System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting information on hobbies and preferences of a user and a means of transportation; logger means for collecting movement data of the user; means for analyzing the collected movement data; and means for proposing a going-out plan based on an analysis result.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] Many people refer to magazines and the Internet when deciding where to go on their days off, but the information they find is often merely a model case and does not match their personal hobbies or patterns of behavior. Furthermore, because gathering information is time-consuming, many people ultimately give up on going out. To solve this problem, a method is needed to propose personalized outing plans that take into account individual hobbies, preferences, and patterns of behavior. [Means for solving the problem]

[0005] The present invention is a system that includes a means for inputting information on a user's hobbies, preferences, and transportation methods, a logger means for collecting the user's movement data, a means for analyzing the collected movement data, and a means for proposing outing plans based on the analysis results. Furthermore, by including a filter means for excluding inappropriate routes and locations from the collected movement data, it is possible to provide the user with safe and interesting outing plans. Furthermore, by including a means for displaying the proposed outing plans to the user and allowing the user to select a plan, the system allows the user to easily select the outing plan that is best for them.

[0006] "User" refers to an individual or individuals who use the system.

[0007] "Hobbies" refers to the activities and areas of interest that a user prefers.

[0008] "Transportation" refers to the means used by the user to travel (e.g., walking, cycling, public transportation).

[0009] "Logger" refers to a device or application that collects user movement data (e.g., GPS data, step counts).

[0010] "Movement data" refers to data such as location information, movement route, and number of steps generated by a user when moving.

[0011] "Analysis means" refers to algorithms or programs that analyze users' behavioral patterns and hobbies and preferences based on collected movement data.

[0012] An "outing plan" refers to a plan that includes places to visit and recommended activities suggested to the user based on the analysis results.

[0013] "Filtering means" refers to a function that allows a user to exclude routes or locations that they deem inappropriate from collected travel data.

[0014] The "selection means" refers to a function that allows the user to select any plan from the proposed outing plans.

[0015] The term "system" refers to the entire component including the above means. [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] The present invention relates to a system that proposes optimal outing plans based on the hobbies, preferences, and behavioral patterns of individual users. This system is composed of a user terminal, a dedicated logger, and a server, and functions as follows.

[0038] First, a user installs the application on a device such as a smartphone, creates an account, and logs in. The user then uses the application to set their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[0039] Next, the user carries a dedicated logger with them when they go out. This logger uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals (e.g., every 30 seconds). The device temporarily stores the data from the logger and uploads it to a server at regular intervals (e.g., every 5 minutes).

[0040] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. This analysis uses algorithms such as clustering and pattern mining. It also performs filtering to exclude routes and locations that the user deems inappropriate (e.g., gambling halls). The analysis results are used to understand the user's behavioral patterns and generate optimal outing plans.

[0041] As a specific example, let's say a user goes out on a day off with a logger and visits hiking spot A and cafe B. Based on this movement data, the server analyzes the user's walking distance, the places they visited, and the amount of time they spent there, and then suggests new hiking courses and cafe-hopping plans that are perfect for the next weekend. For example, a plan that includes hiking spot C and a new cafe D is generated and presented to the user.

[0042] The device displays the outing plans received from the server to the user. The user can select the plan that best suits their interests and conditions from the presented plans. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0043] The purpose of the present invention is to provide users with optimal outing plans through the above-mentioned series of processes, thereby lowering the barriers to going out. This system is expected to increase opportunities to go out and increase economic consumption.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] Users install the application on their smartphone or other device, create an account, and log in.

[0047] Step 2:

[0048] Through the application, users input their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling).

[0049] Step 3:

[0050] The device stores the entered information on hobbies, preferences and means of transportation in an internal database and transmits the data to a server.

[0051] Step 4:

[0052] The server stores the received initial setting data in a database.

[0053] Step 5:

[0054] The user goes out carrying a dedicated logger, which uses GPS to record the user's current location and an accelerometer to count the number of steps taken.

[0055] Step 6:

[0056] The logger sends data on the current location, number of steps taken, and places visited to the device at regular intervals (e.g., every 30 seconds).

[0057] Step 7:

[0058] The device temporarily stores the received movement data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0059] Step 8:

[0060] The server stores the movement data received from the terminal in a database.

[0061] Step 9:

[0062] The server analyzes the user's movement patterns using algorithms such as clustering and pattern mining.

[0063] Step 10:

[0064] The server runs a filter process to exclude routes and locations that the user deems inappropriate, according to their settings.

[0065] Step 11:

[0066] Based on the analysis results, the server generates an outing plan that matches the user's hobbies, preferences, and behavioral patterns. Specifically, it creates multiple plans that include places to visit, recommended time schedules, and means of transportation to use.

[0067] Step 12:

[0068] The server transmits the generated outing plan to the terminal.

[0069] Step 13:

[0070] The terminal displays the outing plan received from the server to the user.

[0071] Step 14:

[0072] The user selects from the plans presented one that best suits their interests and requirements, and the selected plan is saved on the device.

[0073] Step 15:

[0074] Based on the selected plan, the device will link with map apps and reminder functions to support users when they are out and about.

[0075] The above are the specific processing steps of the system. Through this series of processing, the system proposes the most suitable outing plan to the user, making it easier to plan an outing.

[0076] Example 1

[0077] 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."

[0078] Conventional outing plan suggestion systems have difficulty providing optimal plans based on a user's hobbies, preferences, and behavioral patterns. In particular, there is a risk that inappropriate routes or locations may be suggested, which can reduce user satisfaction. Furthermore, systems that can analyze data in real time and smoothly provide users with optimal plans have not yet been fully established. There has been a demand for a system that can solve these issues and provide users with more suitable outing plans.

[0079] 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.

[0080] In this invention, the server includes: a means for a user to input information about hobbies, preferences, and transportation methods into a terminal; a means for the user to carry a logger when going out and collect movement data using a GPS and acceleration sensor; a means for transferring the collected movement data to the terminal at regular intervals and temporarily storing it; a means for uploading data from the terminal to the server at regular intervals; a means for analyzing the movement data stored in the database on the server using algorithms such as clustering and pattern mining; and a means for generating and proposing an optimal outing plan to the user based on the analysis results. This makes it possible to provide an optimal plan based on the user's hobbies, preferences, and behavioral patterns, and to realize high-quality plan proposals that exclude inappropriate routes and locations.

[0081] "Hobbies" refer to the activities and interests that interest a user.

[0082] "Transportation" refers to the method or means used by a user to travel, including walking, cycling, public transportation, etc.

[0083] A "terminal" is a portable electronic device used by a user, such as a smartphone or tablet.

[0084] A "logger" is a device that has a built-in GPS sensor and acceleration sensor and collects user movement data.

[0085] "Analysis" is the process of extracting user behavior patterns and trends from collected data using statistical methods and machine learning algorithms.

[0086] An "outing plan" is a plan of destinations and routes proposed based on the user's hobbies, preferences and behavioral patterns.

[0087] "Filtering" is the process of removing routes or locations that are deemed inappropriate from the collected data.

[0088] A "database" is an information management system that stores collected data in an organized manner and enables efficient access when needed.

[0089] "Clustering" is a machine learning technique that categorizes data points into groups such that the data within each group are more similar to each other than to the data in other groups.

[0090] "Pattern mining" is a data analysis technique for finding useful patterns and regularities from large amounts of data.

[0091] The present invention relates to a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns. This system is composed of a user terminal, a dedicated logger, and a server.

[0092] Hardware and software used

[0093] The system is implemented using the following hardware and software.

[0094] User device: Smartphone or tablet

[0095] Dedicated logger: A device with a built-in GPS sensor and acceleration sensor

[0096] Server: A server for running databases and analytical algorithms

[0097] Examples of software used: Apache Spark (distributed processing framework), Google Maps API (map display)

[0098] Processing Details

[0099] Collecting and storing user preference information

[0100] First, users install a dedicated application on their smartphone or tablet. They launch the application and create an account by entering the necessary personal information. Next, they set their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The device sends this setting information to the server, which stores it in a database.

[0101] Data collection using loggers

[0102] Users carry a dedicated logger when they go out. The logger uses a GPS sensor to record their current location and an accelerometer to measure the number of steps taken. These data are collected at regular intervals (e.g., every 30 seconds).

[0103] Transfer and storage of data to your device

[0104] The logger uses Bluetooth or Wi-Fi to transmit the collected data to the device, which has local storage for temporarily storing this data.

[0105] Uploading data to the server

[0106] The device uploads the collected data to the server at a fixed interval (e.g., every 5 minutes), and the server stores the received data in a database.

[0107] Analyzing movement data

[0108] The server performs analysis based on the travel data stored in the database, extracting user behavior patterns using machine learning algorithms such as clustering and pattern mining, and filtering out routes and locations deemed inappropriate.

[0109] Creating and presenting travel plans

[0110] The server generates an optimal outing plan for the user based on the analysis results. For example, based on the user's history of visiting hiking spot A and cafe B, the server proposes a plan that includes new hiking course C and cafe D. The device displays this plan to the user. The user can then select the plan that best suits their interests and conditions from the displayed plans and carry out the plan.

[0111] Examples of concrete examples and prompts

[0112] As a concrete example, a user goes out on a day off with a dedicated logger and visits hiking spot A and cafe B. Based on this movement data, the server analyzes the distance walked by the user, the places visited, and the time spent there, and suggests new hiking courses and cafe hopping plans that are optimal for the next weekend. For example, a plan including new hiking spot C and cafe D is generated and presented to the user.

[0113] Example prompt sentence:

[0114] "For a user who visited hiking spot A and cafe B last weekend, generate a plan that suggests a new hiking trail (e.g., hiking spot C) and cafe (e.g., cafe D) that are perfect for next weekend."

[0115] As described above, the present invention improves the user's outing experience by collecting and analyzing the user's movement data and providing the optimal outing plan.

[0116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0117] Step 1:

[0118] Users install a dedicated application on their smartphone or tablet and create an account. At this time, they select their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The entered information is sent from the device to a server, which then stores it in a database.

[0119] Input: User preferences and transportation information

[0120] Output: Configuration information stored in a database on the server

[0121] Specific operation: The user enters information into the input form and presses the submit button. The device packages the configuration information in JSON format and sends it to the server via an HTTPS request. The server analyzes the received data and records it in a database.

[0122] Step 2:

[0123] Users carry a dedicated logger when they go out. The logger uses a GPS sensor and an acceleration sensor to measure their current location and number of steps, and saves the collected data in local storage at regular intervals (e.g., every 30 seconds).

[0124] Input: None (logger actively collects data)

[0125] Output: Movement data stored in the local storage of the logger

[0126] How it works: The logger's GPS sensor acquires location information, and the accelerometer detects movement and counts steps. The data obtained from the sensors is recorded periodically in the internal memory.

[0127] Step 3:

[0128] The logger uses Bluetooth or Wi-Fi to transmit the collected data to the user's device, which temporarily stores the data.

[0129] Input: Movement data from the logger

[0130] Output: Travel data stored in the device's local storage

[0131] Specific operation: The logger pairs with the device using a communication module (e.g., Bluetooth or Wi-Fi). Data is sent from the logger to the device in batches. The device records the received data in local storage.

[0132] Step 4:

[0133] The device uploads the movement data stored in the local storage to the server at a fixed frequency (e.g., every 5 minutes), and the server stores the received data in a database.

[0134] Input: Travel data stored on the device

[0135] Output: Movement data stored in the server database

[0136] Specific operation: The device uses a timer to retrieve movement data from local storage at regular intervals and send it to the server via an HTTPS request. The server receives the data and records it in a database.

[0137] Step 5:

[0138] The server analyzes the travel data stored in the database, extracts user behavior patterns using machine learning algorithms such as clustering and pattern mining, and filters out routes and locations deemed inappropriate.

[0139] Input: Movement data stored in the database

[0140] Output: Analysis results (user behavior patterns, exclusion of inappropriate locations)

[0141] Specific operation: The server starts an analysis job and performs data analysis using distributed processing with Apache Spark. It extracts behavioral patterns through cluster analysis and pattern mining, and lists and eliminates inappropriate locations.

[0142] Step 6:

[0143] The server generates an optimal outing plan for the user based on the analysis results. For example, it recommends new destinations that correspond to past visit history and generates the plan. The device displays the plan received from the server to the user.

[0144] Input: Analysis results

[0145] Output: Generated itinerary

[0146] How it works: Based on the analysis results, the server uses the AI ​​model to generate a new outing plan, which is then sent to the device, where the plan is displayed by the device application.

[0147] Step 7:

[0148] Users can select from the displayed outing plans that best suit their interests and requirements. The selected plan is saved on the device and will be linked to the map app and reminder function on the day of the trip to support the outing.

[0149] Input: Displayed outing plan

[0150] Output: User selected outing plan

[0151] How it works: The user browses through the proposed plans on the application interface and taps to select the plan they are interested in. The selected plan is saved in the device's local storage and linked to the map app and reminders.

[0152] (Application example 1)

[0153] 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."

[0154] In today's world, it is difficult for users to efficiently plan outings based on their hobbies, preferences, and behavioral patterns, and it is especially challenging to make optimal choices when it comes to food delivery. Furthermore, due to time and resource constraints, users are likely to miss suitable options. Additionally, there is a lack of systems that make suggestions based on individual user preferences and past behavioral data. The purpose of this invention is to solve these problems and provide users with optimal options.

[0155] 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.

[0156] In this invention, the server includes means for inputting information on the user's hobbies, preferences, and transportation means, logger means for collecting the user's movement data, means for analyzing the collected movement data, and means for proposing an outing plan or a food delivery plan based on the analysis results, thereby enabling the user to receive suggestions for an optimal outing plan or a food delivery plan based on their own behavioral patterns and hobbies and preferences.

[0157] "User" refers to an individual or organization that uses the system and provides travel data by inputting information on hobbies, preferences, and means of transportation.

[0158] "Hobbies and preferences" refers to the activities and interests of the user, and are the basic information that allows the system to propose the optimal plan.

[0159] "Transportation" refers to the means used by the user when traveling, and includes walking, cycling, public transportation, and cars.

[0160] "Logger means" refers to a device or program that collects user movement data, and records location information and movement data using a GPS, acceleration sensor, etc.

[0161] "Movement data" refers to data such as location information, number of steps, and duration of stay when a user actually moves, and is collected by a logger means.

[0162] "Analysis means" refers to a device or program that analyzes a user's behavioral patterns using a data analysis algorithm based on collected movement data and the user's hobbies and preferences.

[0163] An "outing plan" refers to a specific action plan for when a user goes out based on the user's hobbies, preferences, and behavioral patterns, and includes places to visit, routes, and the like.

[0164] A "food delivery plan" is a plan that suggests the most suitable restaurant and menu based on a user's preferences, past ordering history, and behavioral patterns.

[0165] The "suggestion means" refers to a device or program that generates an outing plan or food delivery plan that is suited to the user's hobbies, preferences, and behavioral patterns based on the analysis results and presents it to the user.

[0166] "Server" refers to a central processing unit or computer system that analyzes collected travel data and user preferences and proposes optimal plans.

[0167] "Filtering means" refers to devices or programs that perform processing to remove inappropriate routes or locations from collected movement data.

[0168] This invention is a system that proposes optimal outing plans and food delivery plans based on information on a user's hobbies, preferences, and transportation methods. This system is composed of a user terminal, a dedicated logger, and a server, and operates in the following manner.

[0169] Functionality Overview

[0170] Users install a dedicated application on their smartphone or other device, create an account, and log in. Next, they enter their hobbies and preferences (e.g., Chinese food, hiking, etc.) and preferred means of transportation (e.g., walking, cycling, train, etc.) through the application. This information is sent from the device to the server and stored in a database.

[0171] Data collection and analysis

[0172] When users go out, they carry a dedicated logger, which uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the user's device at regular intervals (e.g., every 30 seconds), where it is temporarily stored. It is then uploaded to a server at regular intervals (e.g., every 5 minutes). The server stores the received data in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. Data analysis algorithms such as clustering and pattern mining are used in this analysis. Filtering is also performed to exclude inappropriate routes and locations.

[0173] Plan proposal

[0174] The server generates outing plans and food delivery plans based on the analysis results. For example, if a user has previously visited hiking spot A and cafe B and these are registered, the server will suggest new hiking trails and cafes for the next weekend. These suggestions are displayed on the user's device, allowing the user to select based on their interests and convenience.

[0175] Hardware and software used

[0176] In implementing the present invention, the following hardware and software are used:

[0177] User device: smartphone or tablet

[0178] Dedicated logger: A device equipped with a GPS and accelerometer

[0179] Server: a computer system capable of running a database and data analysis algorithms

[0180] On the server side, the following data processing and calculations are performed:

[0181] Clustering: Grouping user behavior patterns based on collected movement data.

[0182] Pattern mining: Extracting general trends from users' past behavioral patterns and generating new plans.

[0183] Filtering: Filter out inappropriate routes and locations.

[0184] Specific examples

[0185] Assume a user likes Chinese food, dislikes spicy food, and has recently been ordering sushi frequently. We send the following prompt to the generative AI model as a food delivery plan suggestion:

[0186] The user likes Chinese food but doesn't like spicy food. Recently, he has been enjoying sushi and ordering it frequently. Please suggest the best restaurant and menu for him based on his travel patterns and past ordering history.

[0187] The purpose of this invention is to propose optimal plans based on the user's interests and behavior through the collection and analysis of various user data, which not only allows the user to efficiently choose their outings and meals, but also helps them discover new destinations and restaurants.

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] The user installs the application on their device, creates an account, and logs in.

[0191] Input: User account information

[0192] Output: A message indicating successful account creation and login

[0193] Specific operation: The user creates an account by entering their name, email address, password, etc. After the account is created, they log in to the application.

[0194] Step 2:

[0195] The user inputs information about their interests and transportation.

[0196] Input: Hobbies and preferences (e.g., Chinese food, hiking, etc.), mode of transportation (e.g., walking, cycling, etc.)

[0197] Output: Hobbies, preferences and transportation information stored on the server

[0198] Specific operation: The user enters information about their hobbies, preferences, and preferred means of transportation on the application's settings screen. This information is sent from the device to the server and stored in a database.

[0199] Step 3:

[0200] A dedicated logger collects user movement data.

[0201] Input: User location information, number of steps

[0202] Output: Temporarily saved movement data

[0203] How it works: The logger uses GPS and an accelerometer to record the user's current location and number of steps at regular intervals (e.g., every 30 seconds). This data is temporarily stored on the device.

[0204] Step 4:

[0205] The terminal uploads the collected data to the server at a fixed frequency.

[0206] Input: Movement data from the logger

[0207] Output: Movement data sent to the server

[0208] Specific operation: The device sends the data obtained from the logger to the server at a fixed frequency (e.g., every 5 minutes). The sent data is stored in a database.

[0209] Step 5:

[0210] The server analyzes the collected movement data.

[0211] Input: Movement data stored on the server

[0212] Output: Analysis results (data based on user behavior patterns and hobbies and preferences)

[0213] How it works: The server analyzes the travel data and extracts user behavior patterns using algorithms such as clustering and pattern mining. It also filters out inappropriate routes and locations.

[0214] Step 6:

[0215] The server generates a plan based on the analysis results.

[0216] Input: Analysis results, filtering results

[0217] Output: Best outing plan or food delivery plan

[0218] Specific operation: The server generates the best outing plan or food delivery plan for the next weekend based on the user's behavioral patterns and hobbies and preferences. The generated plan is saved on the server.

[0219] Step 7:

[0220] The terminal displays the plan received from the server to the user.

[0221] Input: Plan sent from the server

[0222] Output: Plan displayed on terminal

[0223] Specific operation: The plan generated by the server is sent to the device, which displays the plan to the user, allowing the user to select a plan that suits their interests and requirements.

[0224] Step 8:

[0225] The user selects a plan and goes out or arranges food delivery.

[0226] Input: The plan selected by the user

[0227] Output: Confirmation of outing plans and delivery orders

[0228] What it does: The user selects the plan they're interested in and plans to go out, or order food delivery. The selection is saved on the device and linked to reminders and map apps.

[0229] 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.

[0230] The present invention combines an emotion engine with a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns. This system is composed of a user terminal, a dedicated logger, an emotion engine, and a server, and functions as follows:

[0231] First, a user installs the application on a device such as a smartphone, creates an account, and logs in. The user then uses the application to set their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[0232] Next, the user carries a dedicated logger with them when they go out. This logger uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals (e.g., every 30 seconds). The device temporarily stores the data from the logger and uploads it to a server at regular intervals (e.g., every 5 minutes).

[0233] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. This analysis uses algorithms such as clustering and pattern mining. It also performs filtering to exclude routes and locations that the user deems inappropriate (e.g., gambling halls).

[0234] Furthermore, in this invention, by combining an emotion engine, it is possible to recognize the user's emotions. The emotion engine reads the user's current emotional state from photos, voice, text messages, etc., and collects the emotion data. The collected emotion data is processed by an analysis means and used to understand what emotions the user has under what circumstances.

[0235] Specifically, suppose a user goes out with a logger over the weekend and visits hiking spot A and cafe B. At this time, the emotion engine recognizes the user's facial photo and analyzes emotions such as joy or fatigue. This emotion data is sent to the server and analyzed along with other movement data. For example, if the server determines that the user had a great time at hiking spot A but was somewhat tired at cafe B, the server can suggest a new, more relaxing cafe the next time the server proposes a plan.

[0236] The server generates multiple optimal outing plans for the user based on comprehensive data, including the results of sentiment analysis. These plans include places to visit, a recommended time schedule, and the means of transportation to be used. The server sends the generated plans to the device, which displays them to the user. The user can then select the plan that best suits their interests and requirements from the plans provided. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0237] In this way, by combining the emotion engine, it becomes possible to provide more personalized outing plans based on the user's emotional state. This system is expected to provide users with a more satisfying outing experience and increase the number of opportunities to go out.

[0238] The processing flow will be explained below.

[0239] Step 1:

[0240] Users install the application on their smartphone or other device, create an account, and log in.

[0241] Step 2:

[0242] Through the application, users input their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling).

[0243] Step 3:

[0244] The device stores the entered information on hobbies, preferences and means of transportation in an internal database and transmits the data to a server.

[0245] Step 4:

[0246] The server stores the received initial setting data in a database.

[0247] Step 5:

[0248] When users go out, they carry a dedicated logger that uses GPS to record their current location and an accelerometer to measure the number of steps taken.

[0249] Step 6:

[0250] The logger sends data on the current location, number of steps taken, and places visited to the device at regular intervals (e.g., every 30 seconds).

[0251] Step 7:

[0252] The device temporarily stores the received movement data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0253] Step 8:

[0254] The server stores the movement data received from the terminal in a database.

[0255] Step 9:

[0256] The server analyzes the user's movement patterns using algorithms such as clustering and pattern mining.

[0257] Step 10:

[0258] The server runs a filter process to exclude routes and locations that the user deems inappropriate, according to their settings.

[0259] Step 11:

[0260] The emotion engine recognizes the user's emotional state from photos, voice, text messages, etc. and sends that data to the device.

[0261] Step 12:

[0262] The device temporarily stores the emotion data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0263] Step 13:

[0264] The server analyzes the emotion data along with the movement data and builds a database to understand what emotions the user feels in what situations.

[0265] Step 14:

[0266] Based on the analysis results, the server generates an outing plan that matches the user's hobbies, preferences, behavioral patterns, and emotional state. Each plan includes places to visit, a recommended time schedule, and the means of transportation to use.

[0267] Step 15:

[0268] The server transmits the generated outing plan to the terminal.

[0269] Step 16:

[0270] The terminal displays the outing plan received from the server to the user.

[0271] Step 17:

[0272] The user selects from the presented plans the one that best suits their interests, conditions, and emotional state, and the selected plan is saved on the device.

[0273] Step 18:

[0274] Based on the selected plan, the device will link with map apps and reminder functions to support users when they are out and about.

[0275] These are the specific processing steps of the system that combines the emotion engine. Through this series of processes, the system proposes the most suitable outing plan to the user, helping them to have a more fulfilling outing.

[0276] Example 2

[0277] 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."

[0278] Conventional systems propose trip plans based on a user's hobbies, preferences, and transportation methods, but they do not take into account the user's emotional state, making it difficult to provide personalized and optimized plans. Furthermore, they lack the ability to filter out inappropriate routes and locations, making it impossible to fully guarantee user safety and satisfaction.

[0279] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information on the user's hobbies, preferences, and means of transportation, a location information collecting means for collecting the user's movement data, an analyzing means for analyzing the collected movement data, a suggesting means for proposing an outing plan based on the analysis results, an emotion recognizing means for collecting and analyzing the user's emotion data, and an adjusting means for adjusting the proposed content based on the emotion data. This makes it possible to provide a more personalized outing plan that takes the user's emotional state into consideration.

[0280] "User" refers to an individual who uses this system to create their own travel plans.

[0281] "Hobbies" refers to activities and areas of interest that a user has.

[0282] "Transportation" refers to the means used by the user to travel (e.g., walking, cycling, public transportation).

[0283] "Location information collection means" refers to a device equipped with a GPS function or an acceleration sensor used to obtain the user's current location and travel route.

[0284] "Analysis means" refers to an algorithm that analyzes the user's behavioral patterns based on collected data and generates an appropriate outing plan.

[0285] "Proposal means" refers to a function that presents the user with the optimal outing plan based on the analysis results.

[0286] "Emotion recognition means" refers to technology for analyzing a user's emotional state from photographs, voice, text messages, etc.

[0287] The "adjustment means" refers to a function for adjusting the contents of the outing plan based on emotion data to suit the user's condition and preferences.

[0288] "Filtering measures" refers to the ability to filter out inappropriate routes or locations from collected data.

[0289] An "outing plan" refers to a plan including an activity schedule and places to visit when going out that is proposed to the user.

[0290] "Server" refers to the central processing unit that receives, stores, analyzes, and generates the optimal plan from the data sent by the user.

[0291] "Terminal" refers to an electronic device, such as a smartphone or tablet, that a user uses to enter information or receive results.

[0292] The present invention is a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns, and combines emotion recognition functionality. This system is composed of a user terminal, a location information collection device, an emotion recognition engine, and a server.

[0293] First, the user installs a dedicated application on a device such as a smartphone, creates an account, and logs in. Through the application, the user sets their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). This setting information is sent to the server via the device and stored in a database.

[0294] Next, when users go out, they carry a location information collection device equipped with a GPS and acceleration sensor. This device records their current location and number of steps at regular intervals (e.g., every 30 seconds). The collected data is sent to the device at regular intervals, where it is temporarily stored and uploaded to a server at regular intervals (e.g., every 5 minutes).

[0295] The server stores the travel data received from the device in a database. The server then analyzes the user's behavioral patterns using algorithms such as clustering and pattern mining. It also performs filtering to exclude inappropriate routes and locations.

[0296] Furthermore, it is possible to recognize the user's emotions using an emotion recognition engine. This engine reads the user's current emotional state from photos, voice, text messages, etc. and collects that data. The emotion data is analyzed and used to understand what emotions the user feels in what situations.

[0297] Specifically, consider the case where a user visits hiking spot A and cafe B over the weekend with a location information collection device. At this time, the emotion recognition engine analyzes the user's facial photo and reads emotions such as joy or fatigue. This emotion data is sent to the server and analyzed together with other movement data. For example, if the analysis shows that the user had a great time at hiking spot A but felt somewhat tired at cafe B, the server can suggest a new relaxing cafe when proposing a plan for the next trip.

[0298] The server generates multiple optimal outing plans for the user based on comprehensive data, including the results of sentiment analysis. These plans include places to visit, recommended time schedules, and means of transportation to use. The server sends the generated plans to the device, which displays them to the user. The user can select the plan that best suits their interests and requirements from the plans provided. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0299] An example prompt is, "Please suggest an appropriate outing plan based on the user's hobbies, preferences, and emotional data. For example, if the user had a great time at hiking spot A but felt a bit tired at cafe B, please tell us specifically what elements you should include in your next suggestion."

[0300] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0301] Step 1:

[0302] The user installs a dedicated application on their smartphone, creates an account, and logs in. As input information, they enter basic user information (such as name and email address) and a password. The account information is sent to the server and registered in the database. As output, the user is able to access the main screen of the application.

[0303] Step 2:

[0304] Through the application, users set their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The input information is temporarily stored on the device and sent to the server by pressing the send button. The server stores the received data in a database. As an output, the setting information is reflected in the user's profile.

[0305] Step 3:

[0306] When a user goes out, they carry a location information collection device (logger) equipped with a built-in GPS and acceleration sensor. The logger records their current location and number of steps at regular intervals (e.g., every 30 seconds). This data is sent from the logger to a device, which temporarily stores the data. The input information is the location information and step count data from the logger. The output is the temporarily stored movement data.

[0307] Step 4:

[0308] The device uploads movement data to the server at a fixed frequency (e.g., every 5 minutes). The server stores the received movement data in a database. The input information is the movement data sent from the device. This data is later subjected to behavioral pattern analysis. The output is movement data stored in the database.

[0309] Step 5:

[0310] The server starts the process of analyzing the movement data. Clustering and pattern mining algorithms are used to analyze user behavior patterns. The input information is the movement data stored in the database. Data manipulation is performed on identified patterns and frequently visited locations. The output is the behavior pattern information resulting from the analysis.

[0311] Step 6:

[0312] The server filters the collected travel data to remove inappropriate routes and locations. For example, it excludes areas considered dangerous or gambling establishments. The input information is behavioral pattern information and route information. Data manipulation involves scrutinizing the data based on the specified filter conditions. The output is information about safe routes after filtering.

[0313] Step 7:

[0314] The emotion recognition engine collects emotional data as users interact with photos, voice, text messages, etc. For example, a user may upload a photo of their face taken at a location they visited to the app. The input information is emotion-related data provided by the user. The emotion recognition engine analyzes this data and identifies the user's emotional state. Emotion data is generated as output and sent to the server.

[0315] Step 8:

[0316] The server analyzes the emotional data and determines the user's emotional state. For example, it recognizes whether the user is happy or tired in a particular place. The input information is emotional data. The data analysis identifies the user's emotional state. The output is the analyzed emotional information.

[0317] Step 9:

[0318] The server integrates the travel data and emotional data to generate an optimal outing plan for the user. For example, it creates a plan taking into account the places the user has enjoyed and where they can relax. The input information is behavioral pattern information and emotional information. As a data manipulation, both data are combined to generate a new outing plan. The output is multiple recommended outing plans.

[0319] Step 10:

[0320] The server sends the generated outing plan to the device, which then displays it to the user. The user selects the plan that best suits their interests and requirements. The input information is the recommended plan. After selection, the plan is saved on the device and is ready to support the outing in conjunction with the map app and reminder function. The output is the selected outing plan.

[0321] The above is the specific processing flow of the program of this system.

[0322] (Application example 2)

[0323] 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."

[0324] Conventional systems could suggest outing plans based on the user's hobbies, preferences, and transportation methods, but they did not take the user's emotional state into account, making it difficult to provide personalized and satisfying suggestions. Furthermore, they had limited means to evaluate the appropriateness of the proposed plans and were unable to respond to the user's real-time emotional changes. Therefore, a system that can further improve user satisfaction is needed.

[0325] The identification process by the identification 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 inputting information on the user's hobbies, preferences, and means of transportation, device means for collecting the user's movement data, means for analyzing the collected movement data, means for collecting and analyzing the user's emotional data, and means for proposing outing plans and optimal products and services based on the analysis results. This makes it possible to grasp the user's emotional state in real time and propose more appropriate outing plans, products, and services that will provide greater satisfaction.

[0326] "User's hobbies and preferences" refers to information about areas that indicate the user's personal favorite activities and interests.

[0327] "Transportation" refers to the means used by users to get around (walking, cycling, public transportation, etc.).

[0328] "Device means" refers to hardware and software for collecting user movement data.

[0329] "Emotion data" is data that represents the user's emotional state and is analyzed from photographs, voice, text messages, and the like.

[0330] "Server" refers to a remotely located computer system for storing, analyzing, and managing data.

[0331] "Outing Plan" refers to suggestions about recommended places and activities for the user to visit, as well as the means of transportation to use when doing so.

[0332] "Goods and Services" refers to items and activities offered to users in virtual and physical stores.

[0333] The present invention relates to a system that proposes optimal outing plans, products, and services based on a user's hobbies, preferences, and behavioral patterns. This system includes a user terminal, a dedicated device, an emotion engine, and a server. Specific embodiments of the system are described below.

[0334] First, a user installs the application on a device such as a smartphone or head-mounted display (HMD), creates an account, and logs in. Through the application, the user sets their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and mode of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[0335] Next, when users go out, they carry a dedicated device (such as smart glasses or a dedicated logger). This device uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals and uploaded from the device to a server.

[0336] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and preferences. Algorithms such as clustering and pattern mining are used for the analysis. Filtering is also performed to exclude routes and locations that the user deems inappropriate.

[0337] Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions in real time. The emotion engine reads the user's current emotional state from photos, voice, text messages, etc. and collects that data. The collected emotion data is processed by an analysis means and converted into data based on the user's emotional state.

[0338] For example, if a user is trying on new clothes in a virtual store, and the emotion engine recognizes a happy expression, the server will suggest similar clothes based on that emotion data. On the other hand, if the user shows a tired expression, the server will suggest items that will help them relax.

[0339] The server generates multiple optimal outing plans, products, and services for the user based on comprehensive data, including the results of sentiment analysis. The generated plan and product suggestions are sent to the device, which displays them to the user. The user then selects the most interesting plan or product from the provided options. The selected plan or product is saved on the device and works in conjunction with the map app and reminder function to support the user's actions.

[0340] For example, the following prompt sentences are used:

[0341] "When a user is trying on a new dress in a virtual try-on, if they show a happy expression, suggest other similar options. On the other hand, if they show a tired expression, suggest items that will help them relax. Also, include detailed descriptions and pricing information with all suggestions."

[0342] In this way, by combining the emotion engine, it becomes possible to propose more personalized outing plans and products / services based on the user's emotional state. This system is expected to provide users with a more satisfying experience.

[0343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0344] Step 1:

[0345] The user installs the application on their smartphone or head-mounted display (HMD) and enters information about their hobbies, preferences, and transportation. When the user creates an account and logs in, this information is sent to the server and stored in a database. The input in this step is the user's hobbies, preferences, and transportation information, and the output is a database in which this information is saved.

[0346] Step 2:

[0347] When a user goes out, they carry a dedicated device (e.g., smart glasses or a dedicated logger). This device uses GPS to record their current location and an acceleration sensor to measure the number of steps taken, and collects the data. The device sends the data to the terminal at regular intervals (e.g., every 30 seconds). The input in this step is movement data, and the output is data temporarily stored on the terminal.

[0348] Step 3:

[0349] The device uploads the movement data from the logger to the server at a fixed frequency (e.g., every 5 minutes). The server stores the received movement data in a database and prepares it for later analysis. The input in this step is the movement data sent from the logger, and the output is the movement data stored in the database.

[0350] Step 4:

[0351] The server analyzes the travel data stored in the database and analyzes the user's behavioral patterns and hobbies and preferences using algorithms such as clustering and pattern mining. As a result of the analysis, inappropriate routes and locations are eliminated and appropriate locations for the user are identified. The input in this step is the travel data stored in the database, and the output is the analyzed behavioral patterns and hobbies and preferences information.

[0352] Step 5:

[0353] The emotion engine collects and analyzes the user's emotional data from photos, voice, text messages, etc. to understand the user's current emotional state. The analysis results are sent to the server and analyzed together with other movement data. The input in this step is emotional data such as photos, voice, and text messages, and the output is data indicating the user's emotional state.

[0354] Step 6:

[0355] The server generates optimal outing plans and product / service suggestions for the user based on the comprehensive analysis of the collected emotion data and movement data. The server generates multiple candidate plans and products and sends them to the device. The input in this step is the analyzed emotion data and movement data, and the output is the generated outing plans and product / service suggestions.

[0356] Step 7:

[0357] The device displays the proposals sent from the server to the user, and the user selects the most suitable plan or product from them. The selected plan or product is saved on the device and linked to the map app and reminder function to support the user's actions. The input in this step is the proposals sent from the server, and the output is the saving and linking of the plan or product selected by the user.

[0358] Additionally, the prompt sentence is used as an example:

[0359] "When a user is trying on a new dress in a virtual try-on, if they show a happy expression, suggest other similar options. On the other hand, if they show a tired expression, suggest items that will help them relax. Also, include detailed descriptions and pricing information with all suggestions."

[0360] 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.

[0361] 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.

[0362] 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.

[0363] [Second embodiment]

[0364] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0365] 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.

[0366] 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).

[0367] 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.

[0368] 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.

[0369] 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).

[0370] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 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."

[0376] The present invention relates to a system that proposes optimal outing plans based on the hobbies, preferences, and behavioral patterns of individual users. This system is composed of a user terminal, a dedicated logger, and a server, and functions as follows.

[0377] First, a user installs the application on a device such as a smartphone, creates an account, and logs in. The user then uses the application to set their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[0378] Next, the user carries a dedicated logger with them when they go out. This logger uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals (e.g., every 30 seconds). The device temporarily stores the data from the logger and uploads it to a server at regular intervals (e.g., every 5 minutes).

[0379] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. This analysis uses algorithms such as clustering and pattern mining. It also performs filtering to exclude routes and locations that the user deems inappropriate (e.g., gambling halls). The analysis results are used to understand the user's behavioral patterns and generate optimal outing plans.

[0380] As a specific example, let's say a user goes out on a day off with a logger and visits hiking spot A and cafe B. Based on this movement data, the server analyzes the user's walking distance, the places they visited, and the amount of time they spent there, and then suggests new hiking courses and cafe-hopping plans that are perfect for the next weekend. For example, a plan that includes hiking spot C and a new cafe D is generated and presented to the user.

[0381] The device displays the outing plans received from the server to the user. The user can select the plan that best suits their interests and conditions from the presented plans. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0382] The purpose of the present invention is to provide users with optimal outing plans through the above-mentioned series of processes, thereby lowering the barriers to going out. This system is expected to increase opportunities to go out and increase economic consumption.

[0383] The processing flow will be explained below.

[0384] Step 1:

[0385] Users install the application on their smartphone or other device, create an account, and log in.

[0386] Step 2:

[0387] Through the application, users input their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling).

[0388] Step 3:

[0389] The device stores the entered information on hobbies, preferences and means of transportation in an internal database and transmits the data to a server.

[0390] Step 4:

[0391] The server stores the received initial setting data in a database.

[0392] Step 5:

[0393] The user goes out carrying a dedicated logger, which uses GPS to record the user's current location and an accelerometer to count the number of steps taken.

[0394] Step 6:

[0395] The logger sends data on the current location, number of steps taken, and places visited to the device at regular intervals (e.g., every 30 seconds).

[0396] Step 7:

[0397] The device temporarily stores the received movement data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0398] Step 8:

[0399] The server stores the movement data received from the terminal in a database.

[0400] Step 9:

[0401] The server analyzes the user's movement patterns using algorithms such as clustering and pattern mining.

[0402] Step 10:

[0403] The server runs a filter process to exclude routes and locations that the user deems inappropriate, according to their settings.

[0404] Step 11:

[0405] Based on the analysis results, the server generates an outing plan that matches the user's hobbies, preferences, and behavioral patterns. Specifically, it creates multiple plans that include places to visit, recommended time schedules, and means of transportation to use.

[0406] Step 12:

[0407] The server transmits the generated outing plan to the terminal.

[0408] Step 13:

[0409] The terminal displays the outing plan received from the server to the user.

[0410] Step 14:

[0411] The user selects from the plans presented one that best suits their interests and requirements, and the selected plan is saved on the device.

[0412] Step 15:

[0413] Based on the selected plan, the device will link with map apps and reminder functions to support users when they are out and about.

[0414] The above are the specific processing steps of the system. Through this series of processing, the system proposes the most suitable outing plan to the user, making it easier to plan an outing.

[0415] Example 1

[0416] 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."

[0417] Conventional outing plan suggestion systems have difficulty providing optimal plans based on a user's hobbies, preferences, and behavioral patterns. In particular, there is a risk that inappropriate routes or locations may be suggested, which can reduce user satisfaction. Furthermore, systems that can analyze data in real time and smoothly provide users with optimal plans have not yet been fully established. There has been a demand for a system that can solve these issues and provide users with more suitable outing plans.

[0418] 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.

[0419] In this invention, the server includes: a means for a user to input information about hobbies, preferences, and transportation methods into a terminal; a means for the user to carry a logger when going out and collect movement data using a GPS and acceleration sensor; a means for transferring the collected movement data to the terminal at regular intervals and temporarily storing it; a means for uploading data from the terminal to the server at regular intervals; a means for analyzing the movement data stored in the database on the server using algorithms such as clustering and pattern mining; and a means for generating and proposing an optimal outing plan to the user based on the analysis results. This makes it possible to provide an optimal plan based on the user's hobbies, preferences, and behavioral patterns, and to realize high-quality plan proposals that exclude inappropriate routes and locations.

[0420] "Hobbies" refer to the activities and interests that interest a user.

[0421] "Transportation" refers to the method or means used by a user to travel, including walking, cycling, public transportation, etc.

[0422] A "terminal" is a portable electronic device used by a user, such as a smartphone or tablet.

[0423] A "logger" is a device that has a built-in GPS sensor and acceleration sensor and collects user movement data.

[0424] "Analysis" is the process of extracting user behavior patterns and trends from collected data using statistical methods and machine learning algorithms.

[0425] An "outing plan" is a plan of destinations and routes proposed based on the user's hobbies, preferences and behavioral patterns.

[0426] "Filtering" is the process of removing routes or locations that are deemed inappropriate from the collected data.

[0427] A "database" is an information management system that stores collected data in an organized manner and enables efficient access when needed.

[0428] "Clustering" is a machine learning technique that categorizes data points into groups such that the data within each group are more similar to each other than to the data in other groups.

[0429] "Pattern mining" is a data analysis technique for finding useful patterns and regularities from large amounts of data.

[0430] The present invention relates to a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns. This system is composed of a user terminal, a dedicated logger, and a server.

[0431] Hardware and software used

[0432] The system is implemented using the following hardware and software.

[0433] User device: Smartphone or tablet

[0434] Dedicated logger: A device with a built-in GPS sensor and acceleration sensor

[0435] Server: A server for running databases and analytical algorithms

[0436] Examples of software used: Apache Spark (distributed processing framework), Google Maps API (map display)

[0437] Processing Details

[0438] Collecting and storing user preference information

[0439] First, users install a dedicated application on their smartphone or tablet. They launch the application and create an account by entering the necessary personal information. Next, they set their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The device sends this setting information to the server, which stores it in a database.

[0440] Data collection using loggers

[0441] Users carry a dedicated logger when they go out. The logger uses a GPS sensor to record their current location and an accelerometer to measure the number of steps taken. These data are collected at regular intervals (e.g., every 30 seconds).

[0442] Transfer and storage of data to your device

[0443] The logger uses Bluetooth or Wi-Fi to transmit the collected data to the device, which has local storage for temporarily storing this data.

[0444] Uploading data to the server

[0445] The device uploads the collected data to the server at a fixed interval (e.g., every 5 minutes), and the server stores the received data in a database.

[0446] Analyzing movement data

[0447] The server performs analysis based on the travel data stored in the database, extracting user behavior patterns using machine learning algorithms such as clustering and pattern mining, and filtering out routes and locations deemed inappropriate.

[0448] Creating and presenting travel plans

[0449] The server generates an optimal outing plan for the user based on the analysis results. For example, based on the user's history of visiting hiking spot A and cafe B, the server proposes a plan that includes new hiking course C and cafe D. The device displays this plan to the user. The user can then select the plan that best suits their interests and conditions from the displayed plans and carry out the plan.

[0450] Examples of concrete examples and prompts

[0451] As a concrete example, a user goes out on a day off with a dedicated logger and visits hiking spot A and cafe B. Based on this movement data, the server analyzes the distance walked by the user, the places visited, and the time spent there, and suggests new hiking courses and cafe hopping plans that are optimal for the next weekend. For example, a plan including new hiking spot C and cafe D is generated and presented to the user.

[0452] Example prompt sentence:

[0453] "For a user who visited hiking spot A and cafe B last weekend, generate a plan that suggests a new hiking trail (e.g., hiking spot C) and cafe (e.g., cafe D) that are perfect for next weekend."

[0454] As described above, the present invention improves the user's outing experience by collecting and analyzing the user's movement data and providing the optimal outing plan.

[0455] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0456] Step 1:

[0457] Users install a dedicated application on their smartphone or tablet and create an account. At this time, they select their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The entered information is sent from the device to a server, which then stores it in a database.

[0458] Input: User preferences and transportation information

[0459] Output: Configuration information stored in a database on the server

[0460] Specific operation: The user enters information into the input form and presses the submit button. The device packages the configuration information in JSON format and sends it to the server via an HTTPS request. The server analyzes the received data and records it in a database.

[0461] Step 2:

[0462] Users carry a dedicated logger when they go out. The logger uses a GPS sensor and an acceleration sensor to measure their current location and number of steps, and saves the collected data in local storage at regular intervals (e.g., every 30 seconds).

[0463] Input: None (logger actively collects data)

[0464] Output: Movement data stored in the local storage of the logger

[0465] How it works: The logger's GPS sensor acquires location information, and the accelerometer detects movement and counts steps. The data obtained from the sensors is recorded periodically in the internal memory.

[0466] Step 3:

[0467] The logger uses Bluetooth or Wi-Fi to transmit the collected data to the user's device, which temporarily stores the data.

[0468] Input: Movement data from the logger

[0469] Output: Travel data stored in the device's local storage

[0470] Specific operation: The logger pairs with the device using a communication module (e.g., Bluetooth or Wi-Fi). Data is sent from the logger to the device in batches. The device records the received data in local storage.

[0471] Step 4:

[0472] The device uploads the movement data stored in the local storage to the server at a fixed frequency (e.g., every 5 minutes), and the server stores the received data in a database.

[0473] Input: Travel data stored on the device

[0474] Output: Movement data stored in the server database

[0475] Specific operation: The device uses a timer to retrieve movement data from local storage at regular intervals and send it to the server via an HTTPS request. The server receives the data and records it in a database.

[0476] Step 5:

[0477] The server analyzes the travel data stored in the database, extracts user behavior patterns using machine learning algorithms such as clustering and pattern mining, and filters out routes and locations deemed inappropriate.

[0478] Input: Movement data stored in the database

[0479] Output: Analysis results (user behavior patterns, exclusion of inappropriate locations)

[0480] Specific operation: The server starts an analysis job and performs data analysis using distributed processing with Apache Spark. It extracts behavioral patterns through cluster analysis and pattern mining, and lists and eliminates inappropriate locations.

[0481] Step 6:

[0482] The server generates an optimal outing plan for the user based on the analysis results. For example, it recommends new destinations that correspond to past visit history and generates the plan. The device displays the plan received from the server to the user.

[0483] Input: Analysis results

[0484] Output: Generated itinerary

[0485] How it works: Based on the analysis results, the server uses the AI ​​model to generate a new outing plan, which is then sent to the device, where the plan is displayed by the device application.

[0486] Step 7:

[0487] Users can select from the displayed outing plans that best suit their interests and requirements. The selected plan is saved on the device and will be linked to the map app and reminder function on the day of the trip to support the outing.

[0488] Input: Displayed outing plan

[0489] Output: User selected outing plan

[0490] How it works: The user browses through the proposed plans on the application interface and taps to select the plan they are interested in. The selected plan is saved in the device's local storage and linked to the map app and reminders.

[0491] (Application example 1)

[0492] 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."

[0493] In today's world, it is difficult for users to efficiently plan outings based on their hobbies, preferences, and behavioral patterns, and it is especially challenging to make optimal choices when it comes to food delivery. Furthermore, due to time and resource constraints, users are likely to miss suitable options. Additionally, there is a lack of systems that make suggestions based on individual user preferences and past behavioral data. The purpose of this invention is to solve these problems and provide users with optimal options.

[0494] 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.

[0495] In this invention, the server includes means for inputting information on the user's hobbies, preferences, and transportation means, logger means for collecting the user's movement data, means for analyzing the collected movement data, and means for proposing an outing plan or a food delivery plan based on the analysis results, thereby enabling the user to receive suggestions for an optimal outing plan or a food delivery plan based on their own behavioral patterns and hobbies and preferences.

[0496] "User" refers to an individual or organization that uses the system and provides travel data by inputting information on hobbies, preferences, and means of transportation.

[0497] "Hobbies and preferences" refers to the activities and interests of the user, and are the basic information that allows the system to propose the optimal plan.

[0498] "Transportation" refers to the means used by the user when traveling, and includes walking, cycling, public transportation, and cars.

[0499] "Logger means" refers to a device or program that collects user movement data, and records location information and movement data using a GPS, acceleration sensor, etc.

[0500] "Movement data" refers to data such as location information, number of steps, and duration of stay when a user actually moves, and is collected by a logger means.

[0501] "Analysis means" refers to a device or program that analyzes a user's behavioral patterns using a data analysis algorithm based on collected movement data and the user's hobbies and preferences.

[0502] An "outing plan" refers to a specific action plan for when a user goes out based on the user's hobbies, preferences, and behavioral patterns, and includes places to visit, routes, and the like.

[0503] A "food delivery plan" is a plan that suggests the most suitable restaurant and menu based on a user's preferences, past ordering history, and behavioral patterns.

[0504] The "suggestion means" refers to a device or program that generates an outing plan or food delivery plan that is suited to the user's hobbies, preferences, and behavioral patterns based on the analysis results and presents it to the user.

[0505] "Server" refers to a central processing unit or computer system that analyzes collected travel data and user preferences and proposes optimal plans.

[0506] "Filtering means" refers to devices or programs that perform processing to remove inappropriate routes or locations from collected movement data.

[0507] This invention is a system that proposes optimal outing plans and food delivery plans based on information on a user's hobbies, preferences, and transportation methods. This system is composed of a user terminal, a dedicated logger, and a server, and operates in the following manner.

[0508] Functionality Overview

[0509] Users install a dedicated application on their smartphone or other device, create an account, and log in. Next, they enter their hobbies and preferences (e.g., Chinese food, hiking, etc.) and preferred means of transportation (e.g., walking, cycling, train, etc.) through the application. This information is sent from the device to the server and stored in a database.

[0510] Data collection and analysis

[0511] When users go out, they carry a dedicated logger, which uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the user's device at regular intervals (e.g., every 30 seconds), where it is temporarily stored. It is then uploaded to a server at regular intervals (e.g., every 5 minutes). The server stores the received data in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. Data analysis algorithms such as clustering and pattern mining are used in this analysis. Filtering is also performed to exclude inappropriate routes and locations.

[0512] Plan proposal

[0513] The server generates outing plans and food delivery plans based on the analysis results. For example, if a user has previously visited hiking spot A and cafe B and these are registered, the server will suggest new hiking trails and cafes for the next weekend. These suggestions are displayed on the user's device, allowing the user to select based on their interests and convenience.

[0514] Hardware and software used

[0515] In implementing the present invention, the following hardware and software are used:

[0516] User device: smartphone or tablet

[0517] Dedicated logger: A device equipped with a GPS and accelerometer

[0518] Server: a computer system capable of running a database and data analysis algorithms

[0519] On the server side, the following data processing and calculations are performed:

[0520] Clustering: Grouping user behavior patterns based on collected movement data.

[0521] Pattern mining: Extracting general trends from users' past behavioral patterns and generating new plans.

[0522] Filtering: Filter out inappropriate routes and locations.

[0523] Specific examples

[0524] Assume a user likes Chinese food, dislikes spicy food, and has recently been ordering sushi frequently. We send the following prompt to the generative AI model as a food delivery plan suggestion:

[0525] The user likes Chinese food but doesn't like spicy food. Recently, he has been enjoying sushi and ordering it frequently. Please suggest the best restaurant and menu for him based on his travel patterns and past ordering history.

[0526] The purpose of this invention is to propose optimal plans based on the user's interests and behavior through the collection and analysis of various user data, which not only allows the user to efficiently choose their outings and meals, but also helps them discover new destinations and restaurants.

[0527] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0528] Step 1:

[0529] The user installs the application on their device, creates an account, and logs in.

[0530] Input: User account information

[0531] Output: A message indicating successful account creation and login

[0532] Specific operation: The user creates an account by entering their name, email address, password, etc. After the account is created, they log in to the application.

[0533] Step 2:

[0534] The user inputs information about their interests and transportation.

[0535] Input: Hobbies and preferences (e.g., Chinese food, hiking, etc.), mode of transportation (e.g., walking, cycling, etc.)

[0536] Output: Hobbies, preferences and transportation information stored on the server

[0537] Specific operation: The user enters information about their hobbies, preferences, and preferred means of transportation on the application's settings screen. This information is sent from the device to the server and stored in a database.

[0538] Step 3:

[0539] A dedicated logger collects user movement data.

[0540] Input: User location information, number of steps

[0541] Output: Temporarily saved movement data

[0542] How it works: The logger uses GPS and an accelerometer to record the user's current location and number of steps at regular intervals (e.g., every 30 seconds). This data is temporarily stored on the device.

[0543] Step 4:

[0544] The terminal uploads the collected data to the server at a fixed frequency.

[0545] Input: Movement data from the logger

[0546] Output: Movement data sent to the server

[0547] Specific operation: The device sends the data obtained from the logger to the server at a fixed frequency (e.g., every 5 minutes). The sent data is stored in a database.

[0548] Step 5:

[0549] The server analyzes the collected movement data.

[0550] Input: Movement data stored on the server

[0551] Output: Analysis results (data based on user behavior patterns and hobbies and preferences)

[0552] How it works: The server analyzes the travel data and extracts user behavior patterns using algorithms such as clustering and pattern mining. It also filters out inappropriate routes and locations.

[0553] Step 6:

[0554] The server generates a plan based on the analysis results.

[0555] Input: Analysis results, filtering results

[0556] Output: Best outing plan or food delivery plan

[0557] Specific operation: The server generates the best outing plan or food delivery plan for the next weekend based on the user's behavioral patterns and hobbies and preferences. The generated plan is saved on the server.

[0558] Step 7:

[0559] The terminal displays the plan received from the server to the user.

[0560] Input: Plan sent from the server

[0561] Output: Plan displayed on terminal

[0562] Specific operation: The plan generated by the server is sent to the device, which displays the plan to the user, allowing the user to select a plan that suits their interests and requirements.

[0563] Step 8:

[0564] The user selects a plan and goes out or arranges food delivery.

[0565] Input: The plan selected by the user

[0566] Output: Confirmation of outing plans and delivery orders

[0567] What it does: The user selects the plan they're interested in and plans to go out, or order food delivery. The selection is saved on the device and linked to reminders and map apps.

[0568] 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.

[0569] The present invention combines an emotion engine with a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns. This system is composed of a user terminal, a dedicated logger, an emotion engine, and a server, and functions as follows:

[0570] First, a user installs the application on a device such as a smartphone, creates an account, and logs in. The user then uses the application to set their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[0571] Next, the user carries a dedicated logger with them when they go out. This logger uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals (e.g., every 30 seconds). The device temporarily stores the data from the logger and uploads it to a server at regular intervals (e.g., every 5 minutes).

[0572] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. This analysis uses algorithms such as clustering and pattern mining. It also performs filtering to exclude routes and locations that the user deems inappropriate (e.g., gambling halls).

[0573] Furthermore, in this invention, by combining an emotion engine, it is possible to recognize the user's emotions. The emotion engine reads the user's current emotional state from photos, voice, text messages, etc., and collects the emotion data. The collected emotion data is processed by an analysis means and used to understand what emotions the user has under what circumstances.

[0574] Specifically, suppose a user goes out with a logger over the weekend and visits hiking spot A and cafe B. At this time, the emotion engine recognizes the user's facial photo and analyzes emotions such as joy or fatigue. This emotion data is sent to the server and analyzed along with other movement data. For example, if the server determines that the user had a great time at hiking spot A but was somewhat tired at cafe B, the server can suggest a new, more relaxing cafe the next time the server proposes a plan.

[0575] The server generates multiple optimal outing plans for the user based on comprehensive data, including the results of sentiment analysis. These plans include places to visit, a recommended time schedule, and the means of transportation to be used. The server sends the generated plans to the device, which displays them to the user. The user can then select the plan that best suits their interests and requirements from the plans provided. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0576] In this way, by combining the emotion engine, it becomes possible to provide more personalized outing plans based on the user's emotional state. This system is expected to provide users with a more satisfying outing experience and increase the number of opportunities to go out.

[0577] The processing flow will be explained below.

[0578] Step 1:

[0579] Users install the application on their smartphone or other device, create an account, and log in.

[0580] Step 2:

[0581] Through the application, users input their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling).

[0582] Step 3:

[0583] The device stores the entered information on hobbies, preferences and means of transportation in an internal database and transmits the data to a server.

[0584] Step 4:

[0585] The server stores the received initial setting data in a database.

[0586] Step 5:

[0587] When users go out, they carry a dedicated logger that uses GPS to record their current location and an accelerometer to measure the number of steps taken.

[0588] Step 6:

[0589] The logger sends data on the current location, number of steps taken, and places visited to the device at regular intervals (e.g., every 30 seconds).

[0590] Step 7:

[0591] The device temporarily stores the received movement data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0592] Step 8:

[0593] The server stores the movement data received from the terminal in a database.

[0594] Step 9:

[0595] The server analyzes the user's movement patterns using algorithms such as clustering and pattern mining.

[0596] Step 10:

[0597] The server runs a filter process to exclude routes and locations that the user deems inappropriate, according to their settings.

[0598] Step 11:

[0599] The emotion engine recognizes the user's emotional state from photos, voice, text messages, etc. and sends that data to the device.

[0600] Step 12:

[0601] The device temporarily stores the emotion data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0602] Step 13:

[0603] The server analyzes the emotion data along with the movement data and builds a database to understand what emotions the user feels in what situations.

[0604] Step 14:

[0605] Based on the analysis results, the server generates an outing plan that matches the user's hobbies, preferences, behavioral patterns, and emotional state. Each plan includes places to visit, a recommended time schedule, and the means of transportation to use.

[0606] Step 15:

[0607] The server transmits the generated outing plan to the terminal.

[0608] Step 16:

[0609] The terminal displays the outing plan received from the server to the user.

[0610] Step 17:

[0611] The user selects from the presented plans the one that best suits their interests, conditions, and emotional state, and the selected plan is saved on the device.

[0612] Step 18:

[0613] Based on the selected plan, the device will link with map apps and reminder functions to support users when they are out and about.

[0614] These are the specific processing steps of the system that combines the emotion engine. Through this series of processes, the system proposes the most suitable outing plan to the user, helping them to have a more fulfilling outing.

[0615] Example 2

[0616] 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."

[0617] Conventional systems propose trip plans based on a user's hobbies, preferences, and transportation methods, but they do not take into account the user's emotional state, making it difficult to provide personalized and optimized plans. Furthermore, they lack the ability to filter out inappropriate routes and locations, making it impossible to fully guarantee user safety and satisfaction.

[0618] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information on the user's hobbies, preferences, and means of transportation, a location information collecting means for collecting the user's movement data, an analyzing means for analyzing the collected movement data, a suggesting means for proposing an outing plan based on the analysis results, an emotion recognizing means for collecting and analyzing the user's emotion data, and an adjusting means for adjusting the proposed content based on the emotion data. This makes it possible to provide a more personalized outing plan that takes the user's emotional state into consideration.

[0619] "User" refers to an individual who uses this system to create their own travel plans.

[0620] "Hobbies" refers to activities and areas of interest that a user has.

[0621] "Transportation" refers to the means used by the user to travel (e.g., walking, cycling, public transportation).

[0622] "Location information collection means" refers to a device equipped with a GPS function or an acceleration sensor used to obtain the user's current location and travel route.

[0623] "Analysis means" refers to an algorithm that analyzes the user's behavioral patterns based on collected data and generates an appropriate outing plan.

[0624] "Proposal means" refers to a function that presents the user with the optimal outing plan based on the analysis results.

[0625] "Emotion recognition means" refers to technology for analyzing a user's emotional state from photographs, voice, text messages, etc.

[0626] The "adjustment means" refers to a function for adjusting the contents of the outing plan based on emotion data to suit the user's condition and preferences.

[0627] "Filtering measures" refers to the ability to filter out inappropriate routes or locations from collected data.

[0628] An "outing plan" refers to a plan including an activity schedule and places to visit when going out that is proposed to the user.

[0629] "Server" refers to the central processing unit that receives, stores, analyzes, and generates the optimal plan from the data sent by the user.

[0630] "Terminal" refers to an electronic device, such as a smartphone or tablet, that a user uses to enter information or receive results.

[0631] The present invention is a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns, and combines emotion recognition functionality. This system is composed of a user terminal, a location information collection device, an emotion recognition engine, and a server.

[0632] First, the user installs a dedicated application on a device such as a smartphone, creates an account, and logs in. Through the application, the user sets their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). This setting information is sent to the server via the device and stored in a database.

[0633] Next, when users go out, they carry a location information collection device equipped with a GPS and acceleration sensor. This device records their current location and number of steps at regular intervals (e.g., every 30 seconds). The collected data is sent to the device at regular intervals, where it is temporarily stored and uploaded to a server at regular intervals (e.g., every 5 minutes).

[0634] The server stores the travel data received from the device in a database. The server then analyzes the user's behavioral patterns using algorithms such as clustering and pattern mining. It also performs filtering to exclude inappropriate routes and locations.

[0635] Furthermore, it is possible to recognize the user's emotions using an emotion recognition engine. This engine reads the user's current emotional state from photos, voice, text messages, etc. and collects that data. The emotion data is analyzed and used to understand what emotions the user feels in what situations.

[0636] Specifically, consider the case where a user visits hiking spot A and cafe B over the weekend with a location information collection device. At this time, the emotion recognition engine analyzes the user's facial photo and reads emotions such as joy or fatigue. This emotion data is sent to the server and analyzed together with other movement data. For example, if the analysis shows that the user had a great time at hiking spot A but felt somewhat tired at cafe B, the server can suggest a new relaxing cafe when proposing a plan for the next trip.

[0637] The server generates multiple optimal outing plans for the user based on comprehensive data, including the results of sentiment analysis. These plans include places to visit, recommended time schedules, and means of transportation to use. The server sends the generated plans to the device, which displays them to the user. The user can select the plan that best suits their interests and requirements from the plans provided. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0638] An example prompt is, "Please suggest an appropriate outing plan based on the user's hobbies, preferences, and emotional data. For example, if the user had a great time at hiking spot A but felt a bit tired at cafe B, please tell us specifically what elements you should include in your next suggestion."

[0639] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0640] Step 1:

[0641] The user installs a dedicated application on their smartphone, creates an account, and logs in. As input information, they enter basic user information (such as name and email address) and a password. The account information is sent to the server and registered in the database. As output, the user is able to access the main screen of the application.

[0642] Step 2:

[0643] Through the application, users set their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The input information is temporarily stored on the device and sent to the server by pressing the send button. The server stores the received data in a database. As an output, the setting information is reflected in the user's profile.

[0644] Step 3:

[0645] When a user goes out, they carry a location information collection device (logger) equipped with a built-in GPS and acceleration sensor. The logger records their current location and number of steps at regular intervals (e.g., every 30 seconds). This data is sent from the logger to a device, which temporarily stores the data. The input information is the location information and step count data from the logger. The output is the temporarily stored movement data.

[0646] Step 4:

[0647] The device uploads movement data to the server at a fixed frequency (e.g., every 5 minutes). The server stores the received movement data in a database. The input information is the movement data sent from the device. This data is later subjected to behavioral pattern analysis. The output is movement data stored in the database.

[0648] Step 5:

[0649] The server starts the process of analyzing the movement data. Clustering and pattern mining algorithms are used to analyze user behavior patterns. The input information is the movement data stored in the database. Data manipulation is performed on identified patterns and frequently visited locations. The output is the behavior pattern information resulting from the analysis.

[0650] Step 6:

[0651] The server filters the collected travel data to remove inappropriate routes and locations. For example, it excludes areas considered dangerous or gambling establishments. The input information is behavioral pattern information and route information. Data manipulation involves scrutinizing the data based on the specified filter conditions. The output is information about safe routes after filtering.

[0652] Step 7:

[0653] The emotion recognition engine collects emotional data as users interact with photos, voice, text messages, etc. For example, a user may upload a photo of their face taken at a location they visited to the app. The input information is emotion-related data provided by the user. The emotion recognition engine analyzes this data and identifies the user's emotional state. Emotion data is generated as output and sent to the server.

[0654] Step 8:

[0655] The server analyzes the emotional data and determines the user's emotional state. For example, it recognizes whether the user is happy or tired in a particular place. The input information is emotional data. The data analysis identifies the user's emotional state. The output is the analyzed emotional information.

[0656] Step 9:

[0657] The server integrates the travel data and emotional data to generate an optimal outing plan for the user. For example, it creates a plan taking into account the places the user has enjoyed and where they can relax. The input information is behavioral pattern information and emotional information. As a data manipulation, both data are combined to generate a new outing plan. The output is multiple recommended outing plans.

[0658] Step 10:

[0659] The server sends the generated outing plan to the device, which then displays it to the user. The user selects the plan that best suits their interests and requirements. The input information is the recommended plan. After selection, the plan is saved on the device and is ready to support the outing in conjunction with the map app and reminder function. The output is the selected outing plan.

[0660] The above is the specific processing flow of the program of this system.

[0661] (Application example 2)

[0662] 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."

[0663] Conventional systems could suggest outing plans based on the user's hobbies, preferences, and transportation methods, but they did not take the user's emotional state into account, making it difficult to provide personalized and satisfying suggestions. Furthermore, they had limited means to evaluate the appropriateness of the proposed plans and were unable to respond to the user's real-time emotional changes. Therefore, a system that can further improve user satisfaction is needed.

[0664] The identification process by the identification 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 inputting information on the user's hobbies, preferences, and means of transportation, device means for collecting the user's movement data, means for analyzing the collected movement data, means for collecting and analyzing the user's emotional data, and means for proposing outing plans and optimal products and services based on the analysis results. This makes it possible to grasp the user's emotional state in real time and propose more appropriate outing plans, products, and services that will provide greater satisfaction.

[0665] "User's hobbies and preferences" refers to information about areas that indicate the user's personal favorite activities and interests.

[0666] "Transportation" refers to the means used by users to get around (walking, cycling, public transportation, etc.).

[0667] "Device means" refers to hardware and software for collecting user movement data.

[0668] "Emotion data" is data that represents the user's emotional state and is analyzed from photographs, voice, text messages, and the like.

[0669] "Server" refers to a remotely located computer system for storing, analyzing, and managing data.

[0670] "Outing Plan" refers to suggestions about recommended places and activities for the user to visit, as well as the means of transportation to use when doing so.

[0671] "Goods and Services" refers to items and activities offered to users in virtual and physical stores.

[0672] The present invention relates to a system that proposes optimal outing plans, products, and services based on a user's hobbies, preferences, and behavioral patterns. This system includes a user terminal, a dedicated device, an emotion engine, and a server. Specific embodiments of the system are described below.

[0673] First, a user installs the application on a device such as a smartphone or head-mounted display (HMD), creates an account, and logs in. Through the application, the user sets their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and mode of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[0674] Next, when users go out, they carry a dedicated device (such as smart glasses or a dedicated logger). This device uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals and uploaded from the device to a server.

[0675] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and preferences. Algorithms such as clustering and pattern mining are used for the analysis. Filtering is also performed to exclude routes and locations that the user deems inappropriate.

[0676] Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions in real time. The emotion engine reads the user's current emotional state from photos, voice, text messages, etc. and collects that data. The collected emotion data is processed by an analysis means and converted into data based on the user's emotional state.

[0677] For example, if a user is trying on new clothes in a virtual store, and the emotion engine recognizes a happy expression, the server will suggest similar clothes based on that emotion data. On the other hand, if the user shows a tired expression, the server will suggest items that will help them relax.

[0678] The server generates multiple optimal outing plans, products, and services for the user based on comprehensive data, including the results of sentiment analysis. The generated plan and product suggestions are sent to the device, which displays them to the user. The user then selects the most interesting plan or product from the provided options. The selected plan or product is saved on the device and works in conjunction with the map app and reminder function to support the user's actions.

[0679] For example, the following prompt sentences are used:

[0680] "When a user is trying on a new dress in a virtual try-on, if they show a happy expression, suggest other similar options. On the other hand, if they show a tired expression, suggest items that will help them relax. Also, include detailed descriptions and pricing information with all suggestions."

[0681] In this way, by combining the emotion engine, it becomes possible to propose more personalized outing plans and products / services based on the user's emotional state. This system is expected to provide users with a more satisfying experience.

[0682] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0683] Step 1:

[0684] The user installs the application on their smartphone or head-mounted display (HMD) and enters information about their hobbies, preferences, and transportation. When the user creates an account and logs in, this information is sent to the server and stored in a database. The input in this step is the user's hobbies, preferences, and transportation information, and the output is a database in which this information is saved.

[0685] Step 2:

[0686] When a user goes out, they carry a dedicated device (e.g., smart glasses or a dedicated logger). This device uses GPS to record their current location and an acceleration sensor to measure the number of steps taken, and collects the data. The device sends the data to the terminal at regular intervals (e.g., every 30 seconds). The input in this step is movement data, and the output is data temporarily stored on the terminal.

[0687] Step 3:

[0688] The device uploads the movement data from the logger to the server at a fixed frequency (e.g., every 5 minutes). The server stores the received movement data in a database and prepares it for later analysis. The input in this step is the movement data sent from the logger, and the output is the movement data stored in the database.

[0689] Step 4:

[0690] The server analyzes the travel data stored in the database and analyzes the user's behavioral patterns and hobbies and preferences using algorithms such as clustering and pattern mining. As a result of the analysis, inappropriate routes and locations are eliminated and appropriate locations for the user are identified. The input in this step is the travel data stored in the database, and the output is the analyzed behavioral patterns and hobbies and preferences information.

[0691] Step 5:

[0692] The emotion engine collects and analyzes the user's emotional data from photos, voice, text messages, etc. to understand the user's current emotional state. The analysis results are sent to the server and analyzed together with other movement data. The input in this step is emotional data such as photos, voice, and text messages, and the output is data indicating the user's emotional state.

[0693] Step 6:

[0694] The server generates optimal outing plans and product / service suggestions for the user based on the comprehensive analysis of the collected emotion data and movement data. The server generates multiple candidate plans and products and sends them to the device. The input in this step is the analyzed emotion data and movement data, and the output is the generated outing plans and product / service suggestions.

[0695] Step 7:

[0696] The device displays the proposals sent from the server to the user, and the user selects the most suitable plan or product from them. The selected plan or product is saved on the device and linked to the map app and reminder function to support the user's actions. The input in this step is the proposals sent from the server, and the output is the saving and linking of the plan or product selected by the user.

[0697] Additionally, the prompt sentence is used as an example:

[0698] "When a user is trying on a new dress in a virtual try-on, if they show a happy expression, suggest other similar options. On the other hand, if they show a tired expression, suggest items that will help them relax. Also, include detailed descriptions and pricing information with all suggestions."

[0699] 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.

[0700] 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.

[0701] 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.

[0702] [Third embodiment]

[0703] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0704] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0705] 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).

[0706] 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.

[0707] 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.

[0708] 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).

[0709] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] 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."

[0715] The present invention relates to a system that proposes optimal outing plans based on the hobbies, preferences, and behavioral patterns of individual users. This system is composed of a user terminal, a dedicated logger, and a server, and functions as follows.

[0716] First, a user installs the application on a device such as a smartphone, creates an account, and logs in. The user then uses the application to set their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[0717] Next, the user carries a dedicated logger with them when they go out. This logger uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals (e.g., every 30 seconds). The device temporarily stores the data from the logger and uploads it to a server at regular intervals (e.g., every 5 minutes).

[0718] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. This analysis uses algorithms such as clustering and pattern mining. It also performs filtering to exclude routes and locations that the user deems inappropriate (e.g., gambling halls). The analysis results are used to understand the user's behavioral patterns and generate optimal outing plans.

[0719] As a specific example, let's say a user goes out on a day off with a logger and visits hiking spot A and cafe B. Based on this movement data, the server analyzes the user's walking distance, the places they visited, and the amount of time they spent there, and then suggests new hiking courses and cafe-hopping plans that are perfect for the next weekend. For example, a plan that includes hiking spot C and a new cafe D is generated and presented to the user.

[0720] The device displays the outing plans received from the server to the user. The user can select the plan that best suits their interests and conditions from the presented plans. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0721] The purpose of the present invention is to provide users with optimal outing plans through the above-mentioned series of processes, thereby lowering the barriers to going out. This system is expected to increase opportunities to go out and increase economic consumption.

[0722] The processing flow will be explained below.

[0723] Step 1:

[0724] Users install the application on their smartphone or other device, create an account, and log in.

[0725] Step 2:

[0726] Through the application, users input their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling).

[0727] Step 3:

[0728] The device stores the entered information on hobbies, preferences and means of transportation in an internal database and transmits the data to a server.

[0729] Step 4:

[0730] The server stores the received initial setting data in a database.

[0731] Step 5:

[0732] The user goes out carrying a dedicated logger, which uses GPS to record the user's current location and an accelerometer to count the number of steps taken.

[0733] Step 6:

[0734] The logger sends data on the current location, number of steps taken, and places visited to the device at regular intervals (e.g., every 30 seconds).

[0735] Step 7:

[0736] The device temporarily stores the received movement data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0737] Step 8:

[0738] The server stores the movement data received from the terminal in a database.

[0739] Step 9:

[0740] The server analyzes the user's movement patterns using algorithms such as clustering and pattern mining.

[0741] Step 10:

[0742] The server runs a filter process to exclude routes and locations that the user deems inappropriate, according to their settings.

[0743] Step 11:

[0744] Based on the analysis results, the server generates an outing plan that matches the user's hobbies, preferences, and behavioral patterns. Specifically, it creates multiple plans that include places to visit, recommended time schedules, and means of transportation to use.

[0745] Step 12:

[0746] The server transmits the generated outing plan to the terminal.

[0747] Step 13:

[0748] The terminal displays the outing plan received from the server to the user.

[0749] Step 14:

[0750] The user selects from the plans presented one that best suits their interests and requirements, and the selected plan is saved on the device.

[0751] Step 15:

[0752] Based on the selected plan, the device will link with map apps and reminder functions to support users when they are out and about.

[0753] The above are the specific processing steps of the system. Through this series of processing, the system proposes the most suitable outing plan to the user, making it easier to plan an outing.

[0754] Example 1

[0755] 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."

[0756] Conventional outing plan suggestion systems have difficulty providing optimal plans based on a user's hobbies, preferences, and behavioral patterns. In particular, there is a risk that inappropriate routes or locations may be suggested, which can reduce user satisfaction. Furthermore, systems that can analyze data in real time and smoothly provide users with optimal plans have not yet been fully established. There has been a demand for a system that can solve these issues and provide users with more suitable outing plans.

[0757] 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.

[0758] In this invention, the server includes: a means for a user to input information about hobbies, preferences, and transportation methods into a terminal; a means for the user to carry a logger when going out and collect movement data using a GPS and acceleration sensor; a means for transferring the collected movement data to the terminal at regular intervals and temporarily storing it; a means for uploading data from the terminal to the server at regular intervals; a means for analyzing the movement data stored in the database on the server using algorithms such as clustering and pattern mining; and a means for generating and proposing an optimal outing plan to the user based on the analysis results. This makes it possible to provide an optimal plan based on the user's hobbies, preferences, and behavioral patterns, and to realize high-quality plan proposals that exclude inappropriate routes and locations.

[0759] "Hobbies" refer to the activities and interests that interest a user.

[0760] "Transportation" refers to the method or means used by a user to travel, including walking, cycling, public transportation, etc.

[0761] A "terminal" is a portable electronic device used by a user, such as a smartphone or tablet.

[0762] A "logger" is a device that has a built-in GPS sensor and acceleration sensor and collects user movement data.

[0763] "Analysis" is the process of extracting user behavior patterns and trends from collected data using statistical methods and machine learning algorithms.

[0764] An "outing plan" is a plan of destinations and routes proposed based on the user's hobbies, preferences and behavioral patterns.

[0765] "Filtering" is the process of removing routes or locations that are deemed inappropriate from the collected data.

[0766] A "database" is an information management system that stores collected data in an organized manner and enables efficient access when needed.

[0767] "Clustering" is a machine learning technique that categorizes data points into groups such that the data within each group are more similar to each other than to the data in other groups.

[0768] "Pattern mining" is a data analysis technique for finding useful patterns and regularities from large amounts of data.

[0769] The present invention relates to a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns. This system is composed of a user terminal, a dedicated logger, and a server.

[0770] Hardware and software used

[0771] The system is implemented using the following hardware and software.

[0772] User device: Smartphone or tablet

[0773] Dedicated logger: A device with a built-in GPS sensor and acceleration sensor

[0774] Server: A server for running databases and analytical algorithms

[0775] Examples of software used: Apache Spark (distributed processing framework), Google Maps API (map display)

[0776] Processing Details

[0777] Collecting and storing user preference information

[0778] First, users install a dedicated application on their smartphone or tablet. They launch the application and create an account by entering the necessary personal information. Next, they set their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The device sends this setting information to the server, which stores it in a database.

[0779] Data collection using loggers

[0780] Users carry a dedicated logger when they go out. The logger uses a GPS sensor to record their current location and an accelerometer to measure the number of steps taken. These data are collected at regular intervals (e.g., every 30 seconds).

[0781] Transfer and storage of data to your device

[0782] The logger uses Bluetooth or Wi-Fi to transmit the collected data to the device, which has local storage for temporarily storing this data.

[0783] Uploading data to the server

[0784] The device uploads the collected data to the server at a fixed interval (e.g., every 5 minutes), and the server stores the received data in a database.

[0785] Analyzing movement data

[0786] The server performs analysis based on the travel data stored in the database, extracting user behavior patterns using machine learning algorithms such as clustering and pattern mining, and filtering out routes and locations deemed inappropriate.

[0787] Creating and presenting travel plans

[0788] The server generates an optimal outing plan for the user based on the analysis results. For example, based on the user's history of visiting hiking spot A and cafe B, the server proposes a plan that includes new hiking course C and cafe D. The device displays this plan to the user. The user can then select the plan that best suits their interests and conditions from the displayed plans and carry out the plan.

[0789] Examples of concrete examples and prompts

[0790] As a concrete example, a user goes out on a day off with a dedicated logger and visits hiking spot A and cafe B. Based on this movement data, the server analyzes the distance walked by the user, the places visited, and the time spent there, and suggests new hiking courses and cafe hopping plans that are optimal for the next weekend. For example, a plan including new hiking spot C and cafe D is generated and presented to the user.

[0791] Example prompt sentence:

[0792] "For a user who visited hiking spot A and cafe B last weekend, generate a plan that suggests a new hiking trail (e.g., hiking spot C) and cafe (e.g., cafe D) that are perfect for next weekend."

[0793] As described above, the present invention improves the user's outing experience by collecting and analyzing the user's movement data and providing the optimal outing plan.

[0794] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0795] Step 1:

[0796] Users install a dedicated application on their smartphone or tablet and create an account. At this time, they select their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The entered information is sent from the device to a server, which then stores it in a database.

[0797] Input: User preferences and transportation information

[0798] Output: Configuration information stored in a database on the server

[0799] Specific operation: The user enters information into the input form and presses the submit button. The device packages the configuration information in JSON format and sends it to the server via an HTTPS request. The server analyzes the received data and records it in a database.

[0800] Step 2:

[0801] Users carry a dedicated logger when they go out. The logger uses a GPS sensor and an acceleration sensor to measure their current location and number of steps, and saves the collected data in local storage at regular intervals (e.g., every 30 seconds).

[0802] Input: None (logger actively collects data)

[0803] Output: Movement data stored in the local storage of the logger

[0804] How it works: The logger's GPS sensor acquires location information, and the accelerometer detects movement and counts steps. The data obtained from the sensors is recorded periodically in the internal memory.

[0805] Step 3:

[0806] The logger uses Bluetooth or Wi-Fi to transmit the collected data to the user's device, which temporarily stores the data.

[0807] Input: Movement data from the logger

[0808] Output: Travel data stored in the device's local storage

[0809] Specific operation: The logger pairs with the device using a communication module (e.g., Bluetooth or Wi-Fi). Data is sent from the logger to the device in batches. The device records the received data in local storage.

[0810] Step 4:

[0811] The device uploads the movement data stored in the local storage to the server at a fixed frequency (e.g., every 5 minutes), and the server stores the received data in a database.

[0812] Input: Travel data stored on the device

[0813] Output: Movement data stored in the server database

[0814] Specific operation: The device uses a timer to retrieve movement data from local storage at regular intervals and send it to the server via an HTTPS request. The server receives the data and records it in a database.

[0815] Step 5:

[0816] The server analyzes the travel data stored in the database, extracts user behavior patterns using machine learning algorithms such as clustering and pattern mining, and filters out routes and locations deemed inappropriate.

[0817] Input: Movement data stored in the database

[0818] Output: Analysis results (user behavior patterns, exclusion of inappropriate locations)

[0819] Specific operation: The server starts an analysis job and performs data analysis using distributed processing with Apache Spark. It extracts behavioral patterns through cluster analysis and pattern mining, and lists and eliminates inappropriate locations.

[0820] Step 6:

[0821] The server generates an optimal outing plan for the user based on the analysis results. For example, it recommends new destinations that correspond to past visit history and generates the plan. The device displays the plan received from the server to the user.

[0822] Input: Analysis results

[0823] Output: Generated itinerary

[0824] How it works: Based on the analysis results, the server uses the AI ​​model to generate a new outing plan, which is then sent to the device, where the plan is displayed by the device application.

[0825] Step 7:

[0826] Users can select from the displayed outing plans that best suit their interests and requirements. The selected plan is saved on the device and will be linked to the map app and reminder function on the day of the trip to support the outing.

[0827] Input: Displayed outing plan

[0828] Output: User selected outing plan

[0829] How it works: The user browses through the proposed plans on the application interface and taps to select the plan they are interested in. The selected plan is saved in the device's local storage and linked to the map app and reminders.

[0830] (Application example 1)

[0831] 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."

[0832] In today's world, it is difficult for users to efficiently plan outings based on their hobbies, preferences, and behavioral patterns, and it is especially challenging to make optimal choices when it comes to food delivery. Furthermore, due to time and resource constraints, users are likely to miss suitable options. Additionally, there is a lack of systems that make suggestions based on individual user preferences and past behavioral data. The purpose of this invention is to solve these problems and provide users with optimal options.

[0833] 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.

[0834] In this invention, the server includes means for inputting information on the user's hobbies, preferences, and transportation means, logger means for collecting the user's movement data, means for analyzing the collected movement data, and means for proposing an outing plan or a food delivery plan based on the analysis results, thereby enabling the user to receive suggestions for an optimal outing plan or a food delivery plan based on their own behavioral patterns and hobbies and preferences.

[0835] "User" refers to an individual or organization that uses the system and provides travel data by inputting information on hobbies, preferences, and means of transportation.

[0836] "Hobbies and preferences" refers to the activities and interests of the user, and are the basic information that allows the system to propose the optimal plan.

[0837] "Transportation" refers to the means used by the user when traveling, and includes walking, cycling, public transportation, and cars.

[0838] "Logger means" refers to a device or program that collects user movement data, and records location information and movement data using a GPS, acceleration sensor, etc.

[0839] "Movement data" refers to data such as location information, number of steps, and duration of stay when a user actually moves, and is collected by a logger means.

[0840] "Analysis means" refers to a device or program that analyzes a user's behavioral patterns using a data analysis algorithm based on collected movement data and the user's hobbies and preferences.

[0841] An "outing plan" refers to a specific action plan for when a user goes out based on the user's hobbies, preferences, and behavioral patterns, and includes places to visit, routes, and the like.

[0842] A "food delivery plan" is a plan that suggests the most suitable restaurant and menu based on a user's preferences, past ordering history, and behavioral patterns.

[0843] The "suggestion means" refers to a device or program that generates an outing plan or food delivery plan that is suited to the user's hobbies, preferences, and behavioral patterns based on the analysis results and presents it to the user.

[0844] "Server" refers to a central processing unit or computer system that analyzes collected travel data and user preferences and proposes optimal plans.

[0845] "Filtering means" refers to devices or programs that perform processing to remove inappropriate routes or locations from collected movement data.

[0846] This invention is a system that proposes optimal outing plans and food delivery plans based on information on a user's hobbies, preferences, and transportation methods. This system is composed of a user terminal, a dedicated logger, and a server, and operates in the following manner.

[0847] Functionality Overview

[0848] Users install a dedicated application on their smartphone or other device, create an account, and log in. Next, they enter their hobbies and preferences (e.g., Chinese food, hiking, etc.) and preferred means of transportation (e.g., walking, cycling, train, etc.) through the application. This information is sent from the device to the server and stored in a database.

[0849] Data collection and analysis

[0850] When users go out, they carry a dedicated logger, which uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the user's device at regular intervals (e.g., every 30 seconds), where it is temporarily stored. It is then uploaded to a server at regular intervals (e.g., every 5 minutes). The server stores the received data in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. Data analysis algorithms such as clustering and pattern mining are used in this analysis. Filtering is also performed to exclude inappropriate routes and locations.

[0851] Plan proposal

[0852] The server generates outing plans and food delivery plans based on the analysis results. For example, if a user has previously visited hiking spot A and cafe B and these are registered, the server will suggest new hiking trails and cafes for the next weekend. These suggestions are displayed on the user's device, allowing the user to select based on their interests and convenience.

[0853] Hardware and software used

[0854] In implementing the present invention, the following hardware and software are used:

[0855] User device: smartphone or tablet

[0856] Dedicated logger: A device equipped with a GPS and accelerometer

[0857] Server: a computer system capable of running a database and data analysis algorithms

[0858] On the server side, the following data processing and calculations are performed:

[0859] Clustering: Grouping user behavior patterns based on collected movement data.

[0860] Pattern mining: Extracting general trends from users' past behavioral patterns and generating new plans.

[0861] Filtering: Filter out inappropriate routes and locations.

[0862] Specific examples

[0863] Assume a user likes Chinese food, dislikes spicy food, and has recently been ordering sushi frequently. We send the following prompt to the generative AI model as a food delivery plan suggestion:

[0864] The user likes Chinese food but doesn't like spicy food. Recently, he has been enjoying sushi and ordering it frequently. Please suggest the best restaurant and menu for him based on his travel patterns and past ordering history.

[0865] The purpose of this invention is to propose optimal plans based on the user's interests and behavior through the collection and analysis of various user data, which not only allows the user to efficiently choose their outings and meals, but also helps them discover new destinations and restaurants.

[0866] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0867] Step 1:

[0868] The user installs the application on their device, creates an account, and logs in.

[0869] Input: User account information

[0870] Output: A message indicating successful account creation and login

[0871] Specific operation: The user creates an account by entering their name, email address, password, etc. After the account is created, they log in to the application.

[0872] Step 2:

[0873] The user inputs information about their interests and transportation.

[0874] Input: Hobbies and preferences (e.g., Chinese food, hiking, etc.), mode of transportation (e.g., walking, cycling, etc.)

[0875] Output: Hobbies, preferences and transportation information stored on the server

[0876] Specific operation: The user enters information about their hobbies, preferences, and preferred means of transportation on the application's settings screen. This information is sent from the device to the server and stored in a database.

[0877] Step 3:

[0878] A dedicated logger collects user movement data.

[0879] Input: User location information, number of steps

[0880] Output: Temporarily saved movement data

[0881] How it works: The logger uses GPS and an accelerometer to record the user's current location and number of steps at regular intervals (e.g., every 30 seconds). This data is temporarily stored on the device.

[0882] Step 4:

[0883] The terminal uploads the collected data to the server at a fixed frequency.

[0884] Input: Movement data from the logger

[0885] Output: Movement data sent to the server

[0886] Specific operation: The device sends the data obtained from the logger to the server at a fixed frequency (e.g., every 5 minutes). The sent data is stored in a database.

[0887] Step 5:

[0888] The server analyzes the collected movement data.

[0889] Input: Movement data stored on the server

[0890] Output: Analysis results (data based on user behavior patterns and hobbies and preferences)

[0891] How it works: The server analyzes the travel data and extracts user behavior patterns using algorithms such as clustering and pattern mining. It also filters out inappropriate routes and locations.

[0892] Step 6:

[0893] The server generates a plan based on the analysis results.

[0894] Input: Analysis results, filtering results

[0895] Output: Best outing plan or food delivery plan

[0896] Specific operation: The server generates the best outing plan or food delivery plan for the next weekend based on the user's behavioral patterns and hobbies and preferences. The generated plan is saved on the server.

[0897] Step 7:

[0898] The terminal displays the plan received from the server to the user.

[0899] Input: Plan sent from the server

[0900] Output: Plan displayed on terminal

[0901] Specific operation: The plan generated by the server is sent to the device, which displays the plan to the user, allowing the user to select a plan that suits their interests and requirements.

[0902] Step 8:

[0903] The user selects a plan and goes out or arranges food delivery.

[0904] Input: The plan selected by the user

[0905] Output: Confirmation of outing plans and delivery orders

[0906] What it does: The user selects the plan they're interested in and plans to go out, or order food delivery. The selection is saved on the device and linked to reminders and map apps.

[0907] 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.

[0908] The present invention combines an emotion engine with a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns. This system is composed of a user terminal, a dedicated logger, an emotion engine, and a server, and functions as follows:

[0909] First, a user installs the application on a device such as a smartphone, creates an account, and logs in. The user then uses the application to set their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[0910] Next, the user carries a dedicated logger with them when they go out. This logger uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals (e.g., every 30 seconds). The device temporarily stores the data from the logger and uploads it to a server at regular intervals (e.g., every 5 minutes).

[0911] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. This analysis uses algorithms such as clustering and pattern mining. It also performs filtering to exclude routes and locations that the user deems inappropriate (e.g., gambling halls).

[0912] Furthermore, in this invention, by combining an emotion engine, it is possible to recognize the user's emotions. The emotion engine reads the user's current emotional state from photos, voice, text messages, etc., and collects the emotion data. The collected emotion data is processed by an analysis means and used to understand what emotions the user has under what circumstances.

[0913] Specifically, suppose a user goes out with a logger over the weekend and visits hiking spot A and cafe B. At this time, the emotion engine recognizes the user's facial photo and analyzes emotions such as joy or fatigue. This emotion data is sent to the server and analyzed along with other movement data. For example, if the server determines that the user had a great time at hiking spot A but was somewhat tired at cafe B, the server can suggest a new, more relaxing cafe the next time the server proposes a plan.

[0914] The server generates multiple optimal outing plans for the user based on comprehensive data, including the results of sentiment analysis. These plans include places to visit, a recommended time schedule, and the means of transportation to be used. The server sends the generated plans to the device, which displays them to the user. The user can then select the plan that best suits their interests and requirements from the plans provided. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0915] In this way, by combining the emotion engine, it becomes possible to provide more personalized outing plans based on the user's emotional state. This system is expected to provide users with a more satisfying outing experience and increase the number of opportunities to go out.

[0916] The processing flow will be explained below.

[0917] Step 1:

[0918] Users install the application on their smartphone or other device, create an account, and log in.

[0919] Step 2:

[0920] Through the application, users input their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling).

[0921] Step 3:

[0922] The device stores the entered information on hobbies, preferences and means of transportation in an internal database and transmits the data to a server.

[0923] Step 4:

[0924] The server stores the received initial setting data in a database.

[0925] Step 5:

[0926] When users go out, they carry a dedicated logger that uses GPS to record their current location and an accelerometer to measure the number of steps taken.

[0927] Step 6:

[0928] The logger sends data on the current location, number of steps taken, and places visited to the device at regular intervals (e.g., every 30 seconds).

[0929] Step 7:

[0930] The device temporarily stores the received movement data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0931] Step 8:

[0932] The server stores the movement data received from the terminal in a database.

[0933] Step 9:

[0934] The server analyzes the user's movement patterns using algorithms such as clustering and pattern mining.

[0935] Step 10:

[0936] The server runs a filter process to exclude routes and locations that the user deems inappropriate, according to their settings.

[0937] Step 11:

[0938] The emotion engine recognizes the user's emotional state from photos, voice, text messages, etc. and sends that data to the device.

[0939] Step 12:

[0940] The device temporarily stores the emotion data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[0941] Step 13:

[0942] The server analyzes the emotion data along with the movement data and builds a database to understand what emotions the user feels in what situations.

[0943] Step 14:

[0944] Based on the analysis results, the server generates an outing plan that matches the user's hobbies, preferences, behavioral patterns, and emotional state. Each plan includes places to visit, a recommended time schedule, and the means of transportation to use.

[0945] Step 15:

[0946] The server transmits the generated outing plan to the terminal.

[0947] Step 16:

[0948] The terminal displays the outing plan received from the server to the user.

[0949] Step 17:

[0950] The user selects from the presented plans the one that best suits their interests, conditions, and emotional state, and the selected plan is saved on the device.

[0951] Step 18:

[0952] Based on the selected plan, the device will link with map apps and reminder functions to support users when they are out and about.

[0953] These are the specific processing steps of the system that combines the emotion engine. Through this series of processes, the system proposes the most suitable outing plan to the user, helping them to have a more fulfilling outing.

[0954] Example 2

[0955] 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."

[0956] Conventional systems propose trip plans based on a user's hobbies, preferences, and transportation methods, but they do not take into account the user's emotional state, making it difficult to provide personalized and optimized plans. Furthermore, they lack the ability to filter out inappropriate routes and locations, making it impossible to fully guarantee user safety and satisfaction.

[0957] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information on the user's hobbies, preferences, and means of transportation, a location information collecting means for collecting the user's movement data, an analyzing means for analyzing the collected movement data, a suggesting means for proposing an outing plan based on the analysis results, an emotion recognizing means for collecting and analyzing the user's emotion data, and an adjusting means for adjusting the proposed content based on the emotion data. This makes it possible to provide a more personalized outing plan that takes the user's emotional state into consideration.

[0958] "User" refers to an individual who uses this system to create their own travel plans.

[0959] "Hobbies" refers to activities and areas of interest that a user has.

[0960] "Transportation" refers to the means used by the user to travel (e.g., walking, cycling, public transportation).

[0961] "Location information collection means" refers to a device equipped with a GPS function or an acceleration sensor used to obtain the user's current location and travel route.

[0962] "Analysis means" refers to an algorithm that analyzes the user's behavioral patterns based on collected data and generates an appropriate outing plan.

[0963] "Proposal means" refers to a function that presents the user with the optimal outing plan based on the analysis results.

[0964] "Emotion recognition means" refers to technology for analyzing a user's emotional state from photographs, voice, text messages, etc.

[0965] The "adjustment means" refers to a function for adjusting the contents of the outing plan based on emotion data to suit the user's condition and preferences.

[0966] "Filtering measures" refers to the ability to filter out inappropriate routes or locations from collected data.

[0967] An "outing plan" refers to a plan including an activity schedule and places to visit when going out that is proposed to the user.

[0968] "Server" refers to the central processing unit that receives, stores, analyzes, and generates the optimal plan from the data sent by the user.

[0969] "Terminal" refers to an electronic device, such as a smartphone or tablet, that a user uses to enter information or receive results.

[0970] The present invention is a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns, and combines emotion recognition functionality. This system is composed of a user terminal, a location information collection device, an emotion recognition engine, and a server.

[0971] First, the user installs a dedicated application on a device such as a smartphone, creates an account, and logs in. Through the application, the user sets their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). This setting information is sent to the server via the device and stored in a database.

[0972] Next, when users go out, they carry a location information collection device equipped with a GPS and acceleration sensor. This device records their current location and number of steps at regular intervals (e.g., every 30 seconds). The collected data is sent to the device at regular intervals, where it is temporarily stored and uploaded to a server at regular intervals (e.g., every 5 minutes).

[0973] The server stores the travel data received from the device in a database. The server then analyzes the user's behavioral patterns using algorithms such as clustering and pattern mining. It also performs filtering to exclude inappropriate routes and locations.

[0974] Furthermore, it is possible to recognize the user's emotions using an emotion recognition engine. This engine reads the user's current emotional state from photos, voice, text messages, etc. and collects that data. The emotion data is analyzed and used to understand what emotions the user feels in what situations.

[0975] Specifically, consider the case where a user visits hiking spot A and cafe B over the weekend with a location information collection device. At this time, the emotion recognition engine analyzes the user's facial photo and reads emotions such as joy or fatigue. This emotion data is sent to the server and analyzed together with other movement data. For example, if the analysis shows that the user had a great time at hiking spot A but felt somewhat tired at cafe B, the server can suggest a new relaxing cafe when proposing a plan for the next trip.

[0976] The server generates multiple optimal outing plans for the user based on comprehensive data, including the results of sentiment analysis. These plans include places to visit, recommended time schedules, and means of transportation to use. The server sends the generated plans to the device, which displays them to the user. The user can select the plan that best suits their interests and requirements from the plans provided. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[0977] An example prompt is, "Please suggest an appropriate outing plan based on the user's hobbies, preferences, and emotional data. For example, if the user had a great time at hiking spot A but felt a bit tired at cafe B, please tell us specifically what elements you should include in your next suggestion."

[0978] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0979] Step 1:

[0980] The user installs a dedicated application on their smartphone, creates an account, and logs in. As input information, they enter basic user information (such as name and email address) and a password. The account information is sent to the server and registered in the database. As output, the user is able to access the main screen of the application.

[0981] Step 2:

[0982] Through the application, users set their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The input information is temporarily stored on the device and sent to the server by pressing the send button. The server stores the received data in a database. As an output, the setting information is reflected in the user's profile.

[0983] Step 3:

[0984] When a user goes out, they carry a location information collection device (logger) equipped with a built-in GPS and acceleration sensor. The logger records their current location and number of steps at regular intervals (e.g., every 30 seconds). This data is sent from the logger to a device, which temporarily stores the data. The input information is the location information and step count data from the logger. The output is the temporarily stored movement data.

[0985] Step 4:

[0986] The device uploads movement data to the server at a fixed frequency (e.g., every 5 minutes). The server stores the received movement data in a database. The input information is the movement data sent from the device. This data is later subjected to behavioral pattern analysis. The output is movement data stored in the database.

[0987] Step 5:

[0988] The server starts the process of analyzing the movement data. Clustering and pattern mining algorithms are used to analyze user behavior patterns. The input information is the movement data stored in the database. Data manipulation is performed on identified patterns and frequently visited locations. The output is the behavior pattern information resulting from the analysis.

[0989] Step 6:

[0990] The server filters the collected travel data to remove inappropriate routes and locations. For example, it excludes areas considered dangerous or gambling establishments. The input information is behavioral pattern information and route information. Data manipulation involves scrutinizing the data based on the specified filter conditions. The output is information about safe routes after filtering.

[0991] Step 7:

[0992] The emotion recognition engine collects emotional data as users interact with photos, voice, text messages, etc. For example, a user may upload a photo of their face taken at a location they visited to the app. The input information is emotion-related data provided by the user. The emotion recognition engine analyzes this data and identifies the user's emotional state. Emotion data is generated as output and sent to the server.

[0993] Step 8:

[0994] The server analyzes the emotional data and determines the user's emotional state. For example, it recognizes whether the user is happy or tired in a particular place. The input information is emotional data. The data analysis identifies the user's emotional state. The output is the analyzed emotional information.

[0995] Step 9:

[0996] The server integrates the travel data and emotional data to generate an optimal outing plan for the user. For example, it creates a plan taking into account the places the user has enjoyed and where they can relax. The input information is behavioral pattern information and emotional information. As a data manipulation, both data are combined to generate a new outing plan. The output is multiple recommended outing plans.

[0997] Step 10:

[0998] The server sends the generated outing plan to the device, which then displays it to the user. The user selects the plan that best suits their interests and requirements. The input information is the recommended plan. After selection, the plan is saved on the device and is ready to support the outing in conjunction with the map app and reminder function. The output is the selected outing plan.

[0999] The above is the specific processing flow of the program of this system.

[1000] (Application example 2)

[1001] 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."

[1002] Conventional systems could suggest outing plans based on the user's hobbies, preferences, and transportation methods, but they did not take the user's emotional state into account, making it difficult to provide personalized and satisfying suggestions. Furthermore, they had limited means to evaluate the appropriateness of the proposed plans and were unable to respond to the user's real-time emotional changes. Therefore, a system that can further improve user satisfaction is needed.

[1003] The identification process by the identification 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 inputting information on the user's hobbies, preferences, and means of transportation, device means for collecting the user's movement data, means for analyzing the collected movement data, means for collecting and analyzing the user's emotional data, and means for proposing outing plans and optimal products and services based on the analysis results. This makes it possible to grasp the user's emotional state in real time and propose more appropriate outing plans, products, and services that will provide greater satisfaction.

[1004] "User's hobbies and preferences" refers to information about areas that indicate the user's personal favorite activities and interests.

[1005] "Transportation" refers to the means used by users to get around (walking, cycling, public transportation, etc.).

[1006] "Device means" refers to hardware and software for collecting user movement data.

[1007] "Emotion data" is data that represents the user's emotional state and is analyzed from photographs, voice, text messages, and the like.

[1008] "Server" refers to a remotely located computer system for storing, analyzing, and managing data.

[1009] "Outing Plan" refers to suggestions about recommended places and activities for the user to visit, as well as the means of transportation to use when doing so.

[1010] "Goods and Services" refers to items and activities offered to users in virtual and physical stores.

[1011] The present invention relates to a system that proposes optimal outing plans, products, and services based on a user's hobbies, preferences, and behavioral patterns. This system includes a user terminal, a dedicated device, an emotion engine, and a server. Specific embodiments of the system are described below.

[1012] First, a user installs the application on a device such as a smartphone or head-mounted display (HMD), creates an account, and logs in. Through the application, the user sets their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and mode of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[1013] Next, when users go out, they carry a dedicated device (such as smart glasses or a dedicated logger). This device uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals and uploaded from the device to a server.

[1014] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and preferences. Algorithms such as clustering and pattern mining are used for the analysis. Filtering is also performed to exclude routes and locations that the user deems inappropriate.

[1015] Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions in real time. The emotion engine reads the user's current emotional state from photos, voice, text messages, etc. and collects that data. The collected emotion data is processed by an analysis means and converted into data based on the user's emotional state.

[1016] For example, if a user is trying on new clothes in a virtual store, and the emotion engine recognizes a happy expression, the server will suggest similar clothes based on that emotion data. On the other hand, if the user shows a tired expression, the server will suggest items that will help them relax.

[1017] The server generates multiple optimal outing plans, products, and services for the user based on comprehensive data, including the results of sentiment analysis. The generated plan and product suggestions are sent to the device, which displays them to the user. The user then selects the most interesting plan or product from the provided options. The selected plan or product is saved on the device and works in conjunction with the map app and reminder function to support the user's actions.

[1018] For example, the following prompt sentences are used:

[1019] "When a user is trying on a new dress in a virtual try-on, if they show a happy expression, suggest other similar options. On the other hand, if they show a tired expression, suggest items that will help them relax. Also, include detailed descriptions and pricing information with all suggestions."

[1020] In this way, by combining the emotion engine, it becomes possible to propose more personalized outing plans and products / services based on the user's emotional state. This system is expected to provide users with a more satisfying experience.

[1021] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1022] Step 1:

[1023] The user installs the application on their smartphone or head-mounted display (HMD) and enters information about their hobbies, preferences, and transportation. When the user creates an account and logs in, this information is sent to the server and stored in a database. The input in this step is the user's hobbies, preferences, and transportation information, and the output is a database in which this information is saved.

[1024] Step 2:

[1025] When a user goes out, they carry a dedicated device (e.g., smart glasses or a dedicated logger). This device uses GPS to record their current location and an acceleration sensor to measure the number of steps taken, and collects the data. The device sends the data to the terminal at regular intervals (e.g., every 30 seconds). The input in this step is movement data, and the output is data temporarily stored on the terminal.

[1026] Step 3:

[1027] The device uploads the movement data from the logger to the server at a fixed frequency (e.g., every 5 minutes). The server stores the received movement data in a database and prepares it for later analysis. The input in this step is the movement data sent from the logger, and the output is the movement data stored in the database.

[1028] Step 4:

[1029] The server analyzes the travel data stored in the database and analyzes the user's behavioral patterns and hobbies and preferences using algorithms such as clustering and pattern mining. As a result of the analysis, inappropriate routes and locations are eliminated and appropriate locations for the user are identified. The input in this step is the travel data stored in the database, and the output is the analyzed behavioral patterns and hobbies and preferences information.

[1030] Step 5:

[1031] The emotion engine collects and analyzes the user's emotional data from photos, voice, text messages, etc. to understand the user's current emotional state. The analysis results are sent to the server and analyzed together with other movement data. The input in this step is emotional data such as photos, voice, and text messages, and the output is data indicating the user's emotional state.

[1032] Step 6:

[1033] The server generates optimal outing plans and product / service suggestions for the user based on the comprehensive analysis of the collected emotion data and movement data. The server generates multiple candidate plans and products and sends them to the device. The input in this step is the analyzed emotion data and movement data, and the output is the generated outing plans and product / service suggestions.

[1034] Step 7:

[1035] The device displays the proposals sent from the server to the user, and the user selects the most suitable plan or product from them. The selected plan or product is saved on the device and linked to the map app and reminder function to support the user's actions. The input in this step is the proposals sent from the server, and the output is the saving and linking of the plan or product selected by the user.

[1036] Additionally, the prompt sentence is used as an example:

[1037] "When a user is trying on a new dress in a virtual try-on, if they show a happy expression, suggest other similar options. On the other hand, if they show a tired expression, suggest items that will help them relax. Also, include detailed descriptions and pricing information with all suggestions."

[1038] 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.

[1039] 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.

[1040] 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.

[1041] [Fourth embodiment]

[1042] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1043] 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.

[1044] 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).

[1045] 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.

[1046] 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.

[1047] 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).

[1048] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1049] 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.

[1050] 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.

[1051] 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.

[1052] 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.

[1053] 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.

[1054] 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."

[1055] The present invention relates to a system that proposes optimal outing plans based on the hobbies, preferences, and behavioral patterns of individual users. This system is composed of a user terminal, a dedicated logger, and a server, and functions as follows.

[1056] First, a user installs the application on a device such as a smartphone, creates an account, and logs in. The user then uses the application to set their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[1057] Next, the user carries a dedicated logger with them when they go out. This logger uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals (e.g., every 30 seconds). The device temporarily stores the data from the logger and uploads it to a server at regular intervals (e.g., every 5 minutes).

[1058] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. This analysis uses algorithms such as clustering and pattern mining. It also performs filtering to exclude routes and locations that the user deems inappropriate (e.g., gambling halls). The analysis results are used to understand the user's behavioral patterns and generate optimal outing plans.

[1059] As a specific example, let's say a user goes out on a day off with a logger and visits hiking spot A and cafe B. Based on this movement data, the server analyzes the user's walking distance, the places they visited, and the amount of time they spent there, and then suggests new hiking courses and cafe-hopping plans that are perfect for the next weekend. For example, a plan that includes hiking spot C and a new cafe D is generated and presented to the user.

[1060] The device displays the outing plans received from the server to the user. The user can select the plan that best suits their interests and conditions from the presented plans. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[1061] The purpose of the present invention is to provide users with optimal outing plans through the above-mentioned series of processes, thereby lowering the barriers to going out. This system is expected to increase opportunities to go out and increase economic consumption.

[1062] The processing flow will be explained below.

[1063] Step 1:

[1064] Users install the application on their smartphone or other device, create an account, and log in.

[1065] Step 2:

[1066] Through the application, users input their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling).

[1067] Step 3:

[1068] The device stores the entered information on hobbies, preferences and means of transportation in an internal database and transmits the data to a server.

[1069] Step 4:

[1070] The server stores the received initial setting data in a database.

[1071] Step 5:

[1072] The user goes out carrying a dedicated logger, which uses GPS to record the user's current location and an accelerometer to count the number of steps taken.

[1073] Step 6:

[1074] The logger sends data on the current location, number of steps taken, and places visited to the device at regular intervals (e.g., every 30 seconds).

[1075] Step 7:

[1076] The device temporarily stores the received movement data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[1077] Step 8:

[1078] The server stores the movement data received from the terminal in a database.

[1079] Step 9:

[1080] The server analyzes the user's movement patterns using algorithms such as clustering and pattern mining.

[1081] Step 10:

[1082] The server runs a filter process to exclude routes and locations that the user deems inappropriate, according to their settings.

[1083] Step 11:

[1084] Based on the analysis results, the server generates an outing plan that matches the user's hobbies, preferences, and behavioral patterns. Specifically, it creates multiple plans that include places to visit, recommended time schedules, and means of transportation to use.

[1085] Step 12:

[1086] The server transmits the generated outing plan to the terminal.

[1087] Step 13:

[1088] The terminal displays the outing plan received from the server to the user.

[1089] Step 14:

[1090] The user selects from the plans presented one that best suits their interests and requirements, and the selected plan is saved on the device.

[1091] Step 15:

[1092] Based on the selected plan, the device will link with map apps and reminder functions to support users when they are out and about.

[1093] The above are the specific processing steps of the system. Through this series of processing, the system proposes the most suitable outing plan to the user, making it easier to plan an outing.

[1094] Example 1

[1095] 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."

[1096] Conventional outing plan suggestion systems have difficulty providing optimal plans based on a user's hobbies, preferences, and behavioral patterns. In particular, there is a risk that inappropriate routes or locations may be suggested, which can reduce user satisfaction. Furthermore, systems that can analyze data in real time and smoothly provide users with optimal plans have not yet been fully established. There has been a demand for a system that can solve these issues and provide users with more suitable outing plans.

[1097] 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.

[1098] In this invention, the server includes: a means for a user to input information about hobbies, preferences, and transportation methods into a terminal; a means for the user to carry a logger when going out and collect movement data using a GPS and acceleration sensor; a means for transferring the collected movement data to the terminal at regular intervals and temporarily storing it; a means for uploading data from the terminal to the server at regular intervals; a means for analyzing the movement data stored in the database on the server using algorithms such as clustering and pattern mining; and a means for generating and proposing an optimal outing plan to the user based on the analysis results. This makes it possible to provide an optimal plan based on the user's hobbies, preferences, and behavioral patterns, and to realize high-quality plan proposals that exclude inappropriate routes and locations.

[1099] "Hobbies" refer to the activities and interests that interest a user.

[1100] "Transportation" refers to the method or means used by a user to travel, including walking, cycling, public transportation, etc.

[1101] A "terminal" is a portable electronic device used by a user, such as a smartphone or tablet.

[1102] A "logger" is a device that has a built-in GPS sensor and acceleration sensor and collects user movement data.

[1103] "Analysis" is the process of extracting user behavior patterns and trends from collected data using statistical methods and machine learning algorithms.

[1104] An "outing plan" is a plan of destinations and routes proposed based on the user's hobbies, preferences and behavioral patterns.

[1105] "Filtering" is the process of removing routes or locations that are deemed inappropriate from the collected data.

[1106] A "database" is an information management system that stores collected data in an organized manner and enables efficient access when needed.

[1107] "Clustering" is a machine learning technique that categorizes data points into groups such that the data within each group are more similar to each other than to the data in other groups.

[1108] "Pattern mining" is a data analysis technique for finding useful patterns and regularities from large amounts of data.

[1109] The present invention relates to a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns. This system is composed of a user terminal, a dedicated logger, and a server.

[1110] Hardware and software used

[1111] The system is implemented using the following hardware and software.

[1112] User device: Smartphone or tablet

[1113] Dedicated logger: A device with a built-in GPS sensor and acceleration sensor

[1114] Server: A server for running databases and analytical algorithms

[1115] Examples of software used: Apache Spark (distributed processing framework), Google Maps API (map display)

[1116] Processing Details

[1117] Collecting and storing user preference information

[1118] First, users install a dedicated application on their smartphone or tablet. They launch the application and create an account by entering the necessary personal information. Next, they set their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The device sends this setting information to the server, which stores it in a database.

[1119] Data collection using loggers

[1120] Users carry a dedicated logger when they go out. The logger uses a GPS sensor to record their current location and an accelerometer to measure the number of steps taken. These data are collected at regular intervals (e.g., every 30 seconds).

[1121] Transfer and storage of data to your device

[1122] The logger uses Bluetooth or Wi-Fi to transmit the collected data to the device, which has local storage for temporarily storing this data.

[1123] Uploading data to the server

[1124] The device uploads the collected data to the server at a fixed interval (e.g., every 5 minutes), and the server stores the received data in a database.

[1125] Analyzing movement data

[1126] The server performs analysis based on the travel data stored in the database, extracting user behavior patterns using machine learning algorithms such as clustering and pattern mining, and filtering out routes and locations deemed inappropriate.

[1127] Creating and presenting travel plans

[1128] The server generates an optimal outing plan for the user based on the analysis results. For example, based on the user's history of visiting hiking spot A and cafe B, the server proposes a plan that includes new hiking course C and cafe D. The device displays this plan to the user. The user can then select the plan that best suits their interests and conditions from the displayed plans and carry out the plan.

[1129] Examples of concrete examples and prompts

[1130] As a concrete example, a user goes out on a day off with a dedicated logger and visits hiking spot A and cafe B. Based on this movement data, the server analyzes the distance walked by the user, the places visited, and the time spent there, and suggests new hiking courses and cafe hopping plans that are optimal for the next weekend. For example, a plan including new hiking spot C and cafe D is generated and presented to the user.

[1131] Example prompt sentence:

[1132] "For a user who visited hiking spot A and cafe B last weekend, generate a plan that suggests a new hiking trail (e.g., hiking spot C) and cafe (e.g., cafe D) that are perfect for next weekend."

[1133] As described above, the present invention improves the user's outing experience by collecting and analyzing the user's movement data and providing the optimal outing plan.

[1134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1135] Step 1:

[1136] Users install a dedicated application on their smartphone or tablet and create an account. At this time, they select their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The entered information is sent from the device to a server, which then stores it in a database.

[1137] Input: User preferences and transportation information

[1138] Output: Configuration information stored in a database on the server

[1139] Specific operation: The user enters information into the input form and presses the submit button. The device packages the configuration information in JSON format and sends it to the server via an HTTPS request. The server analyzes the received data and records it in a database.

[1140] Step 2:

[1141] Users carry a dedicated logger when they go out. The logger uses a GPS sensor and an acceleration sensor to measure their current location and number of steps, and saves the collected data in local storage at regular intervals (e.g., every 30 seconds).

[1142] Input: None (logger actively collects data)

[1143] Output: Movement data stored in the local storage of the logger

[1144] How it works: The logger's GPS sensor acquires location information, and the accelerometer detects movement and counts steps. The data obtained from the sensors is recorded periodically in the internal memory.

[1145] Step 3:

[1146] The logger uses Bluetooth or Wi-Fi to transmit the collected data to the user's device, which temporarily stores the data.

[1147] Input: Movement data from the logger

[1148] Output: Travel data stored in the device's local storage

[1149] Specific operation: The logger pairs with the device using a communication module (e.g., Bluetooth or Wi-Fi). Data is sent from the logger to the device in batches. The device records the received data in local storage.

[1150] Step 4:

[1151] The device uploads the movement data stored in the local storage to the server at a fixed frequency (e.g., every 5 minutes), and the server stores the received data in a database.

[1152] Input: Travel data stored on the device

[1153] Output: Movement data stored in the server database

[1154] Specific operation: The device uses a timer to retrieve movement data from local storage at regular intervals and send it to the server via an HTTPS request. The server receives the data and records it in a database.

[1155] Step 5:

[1156] The server analyzes the travel data stored in the database, extracts user behavior patterns using machine learning algorithms such as clustering and pattern mining, and filters out routes and locations deemed inappropriate.

[1157] Input: Movement data stored in the database

[1158] Output: Analysis results (user behavior patterns, exclusion of inappropriate locations)

[1159] Specific operation: The server starts an analysis job and performs data analysis using distributed processing with Apache Spark. It extracts behavioral patterns through cluster analysis and pattern mining, and lists and eliminates inappropriate locations.

[1160] Step 6:

[1161] The server generates an optimal outing plan for the user based on the analysis results. For example, it recommends new destinations that correspond to past visit history and generates the plan. The device displays the plan received from the server to the user.

[1162] Input: Analysis results

[1163] Output: Generated itinerary

[1164] How it works: Based on the analysis results, the server uses the AI ​​model to generate a new outing plan, which is then sent to the device, where the plan is displayed by the device application.

[1165] Step 7:

[1166] Users can select from the displayed outing plans that best suit their interests and requirements. The selected plan is saved on the device and will be linked to the map app and reminder function on the day of the trip to support the outing.

[1167] Input: Displayed outing plan

[1168] Output: User selected outing plan

[1169] How it works: The user browses through the proposed plans on the application interface and taps to select the plan they are interested in. The selected plan is saved in the device's local storage and linked to the map app and reminders.

[1170] (Application example 1)

[1171] 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."

[1172] In today's world, it is difficult for users to efficiently plan outings based on their hobbies, preferences, and behavioral patterns, and it is especially challenging to make optimal choices when it comes to food delivery. Furthermore, due to time and resource constraints, users are likely to miss suitable options. Additionally, there is a lack of systems that make suggestions based on individual user preferences and past behavioral data. The purpose of this invention is to solve these problems and provide users with optimal options.

[1173] 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.

[1174] In this invention, the server includes means for inputting information on the user's hobbies, preferences, and transportation means, logger means for collecting the user's movement data, means for analyzing the collected movement data, and means for proposing an outing plan or a food delivery plan based on the analysis results, thereby enabling the user to receive suggestions for an optimal outing plan or a food delivery plan based on their own behavioral patterns and hobbies and preferences.

[1175] "User" refers to an individual or organization that uses the system and provides travel data by inputting information on hobbies, preferences, and means of transportation.

[1176] "Hobbies and preferences" refers to the activities and interests of the user, and are the basic information that allows the system to propose the optimal plan.

[1177] "Transportation" refers to the means used by the user when traveling, and includes walking, cycling, public transportation, and cars.

[1178] "Logger means" refers to a device or program that collects user movement data, and records location information and movement data using a GPS, acceleration sensor, etc.

[1179] "Movement data" refers to data such as location information, number of steps, and duration of stay when a user actually moves, and is collected by a logger means.

[1180] "Analysis means" refers to a device or program that analyzes a user's behavioral patterns using a data analysis algorithm based on collected movement data and the user's hobbies and preferences.

[1181] An "outing plan" refers to a specific action plan for when a user goes out based on the user's hobbies, preferences, and behavioral patterns, and includes places to visit, routes, and the like.

[1182] A "food delivery plan" is a plan that suggests the most suitable restaurant and menu based on a user's preferences, past ordering history, and behavioral patterns.

[1183] The "suggestion means" refers to a device or program that generates an outing plan or food delivery plan that is suited to the user's hobbies, preferences, and behavioral patterns based on the analysis results and presents it to the user.

[1184] "Server" refers to a central processing unit or computer system that analyzes collected travel data and user preferences and proposes optimal plans.

[1185] "Filtering means" refers to devices or programs that perform processing to remove inappropriate routes or locations from collected movement data.

[1186] This invention is a system that proposes optimal outing plans and food delivery plans based on information on a user's hobbies, preferences, and transportation methods. This system is composed of a user terminal, a dedicated logger, and a server, and operates in the following manner.

[1187] Functionality Overview

[1188] Users install a dedicated application on their smartphone or other device, create an account, and log in. Next, they enter their hobbies and preferences (e.g., Chinese food, hiking, etc.) and preferred means of transportation (e.g., walking, cycling, train, etc.) through the application. This information is sent from the device to the server and stored in a database.

[1189] Data collection and analysis

[1190] When users go out, they carry a dedicated logger, which uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the user's device at regular intervals (e.g., every 30 seconds), where it is temporarily stored. It is then uploaded to a server at regular intervals (e.g., every 5 minutes). The server stores the received data in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. Data analysis algorithms such as clustering and pattern mining are used in this analysis. Filtering is also performed to exclude inappropriate routes and locations.

[1191] Plan proposal

[1192] The server generates outing plans and food delivery plans based on the analysis results. For example, if a user has previously visited hiking spot A and cafe B and these are registered, the server will suggest new hiking trails and cafes for the next weekend. These suggestions are displayed on the user's device, allowing the user to select based on their interests and convenience.

[1193] Hardware and software used

[1194] In implementing the present invention, the following hardware and software are used:

[1195] User device: smartphone or tablet

[1196] Dedicated logger: A device equipped with a GPS and accelerometer

[1197] Server: a computer system capable of running a database and data analysis algorithms

[1198] On the server side, the following data processing and calculations are performed:

[1199] Clustering: Grouping user behavior patterns based on collected movement data.

[1200] Pattern mining: Extracting general trends from users' past behavioral patterns and generating new plans.

[1201] Filtering: Filter out inappropriate routes and locations.

[1202] Specific examples

[1203] Assume a user likes Chinese food, dislikes spicy food, and has recently been ordering sushi frequently. We send the following prompt to the generative AI model as a food delivery plan suggestion:

[1204] The user likes Chinese food but doesn't like spicy food. Recently, he has been enjoying sushi and ordering it frequently. Please suggest the best restaurant and menu for him based on his travel patterns and past ordering history.

[1205] The purpose of this invention is to propose optimal plans based on the user's interests and behavior through the collection and analysis of various user data, which not only allows the user to efficiently choose their outings and meals, but also helps them discover new destinations and restaurants.

[1206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1207] Step 1:

[1208] The user installs the application on their device, creates an account, and logs in.

[1209] Input: User account information

[1210] Output: A message indicating successful account creation and login

[1211] Specific operation: The user creates an account by entering their name, email address, password, etc. After the account is created, they log in to the application.

[1212] Step 2:

[1213] The user inputs information about their interests and transportation.

[1214] Input: Hobbies and preferences (e.g., Chinese food, hiking, etc.), mode of transportation (e.g., walking, cycling, etc.)

[1215] Output: Hobbies, preferences and transportation information stored on the server

[1216] Specific operation: The user enters information about their hobbies, preferences, and preferred means of transportation on the application's settings screen. This information is sent from the device to the server and stored in a database.

[1217] Step 3:

[1218] A dedicated logger collects user movement data.

[1219] Input: User location information, number of steps

[1220] Output: Temporarily saved movement data

[1221] How it works: The logger uses GPS and an accelerometer to record the user's current location and number of steps at regular intervals (e.g., every 30 seconds). This data is temporarily stored on the device.

[1222] Step 4:

[1223] The terminal uploads the collected data to the server at a fixed frequency.

[1224] Input: Movement data from the logger

[1225] Output: Movement data sent to the server

[1226] Specific operation: The device sends the data obtained from the logger to the server at a fixed frequency (e.g., every 5 minutes). The sent data is stored in a database.

[1227] Step 5:

[1228] The server analyzes the collected movement data.

[1229] Input: Movement data stored on the server

[1230] Output: Analysis results (data based on user behavior patterns and hobbies and preferences)

[1231] How it works: The server analyzes the travel data and extracts user behavior patterns using algorithms such as clustering and pattern mining. It also filters out inappropriate routes and locations.

[1232] Step 6:

[1233] The server generates a plan based on the analysis results.

[1234] Input: Analysis results, filtering results

[1235] Output: Best outing plan or food delivery plan

[1236] Specific operation: The server generates the best outing plan or food delivery plan for the next weekend based on the user's behavioral patterns and hobbies and preferences. The generated plan is saved on the server.

[1237] Step 7:

[1238] The terminal displays the plan received from the server to the user.

[1239] Input: Plan sent from the server

[1240] Output: Plan displayed on terminal

[1241] Specific operation: The plan generated by the server is sent to the device, which displays the plan to the user, allowing the user to select a plan that suits their interests and requirements.

[1242] Step 8:

[1243] The user selects a plan and goes out or arranges food delivery.

[1244] Input: The plan selected by the user

[1245] Output: Confirmation of outing plans and delivery orders

[1246] What it does: The user selects the plan they're interested in and plans to go out, or order food delivery. The selection is saved on the device and linked to reminders and map apps.

[1247] 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.

[1248] The present invention combines an emotion engine with a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns. This system is composed of a user terminal, a dedicated logger, an emotion engine, and a server, and functions as follows:

[1249] First, a user installs the application on a device such as a smartphone, creates an account, and logs in. The user then uses the application to set their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[1250] Next, the user carries a dedicated logger with them when they go out. This logger uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals (e.g., every 30 seconds). The device temporarily stores the data from the logger and uploads it to a server at regular intervals (e.g., every 5 minutes).

[1251] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and hobbies and preferences. This analysis uses algorithms such as clustering and pattern mining. It also performs filtering to exclude routes and locations that the user deems inappropriate (e.g., gambling halls).

[1252] Furthermore, in this invention, by combining an emotion engine, it is possible to recognize the user's emotions. The emotion engine reads the user's current emotional state from photos, voice, text messages, etc., and collects the emotion data. The collected emotion data is processed by an analysis means and used to understand what emotions the user has under what circumstances.

[1253] Specifically, suppose a user goes out with a logger over the weekend and visits hiking spot A and cafe B. At this time, the emotion engine recognizes the user's facial photo and analyzes emotions such as joy or fatigue. This emotion data is sent to the server and analyzed along with other movement data. For example, if the server determines that the user had a great time at hiking spot A but was somewhat tired at cafe B, the server can suggest a new, more relaxing cafe the next time the server proposes a plan.

[1254] The server generates multiple optimal outing plans for the user based on comprehensive data, including the results of sentiment analysis. These plans include places to visit, a recommended time schedule, and the means of transportation to be used. The server sends the generated plans to the device, which displays them to the user. The user can then select the plan that best suits their interests and requirements from the plans provided. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[1255] In this way, by combining the emotion engine, it becomes possible to provide more personalized outing plans based on the user's emotional state. This system is expected to provide users with a more satisfying outing experience and increase the number of opportunities to go out.

[1256] The processing flow will be explained below.

[1257] Step 1:

[1258] Users install the application on their smartphone or other device, create an account, and log in.

[1259] Step 2:

[1260] Through the application, users input their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and preferred means of transportation (e.g., walking, cycling).

[1261] Step 3:

[1262] The device stores the entered information on hobbies, preferences and means of transportation in an internal database and transmits the data to a server.

[1263] Step 4:

[1264] The server stores the received initial setting data in a database.

[1265] Step 5:

[1266] When users go out, they carry a dedicated logger that uses GPS to record their current location and an accelerometer to measure the number of steps taken.

[1267] Step 6:

[1268] The logger sends data on the current location, number of steps taken, and places visited to the device at regular intervals (e.g., every 30 seconds).

[1269] Step 7:

[1270] The device temporarily stores the received movement data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[1271] Step 8:

[1272] The server stores the movement data received from the terminal in a database.

[1273] Step 9:

[1274] The server analyzes the user's movement patterns using algorithms such as clustering and pattern mining.

[1275] Step 10:

[1276] The server runs a filter process to exclude routes and locations that the user deems inappropriate, according to their settings.

[1277] Step 11:

[1278] The emotion engine recognizes the user's emotional state from photos, voice, text messages, etc. and sends that data to the device.

[1279] Step 12:

[1280] The device temporarily stores the emotion data and uploads it to the server at a fixed interval (e.g., every 5 minutes).

[1281] Step 13:

[1282] The server analyzes the emotion data along with the movement data and builds a database to understand what emotions the user feels in what situations.

[1283] Step 14:

[1284] Based on the analysis results, the server generates an outing plan that matches the user's hobbies, preferences, behavioral patterns, and emotional state. Each plan includes places to visit, a recommended time schedule, and the means of transportation to use.

[1285] Step 15:

[1286] The server transmits the generated outing plan to the terminal.

[1287] Step 16:

[1288] The terminal displays the outing plan received from the server to the user.

[1289] Step 17:

[1290] The user selects from the presented plans the one that best suits their interests, conditions, and emotional state, and the selected plan is saved on the device.

[1291] Step 18:

[1292] Based on the selected plan, the device will link with map apps and reminder functions to support users when they are out and about.

[1293] These are the specific processing steps of the system that combines the emotion engine. Through this series of processes, the system proposes the most suitable outing plan to the user, helping them to have a more fulfilling outing.

[1294] Example 2

[1295] 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."

[1296] Conventional systems propose trip plans based on a user's hobbies, preferences, and transportation methods, but they do not take into account the user's emotional state, making it difficult to provide personalized and optimized plans. Furthermore, they lack the ability to filter out inappropriate routes and locations, making it impossible to fully guarantee user safety and satisfaction.

[1297] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting information on the user's hobbies, preferences, and means of transportation, a location information collecting means for collecting the user's movement data, an analyzing means for analyzing the collected movement data, a suggesting means for proposing an outing plan based on the analysis results, an emotion recognizing means for collecting and analyzing the user's emotion data, and an adjusting means for adjusting the proposed content based on the emotion data. This makes it possible to provide a more personalized outing plan that takes the user's emotional state into consideration.

[1298] "User" refers to an individual who uses this system to create their own travel plans.

[1299] "Hobbies" refers to activities and areas of interest that a user has.

[1300] "Transportation" refers to the means used by the user to travel (e.g., walking, cycling, public transportation).

[1301] "Location information collection means" refers to a device equipped with a GPS function or an acceleration sensor used to obtain the user's current location and travel route.

[1302] "Analysis means" refers to an algorithm that analyzes the user's behavioral patterns based on collected data and generates an appropriate outing plan.

[1303] "Proposal means" refers to a function that presents the user with the optimal outing plan based on the analysis results.

[1304] "Emotion recognition means" refers to technology for analyzing a user's emotional state from photographs, voice, text messages, etc.

[1305] The "adjustment means" refers to a function for adjusting the contents of the outing plan based on emotion data to suit the user's condition and preferences.

[1306] "Filtering measures" refers to the ability to filter out inappropriate routes or locations from collected data.

[1307] An "outing plan" refers to a plan including an activity schedule and places to visit when going out that is proposed to the user.

[1308] "Server" refers to the central processing unit that receives, stores, analyzes, and generates the optimal plan from the data sent by the user.

[1309] "Terminal" refers to an electronic device, such as a smartphone or tablet, that a user uses to enter information or receive results.

[1310] The present invention is a system that proposes optimal outing plans based on a user's hobbies, preferences, and behavioral patterns, and combines emotion recognition functionality. This system is composed of a user terminal, a location information collection device, an emotion recognition engine, and a server.

[1311] First, the user installs a dedicated application on a device such as a smartphone, creates an account, and logs in. Through the application, the user sets their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). This setting information is sent to the server via the device and stored in a database.

[1312] Next, when users go out, they carry a location information collection device equipped with a GPS and acceleration sensor. This device records their current location and number of steps at regular intervals (e.g., every 30 seconds). The collected data is sent to the device at regular intervals, where it is temporarily stored and uploaded to a server at regular intervals (e.g., every 5 minutes).

[1313] The server stores the travel data received from the device in a database. The server then analyzes the user's behavioral patterns using algorithms such as clustering and pattern mining. It also performs filtering to exclude inappropriate routes and locations.

[1314] Furthermore, it is possible to recognize the user's emotions using an emotion recognition engine. This engine reads the user's current emotional state from photos, voice, text messages, etc. and collects that data. The emotion data is analyzed and used to understand what emotions the user feels in what situations.

[1315] Specifically, consider the case where a user visits hiking spot A and cafe B over the weekend with a location information collection device. At this time, the emotion recognition engine analyzes the user's facial photo and reads emotions such as joy or fatigue. This emotion data is sent to the server and analyzed together with other movement data. For example, if the analysis shows that the user had a great time at hiking spot A but felt somewhat tired at cafe B, the server can suggest a new relaxing cafe when proposing a plan for the next trip.

[1316] The server generates multiple optimal outing plans for the user based on comprehensive data, including the results of sentiment analysis. These plans include places to visit, recommended time schedules, and means of transportation to use. The server sends the generated plans to the device, which displays them to the user. The user can select the plan that best suits their interests and requirements from the plans provided. The selected plan is saved on the device and works in conjunction with the map app and reminder function to support the user's outings.

[1317] An example prompt is, "Please suggest an appropriate outing plan based on the user's hobbies, preferences, and emotional data. For example, if the user had a great time at hiking spot A but felt a bit tired at cafe B, please tell us specifically what elements you should include in your next suggestion."

[1318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1319] Step 1:

[1320] The user installs a dedicated application on their smartphone, creates an account, and logs in. As input information, they enter basic user information (such as name and email address) and a password. The account information is sent to the server and registered in the database. As output, the user is able to access the main screen of the application.

[1321] Step 2:

[1322] Through the application, users set their hobbies and preferences (e.g., hiking, cafe hopping) and mode of transportation (e.g., walking, cycling, public transportation). The input information is temporarily stored on the device and sent to the server by pressing the send button. The server stores the received data in a database. As an output, the setting information is reflected in the user's profile.

[1323] Step 3:

[1324] When a user goes out, they carry a location information collection device (logger) equipped with a built-in GPS and acceleration sensor. The logger records their current location and number of steps at regular intervals (e.g., every 30 seconds). This data is sent from the logger to a device, which temporarily stores the data. The input information is the location information and step count data from the logger. The output is the temporarily stored movement data.

[1325] Step 4:

[1326] The device uploads movement data to the server at a fixed frequency (e.g., every 5 minutes). The server stores the received movement data in a database. The input information is the movement data sent from the device. This data is later subjected to behavioral pattern analysis. The output is movement data stored in the database.

[1327] Step 5:

[1328] The server starts the process of analyzing the movement data. Clustering and pattern mining algorithms are used to analyze user behavior patterns. The input information is the movement data stored in the database. Data manipulation is performed on identified patterns and frequently visited locations. The output is the behavior pattern information resulting from the analysis.

[1329] Step 6:

[1330] The server filters the collected travel data to remove inappropriate routes and locations. For example, it excludes areas considered dangerous or gambling establishments. The input information is behavioral pattern information and route information. Data manipulation involves scrutinizing the data based on the specified filter conditions. The output is information about safe routes after filtering.

[1331] Step 7:

[1332] The emotion recognition engine collects emotional data as users interact with photos, voice, text messages, etc. For example, a user may upload a photo of their face taken at a location they visited to the app. The input information is emotion-related data provided by the user. The emotion recognition engine analyzes this data and identifies the user's emotional state. Emotion data is generated as output and sent to the server.

[1333] Step 8:

[1334] The server analyzes the emotional data and determines the user's emotional state. For example, it recognizes whether the user is happy or tired in a particular place. The input information is emotional data. The data analysis identifies the user's emotional state. The output is the analyzed emotional information.

[1335] Step 9:

[1336] The server integrates the travel data and emotional data to generate an optimal outing plan for the user. For example, it creates a plan taking into account the places the user has enjoyed and where they can relax. The input information is behavioral pattern information and emotional information. As a data manipulation, both data are combined to generate a new outing plan. The output is multiple recommended outing plans.

[1337] Step 10:

[1338] The server sends the generated outing plan to the device, which then displays it to the user. The user selects the plan that best suits their interests and requirements. The input information is the recommended plan. After selection, the plan is saved on the device and is ready to support the outing in conjunction with the map app and reminder function. The output is the selected outing plan.

[1339] The above is the specific processing flow of the program of this system.

[1340] (Application example 2)

[1341] 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."

[1342] Conventional systems could suggest outing plans based on the user's hobbies, preferences, and transportation methods, but they did not take the user's emotional state into account, making it difficult to provide personalized and satisfying suggestions. Furthermore, they had limited means to evaluate the appropriateness of the proposed plans and were unable to respond to the user's real-time emotional changes. Therefore, a system that can further improve user satisfaction is needed.

[1343] The identification process by the identification 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 inputting information on the user's hobbies, preferences, and means of transportation, device means for collecting the user's movement data, means for analyzing the collected movement data, means for collecting and analyzing the user's emotional data, and means for proposing outing plans and optimal products and services based on the analysis results. This makes it possible to grasp the user's emotional state in real time and propose more appropriate outing plans, products, and services that will provide greater satisfaction.

[1344] "User's hobbies and preferences" refers to information about areas that indicate the user's personal favorite activities and interests.

[1345] "Transportation" refers to the means used by users to get around (walking, cycling, public transportation, etc.).

[1346] "Device means" refers to hardware and software for collecting user movement data.

[1347] "Emotion data" is data that represents the user's emotional state and is analyzed from photographs, voice, text messages, and the like.

[1348] "Server" refers to a remotely located computer system for storing, analyzing, and managing data.

[1349] "Outing Plan" refers to suggestions about recommended places and activities for the user to visit, as well as the means of transportation to use when doing so.

[1350] "Goods and Services" refers to items and activities offered to users in virtual and physical stores.

[1351] The present invention relates to a system that proposes optimal outing plans, products, and services based on a user's hobbies, preferences, and behavioral patterns. This system includes a user terminal, a dedicated device, an emotion engine, and a server. Specific embodiments of the system are described below.

[1352] First, a user installs the application on a device such as a smartphone or head-mounted display (HMD), creates an account, and logs in. Through the application, the user sets their hobbies and preferences (e.g., hiking, cafe hopping, etc.) and mode of transportation (e.g., walking, cycling, public transportation, etc.). This setting information is sent to the server via the device and stored in a database.

[1353] Next, when users go out, they carry a dedicated device (such as smart glasses or a dedicated logger). This device uses GPS to record their current location and an acceleration sensor to measure the number of steps taken. The collected movement data is sent to the device at regular intervals and uploaded from the device to a server.

[1354] The server stores the travel data received from the device in a database and analyzes it based on the user's behavioral patterns and preferences. Algorithms such as clustering and pattern mining are used for the analysis. Filtering is also performed to exclude routes and locations that the user deems inappropriate.

[1355] Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotions in real time. The emotion engine reads the user's current emotional state from photos, voice, text messages, etc. and collects that data. The collected emotion data is processed by an analysis means and converted into data based on the user's emotional state.

[1356] For example, if a user is trying on new clothes in a virtual store, and the emotion engine recognizes a happy expression, the server will suggest similar clothes based on that emotion data. On the other hand, if the user shows a tired expression, the server will suggest items that will help them relax.

[1357] The server generates multiple optimal outing plans, products, and services for the user based on comprehensive data, including the results of sentiment analysis. The generated plan and product suggestions are sent to the device, which displays them to the user. The user then selects the most interesting plan or product from the provided options. The selected plan or product is saved on the device and works in conjunction with the map app and reminder function to support the user's actions.

[1358] For example, the following prompt sentences are used:

[1359] "When a user is trying on a new dress in a virtual try-on, if they show a happy expression, suggest other similar options. On the other hand, if they show a tired expression, suggest items that will help them relax. Also, include detailed descriptions and pricing information with all suggestions."

[1360] In this way, by combining the emotion engine, it becomes possible to propose more personalized outing plans and products / services based on the user's emotional state. This system is expected to provide users with a more satisfying experience.

[1361] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1362] Step 1:

[1363] The user installs the application on their smartphone or head-mounted display (HMD) and enters information about their hobbies, preferences, and transportation. When the user creates an account and logs in, this information is sent to the server and stored in a database. The input in this step is the user's hobbies, preferences, and transportation information, and the output is a database in which this information is saved.

[1364] Step 2:

[1365] When a user goes out, they carry a dedicated device (e.g., smart glasses or a dedicated logger). This device uses GPS to record their current location and an acceleration sensor to measure the number of steps taken, and collects the data. The device sends the data to the terminal at regular intervals (e.g., every 30 seconds). The input in this step is movement data, and the output is data temporarily stored on the terminal.

[1366] Step 3:

[1367] The device uploads the movement data from the logger to the server at a fixed frequency (e.g., every 5 minutes). The server stores the received movement data in a database and prepares it for later analysis. The input in this step is the movement data sent from the logger, and the output is the movement data stored in the database.

[1368] Step 4:

[1369] The server analyzes the travel data stored in the database and analyzes the user's behavioral patterns and hobbies and preferences using algorithms such as clustering and pattern mining. As a result of the analysis, inappropriate routes and locations are eliminated and appropriate locations for the user are identified. The input in this step is the travel data stored in the database, and the output is the analyzed behavioral patterns and hobbies and preferences information.

[1370] Step 5:

[1371] The emotion engine collects and analyzes the user's emotional data from photos, voice, text messages, etc. to understand the user's current emotional state. The analysis results are sent to the server and analyzed together with other movement data. The input in this step is emotional data such as photos, voice, and text messages, and the output is data indicating the user's emotional state.

[1372] Step 6:

[1373] The server generates optimal outing plans and product / service suggestions for the user based on the comprehensive analysis of the collected emotion data and movement data. The server generates multiple candidate plans and products and sends them to the device. The input in this step is the analyzed emotion data and movement data, and the output is the generated outing plans and product / service suggestions.

[1374] Step 7:

[1375] The device displays the proposals sent from the server to the user, and the user selects the most suitable plan or product from them. The selected plan or product is saved on the device and linked to the map app and reminder function to support the user's actions. The input in this step is the proposals sent from the server, and the output is the saving and linking of the plan or product selected by the user.

[1376] Additionally, the prompt sentence is used as an example:

[1377] "When a user is trying on a new dress in a virtual try-on, if they show a happy expression, suggest other similar options. On the other hand, if they show a tired expression, suggest items that will help them relax. Also, include detailed descriptions and pricing information with all suggestions."

[1378] 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.

[1379] 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.

[1380] 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 robot 414.

[1381] 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.

[1382] FIG. 9 is a diagram illustrating 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 actions 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.

[1383] 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.

[1384] 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).

[1385] 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.

[1386] 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."

[1387] 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.

[1388] 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).

[1389] 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.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] The following is further disclosed regarding the above embodiment.

[1400] (Claim 1)

[1401] A means for inputting information on the user's hobbies, preferences and means of transportation;

[1402] a logger means for collecting user movement data;

[1403] means for analyzing the collected movement data;

[1404] A means to suggest outing plans based on the analysis results,

[1405] A system including:

[1406] (Claim 2)

[1407] 2. The system according to claim 1, further comprising a filter means for filtering out inappropriate routes and locations from the collected travel data.

[1408] (Claim 3)

[1409] 2. The system of claim 1, further comprising means for displaying suggested trip plans to a user and allowing the user to select a plan.

[1410] "Example 1"

[1411] (Claim 1)

[1412] A means for a user to input information on hobbies, preferences and means of transportation into the terminal;

[1413] A means for carrying a logger when a user goes out and collecting movement data using a GPS and an acceleration sensor;

[1414] A means for transferring the collected movement data to a terminal at regular intervals and temporarily storing it;

[1415] A means for uploading data from the device to the server at a regular interval;

[1416] A means for analyzing movement data stored in a database on a server using clustering and pattern mining algorithms;

[1417] A means for generating and proposing an optimal outing plan to the user based on the analysis results;

[1418] A system including:

[1419] (Claim 2)

[1420] 2. The system of claim 1, wherein routes and locations that are deemed inappropriate in the analysis are filtered out.

[1421] (Claim 3)

[1422] 2. The system according to claim 1, further comprising means for displaying the outing plans received from the server on the terminal and allowing the user to select a plan.

[1423] "Application Example 1"

[1424] (Claim 1)

[1425] A means for inputting information on the user's hobbies, preferences and means of transportation;

[1426] a logger means for collecting user movement data;

[1427] means for analyzing the collected movement data;

[1428] A means for proposing outing plans or food delivery plans based on the analysis results;

[1429] A system including:

[1430] (Claim 2)

[1431] 2. The system according to claim 1, further comprising a filter means for filtering out inappropriate routes and locations from the collected travel data.

[1432] (Claim 3)

[1433] 2. The system according to claim 1, further comprising means for displaying suggested outing plans or food delivery plans to a user and allowing the user to select a plan.

[1434] "Example 2: Combining Emotion Engines"

[1435] (Claim 1)

[1436] A means for inputting information on the user's hobbies, preferences and means of transportation;

[1437] location information collection means for collecting user movement data;

[1438] an analysis means for analyzing the collected movement data;

[1439] A proposal method for proposing outing plans based on the analysis results;

[1440] emotion recognition means for collecting and analyzing user emotion data;

[1441] an adjustment means for adjusting the proposal content based on the emotion data;

[1442] A system including:

[1443] (Claim 2)

[1444] Includes filtering measures to filter out inappropriate routes and locations from the collected travel data

[1445] 2. The system of claim 1.

[1446] (Claim 3)

[1447] Displaying suggested travel plans to the user and allowing the user to select a plan

[1448] 2. The system of claim 1.

[1449] "Application example 2 when combining emotion engines"

[1450] (Claim 1)

[1451] A means for inputting information on the user's hobbies, preferences and means of transportation;

[1452] A device means for collecting user movement data;

[1453] means for analyzing the collected movement data;

[1454] means for collecting and analyzing user emotion data;

[1455] A means to propose outing plans and optimal products and services based on the analysis results,

[1456] A system including:

[1457] (Claim 2)

[1458] 2. The system according to claim 1, further comprising a filter means for filtering out inappropriate routes and locations from the collected travel data.

[1459] (Claim 3)

[1460] 2. The system according to claim 1, further comprising means for displaying suggested travel plans and products and services to the user and allowing the user to select a plan. [Explanation of symbols]

[1461] 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 means for inputting information on the user's hobbies, preferences and means of transportation; a logger means for collecting user movement data; means for analyzing the collected movement data; A means to suggest outing plans based on the analysis results, A system including:

2. 2. The system according to claim 1, further comprising a filter means for filtering out inappropriate routes and locations from the collected travel data.

3. 2. The system of claim 1, further comprising means for displaying suggested trip plans to a user and allowing the user to select a plan.

Images