaction and impressions are sent to the server to help improve the accuracy of future plan generation.

[0463] Input: User feedback data

[0464] Output: Feedback data accumulation and analysis results

[0465] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0466] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0467] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0468] [Second embodiment]

[0469] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0470] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0471] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0472] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0473] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0474] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0475] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0476] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0477] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0478] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0479] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0480] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0481] MODE FOR CARRYING OUT THE INVENTION

[0482] The present invention provides a specific embodiment of a system that automatically generates an optimal plan taking into consideration the user's mood, what they want to see and do, the required time, and costs, and even handles reservations and transportation arrangements. This system is composed of terminals, a server, and multiple service provider systems in cooperation with each other.

[0483] User Interface Design

[0484] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time it will take, the cost, etc. This form allows users to easily input and submit their wishes and requirements.

[0485] Data Acquisition and Analysis Module

[0486] The data entered by the user into the form is sent from the device to the server, which then analyzes the data and pulls out multiple candidates from a database that match the user's preferences and criteria. This analysis is done using machine learning algorithms and rule-based filters.

[0487] Optimal plan generation module

[0488] The server then generates the optimal plan based on the analysis results, taking into account the user's preferences and conditions. This plan generation process also references the user's past choices and trend information. For example, if a user inputs "I want to visit an art gallery," the server will evaluate the information on multiple galleries and suggest the most suitable one.

[0489] Reservations and transportation arrangements

[0490] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[0491] Presenting and confirming the plan

[0492] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0493] Specific scenario example

[0494] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[0495] 1. User Input

[0496] The device displays an input form, and the user enters "relaxation," "art gallery," "3 hours," and "under 5,000 yen," then clicks the submit button.

[0497] 2. Data Analysis

[0498] The server receives the data sent from the device and analyzes it for "relaxation," "art gallery," "3 hours," and "under 5,000 yen."

[0499] 3. Generating the optimal plan

[0500] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, and determines that this best suits the user's requirements.

[0501] 4. Reservations and transportation arrangements

[0502] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[0503] 5. Presenting the plan

[0504] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[0505] This specific scenario allows users to easily enjoy the optimal plan according to their mood and needs without having to perform cumbersome manual work.

[0506] The processing flow will be explained below.

[0507] Step 1:

[0508] The device displays a form for the user to input their mood, what they want to see, what they want to experience, the time required, and the cost. The user enters these conditions into the form and clicks the submit button.

[0509] Step 2:

[0510] The device sends the input data, including the user's mood, what they want to see, what they want to experience, the time required, and the cost, to the server.

[0511] Step 3:

[0512] The server receives the data sent from the device, analyzes the received data, and generates a list of the best candidates based on the user's input.

[0513] Step 4:

[0514] The server uses machine learning algorithms and rule-based filters to narrow down the list of candidates, taking into account the user's past choices and current trends to identify the most suitable candidates.

[0515] Step 5:

[0516] The server designs an optimal plan, which includes information on candidate spots, travel time, costs, and access methods.

[0517] Step 6:

[0518] The server calls the APIs of various service provider systems to make the necessary reservations, such as reserving tickets to a museum, reserving a table at a cafe, or arranging transportation.

[0519] Step 7:

[0520] The server receives reservation confirmation information from each service provider and reflects it in the optimal plan. Once all reservations and arrangements are complete, the server generates the final plan data.

[0521] Step 8:

[0522] The server sends the final plan to the device, which displays it to the user, providing details and buttons to confirm or change it.

[0523] Step 9:

[0524] The user confirms the plan and clicks the approve button. The contents of the confirmed plan are reflected.

[0525] Step 10:

[0526] The server receives the user's approval and finalizes all bookings, sending the final confirmation information and tickets to the terminal and notifying the user.

[0527] This processing step allows users to automatically obtain the best plan based on their mood and needs without any hassle.

[0528] Example 1

[0529] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0530] In traditional travel and leisure planning, users have to research multiple information sources and manually make reservations and arrangements based on the information. This process is time-consuming and laborious, making it cumbersome for many users. Furthermore, when there are many options, it can be difficult to select the optimal plan. There is a need for a system that can resolve these issues and enable users to efficiently and easily obtain the optimal plan.

[0531] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0532] In this invention, the server includes means for a user to input their mood, what they want to see and experience, the required time, and the cost, means for receiving the input data and analyzing it using a machine learning algorithm or a rule-based filter, means for generating an optimal plan based on the analysis results and taking into account the user's past choices and trend information, means for automatically arranging reservations and transportation required for the generated plan via the API of the linked service provider system, and means for presenting the optimal plan to the user and receiving confirmation. This enables users to efficiently generate, confirm, and approve the optimal plan that best suits their preferences without performing cumbersome manual work.

[0533] A "user" is an individual or group that uses this system and is the entity that inputs conditions such as mood, what they want to see, what they want to experience, required time, and cost.

[0534] "Mood" refers to the emotion or experience the user desires to feel, including states such as relaxation or excitement.

[0535] "What you want to see" refers to places, events, or activities you want to experience.

[0536] "Experiences" refers to activities or events that users would like to experience in real life, such as visiting an art gallery or taking a tea break at a cafe.

[0537] "Duration" refers to the period of time that a User intends to spend on a Plan.

[0538] "Cost" refers to the amount that a User has set as a budget for a Plan.

[0539] "Terminal" refers to the device through which a user accesses the system and makes necessary inputs, including computers, smartphones, tablets, etc.

[0540] "Server" refers to a computer system that receives and analyzes data sent from the terminal, generates the optimal plan based on the conditions, and makes the necessary reservations and arrangements.

[0541] "Database" refers to a system for storing and managing various data, including users' past choices and trend information.

[0542] "Machine learning algorithms" refers to statistical techniques and models used in analyzing data, including methods to derive optimal candidates based on user criteria.

[0543] A "rule-based filter" is a method of analyzing data based on predefined rules and selecting information that meets the user's criteria.

[0544] A "plan" refers to a specific proposal that includes places to visit, experiences, travel time, costs, etc., generated based on user input.

[0545] "API" refers to an interface that allows different systems and services to work together, and includes, for example, a reservation system or a means for arranging transportation.

[0546] "Trend information" refers to current trends and data that may interest users, including information that can be used as a reference for generating plans.

[0547] "Service provider system" refers to a system that provides various services (e.g., ticket reservations, transportation, restaurant reservations) to users.

[0548] MODE FOR CARRYING OUT THE INVENTION

[0549] The present invention provides a specific embodiment of a system that automatically generates an optimal plan taking into consideration the user's mood, what they want to see and do, the required time, and costs, and even handles reservations and transportation arrangements. This system is composed of terminals, a server, and multiple service provider systems in cooperation with each other.

[0550] System Configuration

[0551] The system consists of the following main components:

[0552] Device: A device that allows users to input conditions such as mood, what they want to see, what they want to experience, required time, cost, etc. This includes PCs, smartphones, tablets, etc.

[0553] Server: Receives data sent from devices and analyzes and processes it using machine learning algorithms and rule-based filters, utilizing libraries such as Python's scikit-learn and TensorFlow.

[0554] Database: Stores and manages data including users' past choices and trend information.

[0555] Service provider systems: External systems such as ticket reservation systems, transportation systems, and restaurant reservation systems. They are connected via API.

[0556] Processing flow

[0557] 1. Providing a user interface

[0558] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time required, the cost, etc. This form allows users to easily input and submit their wishes and requirements.

[0559] 2. Data collection and analysis

[0560] The data entered by the user into the form is sent from the device to the server, which then analyzes the data and retrieves multiple candidates from a database that match the user's preferences and criteria. This analysis is performed using machine learning algorithms (e.g., scikit-learn) and rule-based filters.

[0561] 3. Generating the optimal plan

[0562] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes and conditions. For example, if a user inputs "I want to visit an art gallery," the server will evaluate information on multiple galleries and suggest the most suitable one.

[0563] 4. Reservations and transportation arrangements

[0564] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems, such as reserving museum tickets, reserving a seat at a cafe, and arranging a taxi.

[0565] 5. Present and confirm the plan

[0566] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0567] Examples of prompt statements

[0568] Below are some examples of specific prompt sentences.

[0569] ---

[0570] User Input

[0571] The device displays a prompt form, and the user enters "relaxation," "art gallery," "3 hours," and "under 5,000 yen," then clicks the submit button.

[0572] Data analysis

[0573] The server receives the data sent from the device and analyzes the options: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[0574] Generating an optimal plan

[0575] The server generates a plan that includes a "special exhibition at the museum" and a "tea break at a nearby cafe."

[0576] Reservations and transportation arrangements

[0577] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[0578] Presenting the plan

[0579] The server sends the generated plan to the device, and the device displays the final plan to the user, who then checks and approves the plan.

[0580] ---

[0581] This specific operation allows the user to easily enjoy the optimal plan according to their mood and desires without having to perform cumbersome manual work.

[0582] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0583] Step 1: The user enters conditions such as mood, what they want to see, what they want to experience, required time, and cost.

[0584] Input: The user enters conditions such as "relaxation," "art gallery," "3 hours," and "under 5,000 yen" into the input form on their device.

[0585] Processing operations: The terminal receives user input and stores it as internal data.

[0586] Output: The device generates the user input data and is ready to send it to the server.

[0587] Step 2: The device sends the user's input data to the server.

[0588] Input: User input data stored on the device.

[0589] Processing operation: The terminal sends the input data to the server.

[0590] Output: The server receives the user's input data.

[0591] Step 3: The server parses the user's input data.

[0592] Input: User input data received by the server, such as "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[0593] Processing behavior: The server analyzes the data using machine learning algorithms (e.g., scikit-learn) and rule-based filters. This analysis pulls potential matches from the database.

[0594] Output: The analysis results in a list of candidates that match the user's criteria.

[0595] Step 4: The server generates an optimal plan.

[0596] Input: A list of candidates obtained as a result of the analysis.

[0597] Processing operation: The server generates the optimal plan by taking into account the user's past selection history and current trend information. For example, a special exhibition at the National Art Center, Tokyo is selected from among several art galleries, and a tea break at a nearby cafe is included in the plan.

[0598] Output: The generated optimal plan data.

[0599] Step 5: The server automatically arranges any necessary reservations and transportation.

[0600] Input: Generated optimal plan data.

[0601] Processing operation: The server automatically makes necessary reservations (e.g., museum ticket reservations, cafe seat reservations) and transportation (e.g., taxi arrangements) through the APIs of various linked service provider systems.

[0602] Output: Data on completed reservation and transportation arrangements.

[0603] Step 6: The server sends the final plan to the device.

[0604] Input: Data for completed reservations and transportation arrangements.

[0605] Processing operation: The server sends the final plan data to the terminal.

[0606] Output: The device receives the final plan.

[0607] Step 7: The device presents the final plan to the user for confirmation.

[0608] Input: The final plan data sent to the device.

[0609] Processing behavior: The terminal displays the final plan to the user, who reviews the plan and approves it if satisfied.

[0610] Output: If user approval is obtained, the final plan is confirmed.

[0611] (Application example 1)

[0612] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0613] With traditional food delivery services, users had to manually search for the food and restaurant that best suited their mood and preferences, and then make individual reservations and arrangements, which was time-consuming and tedious. It was also difficult to find the optimal plan that perfectly matched a specific mood or budget. This made it difficult for users to enjoy the ideal food delivery experience without going through complicated procedures.

[0614] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0615] In this invention, the server includes means for a user to input their mood, what they want to see and experience, the required time, and the cost, means for receiving and analyzing the input data, means for generating an optimal plan based on the input data, means for automatically arranging reservations and transportation required for the generated plan, means for generating an optimal food delivery plan based on a specific mood, type of food, the required time, and the budget, and automatically making the required reservations and delivery arrangements, and means for presenting the optimal plan to the user and receiving confirmation. This allows users to easily enjoy the optimal food delivery plan that suits their mood and conditions at the time without having to perform cumbersome manual work.

[0616] "User" refers to any individual who uses the System.

[0617] "Mood" refers to the emotions and psychological state that a user is feeling at that time.

[0618] "What you want to see" refers to objects or places that users want to enjoy visually.

[0619] "What you want to experience" refers to the activities or actions that users actually want to experience.

[0620] "Duration" refers to the amount of time a user wants to spend on a particular activity or project.

[0621] "Cost" means the amount you plan to pay for a particular activity or plan.

[0622] "Means" refers to the methods or methods used to achieve a particular purpose.

[0623] "Input Data" refers to information provided by a user to a system.

[0624] "Analyzing" refers to evaluating and processing input data using machine learning algorithms and rule-based filters.

[0625] The "optimal plan" refers to the proposal that best suits the user's mood and conditions.

[0626] "Automatically arranging reservations and transportation" refers to the process by which the system automatically arranges the necessary reservations and transportation for the user.

[0627] "Generated Plan" refers to the specific proposal created by the system based on the user's input data.

[0628] "Food Delivery Plan" refers to a meal delivery plan generated by a user based on their mood, type of food, time required, and budget.

[0629] "Automatically making reservations and delivery arrangements" refers to the process by which the system automatically makes the reservations and delivery arrangements necessary for food delivery services.

[0630] "Presenting the optimal plan to the user" refers to the process of showing the system-generated proposal to the user and asking for their confirmation.

[0631] "Receiving confirmation" refers to the act of the user approving the proposed plan.

[0632] The present invention is a system that automatically generates an optimal food delivery plan taking into consideration the user's mood, desired experience, required time, cost, etc., and makes the necessary reservations and arrangements. The present invention is configured in cooperation with a terminal, a server, and multiple service provider systems. Specific embodiments are described below.

[0633] User Interface Design

[0634] The terminal provides a form for users to input their mood, desired cuisine, required time, budget, etc. This form allows users to easily input and submit their wishes and requirements.

[0635] Data Acquisition and Analysis Module

[0636] The data entered by the user into the form is sent from the device to a server, which then analyzes the data and pulls out multiple candidates from a database that match the user's preferences and criteria. This analysis is done using machine learning algorithms, such as generative AI models, and rule-based filters.

[0637] Optimal plan generation module

[0638] The server then generates the optimal food delivery plan based on the analysis results, taking into account the user's preferences and conditions. This plan generation process also takes into account the user's past choices and current trend information.

[0639] Reservations and Arrangements

[0640] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., food delivery service APIs), including ordering from restaurants and arranging delivery.

[0641] Presenting and confirming the plan

[0642] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0643] Specific scenario example

[0644] Below is a specific example of a user creating a plan with the conditions "relaxed atmosphere," "Italian food," "30 minutes," and "under 2,000 yen."

[0645] 1. User Input

[0646] The device displays an input form, and the user enters "relaxation," "Italian food," "30 minutes," and "under 2,000 yen," and clicks the submit button.

[0647] 2. Data Analysis

[0648] The server receives the data sent from the device and analyzes the criteria: "relaxation," "Italian food," "30 minutes," and "under 2,000 yen."

[0649] 3. Generating the optimal plan

[0650] The server then uses the analysis results to suggest the most suitable restaurant and menu, and generates a plan for it. For example, it generates a plan for "Pasta Carbonara" that takes into account the delivery time, cost, and other factors.

[0651] 4. Reservations and Arrangements

[0652] The server uses the API of the affiliated food delivery service to send the order to the restaurant and arrange for delivery.

[0653] 5. Presenting the plan

[0654] The server sends the generated plan to the terminal, and the terminal displays the final plan to the user, who then checks and approves the plan.

[0655] An example of a prompt is as follows:

[0656] Mood: Relaxed

[0657] Preferred cuisine: Italian

[0658] Duration: 30 minutes

[0659] Budget: 2000 yen

[0660] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0661] Step 1:

[0662] The terminal displays a form for the user to enter their mood, desired cuisine, required time, budget, etc. The user fills in each item and clicks the submit button.

[0663] (Input: User's mood, desired dish, time required, budget. Output: Submitted user data)

[0664] Step 2:

[0665] User data is sent from the device to the server, which then analyzes the received user data.

[0666] (Input: User data sent. Output: Analysis results)

[0667] Step 3:

[0668] The server analyzes the received user data and pulls out candidate restaurants and menus from a database, processing the data using generative AI models and rule-based filters.

[0669] (Input: Analysis requirements (mood, cuisine, time, budget). Output: Candidate list)

[0670] Step 4:

[0671] Based on the analysis results, the server generates a food delivery plan that best suits the user's mood, desired cuisine, required time, and budget. This plan generation process also takes into account the user's past selections and trend information.

[0672] (Input: candidate list. Output: optimal plan)

[0673] Step 5:

[0674] Based on the generated plan, the server automatically makes the necessary reservations and delivery arrangements through the APIs of the various service provider systems it is connected to. Specifically, it uses the API of the food delivery service to send the order to the restaurant and arrange for delivery.

[0675] (Input: Optimal plan. Output: Reservation completion notification)

[0676] Step 6:

[0677] After the necessary reservations and arrangements are completed, the server sends the generated optimal plan to the terminal, which then displays the plan to the user.

[0678] (Input: Reservation completion notification. Output: Plan display)

[0679] Step 7:

[0680] The terminal presents the final plan to the user for review and approval. The user reviews the presented plan and approves it if satisfied.

[0681] (Input: Displayed plan. Output: User approval)

[0682] Step 8:

[0683] Once the user approves the plan, the server will receive the confirmation and begin implementing the plan. Finally, the user can receive food delivery that meets their requirements.

[0684] (Input: User approval. Output: Food delivery)

[0685] The above is a detailed description of the specific operations and processing steps in an actual system.

[0686] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0687] MODE FOR CARRYING OUT THE INVENTION

[0688] This invention is a system that automatically generates an optimal plan by considering the user's mood, what they want to see and experience, the required time, cost, etc., and provides a more accurate plan by combining it with an emotion engine that recognizes the user's emotions. This system is composed of terminals, a server, an emotion engine, and cooperation with multiple service provider systems.

[0689] User Interface Design

[0690] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time it will take, the cost, etc. In this form, users can easily input and submit their wishes and requirements.

[0691] Emotion engine integration

[0692] The emotion engine has the ability to recognize the user's emotions by collecting and analyzing their facial expressions and voice data in real time, in addition to the data input by the user. This allows the emotional data obtained from the user's facial expressions and voice to be reflected in the system and used to generate plans.

[0693] Data Acquisition and Analysis Module

[0694] The data entered by the user into the form and the emotional data recognized by the emotion engine are sent from the device to the server. The server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions. This analysis is performed using machine learning algorithms and rule-based filters.

[0695] Optimal plan generation module

[0696] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. This plan generation process also references the user's past choices and trend information. For example, if a user enters "I want to visit an art gallery" and the emotion engine analyzes that the user is relaxing, the server will suggest a plan centered around relaxing art galleries.

[0697] Reservations and transportation arrangements

[0698] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[0699] Presenting and confirming the plan

[0700] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0701] Specific scenario example

[0702] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[0703] 1. User Input and Emotion Recognition

[0704] The device displays an input form, and the user enters "relax," "art gallery," "3 hours," and "under 5,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "relax."

[0705] 2. Data Analysis

[0706] The server receives the input data and emotion recognition data sent from the device and analyzes the results: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[0707] 3. Generating the optimal plan

[0708] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, determining that this is most suitable for the user's conditions and emotions.

[0709] 4. Reservations and transportation arrangements

[0710] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[0711] 5. Presenting the plan

[0712] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[0713] This specific scenario allows users to easily enjoy the optimal plan according to their mood and desires without having to perform tedious manual tasks. The introduction of the emotion engine makes it possible to provide plans that are more suited to the user's emotions.

[0714] The processing flow will be explained below.

[0715] Step 1:

[0716] The device displays a form for the user to input their mood, what they want to see, what they want to experience, the required time, and the cost. The user enters these conditions into the form and clicks the submit button. The emotion engine also collects and analyzes the user's facial expressions and voice in real time.

[0717] Step 2:

[0718] The device sends the input data and emotional data recognized by the emotion engine to the server. The data includes the user's mood, what they want to see and experience, the required time, the cost, and emotions recognized from facial expressions and voice.

[0719] Step 3:

[0720] The server receives the data sent from the device, analyzes the received data, and generates a list of optimal candidates based on the user's input and emotions.

[0721] Step 4:

[0722] The server uses machine learning algorithms and rule-based filters to narrow down the list of candidates, taking into account the user's past choices and current trend information to identify the most suitable candidates.

[0723] Step 5:

[0724] The server then designs an optimal itinerary, including information on potential spots, travel time, costs, and access methods. For example, it suggests a special exhibition at the National Art Center, Tokyo, taking into account the criteria of "art gallery" and "relaxation."

[0725] Step 6:

[0726] The server calls the APIs of various service provider systems to make the reservations required for the generated plan, such as automatically reserving museum tickets, reserving cafe seats, and arranging transportation.

[0727] Step 7:

[0728] The server receives reservation confirmation information from each service provider and reflects it in the optimal plan. Once all reservations and arrangements are complete, the server generates the final plan data.

[0729] Step 8:

[0730] The server sends the final plan to the device, which displays the plan details to the user and provides confirmation and change buttons.

[0731] Step 9:

[0732] The user checks the plan and clicks the approve button. The confirmed plan contents are sent to the server and are updated accordingly.

[0733] Step 10:

[0734] The server receives the user's approval and finalizes all bookings, sending the final confirmation information and tickets to the terminal and notifying the user.

[0735] This processing step allows users to automatically obtain the optimal plan based on their emotions, wishes, and conditions without any effort. The introduction of an emotion engine makes it possible to provide services that are more adapted to the user's emotions.

[0736] Example 2

[0737] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0738] There is a demand for a system that allows users to easily generate plans that match their moods and desires, and automatically arranges reservations and transportation based on those plans. However, conventional systems generate plans based only on user input data and are unable to take the user's emotions into account, making it difficult to provide plans that match the user's true desires and circumstances.

[0739] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0740] In this invention, the server includes a means for the user to input their mood, what they want to see and experience, the required time, and the cost, a means for collecting and analyzing the user's facial expression and voice data to recognize their emotions, and a means for receiving and analyzing the input data and the recognized emotion data. This makes it possible to generate a plan that takes into account the emotion data in addition to the user's mood and wishes.

[0741] "User" refers to a person who uses the system to generate plans and make reservations.

[0742] "Mood" refers to information that expresses the emotions and psychological state that a user is feeling.

[0743] "What to see" refers to information that describes the places users want to visit or the activities they want to experience.

[0744] "Things you want to experience" refers to information that describes activities or events that users would actually like to experience.

[0745] "Time required" refers to information that indicates the range of time required for the user to execute the plan.

[0746] "Cost" refers to information that represents the range of amounts a user can pay for a plan.

[0747] "Form" refers to the input screen where users can enter their mood, what they want to see, what they want to experience, the time required, and the cost.

[0748] An "emotion engine" refers to a system that has the ability to collect and analyze a user's facial expressions and voice data to recognize their emotions.

[0749] "Database" refers to a collection of information that stores data needed to generate a plan.

[0750] A "machine learning algorithm" refers to a method that uses data to generate a model for making predictions or classifications.

[0751] "Integration" refers to different systems and services exchanging information with each other through APIs.

[0752] "API (Application Program Interface)" refers to the definitions and protocols that allow different software systems to communicate with each other and utilize their functions.

[0753] A "plan" refers to a specific action plan generated based on the user's wishes, conditions, and emotions.

[0754] "Reservation arrangement" refers to the process of securing the necessary reservations and transportation based on the generated plan.

[0755] "Terminal" refers to an electronic device that a user operates and uses to input their moods and preferences.

[0756] "Server" refers to the computer system that receives data from users, analyzes it, generates plans, and arranges reservations.

[0757] MODE FOR CARRYING OUT THE INVENTION

[0758] This invention is a system that automatically generates an optimal plan by considering the user's mood, what they want to see and experience, the required time, cost, etc., and provides a more accurate plan by combining it with an emotion engine that recognizes the user's emotions. This system is composed of terminals, a server, an emotion engine, and cooperation with multiple service provider systems.

[0759] User Interface Design

[0760] The device provides a form for users to input their mood, what they want to see, what they want to experience, the time required, and the cost. This form allows users to easily input and submit their wishes and requirements. For example, users can communicate their specific wishes and conditions by entering "relaxation," "art gallery," "3 hours," and "under 5,000 yen."

[0761] Emotion engine integration

[0762] The emotion engine has the ability to recognize the user's emotions by collecting and analyzing the user's facial expressions and voice data in real time, in addition to the user's input data. This allows the emotional data obtained from the user's facial expressions and voice to be reflected in the system and used to generate plans. For example, if a user makes input with a relaxed expression, the emotion engine will recognize the emotion as "relaxed."

[0763] Data Acquisition and Analysis Module

[0764] The data entered by the user into the form and the emotional data recognized by the emotion engine are sent from the device to the server. The server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions. This analysis is performed using machine learning algorithms and rule-based filters.

[0765] Optimal plan generation module

[0766] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. This plan generation process also references the user's past choices and trend information. For example, if a user enters "I want to visit an art gallery" and the emotion engine analyzes that the user is relaxing, the server will suggest a plan centered around relaxing art galleries.

[0767] Reservations and transportation arrangements

[0768] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[0769] Presenting and confirming the plan

[0770] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0771] Specific scenario example

[0772] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[0773] Input prompt statement:

[0774] I feel relaxed. I want to visit an art gallery. The total time is less than 3 hours. I would like to plan the cost to be less than 5,000 yen.

[0775] 1. User Input and Emotion Recognition:

[0776] The device displays an input form, and the user enters "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "Relax."

[0777] 2. Data Analysis:

[0778] The server receives the input data and emotion recognition data sent from the device and analyzes the results: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[0779] 3. Generate the optimal plan:

[0780] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, and determines that this best suits the user's requirements.

[0781] 4. Booking and Transportation Arrangements:

[0782] The server automatically reserves museum tickets, cafe reservations, and arranges necessary transportation.

[0783] 5. Present the plan:

[0784] The server sends the generated plan to the terminal, which displays the final plan to the user, who then reviews it and approves it if satisfied.

[0785] This specific scenario allows users to easily enjoy the optimal plan according to their mood and desires without having to perform tedious manual tasks. The introduction of the emotion engine makes it possible to provide plans that are more suited to the user's emotions.

[0786] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0787] Step 1: Getting User Input

[0788] What it means: The device displays a form where the user can input their mood, what they want to see and do, how long it will take, and how much it will cost.

[0789] Input: The user enters "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen" into the input form.

[0790] Data processing / calculation: Collects input information and converts it into a data format for sending to the server.

[0791] Output: A data packet containing the input information.

[0792] Specific actions: The user operates the device, enters the required information into the form, and clicks the submit button.

[0793] Step 2: Collecting emotion data

[0794] Specifics: The device uses the built-in camera and microphone to collect the user's facial expression and voice data, which is then analyzed by the emotion engine.

[0795] Input: Facial image captured by a camera and audio data recorded by a microphone.

[0796] Data processing / calculation: The emotion engine analyzes the collected image and audio data and evaluates the user's emotional state.

[0797] Output: Parsed emotion data (e.g. "Relaxed").

[0798] Specific operation: The device captures the user's facial expressions with a camera, records audio with a microphone, and sends this data to the emotion engine.

[0799] Step 3: Sending data

[0800] Specific explanation: The terminal transmits the user's input data and the emotion data recognized by the emotion engine to the server.

[0801] Input: User input data and sentiment data.

[0802] Data processing / calculation: The data is compiled into data packets and sent to the server via the network.

[0803] Output: Data packets sent to the server.

[0804] Specific operation: After the user clicks the send button, the device sends the data to the server.

[0805] Step 4: Data analysis

[0806] Specifically, the server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions.

[0807] Input: User input data and emotion data sent from the device.

[0808] Data processing / calculation: Analyzes data using machine learning algorithms and rule-based filters to extract candidates that match the user's criteria.

[0809] Output: A list of plan candidates.

[0810] Specific operation: The server extracts plan candidates that match the conditions from the database and lists them as analysis results.

[0811] Step 5: Generate an optimal plan

[0812] Specific explanation: Based on the analysis results, the server generates the optimal plan taking into account the user's wishes, conditions, and emotions.

[0813] Input: Parsed user data and plan candidate list.

[0814] Data processing / calculation: The optimal plan is selected by taking into account the user's past choices and trend information.

[0815] Output: Optimal plan.

[0816] Specific operation: The server generates the optimal plan and stores its details in a data format.

[0817] Step 6: Make reservations and arrange transportation

[0818] Specific explanation: Based on the generated plan, the server makes the necessary reservations and arrangements using the APIs of various service provider systems.

[0819] Input: Details of the best plan.

[0820] Data processing / calculation: Sending reservation and arrangement requests through the APIs of various services.

[0821] Output: Reservation confirmation and transportation arrangement information.

[0822] Specific operation: The server uses, for example, the API of a ticket reservation system to reserve tickets for a museum.

[0823] Step 7: Present the generated plan

[0824] Specific explanation: The server sends the final plan and reservation information to the terminal, which displays it to the user.

[0825] Input: Reservation confirmation information and transportation arrangement information.

[0826] Data processing / calculation: Convert the plan into a format that is easy for users to view.

[0827] Output: Final plan information for display.

[0828] Specific operation: The terminal displays the plan information received from the server and provides an interface for the user to review and approve.

[0829] The above is a description of the specific processing steps of the system.

[0830] (Application example 2)

[0831] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0832] Conventional plan generation systems rely on user input data and do not fully consider the user's emotions or momentary mood. Furthermore, the automation of the reservation and arrangement process is limited, placing a heavy burden on the user. Furthermore, in the case of food delivery, the system is insufficient in proposing plans that suit the user's current emotions.

[0833] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input information about their mood, what they want to see and experience, the required time, and the cost; means for analyzing the user's facial expressions and voice to recognize their emotions; means for generating an optimal plan based on the analyzed data and emotional data; means for automatically arranging necessary reservations and transportation based on the generated plan; and means for presenting the optimal plan to the user and receiving confirmation. This makes it possible to provide an optimal plan that takes the user's emotions into consideration and reduce the burden on the user by automating reservation arrangements.

[0834] "User input means" refers to the interface that allows users to input information such as their mood, what they want to see, what they want to experience, the time required, and the cost.

[0835] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions and voice data in real time to recognize the user's current emotions.

[0836] The "means for generating a plan" is an algorithm or system that automatically creates an optimal plan based on the user's input data and emotional data.

[0837] "Means for automatically arranging reservations and transportation" refers to a system that automatically arranges necessary reservations and transportation based on the generated plan. This includes ticket reservations, transportation arrangements, restaurant reservations, etc.

[0838] The "means for presenting a plan to the user and receiving confirmation" refers to an interface that presents the optimal plan to the user visually or audibly, and allows the user to confirm and approve the plan.

[0839] "Means of linking with APIs of various services" refers to an integrated system that uses APIs to exchange information with external services and automatically makes reservations and arrangements.

[0840] "Generative model" refers to a machine learning model or AI algorithm that proposes the optimal plan for the user.

[0841] A "means for generating prompt sentences" is a program or algorithm that automatically generates prompt sentences to be input into a generative AI model.

[0842] This invention is a system that automatically generates an optimal plan by taking into consideration the user's mood, what they want to see and experience, the required time, and costs. Furthermore, by combining it with the functionality of an emotion engine that recognizes the user's emotions, it provides a more accurate plan. This system includes a terminal, a server, an emotion engine, and links with various service provider systems.

[0843] Hardware and Software Configuration

[0844] 1. Device:

[0845] Camera: Used to capture the user's facial expressions and analyze their emotions.

[0846] Microphone: Used to collect your voice data and analyze your voice.

[0847] User interface: Provide a form to input your mood, what you want to see and experience, time required, budget, etc.

[0848] 2. Server:

[0849] Data analysis module: Receives data entered by the user and emotional data recognized by the emotion engine, and analyzes it using machine learning algorithms and rule-based filters.

[0850] Optimal plan generation module: Generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. It also references the user's past choices and trend information.

[0851] Reservation arrangement module: Automatically makes necessary reservations and arrangements through the APIs of various service provider systems.

[0852] 3. Emotion Engine:

[0853] Facial Expression Analysis: Use libraries such as OpenCV to analyze the user's facial expressions and recognize emotions.

[0854] Speech analysis: Use the speech_recognition library to convert speech data into text and analyze user sentiment.

[0855] Program processing overview

[0856] The server receives user input data and emotional data sent from the device in real time and analyzes it in the data analysis module. This analysis uses machine learning algorithms and rule-based filters. Based on the analysis results, the optimal plan generation module generates the optimal plan that suits the user's wishes, conditions, and emotions.

[0857] The reservation arrangement module then automatically makes the necessary reservations and arrangements through the APIs of various service provider systems (e.g., food delivery services). After the optimal plan is generated and all necessary reservations and arrangements are completed, the information is sent to the terminal and the final plan is displayed to the user.

[0858] Specific Examples

[0859] For example, if a user creates a plan with the conditions "I want to eat curry in a relaxed mood," "My budget is under 3,000 yen," and "I want to eat it right away," it will look like this:

[0860] Example prompt sentence:

[0861] A user says they want to eat curry, their budget is under 3000 yen, and they want to eat it immediately. Please suggest the best food delivery plan.

[0862] 1. User Input and Emotion Recognition:

[0863] The device displays an input form, and the user enters "relax," "curry," "immediately," and "under 3,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "relax."

[0864] 2. Data Analysis:

[0865] The server receives the input data and emotion recognition data sent from the device and analyzes the results as "relaxed," "curry," "immediately," and "under 3,000 yen."

[0866] 3. Generate the optimal plan:

[0867] The server selects the optimal delivery menu for the meal plan and determines which is most suitable for the user's conditions and emotions.

[0868] 4. Reservations and Arrangements:

[0869] The server reserves the corresponding menu (for example, a curry set) via the delivery service's API and arranges delivery.

[0870] 5. Plan presentation and confirmation:

[0871] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[0872] This system allows users to easily enjoy the food delivery plan that best suits their mood and conditions. The introduction of an emotion engine makes it possible to provide plans that are even more suited to the user's emotions.

[0873] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0874] Step 1:

[0875] User Input and Emotion Recognition

[0876] Users use the device's input form to enter information such as their mood, what they want to see and experience, the travel time, and the cost. At the same time, the device's camera and microphone collect the user's facial and voice data. The emotion engine analyzes this data and recognizes the user's current emotions.

[0877] Input: User input data (e.g., "Relax," "Curry," "Right away," "Under 3,000 yen"), user facial expression data, voice data

[0878] Output: User's emotion data (e.g. "Relaxed")

[0879] Step 2:

[0880] Data transmission

[0881] The terminal transmits the user's input data and the emotion data analyzed by the emotion engine to the server.

[0882] Input: User input data, user emotion data

[0883] Output: Consolidated data sent to the server

[0884] Step 3:

[0885] Data analysis

[0886] The server analyzes the received data using machine learning algorithms and rule-based filters, filtering and ranking the plans in the database based on the user's preferences, conditions, and emotions.

[0887] Input: User input data, user sentiment data, plan candidates in the database

[0888] Output: A filtered list of plan candidates

[0889] Step 4:

[0890] Generating an optimal plan

[0891] The server then generates the optimal plan for the user based on the data analysis results. This process takes into account the user's past choices and current trends. The generated plan includes a meal plan, such as the optimal curry delivery menu.

[0892] Input: Filtered plan candidate list, historical selection data, trend information

[0893] Output: Optimal plan (e.g. curry delivery menu)

[0894] Step 5:

[0895] Reservations and Arrangements

[0896] Based on the generated optimal plan, the server automatically makes the necessary reservations and arrangements through the APIs of the various related service provider systems. For example, it reserves delivery of a curry set through the API of a food delivery service.

[0897] Input: Optimal plan, API of service provider system

[0898] Output: Booking confirmation data and booking information

[0899] Step 6:

[0900] Plan presentation and confirmation

[0901] The server sends the information of the optimal plan with all reservations and arrangements completed to the terminal, and the terminal displays the final plan to the user and asks for their confirmation and approval.

[0902] Input: Reservation confirmation data, arrangement information, optimal plan

[0903] Output: Plan presented to user, user approval

[0904] Step 7:

[0905] Gathering feedback

[0906] After the user uses the service, the device collects feedback from the user, and the user's satisfaction and impressions are sent to the server to help improve the accuracy of future plan generation.

[0907] Input: User feedback data

[0908] Output: Feedback data accumulation and analysis results

[0909] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0910] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0911] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0912] [Third embodiment]

[0913] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0914] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0915] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0916] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0917] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0918] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0919] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0920] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0921] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0922] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0923] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0924] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0925] MODE FOR CARRYING OUT THE INVENTION

[0926] The present invention provides a specific embodiment of a system that automatically generates an optimal plan taking into consideration the user's mood, what they want to see and do, the required time, and costs, and even handles reservations and transportation arrangements. This system is composed of terminals, a server, and multiple service provider systems in cooperation with each other.

[0927] User Interface Design

[0928] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time it will take, the cost, etc. This form allows users to easily input and submit their wishes and requirements.

[0929] Data Acquisition and Analysis Module

[0930] The data entered by the user into the form is sent from the device to the server, which then analyzes the data and pulls out multiple candidates from a database that match the user's preferences and criteria. This analysis is done using machine learning algorithms and rule-based filters.

[0931] Optimal plan generation module

[0932] The server then generates the optimal plan based on the analysis results, taking into account the user's preferences and conditions. This plan generation process also references the user's past choices and trend information. For example, if a user inputs "I want to visit an art gallery," the server will evaluate the information on multiple galleries and suggest the most suitable one.

[0933] Reservations and transportation arrangements

[0934] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[0935] Presenting and confirming the plan

[0936] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[0937] Specific scenario example

[0938] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[0939] 1. User Input

[0940] The device displays an input form, and the user enters "relaxation," "art gallery," "3 hours," and "under 5,000 yen," then clicks the submit button.

[0941] 2. Data Analysis

[0942] The server receives the data sent from the device and analyzes it for "relaxation," "art gallery," "3 hours," and "under 5,000 yen."

[0943] 3. Generating the optimal plan

[0944] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, and determines that this best suits the user's requirements.

[0945] 4. Reservations and transportation arrangements

[0946] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[0947] 5. Presenting the plan

[0948] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[0949] This specific scenario allows users to easily enjoy the optimal plan according to their mood and needs without having to perform cumbersome manual work.

[0950] The processing flow will be explained below.

[0951] Step 1:

[0952] The device displays a form for the user to input their mood, what they want to see, what they want to experience, the time required, and the cost. The user enters these conditions into the form and clicks the submit button.

[0953] Step 2:

[0954] The device sends the input data, including the user's mood, what they want to see, what they want to experience, the time required, and the cost, to the server.

[0955] Step 3:

[0956] The server receives the data sent from the device, analyzes the received data, and generates a list of the best candidates based on the user's input.

[0957] Step 4:

[0958] The server uses machine learning algorithms and rule-based filters to narrow down the list of candidates, taking into account the user's past choices and current trends to identify the most suitable candidates.

[0959] Step 5:

[0960] The server designs an optimal plan, which includes information on candidate spots, travel time, costs, and access methods.

[0961] Step 6:

[0962] The server calls the APIs of various service provider systems to make the necessary reservations, such as reserving tickets to a museum, reserving a table at a cafe, or arranging transportation.

[0963] Step 7:

[0964] The server receives reservation confirmation information from each service provider and reflects it in the optimal plan. Once all reservations and arrangements are complete, the server generates the final plan data.

[0965] Step 8:

[0966] The server sends the final plan to the device, which displays it to the user, providing details and buttons to confirm or change it.

[0967] Step 9:

[0968] The user confirms the plan and clicks the approve button. The contents of the confirmed plan are reflected.

[0969] Step 10:

[0970] The server receives the user's approval and finalizes all bookings, sending the final confirmation information and tickets to the terminal and notifying the user.

[0971] This processing step allows users to automatically obtain the best plan based on their mood and needs without any hassle.

[0972] Example 1

[0973] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0974] In traditional travel and leisure planning, users have to research multiple information sources and manually make reservations and arrangements based on the information. This process is time-consuming and laborious, making it cumbersome for many users. Furthermore, when there are many options, it can be difficult to select the optimal plan. There is a need for a system that can resolve these issues and enable users to efficiently and easily obtain the optimal plan.

[0975] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0976] In this invention, the server includes means for a user to input their mood, what they want to see and experience, the required time, and the cost, means for receiving the input data and analyzing it using a machine learning algorithm or a rule-based filter, means for generating an optimal plan based on the analysis results and taking into account the user's past choices and trend information, means for automatically arranging reservations and transportation required for the generated plan via the API of the linked service provider system, and means for presenting the optimal plan to the user and receiving confirmation. This enables users to efficiently generate, confirm, and approve the optimal plan that best suits their preferences without performing cumbersome manual work.

[0977] A "user" is an individual or group that uses this system and is the entity that inputs conditions such as mood, what they want to see, what they want to experience, required time, and cost.

[0978] "Mood" refers to the emotion or experience the user desires to feel, including states such as relaxation or excitement.

[0979] "What you want to see" refers to places, events, or activities you want to experience.

[0980] "Experiences" refers to activities or events that users would like to experience in real life, such as visiting an art gallery or taking a tea break at a cafe.

[0981] "Duration" refers to the period of time that a User intends to spend on a Plan.

[0982] "Cost" refers to the amount that a User has set as a budget for a Plan.

[0983] "Terminal" refers to the device through which a user accesses the system and makes necessary inputs, including computers, smartphones, tablets, etc.

[0984] "Server" refers to a computer system that receives and analyzes data sent from the terminal, generates the optimal plan based on the conditions, and makes the necessary reservations and arrangements.

[0985] "Database" refers to a system for storing and managing various data, including users' past choices and trend information.

[0986] "Machine learning algorithms" refers to statistical techniques and models used in analyzing data, including methods to derive optimal candidates based on user criteria.

[0987] A "rule-based filter" is a method of analyzing data based on predefined rules and selecting information that meets the user's criteria.

[0988] A "plan" refers to a specific proposal that includes places to visit, experiences, travel time, costs, etc., generated based on user input.

[0989] "API" refers to an interface that allows different systems and services to work together, and includes, for example, a reservation system or a means for arranging transportation.

[0990] "Trend information" refers to current trends and data that may interest users, including information that can be used as a reference for generating plans.

[0991] "Service provider system" refers to a system that provides various services (e.g., ticket reservations, transportation, restaurant reservations) to users.

[0992] MODE FOR CARRYING OUT THE INVENTION

[0993] The present invention provides a specific embodiment of a system that automatically generates an optimal plan taking into consideration the user's mood, what they want to see and do, the required time, and costs, and even handles reservations and transportation arrangements. This system is composed of terminals, a server, and multiple service provider systems in cooperation with each other.

[0994] System Configuration

[0995] The system consists of the following main components:

[0996] Device: A device that allows users to input conditions such as mood, what they want to see, what they want to experience, required time, cost, etc. This includes PCs, smartphones, tablets, etc.

[0997] Server: Receives data sent from devices and analyzes and processes it using machine learning algorithms and rule-based filters, utilizing libraries such as Python's scikit-learn and TensorFlow.

[0998] Database: Stores and manages data including users' past choices and trend information.

[0999] Service provider systems: External systems such as ticket reservation systems, transportation systems, and restaurant reservation systems. They are connected via API.

[1000] Processing flow

[1001] 1. Providing a user interface

[1002] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time required, the cost, etc. This form allows users to easily input and submit their wishes and requirements.

[1003] 2. Data collection and analysis

[1004] The data entered by the user into the form is sent from the device to the server, which then analyzes the data and retrieves multiple candidates from a database that match the user's preferences and criteria. This analysis is performed using machine learning algorithms (e.g., scikit-learn) and rule-based filters.

[1005] 3. Generating the optimal plan

[1006] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes and conditions. For example, if a user inputs "I want to visit an art gallery," the server will evaluate information on multiple galleries and suggest the most suitable one.

[1007] 4. Reservations and transportation arrangements

[1008] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems, such as reserving museum tickets, reserving a seat at a cafe, and arranging a taxi.

[1009] 5. Present and confirm the plan

[1010] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1011] Examples of prompt statements

[1012] Below are some examples of specific prompt sentences.

[1013] ---

[1014] User Input

[1015] The device displays a prompt form, and the user enters "relaxation," "art gallery," "3 hours," and "under 5,000 yen," then clicks the submit button.

[1016] Data analysis

[1017] The server receives the data sent from the device and analyzes the options: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[1018] Generating an optimal plan

[1019] The server generates a plan that includes a "special exhibition at the museum" and a "tea break at a nearby cafe."

[1020] Reservations and transportation arrangements

[1021] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[1022] Presenting the plan

[1023] The server sends the generated plan to the device, and the device displays the final plan to the user, who then checks and approves the plan.

[1024] ---

[1025] This specific operation allows the user to easily enjoy the optimal plan according to their mood and desires without having to perform cumbersome manual work.

[1026] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1027] Step 1: The user enters conditions such as mood, what they want to see, what they want to experience, required time, and cost.

[1028] Input: The user enters conditions such as "relaxation," "art gallery," "3 hours," and "under 5,000 yen" into the input form on their device.

[1029] Processing operations: The terminal receives user input and stores it as internal data.

[1030] Output: The device generates the user input data and is ready to send it to the server.

[1031] Step 2: The device sends the user's input data to the server.

[1032] Input: User input data stored on the device.

[1033] Processing operation: The terminal sends the input data to the server.

[1034] Output: The server receives the user's input data.

[1035] Step 3: The server parses the user's input data.

[1036] Input: User input data received by the server, such as "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[1037] Processing behavior: The server analyzes the data using machine learning algorithms (e.g., scikit-learn) and rule-based filters. This analysis pulls potential matches from the database.

[1038] Output: The analysis results in a list of candidates that match the user's criteria.

[1039] Step 4: The server generates an optimal plan.

[1040] Input: A list of candidates obtained as a result of the analysis.

[1041] Processing operation: The server generates the optimal plan by taking into account the user's past selection history and current trend information. For example, a special exhibition at the National Art Center, Tokyo is selected from among several art galleries, and a tea break at a nearby cafe is included in the plan.

[1042] Output: The generated optimal plan data.

[1043] Step 5: The server automatically arranges any necessary reservations and transportation.

[1044] Input: Generated optimal plan data.

[1045] Processing operation: The server automatically makes necessary reservations (e.g., museum ticket reservations, cafe seat reservations) and transportation (e.g., taxi arrangements) through the APIs of various linked service provider systems.

[1046] Output: Data on completed reservation and transportation arrangements.

[1047] Step 6: The server sends the final plan to the device.

[1048] Input: Data for completed reservations and transportation arrangements.

[1049] Processing operation: The server sends the final plan data to the terminal.

[1050] Output: The device receives the final plan.

[1051] Step 7: The device presents the final plan to the user for confirmation.

[1052] Input: The final plan data sent to the device.

[1053] Processing behavior: The terminal displays the final plan to the user, who reviews the plan and approves it if satisfied.

[1054] Output: If user approval is obtained, the final plan is confirmed.

[1055] (Application example 1)

[1056] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1057] With traditional food delivery services, users had to manually search for the food and restaurant that best suited their mood and preferences, and then make individual reservations and arrangements, which was time-consuming and tedious. It was also difficult to find the optimal plan that perfectly matched a specific mood or budget. This made it difficult for users to enjoy the ideal food delivery experience without going through complicated procedures.

[1058] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1059] In this invention, the server includes means for a user to input their mood, what they want to see and experience, the required time, and the cost, means for receiving and analyzing the input data, means for generating an optimal plan based on the input data, means for automatically arranging reservations and transportation required for the generated plan, means for generating an optimal food delivery plan based on a specific mood, type of food, the required time, and the budget, and automatically making the required reservations and delivery arrangements, and means for presenting the optimal plan to the user and receiving confirmation. This allows users to easily enjoy the optimal food delivery plan that suits their mood and conditions at the time without having to perform cumbersome manual work.

[1060] "User" refers to any individual who uses the System.

[1061] "Mood" refers to the emotions and psychological state that a user is feeling at that time.

[1062] "What you want to see" refers to objects or places that users want to enjoy visually.

[1063] "What you want to experience" refers to the activities or actions that users actually want to experience.

[1064] "Duration" refers to the amount of time a user wants to spend on a particular activity or project.

[1065] "Cost" means the amount you plan to pay for a particular activity or plan.

[1066] "Means" refers to the methods or methods used to achieve a particular purpose.

[1067] "Input Data" refers to information provided by a user to a system.

[1068] "Analyzing" refers to evaluating and processing input data using machine learning algorithms and rule-based filters.

[1069] The "optimal plan" refers to the proposal that best suits the user's mood and conditions.

[1070] "Automatically arranging reservations and transportation" refers to the process by which the system automatically arranges the necessary reservations and transportation for the user.

[1071] "Generated Plan" refers to the specific proposal created by the system based on the user's input data.

[1072] "Food Delivery Plan" refers to a meal delivery plan generated by a user based on their mood, type of food, time required, and budget.

[1073] "Automatically making reservations and delivery arrangements" refers to the process by which the system automatically makes the reservations and delivery arrangements necessary for food delivery services.

[1074] "Presenting the optimal plan to the user" refers to the process of showing the system-generated proposal to the user and asking for their confirmation.

[1075] "Receiving confirmation" refers to the act of the user approving the proposed plan.

[1076] The present invention is a system that automatically generates an optimal food delivery plan taking into consideration the user's mood, desired experience, required time, cost, etc., and makes the necessary reservations and arrangements. The present invention is configured in cooperation with a terminal, a server, and multiple service provider systems. Specific embodiments are described below.

[1077] User Interface Design

[1078] The terminal provides a form for users to input their mood, desired cuisine, required time, budget, etc. This form allows users to easily input and submit their wishes and requirements.

[1079] Data Acquisition and Analysis Module

[1080] The data entered by the user into the form is sent from the device to a server, which then analyzes the data and pulls out multiple candidates from a database that match the user's preferences and criteria. This analysis is done using machine learning algorithms, such as generative AI models, and rule-based filters.

[1081] Optimal plan generation module

[1082] The server then generates the optimal food delivery plan based on the analysis results, taking into account the user's preferences and conditions. This plan generation process also takes into account the user's past choices and current trend information.

[1083] Reservations and Arrangements

[1084] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., food delivery service APIs), including ordering from restaurants and arranging delivery.

[1085] Presenting and confirming the plan

[1086] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1087] Specific scenario example

[1088] Below is a specific example of a user creating a plan with the conditions "relaxed atmosphere," "Italian food," "30 minutes," and "under 2,000 yen."

[1089] 1. User Input

[1090] The device displays an input form, and the user enters "relaxation," "Italian food," "30 minutes," and "under 2,000 yen," and clicks the submit button.

[1091] 2. Data Analysis

[1092] The server receives the data sent from the device and analyzes the criteria: "relaxation," "Italian food," "30 minutes," and "under 2,000 yen."

[1093] 3. Generating the optimal plan

[1094] The server then uses the analysis results to suggest the most suitable restaurant and menu, and generates a plan for it. For example, it generates a plan for "Pasta Carbonara" that takes into account the delivery time, cost, and other factors.

[1095] 4. Reservations and Arrangements

[1096] The server uses the API of the affiliated food delivery service to send the order to the restaurant and arrange for delivery.

[1097] 5. Presenting the plan

[1098] The server sends the generated plan to the terminal, and the terminal displays the final plan to the user, who then checks and approves the plan.

[1099] An example of a prompt is as follows:

[1100] Mood: Relaxed

[1101] Preferred cuisine: Italian

[1102] Duration: 30 minutes

[1103] Budget: 2000 yen

[1104] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1105] Step 1:

[1106] The terminal displays a form for the user to enter their mood, desired cuisine, required time, budget, etc. The user fills in each item and clicks the submit button.

[1107] (Input: User's mood, desired dish, time required, budget. Output: Submitted user data)

[1108] Step 2:

[1109] User data is sent from the device to the server, which then analyzes the received user data.

[1110] (Input: User data sent. Output: Analysis results)

[1111] Step 3:

[1112] The server analyzes the received user data and pulls out candidate restaurants and menus from a database, processing the data using generative AI models and rule-based filters.

[1113] (Input: Analysis requirements (mood, cuisine, time, budget). Output: Candidate list)

[1114] Step 4:

[1115] Based on the analysis results, the server generates a food delivery plan that best suits the user's mood, desired cuisine, required time, and budget. This plan generation process also takes into account the user's past selections and trend information.

[1116] (Input: candidate list. Output: optimal plan)

[1117] Step 5:

[1118] Based on the generated plan, the server automatically makes the necessary reservations and delivery arrangements through the APIs of the various service provider systems it is connected to. Specifically, it uses the API of the food delivery service to send the order to the restaurant and arrange for delivery.

[1119] (Input: Optimal plan. Output: Reservation completion notification)

[1120] Step 6:

[1121] After the necessary reservations and arrangements are completed, the server sends the generated optimal plan to the terminal, which then displays the plan to the user.

[1122] (Input: Reservation completion notification. Output: Plan display)

[1123] Step 7:

[1124] The terminal presents the final plan to the user for review and approval. The user reviews the presented plan and approves it if satisfied.

[1125] (Input: Displayed plan. Output: User approval)

[1126] Step 8:

[1127] Once the user approves the plan, the server will receive the confirmation and begin implementing the plan. Finally, the user can receive food delivery that meets their requirements.

[1128] (Input: User approval. Output: Food delivery)

[1129] The above is a detailed description of the specific operations and processing steps in an actual system.

[1130] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1131] MODE FOR CARRYING OUT THE INVENTION

[1132] This invention is a system that automatically generates an optimal plan by considering the user's mood, what they want to see and experience, the required time, cost, etc., and provides a more accurate plan by combining it with an emotion engine that recognizes the user's emotions. This system is composed of terminals, a server, an emotion engine, and cooperation with multiple service provider systems.

[1133] User Interface Design

[1134] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time it will take, the cost, etc. In this form, users can easily input and submit their wishes and requirements.

[1135] Emotion engine integration

[1136] The emotion engine has the ability to recognize the user's emotions by collecting and analyzing their facial expressions and voice data in real time, in addition to the data input by the user. This allows the emotional data obtained from the user's facial expressions and voice to be reflected in the system and used to generate plans.

[1137] Data Acquisition and Analysis Module

[1138] The data entered by the user into the form and the emotional data recognized by the emotion engine are sent from the device to the server. The server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions. This analysis is performed using machine learning algorithms and rule-based filters.

[1139] Optimal plan generation module

[1140] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. This plan generation process also references the user's past choices and trend information. For example, if a user enters "I want to visit an art gallery" and the emotion engine analyzes that the user is relaxing, the server will suggest a plan centered around relaxing art galleries.

[1141] Reservations and transportation arrangements

[1142] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[1143] Presenting and confirming the plan

[1144] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1145] Specific scenario example

[1146] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[1147] 1. User Input and Emotion Recognition

[1148] The device displays an input form, and the user enters "relax," "art gallery," "3 hours," and "under 5,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "relax."

[1149] 2. Data Analysis

[1150] The server receives the input data and emotion recognition data sent from the device and analyzes the results: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[1151] 3. Generating the optimal plan

[1152] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, determining that this is most suitable for the user's conditions and emotions.

[1153] 4. Reservations and transportation arrangements

[1154] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[1155] 5. Presenting the plan

[1156] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[1157] This specific scenario allows users to easily enjoy the optimal plan according to their mood and desires without having to perform tedious manual tasks. The introduction of the emotion engine makes it possible to provide plans that are more suited to the user's emotions.

[1158] The processing flow will be explained below.

[1159] Step 1:

[1160] The device displays a form for the user to input their mood, what they want to see, what they want to experience, the required time, and the cost. The user enters these conditions into the form and clicks the submit button. The emotion engine also collects and analyzes the user's facial expressions and voice in real time.

[1161] Step 2:

[1162] The device sends the input data and emotional data recognized by the emotion engine to the server. The data includes the user's mood, what they want to see and experience, the required time, the cost, and emotions recognized from facial expressions and voice.

[1163] Step 3:

[1164] The server receives the data sent from the device, analyzes the received data, and generates a list of optimal candidates based on the user's input and emotions.

[1165] Step 4:

[1166] The server uses machine learning algorithms and rule-based filters to narrow down the list of candidates, taking into account the user's past choices and current trend information to identify the most suitable candidates.

[1167] Step 5:

[1168] The server then designs an optimal itinerary, including information on potential spots, travel time, costs, and access methods. For example, it suggests a special exhibition at the National Art Center, Tokyo, taking into account the criteria of "art gallery" and "relaxation."

[1169] Step 6:

[1170] The server calls the APIs of various service provider systems to make the reservations required for the generated plan, such as automatically reserving museum tickets, reserving cafe seats, and arranging transportation.

[1171] Step 7:

[1172] The server receives reservation confirmation information from each service provider and reflects it in the optimal plan. Once all reservations and arrangements are complete, the server generates the final plan data.

[1173] Step 8:

[1174] The server sends the final plan to the device, which displays the plan details to the user and provides confirmation and change buttons.

[1175] Step 9:

[1176] The user checks the plan and clicks the approve button. The confirmed plan contents are sent to the server and are updated accordingly.

[1177] Step 10:

[1178] The server receives the user's approval and finalizes all bookings, sending the final confirmation information and tickets to the terminal and notifying the user.

[1179] This processing step allows users to automatically obtain the optimal plan based on their emotions, wishes, and conditions without any effort. The introduction of an emotion engine makes it possible to provide services that are more adapted to the user's emotions.

[1180] Example 2

[1181] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1182] There is a demand for a system that allows users to easily generate plans that match their moods and desires, and automatically arranges reservations and transportation based on those plans. However, conventional systems generate plans based only on user input data and are unable to take the user's emotions into account, making it difficult to provide plans that match the user's true desires and circumstances.

[1183] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1184] In this invention, the server includes a means for the user to input their mood, what they want to see and experience, the required time, and the cost, a means for collecting and analyzing the user's facial expression and voice data to recognize their emotions, and a means for receiving and analyzing the input data and the recognized emotion data. This makes it possible to generate a plan that takes into account the emotion data in addition to the user's mood and wishes.

[1185] "User" refers to a person who uses the system to generate plans and make reservations.

[1186] "Mood" refers to information that expresses the emotions and psychological state that a user is feeling.

[1187] "What to see" refers to information that describes the places users want to visit or the activities they want to experience.

[1188] "Things you want to experience" refers to information that describes activities or events that users would actually like to experience.

[1189] "Time required" refers to information that indicates the range of time required for the user to execute the plan.

[1190] "Cost" refers to information that represents the range of amounts a user can pay for a plan.

[1191] "Form" refers to the input screen where users can enter their mood, what they want to see, what they want to experience, the time required, and the cost.

[1192] An "emotion engine" refers to a system that has the ability to collect and analyze a user's facial expressions and voice data to recognize their emotions.

[1193] "Database" refers to a collection of information that stores data needed to generate a plan.

[1194] A "machine learning algorithm" refers to a method that uses data to generate a model for making predictions or classifications.

[1195] "Integration" refers to different systems and services exchanging information with each other through APIs.

[1196] "API (Application Program Interface)" refers to the definitions and protocols that allow different software systems to communicate with each other and utilize their functions.

[1197] A "plan" refers to a specific action plan generated based on the user's wishes, conditions, and emotions.

[1198] "Reservation arrangement" refers to the process of securing the necessary reservations and transportation based on the generated plan.

[1199] "Terminal" refers to an electronic device that a user operates and uses to input their moods and preferences.

[1200] "Server" refers to the computer system that receives data from users, analyzes it, generates plans, and arranges reservations.

[1201] MODE FOR CARRYING OUT THE INVENTION

[1202] This invention is a system that automatically generates an optimal plan by considering the user's mood, what they want to see and experience, the required time, cost, etc., and provides a more accurate plan by combining it with an emotion engine that recognizes the user's emotions. This system is composed of terminals, a server, an emotion engine, and cooperation with multiple service provider systems.

[1203] User Interface Design

[1204] The device provides a form for users to input their mood, what they want to see, what they want to experience, the time required, and the cost. This form allows users to easily input and submit their wishes and requirements. For example, users can communicate their specific wishes and conditions by entering "relaxation," "art gallery," "3 hours," and "under 5,000 yen."

[1205] Emotion engine integration

[1206] The emotion engine has the ability to recognize the user's emotions by collecting and analyzing the user's facial expressions and voice data in real time, in addition to the user's input data. This allows the emotional data obtained from the user's facial expressions and voice to be reflected in the system and used to generate plans. For example, if a user makes input with a relaxed expression, the emotion engine will recognize the emotion as "relaxed."

[1207] Data Acquisition and Analysis Module

[1208] The data entered by the user into the form and the emotional data recognized by the emotion engine are sent from the device to the server. The server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions. This analysis is performed using machine learning algorithms and rule-based filters.

[1209] Optimal plan generation module

[1210] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. This plan generation process also references the user's past choices and trend information. For example, if a user enters "I want to visit an art gallery" and the emotion engine analyzes that the user is relaxing, the server will suggest a plan centered around relaxing art galleries.

[1211] Reservations and transportation arrangements

[1212] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[1213] Presenting and confirming the plan

[1214] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1215] Specific scenario example

[1216] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[1217] Input prompt statement:

[1218] I feel relaxed. I want to visit an art gallery. The total time is less than 3 hours. I would like to plan the cost to be less than 5,000 yen.

[1219] 1. User Input and Emotion Recognition:

[1220] The device displays an input form, and the user enters "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "Relax."

[1221] 2. Data Analysis:

[1222] The server receives the input data and emotion recognition data sent from the device and analyzes the results: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[1223] 3. Generate the optimal plan:

[1224] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, and determines that this best suits the user's requirements.

[1225] 4. Booking and Transportation Arrangements:

[1226] The server automatically reserves museum tickets, cafe reservations, and arranges necessary transportation.

[1227] 5. Present the plan:

[1228] The server sends the generated plan to the terminal, which displays the final plan to the user, who then reviews it and approves it if satisfied.

[1229] This specific scenario allows users to easily enjoy the optimal plan according to their mood and desires without having to perform tedious manual tasks. The introduction of the emotion engine makes it possible to provide plans that are more suited to the user's emotions.

[1230] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1231] Step 1: Getting User Input

[1232] What it means: The device displays a form where the user can input their mood, what they want to see and do, how long it will take, and how much it will cost.

[1233] Input: The user enters "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen" into the input form.

[1234] Data processing / calculation: Collects input information and converts it into a data format for sending to the server.

[1235] Output: A data packet containing the input information.

[1236] Specific actions: The user operates the device, enters the required information into the form, and clicks the submit button.

[1237] Step 2: Collecting emotion data

[1238] Specifics: The device uses the built-in camera and microphone to collect the user's facial expression and voice data, which is then analyzed by the emotion engine.

[1239] Input: Facial image captured by a camera and audio data recorded by a microphone.

[1240] Data processing / calculation: The emotion engine analyzes the collected image and audio data and evaluates the user's emotional state.

[1241] Output: Parsed emotion data (e.g. "Relaxed").

[1242] Specific operation: The device captures the user's facial expressions with a camera, records audio with a microphone, and sends this data to the emotion engine.

[1243] Step 3: Sending data

[1244] Specific explanation: The terminal transmits the user's input data and the emotion data recognized by the emotion engine to the server.

[1245] Input: User input data and sentiment data.

[1246] Data processing / calculation: The data is compiled into data packets and sent to the server via the network.

[1247] Output: Data packets sent to the server.

[1248] Specific operation: After the user clicks the send button, the device sends the data to the server.

[1249] Step 4: Data analysis

[1250] Specifically, the server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions.

[1251] Input: User input data and emotion data sent from the device.

[1252] Data processing / calculation: Analyzes data using machine learning algorithms and rule-based filters to extract candidates that match the user's criteria.

[1253] Output: A list of plan candidates.

[1254] Specific operation: The server extracts plan candidates that match the conditions from the database and lists them as analysis results.

[1255] Step 5: Generate an optimal plan

[1256] Specific explanation: Based on the analysis results, the server generates the optimal plan taking into account the user's wishes, conditions, and emotions.

[1257] Input: Parsed user data and plan candidate list.

[1258] Data processing / calculation: The optimal plan is selected by taking into account the user's past choices and trend information.

[1259] Output: Optimal plan.

[1260] Specific operation: The server generates the optimal plan and stores its details in a data format.

[1261] Step 6: Make reservations and arrange transportation

[1262] Specific explanation: Based on the generated plan, the server makes the necessary reservations and arrangements using the APIs of various service provider systems.

[1263] Input: Details of the best plan.

[1264] Data processing / calculation: Sending reservation and arrangement requests through the APIs of various services.

[1265] Output: Reservation confirmation and transportation arrangement information.

[1266] Specific operation: The server uses, for example, the API of a ticket reservation system to reserve tickets for a museum.

[1267] Step 7: Present the generated plan

[1268] Specific explanation: The server sends the final plan and reservation information to the terminal, which displays it to the user.

[1269] Input: Reservation confirmation information and transportation arrangement information.

[1270] Data processing / calculation: Convert the plan into a format that is easy for users to view.

[1271] Output: Final plan information for display.

[1272] Specific operation: The terminal displays the plan information received from the server and provides an interface for the user to review and approve.

[1273] The above is a description of the specific processing steps of the system.

[1274] (Application example 2)

[1275] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1276] Conventional plan generation systems rely on user input data and do not fully consider the user's emotions or momentary mood. Furthermore, the automation of the reservation and arrangement process is limited, placing a heavy burden on the user. Furthermore, in the case of food delivery, the system is insufficient in proposing plans that suit the user's current emotions.

[1277] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input information about their mood, what they want to see and experience, the required time, and the cost; means for analyzing the user's facial expressions and voice to recognize their emotions; means for generating an optimal plan based on the analyzed data and emotional data; means for automatically arranging necessary reservations and transportation based on the generated plan; and means for presenting the optimal plan to the user and receiving confirmation. This makes it possible to provide an optimal plan that takes the user's emotions into consideration and reduce the burden on the user by automating reservation arrangements.

[1278] "User input means" refers to the interface that allows users to input information such as their mood, what they want to see, what they want to experience, the time required, and the cost.

[1279] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions and voice data in real time to recognize the user's current emotions.

[1280] The "means for generating a plan" is an algorithm or system that automatically creates an optimal plan based on the user's input data and emotional data.

[1281] "Means for automatically arranging reservations and transportation" refers to a system that automatically arranges necessary reservations and transportation based on the generated plan. This includes ticket reservations, transportation arrangements, restaurant reservations, etc.

[1282] The "means for presenting a plan to the user and receiving confirmation" refers to an interface that presents the optimal plan to the user visually or audibly, and allows the user to confirm and approve the plan.

[1283] "Means of linking with APIs of various services" refers to an integrated system that uses APIs to exchange information with external services and automatically makes reservations and arrangements.

[1284] "Generative model" refers to a machine learning model or AI algorithm that proposes the optimal plan for the user.

[1285] A "means for generating prompt sentences" is a program or algorithm that automatically generates prompt sentences to be input into a generative AI model.

[1286] This invention is a system that automatically generates an optimal plan by taking into consideration the user's mood, what they want to see and experience, the required time, and costs. Furthermore, by combining it with the functionality of an emotion engine that recognizes the user's emotions, it provides a more accurate plan. This system includes a terminal, a server, an emotion engine, and links with various service provider systems.

[1287] Hardware and Software Configuration

[1288] 1. Device:

[1289] Camera: Used to capture the user's facial expressions and analyze their emotions.

[1290] Microphone: Used to collect your voice data and analyze your voice.

[1291] User interface: Provide a form to input your mood, what you want to see and experience, time required, budget, etc.

[1292] 2. Server:

[1293] Data analysis module: Receives data entered by the user and emotional data recognized by the emotion engine, and analyzes it using machine learning algorithms and rule-based filters.

[1294] Optimal plan generation module: Generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. It also references the user's past choices and trend information.

[1295] Reservation arrangement module: Automatically makes necessary reservations and arrangements through the APIs of various service provider systems.

[1296] 3. Emotion Engine:

[1297] Facial Expression Analysis: Use libraries such as OpenCV to analyze the user's facial expressions and recognize emotions.

[1298] Speech analysis: Use the speech_recognition library to convert speech data into text and analyze user sentiment.

[1299] Program processing overview

[1300] The server receives user input data and emotional data sent from the device in real time and analyzes it in the data analysis module. This analysis uses machine learning algorithms and rule-based filters. Based on the analysis results, the optimal plan generation module generates the optimal plan that suits the user's wishes, conditions, and emotions.

[1301] The reservation arrangement module then automatically makes the necessary reservations and arrangements through the APIs of various service provider systems (e.g., food delivery services). After the optimal plan is generated and all necessary reservations and arrangements are completed, the information is sent to the terminal and the final plan is displayed to the user.

[1302] Specific Examples

[1303] For example, if a user creates a plan with the conditions "I want to eat curry in a relaxed mood," "My budget is under 3,000 yen," and "I want to eat it right away," it will look like this:

[1304] Example prompt sentence:

[1305] A user says they want to eat curry, their budget is under 3000 yen, and they want to eat it immediately. Please suggest the best food delivery plan.

[1306] 1. User Input and Emotion Recognition:

[1307] The device displays an input form, and the user enters "relax," "curry," "immediately," and "under 3,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "relax."

[1308] 2. Data Analysis:

[1309] The server receives the input data and emotion recognition data sent from the device and analyzes the results as "relaxed," "curry," "immediately," and "under 3,000 yen."

[1310] 3. Generate the optimal plan:

[1311] The server selects the optimal delivery menu for the meal plan and determines which is most suitable for the user's conditions and emotions.

[1312] 4. Reservations and Arrangements:

[1313] The server reserves the corresponding menu (for example, a curry set) via the delivery service's API and arranges delivery.

[1314] 5. Plan presentation and confirmation:

[1315] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[1316] This system allows users to easily enjoy the food delivery plan that best suits their mood and conditions. The introduction of an emotion engine makes it possible to provide plans that are even more suited to the user's emotions.

[1317] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1318] Step 1:

[1319] User Input and Emotion Recognition

[1320] Users use the device's input form to enter information such as their mood, what they want to see and experience, the travel time, and the cost. At the same time, the device's camera and microphone collect the user's facial and voice data. The emotion engine analyzes this data and recognizes the user's current emotions.

[1321] Input: User input data (e.g., "Relax," "Curry," "Right away," "Under 3,000 yen"), user facial expression data, voice data

[1322] Output: User's emotion data (e.g. "Relaxed")

[1323] Step 2:

[1324] Data transmission

[1325] The terminal transmits the user's input data and the emotion data analyzed by the emotion engine to the server.

[1326] Input: User input data, user emotion data

[1327] Output: Consolidated data sent to the server

[1328] Step 3:

[1329] Data analysis

[1330] The server analyzes the received data using machine learning algorithms and rule-based filters, filtering and ranking the plans in the database based on the user's preferences, conditions, and emotions.

[1331] Input: User input data, user sentiment data, plan candidates in the database

[1332] Output: A filtered list of plan candidates

[1333] Step 4:

[1334] Generating an optimal plan

[1335] The server then generates the optimal plan for the user based on the data analysis results. This process takes into account the user's past choices and current trends. The generated plan includes a meal plan, such as the optimal curry delivery menu.

[1336] Input: Filtered plan candidate list, historical selection data, trend information

[1337] Output: Optimal plan (e.g. curry delivery menu)

[1338] Step 5:

[1339] Reservations and Arrangements

[1340] Based on the generated optimal plan, the server automatically makes the necessary reservations and arrangements through the APIs of the various related service provider systems. For example, it reserves delivery of a curry set through the API of a food delivery service.

[1341] Input: Optimal plan, API of service provider system

[1342] Output: Booking confirmation data and booking information

[1343] Step 6:

[1344] Plan presentation and confirmation

[1345] The server sends the information of the optimal plan with all reservations and arrangements completed to the terminal, and the terminal displays the final plan to the user and asks for their confirmation and approval.

[1346] Input: Reservation confirmation data, arrangement information, optimal plan

[1347] Output: Plan presented to user, user approval

[1348] Step 7:

[1349] Gathering feedback

[1350] After the user uses the service, the device collects feedback from the user, and the user's satisfaction and impressions are sent to the server to help improve the accuracy of future plan generation.

[1351] Input: User feedback data

[1352] Output: Feedback data accumulation and analysis results

[1353] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1354] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1355] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1356] [Fourth embodiment]

[1357] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1358] 7, a 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.

[1359] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1360] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1361] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1362] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1363] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1364] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1365] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1366] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1367] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1368] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1369] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1370] MODE FOR CARRYING OUT THE INVENTION

[1371] The present invention provides a specific embodiment of a system that automatically generates an optimal plan taking into consideration the user's mood, what they want to see and do, the required time, and costs, and even handles reservations and transportation arrangements. This system is composed of terminals, a server, and multiple service provider systems in cooperation with each other.

[1372] User Interface Design

[1373] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time it will take, the cost, etc. This form allows users to easily input and submit their wishes and requirements.

[1374] Data Acquisition and Analysis Module

[1375] The data entered by the user into the form is sent from the device to the server, which then analyzes the data and pulls out multiple candidates from a database that match the user's preferences and criteria. This analysis is done using machine learning algorithms and rule-based filters.

[1376] Optimal plan generation module

[1377] The server then generates the optimal plan based on the analysis results, taking into account the user's preferences and conditions. This plan generation process also references the user's past choices and trend information. For example, if a user inputs "I want to visit an art gallery," the server will evaluate the information on multiple galleries and suggest the most suitable one.

[1378] Reservations and transportation arrangements

[1379] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[1380] Presenting and confirming the plan

[1381] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1382] Specific scenario example

[1383] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[1384] 1. User Input

[1385] The device displays an input form, and the user enters "relaxation," "art gallery," "3 hours," and "under 5,000 yen," then clicks the submit button.

[1386] 2. Data Analysis

[1387] The server receives the data sent from the device and analyzes it for "relaxation," "art gallery," "3 hours," and "under 5,000 yen."

[1388] 3. Generating the optimal plan

[1389] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, and determines that this best suits the user's requirements.

[1390] 4. Reservations and transportation arrangements

[1391] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[1392] 5. Presenting the plan

[1393] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[1394] This specific scenario allows users to easily enjoy the optimal plan according to their mood and needs without having to perform cumbersome manual work.

[1395] The processing flow will be explained below.

[1396] Step 1:

[1397] The device displays a form for the user to input their mood, what they want to see, what they want to experience, the time required, and the cost. The user enters these conditions into the form and clicks the submit button.

[1398] Step 2:

[1399] The device sends the input data, including the user's mood, what they want to see, what they want to experience, the time required, and the cost, to the server.

[1400] Step 3:

[1401] The server receives the data sent from the device, analyzes the received data, and generates a list of the best candidates based on the user's input.

[1402] Step 4:

[1403] The server uses machine learning algorithms and rule-based filters to narrow down the list of candidates, taking into account the user's past choices and current trends to identify the most suitable candidates.

[1404] Step 5:

[1405] The server designs an optimal plan, which includes information on candidate spots, travel time, costs, and access methods.

[1406] Step 6:

[1407] The server calls the APIs of various service provider systems to make the necessary reservations, such as reserving tickets to a museum, reserving a table at a cafe, or arranging transportation.

[1408] Step 7:

[1409] The server receives reservation confirmation information from each service provider and reflects it in the optimal plan. Once all reservations and arrangements are complete, the server generates the final plan data.

[1410] Step 8:

[1411] The server sends the final plan to the device, which displays it to the user, providing details and buttons to confirm or change it.

[1412] Step 9:

[1413] The user confirms the plan and clicks the approve button. The contents of the confirmed plan are reflected.

[1414] Step 10:

[1415] The server receives the user's approval and finalizes all bookings, sending the final confirmation information and tickets to the terminal and notifying the user.

[1416] This processing step allows users to automatically obtain the best plan based on their mood and needs without any hassle.

[1417] Example 1

[1418] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1419] In traditional travel and leisure planning, users have to research multiple information sources and manually make reservations and arrangements based on the information. This process is time-consuming and laborious, making it cumbersome for many users. Furthermore, when there are many options, it can be difficult to select the optimal plan. There is a need for a system that can resolve these issues and enable users to efficiently and easily obtain the optimal plan.

[1420] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1421] In this invention, the server includes means for a user to input their mood, what they want to see and experience, the required time, and the cost, means for receiving the input data and analyzing it using a machine learning algorithm or a rule-based filter, means for generating an optimal plan based on the analysis results and taking into account the user's past choices and trend information, means for automatically arranging reservations and transportation required for the generated plan via the API of the linked service provider system, and means for presenting the optimal plan to the user and receiving confirmation. This enables users to efficiently generate, confirm, and approve the optimal plan that best suits their preferences without performing cumbersome manual work.

[1422] A "user" is an individual or group that uses this system and is the entity that inputs conditions such as mood, what they want to see, what they want to experience, required time, and cost.

[1423] "Mood" refers to the emotion or experience the user desires to feel, including states such as relaxation or excitement.

[1424] "What you want to see" refers to places, events, or activities you want to experience.

[1425] "Experiences" refers to activities or events that users would like to experience in real life, such as visiting an art gallery or taking a tea break at a cafe.

[1426] "Duration" refers to the period of time that a User intends to spend on a Plan.

[1427] "Cost" refers to the amount that a User has set as a budget for a Plan.

[1428] "Terminal" refers to the device through which a user accesses the system and makes necessary inputs, including computers, smartphones, tablets, etc.

[1429] "Server" refers to a computer system that receives and analyzes data sent from the terminal, generates the optimal plan based on the conditions, and makes the necessary reservations and arrangements.

[1430] "Database" refers to a system for storing and managing various data, including users' past choices and trend information.

[1431] "Machine learning algorithms" refers to statistical techniques and models used in analyzing data, including methods to derive optimal candidates based on user criteria.

[1432] A "rule-based filter" is a method of analyzing data based on predefined rules and selecting information that meets the user's criteria.

[1433] A "plan" refers to a specific proposal that includes places to visit, experiences, travel time, costs, etc., generated based on user input.

[1434] "API" refers to an interface that allows different systems and services to work together, and includes, for example, a reservation system or a means for arranging transportation.

[1435] "Trend information" refers to current trends and data that may interest users, including information that can be used as a reference for generating plans.

[1436] "Service provider system" refers to a system that provides various services (e.g., ticket reservations, transportation, restaurant reservations) to users.

[1437] MODE FOR CARRYING OUT THE INVENTION

[1438] The present invention provides a specific embodiment of a system that automatically generates an optimal plan taking into consideration the user's mood, what they want to see and do, the required time, and costs, and even handles reservations and transportation arrangements. This system is composed of terminals, a server, and multiple service provider systems in cooperation with each other.

[1439] System Configuration

[1440] The system consists of the following main components:

[1441] Device: A device that allows users to input conditions such as mood, what they want to see, what they want to experience, required time, cost, etc. This includes PCs, smartphones, tablets, etc.

[1442] Server: Receives data sent from devices and analyzes and processes it using machine learning algorithms and rule-based filters, utilizing libraries such as Python's scikit-learn and TensorFlow.

[1443] Database: Stores and manages data including users' past choices and trend information.

[1444] Service provider systems: External systems such as ticket reservation systems, transportation systems, and restaurant reservation systems. They are connected via API.

[1445] Processing flow

[1446] 1. Providing a user interface

[1447] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time required, the cost, etc. This form allows users to easily input and submit their wishes and requirements.

[1448] 2. Data collection and analysis

[1449] The data entered by the user into the form is sent from the device to the server, which then analyzes the data and retrieves multiple candidates from a database that match the user's preferences and criteria. This analysis is performed using machine learning algorithms (e.g., scikit-learn) and rule-based filters.

[1450] 3. Generating the optimal plan

[1451] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes and conditions. For example, if a user inputs "I want to visit an art gallery," the server will evaluate information on multiple galleries and suggest the most suitable one.

[1452] 4. Reservations and transportation arrangements

[1453] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems, such as reserving museum tickets, reserving a seat at a cafe, and arranging a taxi.

[1454] 5. Present and confirm the plan

[1455] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1456] Examples of prompt statements

[1457] Below are some examples of specific prompt sentences.

[1458] ---

[1459] User Input

[1460] The device displays a prompt form, and the user enters "relaxation," "art gallery," "3 hours," and "under 5,000 yen," then clicks the submit button.

[1461] Data analysis

[1462] The server receives the data sent from the device and analyzes the options: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[1463] Generating an optimal plan

[1464] The server generates a plan that includes a "special exhibition at the museum" and a "tea break at a nearby cafe."

[1465] Reservations and transportation arrangements

[1466] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[1467] Presenting the plan

[1468] The server sends the generated plan to the device, and the device displays the final plan to the user, who then checks and approves the plan.

[1469] ---

[1470] This specific operation allows the user to easily enjoy the optimal plan according to their mood and desires without having to perform cumbersome manual work.

[1471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1472] Step 1: The user enters conditions such as mood, what they want to see, what they want to experience, required time, and cost.

[1473] Input: The user enters conditions such as "relaxation," "art gallery," "3 hours," and "under 5,000 yen" into the input form on their device.

[1474] Processing operations: The terminal receives user input and stores it as internal data.

[1475] Output: The device generates the user input data and is ready to send it to the server.

[1476] Step 2: The device sends the user's input data to the server.

[1477] Input: User input data stored on the device.

[1478] Processing operation: The terminal sends the input data to the server.

[1479] Output: The server receives the user's input data.

[1480] Step 3: The server parses the user's input data.

[1481] Input: User input data received by the server, such as "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[1482] Processing behavior: The server analyzes the data using machine learning algorithms (e.g., scikit-learn) and rule-based filters. This analysis pulls potential matches from the database.

[1483] Output: The analysis results in a list of candidates that match the user's criteria.

[1484] Step 4: The server generates an optimal plan.

[1485] Input: A list of candidates obtained as a result of the analysis.

[1486] Processing operation: The server generates the optimal plan by taking into account the user's past selection history and current trend information. For example, a special exhibition at the National Art Center, Tokyo is selected from among several art galleries, and a tea break at a nearby cafe is included in the plan.

[1487] Output: The generated optimal plan data.

[1488] Step 5: The server automatically arranges any necessary reservations and transportation.

[1489] Input: Generated optimal plan data.

[1490] Processing operation: The server automatically makes necessary reservations (e.g., museum ticket reservations, cafe seat reservations) and transportation (e.g., taxi arrangements) through the APIs of various linked service provider systems.

[1491] Output: Data on completed reservation and transportation arrangements.

[1492] Step 6: The server sends the final plan to the device.

[1493] Input: Data for completed reservations and transportation arrangements.

[1494] Processing operation: The server sends the final plan data to the terminal.

[1495] Output: The device receives the final plan.

[1496] Step 7: The device presents the final plan to the user for confirmation.

[1497] Input: The final plan data sent to the device.

[1498] Processing behavior: The terminal displays the final plan to the user, who reviews the plan and approves it if satisfied.

[1499] Output: If user approval is obtained, the final plan is confirmed.

[1500] (Application example 1)

[1501] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1502] With traditional food delivery services, users had to manually search for the food and restaurant that best suited their mood and preferences, and then make individual reservations and arrangements, which was time-consuming and tedious. It was also difficult to find the optimal plan that perfectly matched a specific mood or budget. This made it difficult for users to enjoy the ideal food delivery experience without going through complicated procedures.

[1503] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1504] In this invention, the server includes means for a user to input their mood, what they want to see and experience, the required time, and the cost, means for receiving and analyzing the input data, means for generating an optimal plan based on the input data, means for automatically arranging reservations and transportation required for the generated plan, means for generating an optimal food delivery plan based on a specific mood, type of food, the required time, and the budget, and automatically making the required reservations and delivery arrangements, and means for presenting the optimal plan to the user and receiving confirmation. This allows users to easily enjoy the optimal food delivery plan that suits their mood and conditions at the time without having to perform cumbersome manual work.

[1505] "User" refers to any individual who uses the System.

[1506] "Mood" refers to the emotions and psychological state that a user is feeling at that time.

[1507] "What you want to see" refers to objects or places that users want to enjoy visually.

[1508] "What you want to experience" refers to the activities or actions that users actually want to experience.

[1509] "Duration" refers to the amount of time a user wants to spend on a particular activity or project.

[1510] "Cost" means the amount you plan to pay for a particular activity or plan.

[1511] "Means" refers to the methods or methods used to achieve a particular purpose.

[1512] "Input Data" refers to information provided by a user to a system.

[1513] "Analyzing" refers to evaluating and processing input data using machine learning algorithms and rule-based filters.

[1514] The "optimal plan" refers to the proposal that best suits the user's mood and conditions.

[1515] "Automatically arranging reservations and transportation" refers to the process by which the system automatically arranges the necessary reservations and transportation for the user.

[1516] "Generated Plan" refers to the specific proposal created by the system based on the user's input data.

[1517] "Food Delivery Plan" refers to a meal delivery plan generated by a user based on their mood, type of food, time required, and budget.

[1518] "Automatically making reservations and delivery arrangements" refers to the process by which the system automatically makes the reservations and delivery arrangements necessary for food delivery services.

[1519] "Presenting the optimal plan to the user" refers to the process of showing the system-generated proposal to the user and asking for their confirmation.

[1520] "Receiving confirmation" refers to the act of the user approving the proposed plan.

[1521] The present invention is a system that automatically generates an optimal food delivery plan taking into consideration the user's mood, desired experience, required time, cost, etc., and makes the necessary reservations and arrangements. The present invention is configured in cooperation with a terminal, a server, and multiple service provider systems. Specific embodiments are described below.

[1522] User Interface Design

[1523] The terminal provides a form for users to input their mood, desired cuisine, required time, budget, etc. This form allows users to easily input and submit their wishes and requirements.

[1524] Data Acquisition and Analysis Module

[1525] The data entered by the user into the form is sent from the device to a server, which then analyzes the data and pulls out multiple candidates from a database that match the user's preferences and criteria. This analysis is done using machine learning algorithms, such as generative AI models, and rule-based filters.

[1526] Optimal plan generation module

[1527] The server then generates the optimal food delivery plan based on the analysis results, taking into account the user's preferences and conditions. This plan generation process also takes into account the user's past choices and current trend information.

[1528] Reservations and Arrangements

[1529] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., food delivery service APIs), including ordering from restaurants and arranging delivery.

[1530] Presenting and confirming the plan

[1531] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1532] Specific scenario example

[1533] Below is a specific example of a user creating a plan with the conditions "relaxed atmosphere," "Italian food," "30 minutes," and "under 2,000 yen."

[1534] 1. User Input

[1535] The device displays an input form, and the user enters "relaxation," "Italian food," "30 minutes," and "under 2,000 yen," and clicks the submit button.

[1536] 2. Data Analysis

[1537] The server receives the data sent from the device and analyzes the criteria: "relaxation," "Italian food," "30 minutes," and "under 2,000 yen."

[1538] 3. Generating the optimal plan

[1539] The server then uses the analysis results to suggest the most suitable restaurant and menu, and generates a plan for it. For example, it generates a plan for "Pasta Carbonara" that takes into account the delivery time, cost, and other factors.

[1540] 4. Reservations and Arrangements

[1541] The server uses the API of the affiliated food delivery service to send the order to the restaurant and arrange for delivery.

[1542] 5. Presenting the plan

[1543] The server sends the generated plan to the terminal, and the terminal displays the final plan to the user, who then checks and approves the plan.

[1544] An example of a prompt is as follows:

[1545] Mood: Relaxed

[1546] Preferred cuisine: Italian

[1547] Duration: 30 minutes

[1548] Budget: 2000 yen

[1549] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1550] Step 1:

[1551] The terminal displays a form for the user to enter their mood, desired cuisine, required time, budget, etc. The user fills in each item and clicks the submit button.

[1552] (Input: User's mood, desired dish, time required, budget. Output: Submitted user data)

[1553] Step 2:

[1554] User data is sent from the device to the server, which then analyzes the received user data.

[1555] (Input: User data sent. Output: Analysis results)

[1556] Step 3:

[1557] The server analyzes the received user data and pulls out candidate restaurants and menus from a database, processing the data using generative AI models and rule-based filters.

[1558] (Input: Analysis requirements (mood, cuisine, time, budget). Output: Candidate list)

[1559] Step 4:

[1560] Based on the analysis results, the server generates a food delivery plan that best suits the user's mood, desired cuisine, required time, and budget. This plan generation process also takes into account the user's past selections and trend information.

[1561] (Input: candidate list. Output: optimal plan)

[1562] Step 5:

[1563] Based on the generated plan, the server automatically makes the necessary reservations and delivery arrangements through the APIs of the various service provider systems it is connected to. Specifically, it uses the API of the food delivery service to send the order to the restaurant and arrange for delivery.

[1564] (Input: Optimal plan. Output: Reservation completion notification)

[1565] Step 6:

[1566] After the necessary reservations and arrangements are completed, the server sends the generated optimal plan to the terminal, which then displays the plan to the user.

[1567] (Input: Reservation completion notification. Output: Plan display)

[1568] Step 7:

[1569] The terminal presents the final plan to the user for review and approval. The user reviews the presented plan and approves it if satisfied.

[1570] (Input: Displayed plan. Output: User approval)

[1571] Step 8:

[1572] Once the user approves the plan, the server will receive the confirmation and begin implementing the plan. Finally, the user can receive food delivery that meets their requirements.

[1573] (Input: User approval. Output: Food delivery)

[1574] The above is a detailed description of the specific operations and processing steps in an actual system.

[1575] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1576] MODE FOR CARRYING OUT THE INVENTION

[1577] This invention is a system that automatically generates an optimal plan by considering the user's mood, what they want to see and experience, the required time, cost, etc., and provides a more accurate plan by combining it with an emotion engine that recognizes the user's emotions. This system is composed of terminals, a server, an emotion engine, and cooperation with multiple service provider systems.

[1578] User Interface Design

[1579] The terminal provides a form for users to input their mood, what they want to see, what they want to experience, the time it will take, the cost, etc. In this form, users can easily input and submit their wishes and requirements.

[1580] Emotion engine integration

[1581] The emotion engine has the ability to recognize the user's emotions by collecting and analyzing their facial expressions and voice data in real time, in addition to the data input by the user. This allows the emotional data obtained from the user's facial expressions and voice to be reflected in the system and used to generate plans.

[1582] Data Acquisition and Analysis Module

[1583] The data entered by the user into the form and the emotional data recognized by the emotion engine are sent from the device to the server. The server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions. This analysis is performed using machine learning algorithms and rule-based filters.

[1584] Optimal plan generation module

[1585] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. This plan generation process also references the user's past choices and trend information. For example, if a user enters "I want to visit an art gallery" and the emotion engine analyzes that the user is relaxing, the server will suggest a plan centered around relaxing art galleries.

[1586] Reservations and transportation arrangements

[1587] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[1588] Presenting and confirming the plan

[1589] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1590] Specific scenario example

[1591] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[1592] 1. User Input and Emotion Recognition

[1593] The device displays an input form, and the user enters "relax," "art gallery," "3 hours," and "under 5,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "relax."

[1594] 2. Data Analysis

[1595] The server receives the input data and emotion recognition data sent from the device and analyzes the results: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[1596] 3. Generating the optimal plan

[1597] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, determining that this is most suitable for the user's conditions and emotions.

[1598] 4. Reservations and transportation arrangements

[1599] The server automatically reserves museum tickets, cafe seats, and arranges necessary transportation.

[1600] 5. Presenting the plan

[1601] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[1602] This specific scenario allows users to easily enjoy the optimal plan according to their mood and desires without having to perform tedious manual tasks. The introduction of the emotion engine makes it possible to provide plans that are more suited to the user's emotions.

[1603] The processing flow will be explained below.

[1604] Step 1:

[1605] The device displays a form for the user to input their mood, what they want to see, what they want to experience, the required time, and the cost. The user enters these conditions into the form and clicks the submit button. The emotion engine also collects and analyzes the user's facial expressions and voice in real time.

[1606] Step 2:

[1607] The device sends the input data and emotional data recognized by the emotion engine to the server. The data includes the user's mood, what they want to see and experience, the required time, the cost, and emotions recognized from facial expressions and voice.

[1608] Step 3:

[1609] The server receives the data sent from the device, analyzes the received data, and generates a list of optimal candidates based on the user's input and emotions.

[1610] Step 4:

[1611] The server uses machine learning algorithms and rule-based filters to narrow down the list of candidates, taking into account the user's past choices and current trend information to identify the most suitable candidates.

[1612] Step 5:

[1613] The server then designs an optimal itinerary, including information on potential spots, travel time, costs, and access methods. For example, it suggests a special exhibition at the National Art Center, Tokyo, taking into account the criteria of "art gallery" and "relaxation."

[1614] Step 6:

[1615] The server calls the APIs of various service provider systems to make the reservations required for the generated plan, such as automatically reserving museum tickets, reserving cafe seats, and arranging transportation.

[1616] Step 7:

[1617] The server receives reservation confirmation information from each service provider and reflects it in the optimal plan. Once all reservations and arrangements are complete, the server generates the final plan data.

[1618] Step 8:

[1619] The server sends the final plan to the device, which displays the plan details to the user and provides confirmation and change buttons.

[1620] Step 9:

[1621] The user checks the plan and clicks the approve button. The confirmed plan contents are sent to the server and are updated accordingly.

[1622] Step 10:

[1623] The server receives the user's approval and finalizes all bookings, sending the final confirmation information and tickets to the terminal and notifying the user.

[1624] This processing step allows users to automatically obtain the optimal plan based on their emotions, wishes, and conditions without any effort. The introduction of an emotion engine makes it possible to provide services that are more adapted to the user's emotions.

[1625] Example 2

[1626] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1627] There is a demand for a system that allows users to easily generate plans that match their moods and desires, and automatically arranges reservations and transportation based on those plans. However, conventional systems generate plans based only on user input data and are unable to take the user's emotions into account, making it difficult to provide plans that match the user's true desires and circumstances.

[1628] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1629] In this invention, the server includes a means for the user to input their mood, what they want to see and experience, the required time, and the cost, a means for collecting and analyzing the user's facial expression and voice data to recognize their emotions, and a means for receiving and analyzing the input data and the recognized emotion data. This makes it possible to generate a plan that takes into account the emotion data in addition to the user's mood and wishes.

[1630] "User" refers to a person who uses the system to generate plans and make reservations.

[1631] "Mood" refers to information that expresses the emotions and psychological state that a user is feeling.

[1632] "What to see" refers to information that describes the places users want to visit or the activities they want to experience.

[1633] "Things you want to experience" refers to information that describes activities or events that users would actually like to experience.

[1634] "Time required" refers to information that indicates the range of time required for the user to execute the plan.

[1635] "Cost" refers to information that represents the range of amounts a user can pay for a plan.

[1636] "Form" refers to the input screen where users can enter their mood, what they want to see, what they want to experience, the time required, and the cost.

[1637] An "emotion engine" refers to a system that has the ability to collect and analyze a user's facial expressions and voice data to recognize their emotions.

[1638] "Database" refers to a collection of information that stores data needed to generate a plan.

[1639] A "machine learning algorithm" refers to a method that uses data to generate a model for making predictions or classifications.

[1640] "Integration" refers to different systems and services exchanging information with each other through APIs.

[1641] "API (Application Program Interface)" refers to the definitions and protocols that allow different software systems to communicate with each other and utilize their functions.

[1642] A "plan" refers to a specific action plan generated based on the user's wishes, conditions, and emotions.

[1643] "Reservation arrangement" refers to the process of securing the necessary reservations and transportation based on the generated plan.

[1644] "Terminal" refers to an electronic device that a user operates and uses to input their moods and preferences.

[1645] "Server" refers to the computer system that receives data from users, analyzes it, generates plans, and arranges reservations.

[1646] MODE FOR CARRYING OUT THE INVENTION

[1647] This invention is a system that automatically generates an optimal plan by considering the user's mood, what they want to see and experience, the required time, cost, etc., and provides a more accurate plan by combining it with an emotion engine that recognizes the user's emotions. This system is composed of terminals, a server, an emotion engine, and cooperation with multiple service provider systems.

[1648] User Interface Design

[1649] The device provides a form for users to input their mood, what they want to see, what they want to experience, the time required, and the cost. This form allows users to easily input and submit their wishes and requirements. For example, users can communicate their specific wishes and conditions by entering "relaxation," "art gallery," "3 hours," and "under 5,000 yen."

[1650] Emotion engine integration

[1651] The emotion engine has the ability to recognize the user's emotions by collecting and analyzing the user's facial expressions and voice data in real time, in addition to the user's input data. This allows the emotional data obtained from the user's facial expressions and voice to be reflected in the system and used to generate plans. For example, if a user makes input with a relaxed expression, the emotion engine will recognize the emotion as "relaxed."

[1652] Data Acquisition and Analysis Module

[1653] The data entered by the user into the form and the emotional data recognized by the emotion engine are sent from the device to the server. The server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions. This analysis is performed using machine learning algorithms and rule-based filters.

[1654] Optimal plan generation module

[1655] The server then generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. This plan generation process also references the user's past choices and trend information. For example, if a user enters "I want to visit an art gallery" and the emotion engine analyzes that the user is relaxing, the server will suggest a plan centered around relaxing art galleries.

[1656] Reservations and transportation arrangements

[1657] Based on the generated plan, the server automatically makes the necessary reservations and arrangements through the APIs of various connected service provider systems (e.g., ticket reservation systems, transportation systems, restaurant reservation systems), such as making museum tickets, cafe reservations, and arranging taxis.

[1658] Presenting and confirming the plan

[1659] After the optimal plan is generated and all necessary reservations and arrangements are completed, the server sends this information to the terminal, which displays the final plan to the user and asks for confirmation and approval. The user reviews the presented plan and approves it if satisfied.

[1660] Specific scenario example

[1661] Below is a specific example of a user creating a plan with the following conditions: "Relaxed mood," "Want to visit an art gallery," "3 hours," and "Under 5,000 yen."

[1662] Input prompt statement:

[1663] I feel relaxed. I want to visit an art gallery. The total time is less than 3 hours. I would like to plan the cost to be less than 5,000 yen.

[1664] 1. User Input and Emotion Recognition:

[1665] The device displays an input form, and the user enters "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "Relax."

[1666] 2. Data Analysis:

[1667] The server receives the input data and emotion recognition data sent from the device and analyzes the results: "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen."

[1668] 3. Generate the optimal plan:

[1669] The server generates a plan that includes a special exhibition at the National Art Center, Tokyo and a tea break at a nearby cafe, and determines that this best suits the user's requirements.

[1670] 4. Booking and Transportation Arrangements:

[1671] The server automatically reserves museum tickets, cafe reservations, and arranges necessary transportation.

[1672] 5. Present the plan:

[1673] The server sends the generated plan to the terminal, which displays the final plan to the user, who then reviews it and approves it if satisfied.

[1674] This specific scenario allows users to easily enjoy the optimal plan according to their mood and desires without having to perform tedious manual tasks. The introduction of the emotion engine makes it possible to provide plans that are more suited to the user's emotions.

[1675] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1676] Step 1: Getting User Input

[1677] What it means: The device displays a form where the user can input their mood, what they want to see and do, how long it will take, and how much it will cost.

[1678] Input: The user enters "Relax," "Art Gallery," "3 hours," and "Under 5,000 yen" into the input form.

[1679] Data processing / calculation: Collects input information and converts it into a data format for sending to the server.

[1680] Output: A data packet containing the input information.

[1681] Specific actions: The user operates the device, enters the required information into the form, and clicks the submit button.

[1682] Step 2: Collecting emotion data

[1683] Specifics: The device uses the built-in camera and microphone to collect the user's facial expression and voice data, which is then analyzed by the emotion engine.

[1684] Input: Facial image captured by a camera and audio data recorded by a microphone.

[1685] Data processing / calculation: The emotion engine analyzes the collected image and audio data and evaluates the user's emotional state.

[1686] Output: Parsed emotion data (e.g. "Relaxed").

[1687] Specific operation: The device captures the user's facial expressions with a camera, records audio with a microphone, and sends this data to the emotion engine.

[1688] Step 3: Sending data

[1689] Specific explanation: The terminal transmits the user's input data and the emotion data recognized by the emotion engine to the server.

[1690] Input: User input data and sentiment data.

[1691] Data processing / calculation: The data is compiled into data packets and sent to the server via the network.

[1692] Output: Data packets sent to the server.

[1693] Specific operation: After the user clicks the send button, the device sends the data to the server.

[1694] Step 4: Data analysis

[1695] Specifically, the server analyzes the received data and retrieves multiple candidates from a database that match the user's wishes, conditions, and emotions.

[1696] Input: User input data and emotion data sent from the device.

[1697] Data processing / calculation: Analyzes data using machine learning algorithms and rule-based filters to extract candidates that match the user's criteria.

[1698] Output: A list of plan candidates.

[1699] Specific operation: The server extracts plan candidates that match the conditions from the database and lists them as analysis results.

[1700] Step 5: Generate an optimal plan

[1701] Specific explanation: Based on the analysis results, the server generates the optimal plan taking into account the user's wishes, conditions, and emotions.

[1702] Input: Parsed user data and plan candidate list.

[1703] Data processing / calculation: The optimal plan is selected by taking into account the user's past choices and trend information.

[1704] Output: Optimal plan.

[1705] Specific operation: The server generates the optimal plan and stores its details in a data format.

[1706] Step 6: Make reservations and arrange transportation

[1707] Specific explanation: Based on the generated plan, the server makes the necessary reservations and arrangements using the APIs of various service provider systems.

[1708] Input: Details of the best plan.

[1709] Data processing / calculation: Sending reservation and arrangement requests through the APIs of various services.

[1710] Output: Reservation confirmation and transportation arrangement information.

[1711] Specific operation: The server uses, for example, the API of a ticket reservation system to reserve tickets for a museum.

[1712] Step 7: Present the generated plan

[1713] Specific explanation: The server sends the final plan and reservation information to the terminal, which displays it to the user.

[1714] Input: Reservation confirmation information and transportation arrangement information.

[1715] Data processing / calculation: Convert the plan into a format that is easy for users to view.

[1716] Output: Final plan information for display.

[1717] Specific operation: The terminal displays the plan information received from the server and provides an interface for the user to review and approve.

[1718] The above is a description of the specific processing steps of the system.

[1719] (Application example 2)

[1720] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1721] Conventional plan generation systems rely on user input data and do not fully consider the user's emotions or momentary mood. Furthermore, the automation of the reservation and arrangement process is limited, placing a heavy burden on the user. Furthermore, in the case of food delivery, the system is insufficient in proposing plans that suit the user's current emotions.

[1722] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input information about their mood, what they want to see and experience, the required time, and the cost; means for analyzing the user's facial expressions and voice to recognize their emotions; means for generating an optimal plan based on the analyzed data and emotional data; means for automatically arranging necessary reservations and transportation based on the generated plan; and means for presenting the optimal plan to the user and receiving confirmation. This makes it possible to provide an optimal plan that takes the user's emotions into consideration and reduce the burden on the user by automating reservation arrangements.

[1723] "User input means" refers to the interface that allows users to input information such as their mood, what they want to see, what they want to experience, the time required, and the cost.

[1724] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions and voice data in real time to recognize the user's current emotions.

[1725] The "means for generating a plan" is an algorithm or system that automatically creates an optimal plan based on the user's input data and emotional data.

[1726] "Means for automatically arranging reservations and transportation" refers to a system that automatically arranges necessary reservations and transportation based on the generated plan. This includes ticket reservations, transportation arrangements, restaurant reservations, etc.

[1727] The "means for presenting a plan to the user and receiving confirmation" refers to an interface that presents the optimal plan to the user visually or audibly, and allows the user to confirm and approve the plan.

[1728] "Means of linking with APIs of various services" refers to an integrated system that uses APIs to exchange information with external services and automatically makes reservations and arrangements.

[1729] "Generative model" refers to a machine learning model or AI algorithm that proposes the optimal plan for the user.

[1730] A "means for generating prompt sentences" is a program or algorithm that automatically generates prompt sentences to be input into a generative AI model.

[1731] This invention is a system that automatically generates an optimal plan by taking into consideration the user's mood, what they want to see and experience, the required time, and costs. Furthermore, by combining it with the functionality of an emotion engine that recognizes the user's emotions, it provides a more accurate plan. This system includes a terminal, a server, an emotion engine, and links with various service provider systems.

[1732] Hardware and Software Configuration

[1733] 1. Device:

[1734] Camera: Used to capture the user's facial expressions and analyze their emotions.

[1735] Microphone: Used to collect your voice data and analyze your voice.

[1736] User interface: Provide a form to input your mood, what you want to see and experience, time required, budget, etc.

[1737] 2. Server:

[1738] Data analysis module: Receives data entered by the user and emotional data recognized by the emotion engine, and analyzes it using machine learning algorithms and rule-based filters.

[1739] Optimal plan generation module: Generates the optimal plan based on the analysis results, taking into account the user's wishes, conditions, and emotions. It also references the user's past choices and trend information.

[1740] Reservation arrangement module: Automatically makes necessary reservations and arrangements through the APIs of various service provider systems.

[1741] 3. Emotion Engine:

[1742] Facial Expression Analysis: Use libraries such as OpenCV to analyze the user's facial expressions and recognize emotions.

[1743] Speech analysis: Use the speech_recognition library to convert speech data into text and analyze user sentiment.

[1744] Program processing overview

[1745] The server receives user input data and emotional data sent from the device in real time and analyzes it in the data analysis module. This analysis uses machine learning algorithms and rule-based filters. Based on the analysis results, the optimal plan generation module generates the optimal plan that suits the user's wishes, conditions, and emotions.

[1746] The reservation arrangement module then automatically makes the necessary reservations and arrangements through the APIs of various service provider systems (e.g., food delivery services). After the optimal plan is generated and all necessary reservations and arrangements are completed, the information is sent to the terminal and the final plan is displayed to the user.

[1747] Specific Examples

[1748] For example, if a user creates a plan with the conditions "I want to eat curry in a relaxed mood," "My budget is under 3,000 yen," and "I want to eat it right away," it will look like this:

[1749] Example prompt sentence:

[1750] A user says they want to eat curry, their budget is under 3000 yen, and they want to eat it immediately. Please suggest the best food delivery plan.

[1751] 1. User Input and Emotion Recognition:

[1752] The device displays an input form, and the user enters "relax," "curry," "immediately," and "under 3,000 yen." At the same time, the emotion engine analyzes the user's facial expressions and voice data and recognizes "relax."

[1753] 2. Data Analysis:

[1754] The server receives the input data and emotion recognition data sent from the device and analyzes the results as "relaxed," "curry," "immediately," and "under 3,000 yen."

[1755] 3. Generate the optimal plan:

[1756] The server selects the optimal delivery menu for the meal plan and determines which is most suitable for the user's conditions and emotions.

[1757] 4. Reservations and Arrangements:

[1758] The server reserves the corresponding menu (for example, a curry set) via the delivery service's API and arranges delivery.

[1759] 5. Plan presentation and confirmation:

[1760] The server sends the generated plan to the terminal, which displays the final plan to the user, who then checks and approves it.

[1761] This system allows users to easily enjoy the food delivery plan that best suits their mood and conditions. The introduction of an emotion engine makes it possible to provide plans that are even more suited to the user's emotions.

[1762] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1763] Step 1:

[1764] User Input and Emotion Recognition

[1765] Users use the device's input form to enter information such as their mood, what they want to see and experience, the travel time, and the cost. At the same time, the device's camera and microphone collect the user's facial and voice data. The emotion engine analyzes this data and recognizes the user's current emotions.

[1766] Input: User input data (e.g., "Relax," "Curry," "Right away," "Under 3,000 yen"), user facial expression data, voice data

[1767] Output: User's emotion data (e.g. "Relaxed")

[1768] Step 2:

[1769] Data transmission

[1770] The terminal transmits the user's input data and the emotion data analyzed by the emotion engine to the server.

[1771] Input: User input data, user emotion data

[1772] Output: Consolidated data sent to the server

[1773] Step 3:

[1774] Data analysis

[1775] The server analyzes the received data using machine learning algorithms and rule-based filters, filtering and ranking the plans in the database based on the user's preferences, conditions, and emotions.

[1776] Input: User input data, user sentiment data, plan candidates in the database

[1777] Output: A filtered list of plan candidates

[1778] Step 4:

[1779] Generating an optimal plan

[1780] The server then generates the optimal plan for the user based on the data analysis results. This process takes into account the user's past choices and current trends. The generated plan includes a meal plan, such as the optimal curry delivery menu.

[1781] Input: Filtered plan candidate list, historical selection data, trend information

[1782] Output: Optimal plan (e.g. curry delivery menu)

[1783] Step 5:

[1784] Reservations and Arrangements

[1785] Based on the generated optimal plan, the server automatically makes the necessary reservations and arrangements through the APIs of the various related service provider systems. For example, it reserves delivery of a curry set through the API of a food delivery service.

[1786] Input: Optimal plan, API of service provider system

[1787] Output: Booking confirmation data and booking information

[1788] Step 6:

[1789] Plan presentation and confirmation

[1790] The server sends the information of the optimal plan with all reservations and arrangements completed to the terminal, and the terminal displays the final plan to the user and asks for their confirmation and approval.

[1791] Input: Reservation confirmation data, arrangement information, optimal plan

[1792] Output: Plan presented to user, user approval

[1793] Step 7:

[1794] Gathering feedback

[1795] After the user uses the service, the device collects feedback from the user, and the user's satisfaction and impressions are sent to the server to help improve the accuracy of future plan generation.

[1796] Input: User feedback data

[1797] Output: Feedback data accumulation and analysis results

[1798] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1799] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1800] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1801] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1802] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1803] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1804] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1805] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1806] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1807] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1808] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1809] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1810] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1811] 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.

[1812] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1813] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1814] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1815] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1816] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1817] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1818] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1819] The following is further disclosed regarding the above embodiment.

[1820] (Claim 1)

[1821] A way for users to input their mood, what they want to see, what they want to experience, how long it will take, and how much it will cost.

[1822] means for receiving and analyzing input data;

[1823] a means for generating an optimal plan based on input data;

[1824] A means to automatically arrange reservations and transportation required for the generated plan, and

[1825] A way to present the best plan to the user and receive confirmation,

[1826] A system including:

[1827] (Claim 2)

[1828] 10. The system of claim 1, wherein the plan is generated taking into account a user's past choices and current trends.

[1829] (Claim 3)

[1830] The system according to claim 1, which works in conjunction with APIs of various services to automatically arrange reservations.

[1831] "Example 1"

[1832] (Claim 1)

[1833] A way for users to input their mood, what they want to see, what they want to experience, how long it will take, and how much it will cost.

[1834] A means for receiving input data and analyzing it using machine learning algorithms and rule-based filters;

[1835] A means to generate an optimal plan based on the analysis results, taking into account the user's past choices and trend information;

[1836] A means to automatically arrange reservations and transportation required for the generated plan through the API of the linked service provider system;

[1837] A way to present the best plan to the user and receive confirmation,

[1838] A system including:

[1839] (Claim 2)

[1840] The system of claim 1, wherein the system retrieves multiple candidates from a database based on user selection criteria and generates an optimal plan.

[1841] (Claim 3)

[1842] 10. The system of claim 1, wherein the system uses a machine learning algorithm to analyze data that matches the user's criteria.

[1843] "Application Example 1"

[1844] (Claim 1)

[1845] A way for users to input their mood, what they want to see, what they want to experience, how long it will take, and how much it will cost.

[1846] means for receiving and analyzing input data;

[1847] a means for generating an optimal plan based on input data;

[1848] A means to automatically arrange reservations and transportation required for the generated plan, and

[1849] A means to generate optimal food delivery plans based on specific moods, cuisine types, time required and budget, and automatically make the necessary reservations and delivery arrangements;

[1850] A way to present the best plan to the user and receive confirmation,

[1851] A system including:

[1852] (Claim 2)

[1853] 10. The system of claim 1, wherein the plan is generated taking into account a user's past choices and current trends.

[1854] (Claim 3)

[1855] The system according to claim 1, which works in conjunction with APIs of various services to automatically arrange reservations.

[1856] "Example 2: Combining Emotion Engines"

[1857] (Claim 1)

[1858] A way for users to input their mood, what they want to see, what they want to experience, how long it will take, and how much it will cost.

[1859] A means of recognizing emotions by collecting and analyzing the user's facial and voice data;

[1860] means for receiving and analyzing input data and recognized emotion data;

[1861] means for generating an optimal plan based on the input data and the emotion data;

[1862] A means to automatically arrange reservations and transportation required for the generated plan, and

[1863] A way to present the best plan to the user and receive confirmation,

[1864] A system including:

[1865] (Claim 2)

[1866] 10. The system of claim 1, wherein the plan is generated taking into account a user's past choices and current trends.

[1867] (Claim 3)

[1868] The system according to claim 1, which cooperates with application program interfaces (APIs) of various services to automatically arrange reservations.

[1869] "Application example 2 when combining emotion engines"

[1870] (Claim 1)

[1871] A way for users to input their mood, what they want to see, what they want to experience, how long it will take, and how much it will cost.

[1872] A means for analyzing input data and the user's facial expressions and voice to recognize emotions;

[1873] means for generating an optimal plan based on the analyzed data and the emotion data;

[1874] A means to automatically arrange necessary reservations and transportation based on the generated plan;

[1875] A way to present the best plan to the user and receive confirmation,

[1876] A system including:

[1877] (Claim 2)

[1878] 2. The system according to claim 1, further comprising: means for generating a plan taking into consideration the user's past choices and current trends; and means for improving the accuracy of the plan by also taking into consideration the user's emotions.

[1879] (Claim 3)

[1880] The system of claim 1 comprises a means for automatically arranging reservations in cooperation with APIs of various services, a means for using a generative model based on response results to propose an optimal plan suitable for the user, and a means for creating prompt sentences and inputting them into the generative AI model. [Explanation of symbols]

[1881] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A way for users to input their mood, what they want to see, what they want to experience, how long it will take, and how much it will cost. means for receiving and analyzing input data; a means for generating an optimal plan based on input data; A means to automatically arrange reservations and transportation required for the generated plan, and A way to present the best plan to the user and receive confirmation, A system including:

2. The system of claim 1 , wherein the plan is generated taking into account the user's past choices and current trends.

3. The system according to claim 1, which cooperates with APIs of various services to automatically arrange reservations.

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