System

Abstract

A system is provided.SOLUTION: This system is provided with a means for collecting the action schedule or past communication use conditions of a user, a means for analyzing the collected data, and for selecting the optimal communication plan or radio network connection point, a means for notifying the user of the selected communication plan or radio network connection point, and a means for automatically switching the communication plan by obtaining the permission of the user.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] In today's world, remote work, business trips, and travel have become commonplace, requiring users to ensure a stable communication environment in various locations. However, many users lack technical knowledge about wireless networks and find it difficult to find the optimal communication plan or wireless network connection point. This results in problems such as poor communication quality and high communication charges. Furthermore, it is not easy to understand the quality situation of communication companies, which varies from region to region, and automatically switch to the optimal communication method. This leaves users without an appropriate means to ensure a stable communication environment, leading to the problem of increased communication charges. [Means for solving the problem]

[0005] The present invention provides a system that collects information about a user's planned activities and past communication usage, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.The present invention solves the above-mentioned problems by providing a system that includes the following means.

[0006] A means of collecting user activity schedules and past communication usage status,

[0007] A means of analyzing the collected data and selecting the optimal communication plan and wireless network connection point;

[0008] A means for notifying users of the selected communication plan and wireless network connection point;

[0009] A system that includes a means for automatically switching communication plans with user permission.

[0010] Furthermore, by providing a means for inputting and saving the user's planned activities and obtaining information on the user's current location, it becomes possible to optimize the communication environment in real time.

[0011] Furthermore, by incorporating a means for analyzing communication quality and data usage using machine learning algorithms, highly accurate recommendations can be made, ensuring users a high-quality and cost-effective communication environment.

[0012] "User" refers to a person who uses a wireless network or communication plan.

[0013] "Planned actions" refers to information about actions or activities that a user plans to take in the future.

[0014] "Communication usage" refers to data regarding how a user has used a wireless network in the past.

[0015] "Means" refers to a method or device used to achieve a particular purpose.

[0016] "Collect" refers to gathering data or information.

[0017] "Analyzing" refers to examining collected data in detail to understand and evaluate its contents.

[0018] A "communication plan" refers to a plan that defines the fee structure and terms of use for the communication services used by a user.

[0019] "Wireless network connection point" refers to an access point or hotspot through which users can communicate wirelessly.

[0020] "Notifying" refers to the act of informing a user of information.

[0021] "Switching" refers to changing the communication plan or wireless network connection point you are currently using.

[0022] "Current location" refers to the location information of the user's current location.

[0023] A "machine learning algorithm" refers to a method that uses data to automatically learn specific patterns and knowledge and generate analytical results.

[0024] "Automatic" means that the system operates autonomously without requiring manual operation by the user.

[0025] "Optimization" refers to adjusting something to get the best results under specific conditions. [Brief explanation of the drawings]

[0026] [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

[0027] 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.

[0028] First, the terms used in the following description will be explained.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] [First embodiment]

[0035] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0036] 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.

[0037] 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).

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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."

[0047] The present invention is a system that collects and analyzes a user's planned activities and past communication usage status, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.

[0048] Explanation of program processing

[0049] 1. Data Collection

[0050] When a user enters their planned activities into the app, the location and duration of their stay are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[0051] The device collects past communication usage data. For example, "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0052] The device obtains the current location information in real time and sends it to the server.

[0053] 2. Data Analysis

[0054] The server analyzes the collected data, learning from users' schedules and past usage patterns, and using machine learning algorithms to identify patterns such as "In London, Cafe B has stable Wi-Fi."

[0055] By comparing the collected data with the database, the optimal communication plan and wireless network connection point are selected.

[0056] 3. Proposal generation

[0057] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0058] The content of this proposal is transmitted to the terminal.

[0059] 4. Notice and Advice

[0060] The device receives the suggestion and notifies the user. Example: "You can save on communication charges by using the WiFi at Cafe B while you are in London. To connect, use the PASS displayed inside the store."

[0061] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[0062] 5. Automatic switching

[0063] The server refers to the stability data of local carriers and recommends the best communication plan for a specific area. Example: "Plans from carrier A are stable in 90% of areas in London."

[0064] The device will send a notification to the user about the automatic switch. For example, "Plan from carrier A is the best option for the area around cafe B. Would you like to switch automatically? [Yes] [No]"

[0065] If the user selects "Yes," the device will automatically change the communication plan.

[0066] Specific examples

[0067] 1. Data Collection

[0068] A user enters information about a business trip to London from October 15 to October 20 into the app. The device saves this information and sends past communication data and current location to the server.

[0069] 2. Data Analysis

[0070] The server analyzes past traffic data to identify the best Wi-Fi spots in a particular area of ​​London. For example, it might find that Cafe B offers a stable speed of 45Mbps.

[0071] 3. Proposal generation

[0072] Based on the user's planned activities, the server generates a suggestion such as "It would be a good idea to use Cafe B's WiFi on October 16th" and sends it to the terminal.

[0073] 4. Notice and Advice

[0074] The device notifies the user, "You can save on communication charges by using the WiFi at Cafe B while you are in London. To connect, use the PASS displayed inside the store." The user confirms the suggestion and is prompted with a confirmation message: "Do you want to use this WiFi spot? [Yes] [No]."

[0075] 5. Automatic switching

[0076] The server references the stability data of telecommunications companies in London and determines that "telecommunications company A is the best option." It then sends a notification to the device proposing an automatic switch, and if the user selects "Yes," the device automatically switches to a different communication plan.

[0077] In this way, the present invention can provide an optimal communication environment in real time and reduce the user's communication charges.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user enters their planned activities into the app. The user's planned location and duration are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[0081] Step 2:

[0082] The device collects data on the user's past communication usage. This data includes the WiFi spots connected to, communication quality, data usage, etc. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0083] Step 3:

[0084] The device acquires real-time location information and transmits the current location information to the server.

[0085] Step 4:

[0086] The server analyzes the collected itinerary, past communication data, and current location information. It uses machine learning algorithms to identify communication quality and cost patterns. For example, it identifies a pattern that Cafe B in London has stable Wi-Fi.

[0087] Step 5:

[0088] The server checks the database of public Wi-Fi spots and data roaming plans to select the optimal communication plan and wireless network connection point. For example, select Cafe B or a new Wi-Fi spot (Cafe C, speed 45Mbps).

[0089] Step 6:

[0090] The server generates a proposal based on the analysis results and sends it to the user's device. Example: A notification is generated saying, "High-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[0091] Step 7:

[0092] The device receives the suggestion from the server and notifies the user. The user confirms the suggestion and decides whether to use it or not. Example: "Do you want to use this WiFi spot? [Yes] [No]"

[0093] Step 8:

[0094] The server refers to the stability data of the telecommunications companies and recommends the best communication plan for a specific area. Example: Showing that Telecommunications Company A's plan is stable in 90% of the area of ​​London.

[0095] Step 9:

[0096] The device sends an automatic switching notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0097] Step 10:

[0098] If the user selects "Yes," the device will automatically change the communication plan.

[0099] Step 11:

[0100] The server continuously collects new communication data and updates the analysis results. Based on the analysis results, the recommendation content is updated as appropriate and notified again. Example: "A new WiFi spot has been found. Please try Cafe C (speed 50Mbps)."

[0101] Example 1

[0102] 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."

[0103] In today's communications environment, it is difficult for users to secure an optimal communication environment in various locations. Furthermore, optimizing a communication plan requires complex manual operations, which takes time and effort. The present invention aims to solve these problems and provide a system that automatically provides users with the optimal communication environment and communication plan.

[0104] 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.

[0105] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data using a machine learning algorithm to select an optimal communication plan and wireless network connection point, and means for notifying the user of the selected communication plan and wireless network connection point, thereby enabling the user to automatically secure an optimal communication environment in real time and optimize their communication plan.

[0106] A "user" is a person who uses this system and is the entity that provides the schedule of activities and past communication usage status.

[0107] The "activity schedule" refers to information about activities that a user has planned for a specific period or location, and includes details of trips, business trips, etc.

[0108] "Communication usage status" is data showing past communication usage records, and specifically includes information such as communication speed, connection frequency, and number of disconnections.

[0109] "Data collection means" refers to methods and devices for acquiring and storing schedules and past communication usage information.

[0110] "Data analysis means" refers to processes and algorithms for analyzing collected data, and specifically includes analytical methods using machine learning algorithms.

[0111] "Communication plan" refers to various plans offered by communication service providers, including terms such as data volume, price, and service content.

[0112] "Wireless network connection point" refers to a location or service where Internet connection is possible, such as Wi-Fi or a mobile network.

[0113] "Notification means" refers to a method or device for informing users of the selected communication plan and wireless network connection point information.

[0114] "Means for automatically switching communication plans" refers to the function or process by which the system automatically changes to the most suitable communication plan after obtaining the user's permission.

[0115] The present invention is a system that collects and analyzes a user's planned activities and past communication usage status, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.

[0116] This system is mainly composed of three entities: a server, a terminal, and a user.

[0117] server:

[0118] The server collects the user's planned activities and past communication usage data. The server then analyzes the collected data using machine learning algorithms to select the optimal communication plan and wireless network connection point. The server then sends the results of these selections to the device and notifies the user.

[0119] Device:

[0120] The device saves the user's planned activities and collects past communication usage information. The device also acquires the user's current location information in real time and sends it to the server. The device notifies the user of the proposal received from the server, and if the user approves, the device automatically switches communication plans.

[0121] User:

[0122] The user opens the application and enters their planned activities, for example, "I'm going on a business trip to London from October 15th to October 20th." The device then records this information and sends it to the server.

[0123] Specifically, the invention is carried out in the following steps.

[0124] 1. Data Collection:

[0125] When a user enters their planned activities into the app, the device stores this information. At the same time, the device collects past communication usage data and transmits real-time location information to the server.

[0126] 2. Data Analysis:

[0127] The server uses machine learning algorithms to analyze the collected data. For example, it can identify patterns based on past data, such as "In London, Cafe B has stable Wi-Fi." The server then compares the data with a database to select the optimal communication plan and wireless network connection point.

[0128] 3. Proposal generation:

[0129] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi hotspots and data roaming plans that fit the user's schedule. The server then sends these recommendations to the device, which then notifies the user. For example, the recommendation might be, "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0130] 4. Notices and Advice:

[0131] After receiving the suggestion, the device will notify the user, for example, by displaying a message such as, "You can save on data charges by using the Wi-Fi at Cafe B while you're in London. To connect, use the PASS displayed inside the store."

[0132] 5. Automatic switching:

[0133] The server references the stability data of local telecommunications companies and selects the optimal communication plan. For example, it may make a decision based on information such as "Telecommunications Company A's plan is stable in 90% of the London area." The server then sends this information to the device, which then notifies the user of the automatic switchover proposal. If the user selects "Yes," the device will automatically change the communication plan.

[0134] This system allows users to use the most suitable wireless network connection point and automatically switch to an efficient communication plan, thereby reducing communication charges and providing an optimal communication environment.

[0135] Examples:

[0136] Example prompt sentence:

[0137] A user enters into the app that he or she will be traveling to London from October 15th to October 20th.

[0138] The server analyzes the situation and suggests that "high-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[0139] The device will notify the user that "You can save on data charges by using Cafe B's WiFi while you are in London."

[0140] The user selects "Yes" and the device automatically changes the communication plan.

[0141] This invention allows users to instantly secure an optimal communication environment, enabling comfortable and economical communication use.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] Data collection

[0145] Input: User's planned activities, past communication usage, current location information

[0146] Operation:

[0147] The user enters their planned activities into the app, such as "Business trip to London from October 15th to October 20th."

[0148] The device stores this information and collects past communication data (e.g., "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times").

[0149] The device uses GPS to obtain real-time location information.

[0150] Output: Sends action plans, past communication data, and location information to the server.

[0151] Step 2:

[0152] Data analysis

[0153] Input: Action schedule sent to the server, past communication data, location information

[0154] Operation:

[0155] The server stores the schedule, past communication data, and location information in a database for centralized management.

[0156] The server uses machine learning algorithms to analyze the collected data and identify patterns, such as "in London, Cafe B has the most reliable Wi-Fi."

[0157] The server checks the database and selects the optimal communication plan and wireless network connection point.

[0158] Output: Proposals for optimal communication plans and wireless network connection points.

[0159] Step 3:

[0160] Proposal generation

[0161] Input: Proposals for optimal communication plans and wireless network connection points based on data analysis

[0162] Operation:

[0163] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0164] The server sends the generated proposal to the terminal.

[0165] Output: A proposal to notify the user.

[0166] Step 4:

[0167] Notices and Advice

[0168] Input: Proposal sent from the server

[0169] Operation:

[0170] The device receives the suggestion and notifies the user. For example, "You can save on communication charges by using the WiFi at Cafe B while you're in London. To connect, use the PASS displayed inside the store."

[0171] The device will display a confirmation message to the user asking, "Do you want to use this WiFi spot? [Yes] [No]."

[0172] The user reviews the notification and selects "Yes" or "No."

[0173] Output: The user's choice ("Yes" or "No").

[0174] Step 5:

[0175] Automatic Switching

[0176] Input: User's choice ("Yes" or "No"), stability data of local carriers

[0177] Operation:

[0178] The server references the stability data of local carriers and recommends the best plan for a specific area. For example, "Plans from carrier A are stable in 90% of the London area."

[0179] The device sends the user a notification of automatic switching, displaying the message "Telecommunications company A's plan is the best for the area around Cafe B. Would you like to automatically switch? [Yes] [No]."

[0180] If the user selects "Yes," the device will automatically change the communication plan.

[0181] Output: Automatically switch to the best communication plan.

[0182] (Application example 1)

[0183] 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."

[0184] In today's food delivery services, users often face challenges due to unstable communication environments, which can hinder smooth ordering and delivery. High communication charges and the difficulty of finding optimal communication spots are also inconvenient for users. There is a need to develop a system that can resolve these issues and provide users with a comfortable and efficient food delivery experience.

[0185] 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.

[0186] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data and selecting an optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for proposing an optimal communication environment based on the user's planned activities and past order history, and means for optimizing food delivery orders using the proposed communication environment. This enables optimal food delivery orders based on the communication environment, allowing users to enjoy reduced communication charges and a smooth ordering process.

[0187] "User's planned activities" refers to the activities and schedule that the user plans to carry out in the future.

[0188] "Past communication usage" refers to historical data on how a user has previously used communications.

[0189] An "optimal communication plan" refers to a pricing plan that best suits the user's needs and situation and enables highly efficient communication.

[0190] "Wireless Network Access Point" means a specific location where Wi-Fi or other wireless communications are available.

[0191] "Means for analyzing data" refers to methods and tools for analyzing collected data and extracting useful information.

[0192] "Means for suggesting a communication environment" refers to a method or system for recommending the optimal communication environment to a user based on the analysis results.

[0193] "Means for automatically switching communication plans" refers to a function that automatically changes to the most suitable communication plan with the user's permission.

[0194] "Food delivery" refers to a service that delivers meals from restaurants and eateries to designated locations.

[0195] This invention is a system that aims to optimize the communication environment in food delivery services, selecting the optimal communication plan and wireless network connection point based on the user's planned activities and past communication usage, and proposing and automatically switching between them.

[0196] Hardware and software used

[0197] Smartphone: This is the main interface device for this system, and obtains the current location using a GPS module.

[0198] Server: The central processing center for data analysis and proposal generation. It runs machine learning algorithms using Google Cloud Machine Learning and Amazon SageMaker.

[0199] Firebase Cloud Messaging (FCM): A messaging service for sending notifications to users.

[0200] Generative AI model: Generates optimal suggestions based on the user's planned activities and communication usage status.

[0201] Data collection

[0202] The device (smartphone) stores the itinerary entered by the user, including information such as the duration of the business trip and the location of the trip. The smartphone's GPS module also obtains the user's current location in real time and sends it to the server along with past communication usage information.

[0203] Data analysis

[0204] The server analyzes the collected data and selects the optimal communication plan and wireless network connection point based on past communication usage and current location information. Machine learning algorithms are used to compare various data and identify the optimal communication environment for a specific area and time period.

[0205] Proposal Generation and Notification

[0206] Based on the results of the analysis on the server, the system generates recommendations for the optimal communication environment for the user. The generated recommendations are sent to the user's device via Firebase Cloud Messaging (FCM). For example, the notification might say, "If you order food delivery from your office between 12:00 and 1:00 PM, this WiFi spot is the best choice."

[0207] Automatic Switching

[0208] If the user confirms and approves the proposal, the device will automatically switch to the most suitable communication plan, optimizing the communication environment and ensuring smooth food delivery orders and deliveries.

[0209] Specific examples

[0210] For example, if a user inputs that they will be staying in London from October 15th to October 20th, the server will identify the most reliable Wi-Fi spot in London based on their past communication usage and current location data. Based on the analysis results, a notification will be sent to their smartphone saying, "The best Wi-Fi spot is Cafe B. It is recommended that you use it between 12:00 and 13:00."

[0211] Prompt Sentence Examples

[0212] Below are some example prompts to input to the generative AI model:

[0213] "Please suggest the most stable WiFi spot and data plan for a user staying in London from October 15th to October 20th."

[0214] This invention allows users to enjoy an optimal communication environment and ensures stable communication even when using food delivery services, which is expected to reduce communication charges and streamline ordering procedures, thereby improving user satisfaction.

[0215] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0216] Step 1: Data collection

[0217] The device (smartphone) stores the itinerary entered by the user. For example, if the user enters "Business trip to London from October 15th to October 20th," that information is saved on the device. The smartphone's GPS module is also used to obtain real-time location information. Furthermore, past communication usage information (e.g., connection speeds and frequency of disconnections at Wi-Fi hotspots used in the past) is also collected, and all this data is sent to the server.

[0218] input:

[0219] User-entered itinerary (e.g., duration of stay, location of stay)

[0220] Current location information (GPS data)

[0221] Past communication usage

[0222] output:

[0223] Composite data sent to the server (planned activities, location information, past communication data)

[0224] Step 2: Data analysis

[0225] The server analyzes data based on the device's planned activities, current location, and past network usage. It uses machine learning algorithms from Google Cloud Machine Learning and Amazon SageMaker to identify the optimal network environment for a specific area and time of day. For example, it identifies the most reliable Wi-Fi hotspot in a specific area of ​​London.

[0226] input:

[0227] Planned activities, current location information, past communication usage

[0228] output:

[0229] Optimal communication plan and wireless network connection point

[0230] Step 3: Proposal Generation

[0231] The server generates recommendations for the optimal communication environment for the user based on the results of the data analysis. Specifically, it generates recommendations such as "The WiFi at Cafe B is the best option while you're in London." These recommendations are then sent to the user's device using Firebase Cloud Messaging (FCM).

[0232] input:

[0233] Data analysis results (optimal communication plan and network connection points)

[0234] output:

[0235] Notification message (optimal communication environment suggestions)

[0236] Step 4: Notification and confirmation

[0237] The device notifies the user of the suggestion sent from the server. The notification might say, for example, "You can save on data charges by using Cafe B's Wi-Fi while you're in London. Would you like to use it? [Yes] [No]," and ask for the user's confirmation.

[0238] input:

[0239] Proposal content (information on the optimal communication environment)

[0240] output:

[0241] User notification message

[0242] User response (yes or no)

[0243] Step 5: Automatic Switching

[0244] The server then sends instructions to automatically switch to the optimal communication plan for the device if the user confirms and approves the proposal. For example, if the user selects "Yes," the device will automatically switch to the selected communication plan, allowing the user to use the optimal communication environment.

[0245] input:

[0246] User response ("Yes")

[0247] output:

[0248] Switching your device's data plan

[0249] Through these steps, users are provided with an optimal communication environment and communication stability is ensured even when using food delivery services.

[0250] 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.

[0251] The present invention is a system that recognizes a user's planned activities, past communication usage, and emotional state, and proposes and automatically switches optimal communication plans and wireless network connection points. The present invention also includes a system that combines an emotion engine, and adjusts the proposal content and communication plan based on the user's emotional state.

[0252] Explanation of program processing

[0253] 1. Data Collection

[0254] The user enters their planned activities into the app. Example: "Business trip to London from October 15th to October 20th."

[0255] The device collects the user's past communication usage data, obtains real-time location information, and sends it to the server. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0256] 2. Recognizing emotional states

[0257] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. Example: "The user is feeling stressed."

[0258] 3. Data Analysis

[0259] The server analyzes the collected itinerary, past communication data, current location information, and the user's emotional state, and uses machine learning algorithms to identify patterns such as "In London, Cafe B has stable Wi-Fi."

[0260] Based on the evaluation results of the emotion engine, the optimal communication plan and wireless network connection point are selected to reduce the user's stress level.

[0261] 4. Proposal generation

[0262] Based on the analysis results and the user's emotional state, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule, and sends them to the device. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0263] 5. Notices and Advice

[0264] The device receives the suggestion and notifies the user. For example, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store."

[0265] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[0266] 6. Automatic Switching

[0267] The server refers to the stability data of local carriers and recommends the best communication plan for a specific area. Example: "Plans from carrier A are stable in 90% of areas in London."

[0268] The device will send an automatic switch notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0269] If the user selects "Yes," the device will automatically change the communication plan.

[0270] Specific examples

[0271] 1. Data Collection

[0272] A user enters information about a business trip to London from October 15 to October 20 into the app. The device saves this information and sends past communication data and current location to the server.

[0273] 2. Recognizing emotional states

[0274] The device collects the user's voice and facial expression data in real time and sends it to the emotion engine, which analyzes it and determines that the user is experiencing stress.

[0275] 3. Data Analysis

[0276] The server analyzes past traffic data to identify the best Wi-Fi spots in a specific area of ​​London. For example, Cafe B may provide a stable speed of 45Mbps.

[0277] Based on the evaluation results of the emotion engine, Cafe B is selected to reduce the user's stress level.

[0278] 4. Proposal generation

[0279] Based on the user's planned activities and emotional state, the server generates a suggestion such as "Use Cafe B's WiFi on October 16th" and sends it to the terminal.

[0280] 5. Notices and Advice

[0281] The device notifies the user, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The user confirms the suggestion and is prompted with a confirmation message: "Do you want to use this WiFi spot? [Yes] [No]."

[0282] 6. Automatic Switching

[0283] The server references the stability data of telecommunications companies in London and determines that "telecommunications company A is the best option." It then sends a notification to the device proposing an automatic switch, and if the user selects "Yes," the device automatically switches to a different communication plan.

[0284] In this way, the system of the present invention provides an optimal communication environment in real time based on the user's emotional state, reducing the user's communication charges and alleviating stress.

[0285] The processing flow will be explained below.

[0286] Step 1:

[0287] The user enters their planned activities into the app. The user's planned location and duration are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[0288] Step 2:

[0289] The device collects the user's past communication usage data, obtains real-time location information, and sends it to the server. The collected data includes information on previously connected WiFi spots, their communication quality, and data usage. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0290] Step 3:

[0291] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. Example: "The user is feeling stressed."

[0292] Step 4:

[0293] The server analyzes the collected schedule, past communication data, current location information, and the user's emotional state. It uses a machine learning algorithm to identify patterns such as "In London, Cafe B has stable Wi-Fi." Based on the evaluation results of the emotion engine, it selects the optimal communication plan and wireless network connection point to reduce the user's stress level.

[0294] Step 5:

[0295] The server generates suggestions based on the analysis results and the user's emotional state and sends them to the user's device. Example: "High-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[0296] Step 6:

[0297] The device receives the suggestion and notifies the user. The notification includes details of the suggested WiFi spot and communication plan, as well as how to connect. For example, "Using the WiFi at Cafe B while you're in London will save you money and reduce stress. To connect, use the PASS displayed in the store."

[0298] Step 7:

[0299] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[0300] Step 8:

[0301] The server refers to the reliability data of the carriers and recommends the best plan for a specific area. Example: "Plans from carrier A are stable in 90% of the area of ​​London."

[0302] Step 9:

[0303] The device sends an automatic switching notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0304] Step 10:

[0305] If the user selects "Yes," the device will automatically change the communication plan.

[0306] Step 11:

[0307] The server continuously collects new communication data and updates the analysis results. Based on the analysis results, the recommendation content is updated as appropriate and notified again. Example: "A new WiFi spot has been found. Please try Cafe C (speed 50Mbps)."

[0308] Example 2

[0309] 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."

[0310] Conventional communication plans and wireless network connection points often cannot always be optimally selected based solely on the user's schedule and past communication usage. Furthermore, since they provide a communication environment without taking the user's emotional state into consideration, they do not contribute to user satisfaction or stress reduction. Therefore, there is a need for a system that takes into account not only the user's schedule and past communication usage, but also the user's emotional state.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0312] In this invention, the server includes means for collecting the user's planned activities and past communication usage status, means for analyzing the collected data and selecting the optimal communication plan and wireless network connection point, and means for analyzing the user's emotional state and adjusting the content of the proposal based on this. This makes it possible to provide the optimal communication environment by comprehensively analyzing the user's planned activities, past communication data, real-time location information, and emotional state.

[0313] "User's planned activities" is information about the activities and movements that the user plans to carry out in the future.

[0314] "Communication usage status" is information indicating what communication means and communication volume the user has used in the past, as well as the connection status at that time.

[0315] "Current location information" refers to the geographical location information of the user's terminal.

[0316] "Emotional state" refers to the user's psychological or emotional state, and is the result of analysis of voice and facial expression data.

[0317] A "communication plan" is a contractual provision that includes the terms of use, fees, speed, etc. of the communication service selected or proposed to the user.

[0318] A "wireless network access point" is a specific access point or hotspot used by a user to connect to the Internet.

[0319] An "emotion engine" is software or hardware that analyzes voice and facial expression data to identify a user's emotional state.

[0320] "Data analysis" is a set of processes used to analyze collected data and find patterns and relationships.

[0321] A "machine learning algorithm" is a type of artificial intelligence technology used in data analysis, which learns from past data and makes future predictions and classifications.

[0322] The "proposal content" is detailed information about the optimal communication plan and wireless network connection points that are presented to the user based on the analysis results.

[0323] "Automatically switching communication plans" refers to the system automatically changing to the most suitable communication plan after obtaining the user's permission.

[0324] This invention is a system that comprehensively analyzes a user's schedule, past communication usage, and emotional state, and then proposes and automatically switches to the optimal communication plan and wireless network connection point.The purpose of this invention is to provide a comfortable communication environment for users and reduce stress.

[0325] The system consists of a server, a terminal, and an emotion engine including a generative AI model. The specific features of each hardware and software are described below.

[0326] Hardware and software used

[0327] Server: A server with high-performance data processing capabilities, such as AWS (Amazon Web Services) or Google Cloud Platform, is used. The server applies machine learning algorithms to analyze large amounts of data in real time.

[0328] Device: A mobile device used by a user, such as a smartphone or tablet, that is equipped with GPS, a camera, and a microphone and collects data about their schedule and emotional state.

[0329] Emotion engine: A generative AI model that analyzes voice and facial expression data to identify a user's emotional state. For example, it uses existing AI engines such as Google's Cloud Vision API or IBM's Watson.

[0330] Explanation of program processing

[0331] 1. Data Collection

[0332] A user inputs a schedule into a smartphone application. For example, the user inputs "Business trip to London from October 15th to October 20th."

[0333] The device saves data on planned activities and collects past communication usage data. It also obtains real-time location information and sends this data to the server. For example, it collects information such as "Connected to Hotel A's WiFi on October 10, 2023, the speed was 20Mbps, and the disconnection frequency was 3 times."

[0334] 2. Recognizing emotional states

[0335] The device collects the user's voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. For example, it may recognize from voice data that the user is "feeling stressed."

[0336] 3. Data Analysis

[0337] The server aggregates the collected data on your planned activities, past communication data, current location, and emotional state. Based on this data, it applies machine learning algorithms to identify the optimal communication plan and wireless network connection point. For example, it might discover a pattern that "in London, Cafe B has stable Wi-Fi."

[0338] Based on the results of the analysis of the user's emotional state, the system selects the optimal communication plan and wireless network connection point to reduce the user's stress level.

[0339] 4. Proposal generation

[0340] Based on the analysis results and the user's emotional state, the server generates a recommendation for the optimal communication plan and wireless network connection point that matches the user's planned activities. For example, it may suggest that "high-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0341] The proposal is sent to the terminal.

[0342] 5. Notices and Advice

[0343] The device then notifies the user of the suggestions received from the server. For example, it displays a message such as, "If you use the Wi-Fi at Cafe B during your stay in London, you can save on communication costs and reduce stress. To connect, use the PASS displayed inside the store."

[0344] The user reviews the suggestion and selects "Do you want to use this WiFi spot? [Yes] [No]."

[0345] 6. Automatic Switching

[0346] The server refers to the stability data of telecommunications companies in London and recommends the most suitable telecommunications company. For example, it may determine that "telecommunications company A is the best."

[0347] The device will send a notification to the user about the automatic switch. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0348] If the user selects "Yes," the device will automatically change the communication plan.

[0349] Specific examples

[0350] The following is an example of a scenario in which the system of the present invention actually works.

[0351] 1. Data collection: The user enters "Business trip to London from October 15th to October 20th" into a smartphone app. The device stores this information and sends past communication data, such as "Connected to Hotel A's WiFi on October 10th, 2023, speed was 20Mbps, and disconnection frequency was 3 times," along with current location information to the server.

[0352] 2. Emotional state recognition: The device captures the user's voice and facial expressions and sends them to the emotion engine, which recognizes that the user is feeling stressed.

[0353] 3. Data Analysis: The server analyzes all the data and determines that Cafe B is the best WiFi spot. It also determines that "Cafe B is suitable for reducing user stress."

[0354] 4. Proposal generation: The server generates a proposal such as "It would be good to use Cafe B's WiFi on October 16th" and sends it to the device.

[0355] 5. Notification and Advice: The device will display the suggestion to the user and prompt them to choose "Do you want to use this WiFi hotspot? [Yes] [No]."

[0356] 6. Automatic switching: The server selects the optimal carrier and notifies the device. If the user approves, the device automatically changes its communication plan.

[0357] The present invention allows users to enjoy an optimal communication environment while reducing stress.

[0358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0359] Step 1: Data collection

[0360] A user inputs their schedule into a smartphone application. Example input data: "Business trip to London from October 15th to October 20th."

[0361] The device stores the planned activity data entered by the user. It also collects past communication usage data (e.g., "Connected to Hotel A's WiFi on October 10, 2023, speed 20Mbps, disconnection frequency 3 times") and real-time location information. Location information is obtained using the GPS function.

[0362] The device sends the collected data to the server. The input is the planned activity, past communication data, and location information, and the output is a data packet containing this information.

[0363] Step 2: Recognizing your emotional state

[0364] The device collects the user's voice and facial expression data in real time. Voice data is acquired using a microphone, and facial expression data is captured using a camera.

[0365] The device sends the collected voice and facial expression data to the emotion engine. The input is voice data and facial expression data, and the output is the analysis result by the emotion engine.

[0366] The emotion engine identifies the user's emotional state through voice and facial expression analysis, for example, determining that the user is feeling stressed.

[0367] Step 3: Data analysis

[0368] The server centrally collects the action schedule, past communication data, current location information, and emotional state sent from the device. These data are the input.

[0369] The server applies machine learning algorithms to analyze the data and identify usage patterns, concluding, for example, that "in London, Cafe B has the best WiFi." The output is the optimal data plan or wireless network connection point.

[0370] Based on the analysis of the user's emotional state, the server selects the optimal communication plan and Wi-Fi hotspot to reduce the user's stress level.

[0371] Step 4: Proposal Generation

[0372] The server generates recommendations for optimal communication plans and wireless network connection points based on the user's planned activities based on the results of data analysis and the user's emotional state. For example, it generates a recommendation that "high-speed WiFi (45Mbps) is available at Cafe B on October 16th." The inputs are the analysis results and the user's emotional state, and the output is the recommendations.

[0373] The server transmits the generated proposal to the terminal.

[0374] Step 5: Inform and advise

[0375] The device notifies the user of the suggestion received from the server. For example, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The input is the suggestion, and the output is the notification to the user.

[0376] The device prompts the user to choose, "Do you want to use this WiFi hotspot? [Yes] [No]." The input is the suggestion, and the output is the user's choice.

[0377] Step 6: Automatic Switching

[0378] The server refers to the stability data of telecommunications companies in London and recommends the most suitable telecommunications company. For example, it determines that "telecommunications company A is the most suitable." The input is the telecommunications company stability data, and the output is the recommended telecommunications company.

[0379] The device sends a notification of automatic switching to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]." The input is the recommended carrier, and the output is the notification to the user.

[0380] If the user selects "Yes," the device will automatically change the communication plan. The input is the user's selection, and the output is the change in communication plan.

[0381] In this way, the system of the present invention comprehensively analyzes the user's planned activities, past communication usage, current location information, and emotional state, and provides the optimal communication environment, thereby reducing the user's stress and saving on communication charges.

[0382] (Application example 2)

[0383] 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."

[0384] Conventional communication plan proposal systems optimize plans based on the user's planned activities and past communication usage, but are unable to consider the user's emotional state or stress level. As a result, the optimal communication environment for the user may not be provided. Furthermore, food delivery apps are also unable to make proposals that take into account the user's planned activities and emotional state, making it difficult to improve user satisfaction. These issues can lead to a decline in user convenience and satisfaction.

[0385] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0386] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data and selecting an optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for recognizing the user's emotional state and adjusting the content of suggestions and communication plans based on the analysis results, and means for collecting past order history and planned activities and suggesting optimal foods and promotions. This makes it possible to provide an optimal communication environment that takes into account the user's emotional state in addition to their planned activities and past communication usage status, and also enables suggestions that improve user satisfaction in food delivery apps.

[0387] "Planned activities" refers to information about activities and plans that the user plans to carry out in the future.

[0388] "Communication usage status" refers to data on past communications made by a user, and refers to a detailed history of communications such as communication volume, communication speed, and number of disconnections.

[0389] "Communication plan" refers to the pricing plans and contract terms for internet and data communications provided by telecommunications companies.

[0390] "Wireless Network Access Point" refers to a location where you can connect to public or corporate Wi-Fi or other wireless communication networks.

[0391] "Emotional state" refers to the user's current feelings and state of mind, and includes emotional states such as stress, happiness, and fatigue.

[0392] "Suggestion content" refers to information such as recommended items, services, plans, and promotions that are generated based on the analysis results and the user's status.

[0393] "Past order history" refers to the history of orders and purchases made by the user up to now, and includes information such as the number of purchases and trends of specific products.

[0394] "Promotion" refers to advertising activities such as offering special offers, discounts, and events to users, and is a means of increasing product sales and user satisfaction.

[0395] An "emotion engine" refers to a software module that analyzes audio and image data to identify a user's emotional state.

[0396] A "machine learning algorithm" is a computational method for analyzing large amounts of data and finding statistical patterns, enabling predictions and classifications.

[0397] This system analyzes a user's schedule, past communication usage, and emotional state to propose optimal communication plans and wireless network connection points, and automatically switches communication plans. In addition, in the case of a food delivery app, it can suggest optimal foods and promotions based on past order history and schedule.

[0398] The system mainly consists of the following elements:

[0399] Server: Collects data, analyzes, generates proposals, notifies, and automatically switches.

[0400] Terminal: Collects input data from the user, recognizes emotional states in real time, communicates with the server, makes suggestions, and stores data.

[0401] User: Provides information such as planned activities, past communication usage, and order history.

[0402] The server includes means for collecting the user's planned activities and past communication usage status, means for analyzing the collected data and selecting the optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for recognizing the user's emotional state and adjusting the content of suggestions and communication plans based on the analysis results, and means for collecting the user's past order history and planned activities and suggesting optimal foods and promotions.

[0403] Hardware and software used

[0404] Hardware: Smartphone, built-in camera, built-in microphone

[0405] Software: Python, TensorFlow (emotion engine), Firebase (cloud database), Flutter (UI development)

[0406] Data processing and calculation

[0407] The server receives the user's planned activities, past communication usage, and emotional state, including voice and facial expression data. This data is analyzed using TensorFlow to identify the user's emotional state. Based on the analysis results, machine learning algorithms select the optimal communication plan and wireless network connection point. Based on past ordering history, the optimal food and promotions for food delivery are also selected. Based on this, recommendations are generated and notified to the user. The notifications are displayed on the smartphone using Flutter and stored in Firebase.

[0408] Specific examples

[0409] For example, if a user plans to go on a business trip to London from October 15th to October 20th, the server receives this information and analyzes their past communication usage and order history. In addition, it can identify whether the user is feeling stressed from voice and facial expression data. Based on this, the server can suggest the optimal communication plan and wireless network connection points at cafes, and notify the user to use Cafe B's WiFi during their stay in London. The food delivery app can also suggest a relaxing tea and pizza set to reduce the user's stress.

[0410] Prompt Sentence Examples

[0411] For example, the following prompt sentence can be input to a generative AI model:

[0412] plaintext

[0413] User ID: 12345

[0414] Planned action: Work from home at 4pm on October 12th

[0415] Past orders: 3 pizzas, 1 sushi, 2 salads

[0416] Current emotional state: Tired

[0417] Generated AI prompt:

[0418] 1. Proposing the best meal set

[0419] 2. Describe the reasons for your recommendation based on your past order history and your current emotional state

[0420] In this way, the present invention can optimize the communication environment and improve user satisfaction with food delivery by making optimal suggestions taking into account the user's planned activities and emotional state.

[0421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0422] Step 1:

[0423] The user inputs their itinerary into the app. For example, this information might be "Business trip to London from October 15th to October 20th." The input data is saved on the device and later sent to the server. The itinerary is the input data, and the saved itinerary is generated as the output data.

[0424] Step 2:

[0425] The device collects past communication usage data and obtains real-time location information. The collected data is stored within the system and sent to the server. Specifically, it includes data such as "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times." The input data is past communication usage and current location, and the output data is generated and sent to the server.

[0426] Step 3:

[0427] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine uses TensorFlow to analyze this data and identify the user's emotional state. Specifically, emotional states such as "stress" and "fatigue" are identified from the user's voice and facial expressions. The collected voice and facial expression data are used as input data, and the analyzed emotional state is generated as output data.

[0428] Step 4:

[0429] The server analyzes the collected itinerary, past communication data, current location information, and the user's emotional state. A machine learning algorithm is then used to identify patterns, such as "In London, Cafe B has stable Wi-Fi." The input data are the itinerary, communication usage, current location, and emotional state, and the analysis results are generated as output data.

[0430] Step 5:

[0431] Based on the emotion engine's evaluation results, the server selects the optimal communication plan or wireless network connection point to reduce the user's stress level. The server evaluates options based on specific parameters and determines the most appropriate choice for the user. Specifically, it may select something like "Telecommunications Company A's plan is stable in 90% of London." The input data are the analysis results and the user's emotional state, and the output data is the optimal communication plan or connection point.

[0432] Step 6:

[0433] Based on the analysis results and the user's emotional state, the server generates recommendations for the optimal WiFi spot or data roaming plan that matches the user's schedule and sends them to the device. For example, a recommendation might be generated such as "We recommend using the WiFi at Cafe B on October 16th." The optimal communication plan or connection point is the input data, and the recommendations are generated as output data.

[0434] Step 7:

[0435] The device receives the suggestions and notifies the user. For example, a notification might say, "If you use the WiFi at Cafe B while you're in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The suggestions are input data, and a notification to the user is generated as output data.

[0436] Step 8:

[0437] The user checks the suggestion and chooses whether to use it. For example, a confirmation message such as "Do you want to use this WiFi spot? [Yes] [No]" is displayed. The user's choice is the input data, and the user's response is generated as the output data.

[0438] Step 9:

[0439] The server references the stability data of local telecommunications companies and recommends the optimal communication plan for a specific area. For example, a proposal such as "Telecommunications Company A's plan is optimal for 90% of the London area" is generated. The input data is regional information and telecommunications company data, and the output data is the optimal communication plan.

[0440] Step 10:

[0441] The device sends a notification of automatic switching to the user. For example, a notification such as "Telecommunications company A's plan is optimal for the area around Cafe B. Would you like to perform automatic switching? [Yes] [No]" is sent. If the user selects "Yes," the device automatically changes the communication plan. The input data are the optimal communication plan and the user's selection, and the output data is generated indicating the execution of automatic switching.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] [Second embodiment]

[0446] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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."

[0458] The present invention is a system that collects and analyzes a user's planned activities and past communication usage status, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.

[0459] Explanation of program processing

[0460] 1. Data Collection

[0461] When a user enters their planned activities into the app, the location and duration of their stay are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[0462] The device collects past communication usage data. For example, "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0463] The device obtains the current location information in real time and sends it to the server.

[0464] 2. Data Analysis

[0465] The server analyzes the collected data, learning from users' schedules and past usage patterns, and using machine learning algorithms to identify patterns such as "In London, Cafe B has stable Wi-Fi."

[0466] By comparing the collected data with the database, the optimal communication plan and wireless network connection point are selected.

[0467] 3. Proposal generation

[0468] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0469] The content of this proposal is transmitted to the terminal.

[0470] 4. Notice and Advice

[0471] The device receives the suggestion and notifies the user. Example: "You can save on communication charges by using the WiFi at Cafe B while you are in London. To connect, use the PASS displayed inside the store."

[0472] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[0473] 5. Automatic switching

[0474] The server refers to the stability data of local carriers and recommends the best communication plan for a specific area. Example: "Plans from carrier A are stable in 90% of areas in London."

[0475] The device will send a notification to the user about the automatic switch. For example, "Plan from carrier A is the best option for the area around cafe B. Would you like to switch automatically? [Yes] [No]"

[0476] If the user selects "Yes," the device will automatically change the communication plan.

[0477] Specific examples

[0478] 1. Data Collection

[0479] A user enters information about a business trip to London from October 15 to October 20 into the app. The device saves this information and sends past communication data and current location to the server.

[0480] 2. Data Analysis

[0481] The server analyzes past traffic data to identify the best Wi-Fi spots in a particular area of ​​London. For example, it might find that Cafe B offers a stable speed of 45Mbps.

[0482] 3. Proposal generation

[0483] Based on the user's planned activities, the server generates a suggestion such as "It would be a good idea to use Cafe B's WiFi on October 16th" and sends it to the terminal.

[0484] 4. Notice and Advice

[0485] The device notifies the user, "You can save on communication charges by using the WiFi at Cafe B while you are in London. To connect, use the PASS displayed inside the store." The user confirms the suggestion and is prompted with a confirmation message: "Do you want to use this WiFi spot? [Yes] [No]."

[0486] 5. Automatic switching

[0487] The server references the stability data of telecommunications companies in London and determines that "telecommunications company A is the best option." It then sends a notification to the device proposing an automatic switch, and if the user selects "Yes," the device automatically switches to a different communication plan.

[0488] In this way, the present invention can provide an optimal communication environment in real time and reduce the user's communication charges.

[0489] The processing flow will be explained below.

[0490] Step 1:

[0491] The user enters their planned activities into the app. The user's planned location and duration are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[0492] Step 2:

[0493] The device collects data on the user's past communication usage. This data includes the WiFi spots connected to, communication quality, data usage, etc. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0494] Step 3:

[0495] The device acquires real-time location information and transmits the current location information to the server.

[0496] Step 4:

[0497] The server analyzes the collected itinerary, past communication data, and current location information. It uses machine learning algorithms to identify communication quality and cost patterns. For example, it identifies a pattern that Cafe B in London has stable Wi-Fi.

[0498] Step 5:

[0499] The server checks the database of public Wi-Fi spots and data roaming plans to select the optimal communication plan and wireless network connection point. For example, select Cafe B or a new Wi-Fi spot (Cafe C, speed 45Mbps).

[0500] Step 6:

[0501] The server generates a proposal based on the analysis results and sends it to the user's device. Example: A notification is generated saying, "High-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[0502] Step 7:

[0503] The device receives the suggestion from the server and notifies the user. The user confirms the suggestion and decides whether to use it or not. Example: "Do you want to use this WiFi spot? [Yes] [No]"

[0504] Step 8:

[0505] The server refers to the stability data of the telecommunications companies and recommends the best communication plan for a specific area. Example: Showing that Telecommunications Company A's plan is stable in 90% of the area of ​​London.

[0506] Step 9:

[0507] The device sends an automatic switching notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0508] Step 10:

[0509] If the user selects "Yes," the device will automatically change the communication plan.

[0510] Step 11:

[0511] The server continuously collects new communication data and updates the analysis results. Based on the analysis results, the recommendation content is updated as appropriate and notified again. Example: "A new WiFi spot has been found. Please try Cafe C (speed 50Mbps)."

[0512] Example 1

[0513] 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."

[0514] In today's communications environment, it is difficult for users to secure an optimal communication environment in various locations. Furthermore, optimizing a communication plan requires complex manual operations, which takes time and effort. The present invention aims to solve these problems and provide a system that automatically provides users with the optimal communication environment and communication plan.

[0515] 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.

[0516] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data using a machine learning algorithm to select an optimal communication plan and wireless network connection point, and means for notifying the user of the selected communication plan and wireless network connection point, thereby enabling the user to automatically secure an optimal communication environment in real time and optimize their communication plan.

[0517] A "user" is a person who uses this system and is the entity that provides the schedule of activities and past communication usage status.

[0518] The "activity schedule" refers to information about activities that a user has planned for a specific period or location, and includes details of trips, business trips, etc.

[0519] "Communication usage status" is data showing past communication usage records, and specifically includes information such as communication speed, connection frequency, and number of disconnections.

[0520] "Data collection means" refers to methods and devices for acquiring and storing schedules and past communication usage information.

[0521] "Data analysis means" refers to processes and algorithms for analyzing collected data, and specifically includes analytical methods using machine learning algorithms.

[0522] "Communication plan" refers to various plans offered by communication service providers, including terms such as data volume, price, and service content.

[0523] "Wireless network connection point" refers to a location or service where Internet connection is possible, such as Wi-Fi or a mobile network.

[0524] "Notification means" refers to a method or device for informing users of the selected communication plan and wireless network connection point information.

[0525] "Means for automatically switching communication plans" refers to the function or process by which the system automatically changes to the most suitable communication plan after obtaining the user's permission.

[0526] The present invention is a system that collects and analyzes a user's planned activities and past communication usage status, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.

[0527] This system is mainly composed of three entities: a server, a terminal, and a user.

[0528] server:

[0529] The server collects the user's planned activities and past communication usage data. The server then analyzes the collected data using machine learning algorithms to select the optimal communication plan and wireless network connection point. The server then sends the results of these selections to the device and notifies the user.

[0530] Device:

[0531] The device saves the user's planned activities and collects past communication usage information. The device also acquires the user's current location information in real time and sends it to the server. The device notifies the user of the proposal received from the server, and if the user approves, the device automatically switches communication plans.

[0532] User:

[0533] The user opens the application and enters their planned activities, for example, "I'm going on a business trip to London from October 15th to October 20th." The device then records this information and sends it to the server.

[0534] Specifically, the invention is carried out in the following steps.

[0535] 1. Data Collection:

[0536] When a user enters their planned activities into the app, the device stores this information. At the same time, the device collects past communication usage data and transmits real-time location information to the server.

[0537] 2. Data Analysis:

[0538] The server uses machine learning algorithms to analyze the collected data. For example, it can identify patterns based on past data, such as "In London, Cafe B has stable Wi-Fi." The server then compares the data with a database to select the optimal communication plan and wireless network connection point.

[0539] 3. Proposal generation:

[0540] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi hotspots and data roaming plans that fit the user's schedule. The server then sends these recommendations to the device, which then notifies the user. For example, the recommendation might be, "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0541] 4. Notices and Advice:

[0542] After receiving the suggestion, the device will notify the user, for example, by displaying a message such as, "You can save on data charges by using the Wi-Fi at Cafe B while you're in London. To connect, use the PASS displayed inside the store."

[0543] 5. Automatic switching:

[0544] The server references the stability data of local telecommunications companies and selects the optimal communication plan. For example, it may make a decision based on information such as "Telecommunications Company A's plan is stable in 90% of the London area." The server then sends this information to the device, which then notifies the user of the automatic switchover proposal. If the user selects "Yes," the device will automatically change the communication plan.

[0545] This system allows users to use the most suitable wireless network connection point and automatically switch to an efficient communication plan, thereby reducing communication charges and providing an optimal communication environment.

[0546] Examples:

[0547] Example prompt sentence:

[0548] A user enters into the app that he or she will be traveling to London from October 15th to October 20th.

[0549] The server analyzes the situation and suggests that "high-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[0550] The device will notify the user that "You can save on data charges by using Cafe B's WiFi while you are in London."

[0551] The user selects "Yes" and the device automatically changes the communication plan.

[0552] This invention allows users to instantly secure an optimal communication environment, enabling comfortable and economical communication use.

[0553] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0554] Step 1:

[0555] Data collection

[0556] Input: User's planned activities, past communication usage, current location information

[0557] Operation:

[0558] The user enters their planned activities into the app, such as "Business trip to London from October 15th to October 20th."

[0559] The device stores this information and collects past communication data (e.g., "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times").

[0560] The device uses GPS to obtain real-time location information.

[0561] Output: Sends action plans, past communication data, and location information to the server.

[0562] Step 2:

[0563] Data analysis

[0564] Input: Action schedule sent to the server, past communication data, location information

[0565] Operation:

[0566] The server stores the schedule, past communication data, and location information in a database for centralized management.

[0567] The server uses machine learning algorithms to analyze the collected data and identify patterns, such as "in London, Cafe B has the most reliable Wi-Fi."

[0568] The server checks the database and selects the optimal communication plan and wireless network connection point.

[0569] Output: Proposals for optimal communication plans and wireless network connection points.

[0570] Step 3:

[0571] Proposal generation

[0572] Input: Proposals for optimal communication plans and wireless network connection points based on data analysis

[0573] Operation:

[0574] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0575] The server sends the generated proposal to the terminal.

[0576] Output: A proposal to notify the user.

[0577] Step 4:

[0578] Notices and Advice

[0579] Input: Proposal sent from the server

[0580] Operation:

[0581] The device receives the suggestion and notifies the user. For example, "You can save on communication charges by using the WiFi at Cafe B while you're in London. To connect, use the PASS displayed inside the store."

[0582] The device will display a confirmation message to the user asking, "Do you want to use this WiFi spot? [Yes] [No]."

[0583] The user reviews the notification and selects "Yes" or "No."

[0584] Output: The user's choice ("Yes" or "No").

[0585] Step 5:

[0586] Automatic Switching

[0587] Input: User's choice ("Yes" or "No"), stability data of local carriers

[0588] Operation:

[0589] The server references the stability data of local carriers and recommends the best plan for a specific area. For example, "Plans from carrier A are stable in 90% of the London area."

[0590] The device sends the user a notification of automatic switching, displaying the message "Telecommunications company A's plan is the best for the area around Cafe B. Would you like to automatically switch? [Yes] [No]."

[0591] If the user selects "Yes," the device will automatically change the communication plan.

[0592] Output: Automatically switch to the best communication plan.

[0593] (Application example 1)

[0594] 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."

[0595] In today's food delivery services, users often face challenges due to unstable communication environments, which can hinder smooth ordering and delivery. High communication charges and the difficulty of finding optimal communication spots are also inconvenient for users. There is a need to develop a system that can resolve these issues and provide users with a comfortable and efficient food delivery experience.

[0596] 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.

[0597] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data and selecting an optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for proposing an optimal communication environment based on the user's planned activities and past order history, and means for optimizing food delivery orders using the proposed communication environment. This enables optimal food delivery orders based on the communication environment, allowing users to enjoy reduced communication charges and a smooth ordering process.

[0598] "User's planned activities" refers to the activities and schedule that the user plans to carry out in the future.

[0599] "Past communication usage" refers to historical data on how a user has previously used communications.

[0600] An "optimal communication plan" refers to a pricing plan that best suits the user's needs and situation and enables highly efficient communication.

[0601] "Wireless Network Access Point" means a specific location where Wi-Fi or other wireless communications are available.

[0602] "Means for analyzing data" refers to methods and tools for analyzing collected data and extracting useful information.

[0603] "Means for suggesting a communication environment" refers to a method or system for recommending the optimal communication environment to a user based on the analysis results.

[0604] "Means for automatically switching communication plans" refers to a function that automatically changes to the most suitable communication plan with the user's permission.

[0605] "Food delivery" refers to a service that delivers meals from restaurants and eateries to designated locations.

[0606] This invention is a system that aims to optimize the communication environment in food delivery services, selecting the optimal communication plan and wireless network connection point based on the user's planned activities and past communication usage, and proposing and automatically switching between them.

[0607] Hardware and software used

[0608] Smartphone: This is the main interface device for this system, and obtains the current location using a GPS module.

[0609] Server: The central processing center for data analysis and proposal generation. It runs machine learning algorithms using Google Cloud Machine Learning and Amazon SageMaker.

[0610] Firebase Cloud Messaging (FCM): A messaging service for sending notifications to users.

[0611] Generative AI model: Generates optimal suggestions based on the user's planned activities and communication usage status.

[0612] Data collection

[0613] The device (smartphone) stores the itinerary entered by the user, including information such as the duration of the business trip and the location of the trip. The smartphone's GPS module also obtains the user's current location in real time and sends it to the server along with past communication usage information.

[0614] Data analysis

[0615] The server analyzes the collected data and selects the optimal communication plan and wireless network connection point based on past communication usage and current location information. Machine learning algorithms are used to compare various data and identify the optimal communication environment for a specific area and time period.

[0616] Proposal Generation and Notification

[0617] Based on the results of the analysis on the server, the system generates recommendations for the optimal communication environment for the user. The generated recommendations are sent to the user's device via Firebase Cloud Messaging (FCM). For example, the notification might say, "If you order food delivery from your office between 12:00 and 1:00 PM, this WiFi spot is the best choice."

[0618] Automatic Switching

[0619] If the user confirms and approves the proposal, the device will automatically switch to the most suitable communication plan, optimizing the communication environment and ensuring smooth food delivery orders and deliveries.

[0620] Specific examples

[0621] For example, if a user inputs that they will be staying in London from October 15th to October 20th, the server will identify the most reliable Wi-Fi spot in London based on their past communication usage and current location data. Based on the analysis results, a notification will be sent to their smartphone saying, "The best Wi-Fi spot is Cafe B. It is recommended that you use it between 12:00 and 13:00."

[0622] Prompt Sentence Examples

[0623] Below are some example prompts to input to the generative AI model:

[0624] "Please suggest the most stable WiFi spot and data plan for a user staying in London from October 15th to October 20th."

[0625] This invention allows users to enjoy an optimal communication environment and ensures stable communication even when using food delivery services, which is expected to reduce communication charges and streamline ordering procedures, thereby improving user satisfaction.

[0626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0627] Step 1: Data collection

[0628] The device (smartphone) stores the itinerary entered by the user. For example, if the user enters "Business trip to London from October 15th to October 20th," that information is saved on the device. The smartphone's GPS module is also used to obtain real-time location information. Furthermore, past communication usage information (e.g., connection speeds and frequency of disconnections at Wi-Fi hotspots used in the past) is also collected, and all this data is sent to the server.

[0629] input:

[0630] User-entered itinerary (e.g., duration of stay, location of stay)

[0631] Current location information (GPS data)

[0632] Past communication usage

[0633] output:

[0634] Composite data sent to the server (planned activities, location information, past communication data)

[0635] Step 2: Data analysis

[0636] The server analyzes data based on the device's planned activities, current location, and past network usage. It uses machine learning algorithms from Google Cloud Machine Learning and Amazon SageMaker to identify the optimal network environment for a specific area and time of day. For example, it identifies the most reliable Wi-Fi hotspot in a specific area of ​​London.

[0637] input:

[0638] Planned activities, current location information, past communication usage

[0639] output:

[0640] Optimal communication plan and wireless network connection point

[0641] Step 3: Proposal Generation

[0642] The server generates recommendations for the optimal communication environment for the user based on the results of the data analysis. Specifically, it generates recommendations such as "The WiFi at Cafe B is the best option while you're in London." These recommendations are then sent to the user's device using Firebase Cloud Messaging (FCM).

[0643] input:

[0644] Data analysis results (optimal communication plan and network connection points)

[0645] output:

[0646] Notification message (optimal communication environment suggestions)

[0647] Step 4: Notification and confirmation

[0648] The device notifies the user of the suggestion sent from the server. The notification might say, for example, "You can save on data charges by using Cafe B's Wi-Fi while you're in London. Would you like to use it? [Yes] [No]," and ask for the user's confirmation.

[0649] input:

[0650] Proposal content (information on the optimal communication environment)

[0651] output:

[0652] User notification message

[0653] User response (yes or no)

[0654] Step 5: Automatic Switching

[0655] The server then sends instructions to automatically switch to the optimal communication plan for the device if the user confirms and approves the proposal. For example, if the user selects "Yes," the device will automatically switch to the selected communication plan, allowing the user to use the optimal communication environment.

[0656] input:

[0657] User response ("Yes")

[0658] output:

[0659] Switching your device's data plan

[0660] Through these steps, users are provided with an optimal communication environment and communication stability is ensured even when using food delivery services.

[0661] 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.

[0662] The present invention is a system that recognizes a user's planned activities, past communication usage, and emotional state, and proposes and automatically switches optimal communication plans and wireless network connection points. The present invention also includes a system that combines an emotion engine, and adjusts the proposal content and communication plan based on the user's emotional state.

[0663] Explanation of program processing

[0664] 1. Data Collection

[0665] The user enters their planned activities into the app. Example: "Business trip to London from October 15th to October 20th."

[0666] The device collects the user's past communication usage data, obtains real-time location information, and sends it to the server. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0667] 2. Recognizing emotional states

[0668] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. Example: "The user is feeling stressed."

[0669] 3. Data Analysis

[0670] The server analyzes the collected itinerary, past communication data, current location information, and the user's emotional state, and uses machine learning algorithms to identify patterns such as "In London, Cafe B has stable Wi-Fi."

[0671] Based on the evaluation results of the emotion engine, the optimal communication plan and wireless network connection point are selected to reduce the user's stress level.

[0672] 4. Proposal generation

[0673] Based on the analysis results and the user's emotional state, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule, and sends them to the device. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0674] 5. Notices and Advice

[0675] The device receives the suggestion and notifies the user. For example, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store."

[0676] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[0677] 6. Automatic Switching

[0678] The server refers to the stability data of local carriers and recommends the best communication plan for a specific area. Example: "Plans from carrier A are stable in 90% of areas in London."

[0679] The device will send an automatic switch notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0680] If the user selects "Yes," the device will automatically change the communication plan.

[0681] Specific examples

[0682] 1. Data Collection

[0683] A user enters information about a business trip to London from October 15 to October 20 into the app. The device saves this information and sends past communication data and current location to the server.

[0684] 2. Recognizing emotional states

[0685] The device collects the user's voice and facial expression data in real time and sends it to the emotion engine, which analyzes it and determines that the user is experiencing stress.

[0686] 3. Data Analysis

[0687] The server analyzes past traffic data to identify the best Wi-Fi spots in a specific area of ​​London. For example, Cafe B may provide a stable speed of 45Mbps.

[0688] Based on the evaluation results of the emotion engine, Cafe B is selected to reduce the user's stress level.

[0689] 4. Proposal generation

[0690] Based on the user's planned activities and emotional state, the server generates a suggestion such as "Use Cafe B's WiFi on October 16th" and sends it to the terminal.

[0691] 5. Notices and Advice

[0692] The device notifies the user, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The user confirms the suggestion and is prompted with a confirmation message: "Do you want to use this WiFi spot? [Yes] [No]."

[0693] 6. Automatic Switching

[0694] The server references the stability data of telecommunications companies in London and determines that "telecommunications company A is the best option." It then sends a notification to the device proposing an automatic switch, and if the user selects "Yes," the device automatically switches to a different communication plan.

[0695] In this way, the system of the present invention provides an optimal communication environment in real time based on the user's emotional state, reducing the user's communication charges and alleviating stress.

[0696] The processing flow will be explained below.

[0697] Step 1:

[0698] The user enters their planned activities into the app. The user's planned location and duration are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[0699] Step 2:

[0700] The device collects the user's past communication usage data, obtains real-time location information, and sends it to the server. The collected data includes information on previously connected WiFi spots, their communication quality, and data usage. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0701] Step 3:

[0702] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. Example: "The user is feeling stressed."

[0703] Step 4:

[0704] The server analyzes the collected schedule, past communication data, current location information, and the user's emotional state. It uses a machine learning algorithm to identify patterns such as "In London, Cafe B has stable Wi-Fi." Based on the evaluation results of the emotion engine, it selects the optimal communication plan and wireless network connection point to reduce the user's stress level.

[0705] Step 5:

[0706] The server generates suggestions based on the analysis results and the user's emotional state and sends them to the user's device. Example: "High-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[0707] Step 6:

[0708] The device receives the suggestion and notifies the user. The notification includes details of the suggested WiFi spot and communication plan, as well as how to connect. For example, "Using the WiFi at Cafe B while you're in London will save you money and reduce stress. To connect, use the PASS displayed in the store."

[0709] Step 7:

[0710] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[0711] Step 8:

[0712] The server refers to the reliability data of the carriers and recommends the best plan for a specific area. Example: "Plans from carrier A are stable in 90% of the area of ​​London."

[0713] Step 9:

[0714] The device sends an automatic switching notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0715] Step 10:

[0716] If the user selects "Yes," the device will automatically change the communication plan.

[0717] Step 11:

[0718] The server continuously collects new communication data and updates the analysis results. Based on the analysis results, the recommendation content is updated as appropriate and notified again. Example: "A new WiFi spot has been found. Please try Cafe C (speed 50Mbps)."

[0719] Example 2

[0720] 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."

[0721] Conventional communication plans and wireless network connection points often cannot always be optimally selected based solely on the user's schedule and past communication usage. Furthermore, since they provide a communication environment without taking the user's emotional state into consideration, they do not contribute to user satisfaction or stress reduction. Therefore, there is a need for a system that takes into account not only the user's schedule and past communication usage, but also the user's emotional state.

[0722] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0723] In this invention, the server includes means for collecting the user's planned activities and past communication usage status, means for analyzing the collected data and selecting the optimal communication plan and wireless network connection point, and means for analyzing the user's emotional state and adjusting the content of the proposal based on this. This makes it possible to provide the optimal communication environment by comprehensively analyzing the user's planned activities, past communication data, real-time location information, and emotional state.

[0724] "User's planned activities" is information about the activities and movements that the user plans to carry out in the future.

[0725] "Communication usage status" is information indicating what communication means and communication volume the user has used in the past, as well as the connection status at that time.

[0726] "Current location information" refers to the geographical location information of the user's terminal.

[0727] "Emotional state" refers to the user's psychological or emotional state, and is the result of analysis of voice and facial expression data.

[0728] A "communication plan" is a contractual provision that includes the terms of use, fees, speed, etc. of the communication service selected or proposed to the user.

[0729] A "wireless network access point" is a specific access point or hotspot used by a user to connect to the Internet.

[0730] An "emotion engine" is software or hardware that analyzes voice and facial expression data to identify a user's emotional state.

[0731] "Data analysis" is a set of processes used to analyze collected data and find patterns and relationships.

[0732] A "machine learning algorithm" is a type of artificial intelligence technology used in data analysis, which learns from past data and makes future predictions and classifications.

[0733] The "proposal content" is detailed information about the optimal communication plan and wireless network connection points that are presented to the user based on the analysis results.

[0734] "Automatically switching communication plans" refers to the system automatically changing to the most suitable communication plan after obtaining the user's permission.

[0735] This invention is a system that comprehensively analyzes a user's schedule, past communication usage, and emotional state, and then proposes and automatically switches to the optimal communication plan and wireless network connection point.The purpose of this invention is to provide a comfortable communication environment for users and reduce stress.

[0736] The system consists of a server, a terminal, and an emotion engine including a generative AI model. The specific features of each hardware and software are described below.

[0737] Hardware and software used

[0738] Server: A server with high-performance data processing capabilities, such as AWS (Amazon Web Services) or Google Cloud Platform, is used. The server applies machine learning algorithms to analyze large amounts of data in real time.

[0739] Device: A mobile device used by a user, such as a smartphone or tablet, that is equipped with GPS, a camera, and a microphone and collects data about their schedule and emotional state.

[0740] Emotion engine: A generative AI model that analyzes voice and facial expression data to identify a user's emotional state. For example, it uses existing AI engines such as Google's Cloud Vision API or IBM's Watson.

[0741] Explanation of program processing

[0742] 1. Data Collection

[0743] A user inputs a schedule into a smartphone application. For example, the user inputs "Business trip to London from October 15th to October 20th."

[0744] The device saves data on planned activities and collects past communication usage data. It also obtains real-time location information and sends this data to the server. For example, it collects information such as "Connected to Hotel A's WiFi on October 10, 2023, the speed was 20Mbps, and the disconnection frequency was 3 times."

[0745] 2. Recognizing emotional states

[0746] The device collects the user's voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. For example, it may recognize from voice data that the user is "feeling stressed."

[0747] 3. Data Analysis

[0748] The server aggregates the collected data on your planned activities, past communication data, current location, and emotional state. Based on this data, it applies machine learning algorithms to identify the optimal communication plan and wireless network connection point. For example, it might discover a pattern that "in London, Cafe B has stable Wi-Fi."

[0749] Based on the results of the analysis of the user's emotional state, the system selects the optimal communication plan and wireless network connection point to reduce the user's stress level.

[0750] 4. Proposal generation

[0751] Based on the analysis results and the user's emotional state, the server generates a recommendation for the optimal communication plan and wireless network connection point that matches the user's planned activities. For example, it may suggest that "high-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0752] The proposal is sent to the terminal.

[0753] 5. Notices and Advice

[0754] The device then notifies the user of the suggestions received from the server. For example, it displays a message such as, "If you use the Wi-Fi at Cafe B during your stay in London, you can save on communication costs and reduce stress. To connect, use the PASS displayed inside the store."

[0755] The user reviews the suggestion and selects "Do you want to use this WiFi spot? [Yes] [No]."

[0756] 6. Automatic Switching

[0757] The server refers to the stability data of telecommunications companies in London and recommends the most suitable telecommunications company. For example, it may determine that "telecommunications company A is the best."

[0758] The device will send a notification to the user about the automatic switch. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0759] If the user selects "Yes," the device will automatically change the communication plan.

[0760] Specific examples

[0761] The following is an example of a scenario in which the system of the present invention actually works.

[0762] 1. Data collection: The user enters "Business trip to London from October 15th to October 20th" into a smartphone app. The device stores this information and sends past communication data, such as "Connected to Hotel A's WiFi on October 10th, 2023, speed was 20Mbps, and disconnection frequency was 3 times," along with current location information to the server.

[0763] 2. Emotional state recognition: The device captures the user's voice and facial expressions and sends them to the emotion engine, which recognizes that the user is feeling stressed.

[0764] 3. Data Analysis: The server analyzes all the data and determines that Cafe B is the best WiFi spot. It also determines that "Cafe B is suitable for reducing user stress."

[0765] 4. Proposal generation: The server generates a proposal such as "It would be good to use Cafe B's WiFi on October 16th" and sends it to the device.

[0766] 5. Notification and Advice: The device will display the suggestion to the user and prompt them to choose "Do you want to use this WiFi hotspot? [Yes] [No]."

[0767] 6. Automatic switching: The server selects the optimal carrier and notifies the device. If the user approves, the device automatically changes its communication plan.

[0768] The present invention allows users to enjoy an optimal communication environment while reducing stress.

[0769] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0770] Step 1: Data collection

[0771] A user inputs their schedule into a smartphone application. Example input data: "Business trip to London from October 15th to October 20th."

[0772] The device stores the planned activity data entered by the user. It also collects past communication usage data (e.g., "Connected to Hotel A's WiFi on October 10, 2023, speed 20Mbps, disconnection frequency 3 times") and real-time location information. Location information is obtained using the GPS function.

[0773] The device sends the collected data to the server. The input is the planned activity, past communication data, and location information, and the output is a data packet containing this information.

[0774] Step 2: Recognizing your emotional state

[0775] The device collects the user's voice and facial expression data in real time. Voice data is acquired using a microphone, and facial expression data is captured using a camera.

[0776] The device sends the collected voice and facial expression data to the emotion engine. The input is voice data and facial expression data, and the output is the analysis result by the emotion engine.

[0777] The emotion engine identifies the user's emotional state through voice and facial expression analysis, for example, determining that the user is feeling stressed.

[0778] Step 3: Data analysis

[0779] The server centrally collects the action schedule, past communication data, current location information, and emotional state sent from the device. These data are the input.

[0780] The server applies machine learning algorithms to analyze the data and identify usage patterns, concluding, for example, that "in London, Cafe B has the best WiFi." The output is the optimal data plan or wireless network connection point.

[0781] Based on the analysis of the user's emotional state, the server selects the optimal communication plan and Wi-Fi hotspot to reduce the user's stress level.

[0782] Step 4: Proposal Generation

[0783] The server generates recommendations for optimal communication plans and wireless network connection points based on the user's planned activities based on the results of data analysis and the user's emotional state. For example, it generates a recommendation that "high-speed WiFi (45Mbps) is available at Cafe B on October 16th." The inputs are the analysis results and the user's emotional state, and the output is the recommendations.

[0784] The server transmits the generated proposal to the terminal.

[0785] Step 5: Inform and advise

[0786] The device notifies the user of the suggestion received from the server. For example, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The input is the suggestion, and the output is the notification to the user.

[0787] The device prompts the user to choose, "Do you want to use this WiFi hotspot? [Yes] [No]." The input is the suggestion, and the output is the user's choice.

[0788] Step 6: Automatic Switching

[0789] The server refers to the stability data of telecommunications companies in London and recommends the most suitable telecommunications company. For example, it determines that "telecommunications company A is the most suitable." The input is the telecommunications company stability data, and the output is the recommended telecommunications company.

[0790] The device sends a notification of automatic switching to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]." The input is the recommended carrier, and the output is the notification to the user.

[0791] If the user selects "Yes," the device will automatically change the communication plan. The input is the user's selection, and the output is the change in communication plan.

[0792] In this way, the system of the present invention comprehensively analyzes the user's planned activities, past communication usage, current location information, and emotional state, and provides the optimal communication environment, thereby reducing the user's stress and saving on communication charges.

[0793] (Application example 2)

[0794] 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."

[0795] Conventional communication plan proposal systems optimize plans based on the user's planned activities and past communication usage, but are unable to consider the user's emotional state or stress level. As a result, the optimal communication environment for the user may not be provided. Furthermore, food delivery apps are also unable to make proposals that take into account the user's planned activities and emotional state, making it difficult to improve user satisfaction. These issues can lead to a decline in user convenience and satisfaction.

[0796] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0797] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data and selecting an optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for recognizing the user's emotional state and adjusting the content of suggestions and communication plans based on the analysis results, and means for collecting past order history and planned activities and suggesting optimal foods and promotions. This makes it possible to provide an optimal communication environment that takes into account the user's emotional state in addition to their planned activities and past communication usage status, and also enables suggestions that improve user satisfaction in food delivery apps.

[0798] "Planned activities" refers to information about activities and plans that the user plans to carry out in the future.

[0799] "Communication usage status" refers to data on past communications made by a user, and refers to a detailed history of communications such as communication volume, communication speed, and number of disconnections.

[0800] "Communication plan" refers to the pricing plans and contract terms for internet and data communications provided by telecommunications companies.

[0801] "Wireless Network Access Point" refers to a location where you can connect to public or corporate Wi-Fi or other wireless communication networks.

[0802] "Emotional state" refers to the user's current feelings and state of mind, and includes emotional states such as stress, happiness, and fatigue.

[0803] "Suggestion content" refers to information such as recommended items, services, plans, and promotions that are generated based on the analysis results and the user's status.

[0804] "Past order history" refers to the history of orders and purchases made by the user up to now, and includes information such as the number of purchases and trends of specific products.

[0805] "Promotion" refers to advertising activities such as offering special offers, discounts, and events to users, and is a means of increasing product sales and user satisfaction.

[0806] An "emotion engine" refers to a software module that analyzes audio and image data to identify a user's emotional state.

[0807] A "machine learning algorithm" is a computational method for analyzing large amounts of data and finding statistical patterns, enabling predictions and classifications.

[0808] This system analyzes a user's schedule, past communication usage, and emotional state to propose optimal communication plans and wireless network connection points, and automatically switches communication plans. In addition, in the case of a food delivery app, it can suggest optimal foods and promotions based on past order history and schedule.

[0809] The system mainly consists of the following elements:

[0810] Server: Collects data, analyzes, generates proposals, notifies, and automatically switches.

[0811] Terminal: Collects input data from the user, recognizes emotional states in real time, communicates with the server, makes suggestions, and stores data.

[0812] User: Provides information such as planned activities, past communication usage, and order history.

[0813] The server includes means for collecting the user's planned activities and past communication usage status, means for analyzing the collected data and selecting the optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for recognizing the user's emotional state and adjusting the content of suggestions and communication plans based on the analysis results, and means for collecting the user's past order history and planned activities and suggesting optimal foods and promotions.

[0814] Hardware and software used

[0815] Hardware: Smartphone, built-in camera, built-in microphone

[0816] Software: Python, TensorFlow (emotion engine), Firebase (cloud database), Flutter (UI development)

[0817] Data processing and calculation

[0818] The server receives the user's planned activities, past communication usage, and emotional state, including voice and facial expression data. This data is analyzed using TensorFlow to identify the user's emotional state. Based on the analysis results, machine learning algorithms select the optimal communication plan and wireless network connection point. Based on past ordering history, the optimal food and promotions for food delivery are also selected. Based on this, recommendations are generated and notified to the user. The notifications are displayed on the smartphone using Flutter and stored in Firebase.

[0819] Specific examples

[0820] For example, if a user plans to go on a business trip to London from October 15th to October 20th, the server receives this information and analyzes their past communication usage and order history. In addition, it can identify whether the user is feeling stressed from voice and facial expression data. Based on this, the server can suggest the optimal communication plan and wireless network connection points at cafes, and notify the user to use Cafe B's WiFi during their stay in London. The food delivery app can also suggest a relaxing tea and pizza set to reduce the user's stress.

[0821] Prompt Sentence Examples

[0822] For example, the following prompt sentence can be input to a generative AI model:

[0823] plaintext

[0824] User ID: 12345

[0825] Planned action: Work from home at 4pm on October 12th

[0826] Past orders: 3 pizzas, 1 sushi, 2 salads

[0827] Current emotional state: Tired

[0828] Generated AI prompt:

[0829] 1. Proposing the best meal set

[0830] 2. Describe the reasons for your recommendation based on your past order history and your current emotional state

[0831] In this way, the present invention can optimize the communication environment and improve user satisfaction with food delivery by making optimal suggestions taking into account the user's planned activities and emotional state.

[0832] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0833] Step 1:

[0834] The user inputs their itinerary into the app. For example, this information might be "Business trip to London from October 15th to October 20th." The input data is saved on the device and later sent to the server. The itinerary is the input data, and the saved itinerary is generated as the output data.

[0835] Step 2:

[0836] The device collects past communication usage data and obtains real-time location information. The collected data is stored within the system and sent to the server. Specifically, it includes data such as "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times." The input data is past communication usage and current location, and the output data is generated and sent to the server.

[0837] Step 3:

[0838] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine uses TensorFlow to analyze this data and identify the user's emotional state. Specifically, emotional states such as "stress" and "fatigue" are identified from the user's voice and facial expressions. The collected voice and facial expression data are used as input data, and the analyzed emotional state is generated as output data.

[0839] Step 4:

[0840] The server analyzes the collected itinerary, past communication data, current location information, and the user's emotional state. A machine learning algorithm is then used to identify patterns, such as "In London, Cafe B has stable Wi-Fi." The input data are the itinerary, communication usage, current location, and emotional state, and the analysis results are generated as output data.

[0841] Step 5:

[0842] Based on the emotion engine's evaluation results, the server selects the optimal communication plan or wireless network connection point to reduce the user's stress level. The server evaluates options based on specific parameters and determines the most appropriate choice for the user. Specifically, it may select something like "Telecommunications Company A's plan is stable in 90% of London." The input data are the analysis results and the user's emotional state, and the output data is the optimal communication plan or connection point.

[0843] Step 6:

[0844] Based on the analysis results and the user's emotional state, the server generates recommendations for the optimal WiFi spot or data roaming plan that matches the user's schedule and sends them to the device. For example, a recommendation might be generated such as "We recommend using the WiFi at Cafe B on October 16th." The optimal communication plan or connection point is the input data, and the recommendations are generated as output data.

[0845] Step 7:

[0846] The device receives the suggestions and notifies the user. For example, a notification might say, "If you use the WiFi at Cafe B while you're in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The suggestions are input data, and a notification to the user is generated as output data.

[0847] Step 8:

[0848] The user checks the suggestion and chooses whether to use it. For example, a confirmation message such as "Do you want to use this WiFi spot? [Yes] [No]" is displayed. The user's choice is the input data, and the user's response is generated as the output data.

[0849] Step 9:

[0850] The server references the stability data of local telecommunications companies and recommends the optimal communication plan for a specific area. For example, a proposal such as "Telecommunications Company A's plan is optimal for 90% of the London area" is generated. The input data is regional information and telecommunications company data, and the output data is the optimal communication plan.

[0851] Step 10:

[0852] The device sends a notification of automatic switching to the user. For example, a notification such as "Telecommunications company A's plan is optimal for the area around Cafe B. Would you like to perform automatic switching? [Yes] [No]" is sent. If the user selects "Yes," the device automatically changes the communication plan. The input data are the optimal communication plan and the user's selection, and the output data is generated indicating the execution of automatic switching.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] [Third embodiment]

[0857] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0858] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0859] 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).

[0860] 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.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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."

[0869] The present invention is a system that collects and analyzes a user's planned activities and past communication usage status, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.

[0870] Explanation of program processing

[0871] 1. Data Collection

[0872] When a user enters their planned activities into the app, the location and duration of their stay are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[0873] The device collects past communication usage data. For example, "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0874] The device obtains the current location information in real time and sends it to the server.

[0875] 2. Data Analysis

[0876] The server analyzes the collected data, learning from users' schedules and past usage patterns, and using machine learning algorithms to identify patterns such as "In London, Cafe B has stable Wi-Fi."

[0877] By comparing the collected data with the database, the optimal communication plan and wireless network connection point are selected.

[0878] 3. Proposal generation

[0879] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0880] The content of this proposal is transmitted to the terminal.

[0881] 4. Notice and Advice

[0882] The device receives the suggestion and notifies the user. Example: "You can save on communication charges by using the WiFi at Cafe B while you are in London. To connect, use the PASS displayed inside the store."

[0883] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[0884] 5. Automatic switching

[0885] The server refers to the stability data of local carriers and recommends the best communication plan for a specific area. Example: "Plans from carrier A are stable in 90% of areas in London."

[0886] The device will send a notification to the user about the automatic switch. For example, "Plan from carrier A is the best option for the area around cafe B. Would you like to switch automatically? [Yes] [No]"

[0887] If the user selects "Yes," the device will automatically change the communication plan.

[0888] Specific examples

[0889] 1. Data Collection

[0890] A user enters information about a business trip to London from October 15 to October 20 into the app. The device saves this information and sends past communication data and current location to the server.

[0891] 2. Data Analysis

[0892] The server analyzes past traffic data to identify the best Wi-Fi spots in a particular area of ​​London. For example, it might find that Cafe B offers a stable speed of 45Mbps.

[0893] 3. Proposal generation

[0894] Based on the user's planned activities, the server generates a suggestion such as "It would be a good idea to use Cafe B's WiFi on October 16th" and sends it to the terminal.

[0895] 4. Notice and Advice

[0896] The device notifies the user, "You can save on communication charges by using the WiFi at Cafe B while you are in London. To connect, use the PASS displayed inside the store." The user confirms the suggestion and is prompted with a confirmation message: "Do you want to use this WiFi spot? [Yes] [No]."

[0897] 5. Automatic switching

[0898] The server references the stability data of telecommunications companies in London and determines that "telecommunications company A is the best option." It then sends a notification to the device proposing an automatic switch, and if the user selects "Yes," the device automatically switches to a different communication plan.

[0899] In this way, the present invention can provide an optimal communication environment in real time and reduce the user's communication charges.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] The user enters their planned activities into the app. The user's planned location and duration are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[0903] Step 2:

[0904] The device collects data on the user's past communication usage. This data includes the WiFi spots connected to, communication quality, data usage, etc. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[0905] Step 3:

[0906] The device acquires real-time location information and transmits the current location information to the server.

[0907] Step 4:

[0908] The server analyzes the collected itinerary, past communication data, and current location information. It uses machine learning algorithms to identify communication quality and cost patterns. For example, it identifies a pattern that Cafe B in London has stable Wi-Fi.

[0909] Step 5:

[0910] The server checks the database of public Wi-Fi spots and data roaming plans to select the optimal communication plan and wireless network connection point. For example, select Cafe B or a new Wi-Fi spot (Cafe C, speed 45Mbps).

[0911] Step 6:

[0912] The server generates a proposal based on the analysis results and sends it to the user's device. Example: A notification is generated saying, "High-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[0913] Step 7:

[0914] The device receives the suggestion from the server and notifies the user. The user confirms the suggestion and decides whether to use it or not. Example: "Do you want to use this WiFi spot? [Yes] [No]"

[0915] Step 8:

[0916] The server refers to the stability data of the telecommunications companies and recommends the best communication plan for a specific area. Example: Showing that Telecommunications Company A's plan is stable in 90% of the area of ​​London.

[0917] Step 9:

[0918] The device sends an automatic switching notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[0919] Step 10:

[0920] If the user selects "Yes," the device will automatically change the communication plan.

[0921] Step 11:

[0922] The server continuously collects new communication data and updates the analysis results. Based on the analysis results, the recommendation content is updated as appropriate and notified again. Example: "A new WiFi spot has been found. Please try Cafe C (speed 50Mbps)."

[0923] Example 1

[0924] 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."

[0925] In today's communications environment, it is difficult for users to secure an optimal communication environment in various locations. Furthermore, optimizing a communication plan requires complex manual operations, which takes time and effort. The present invention aims to solve these problems and provide a system that automatically provides users with the optimal communication environment and communication plan.

[0926] 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.

[0927] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data using a machine learning algorithm to select an optimal communication plan and wireless network connection point, and means for notifying the user of the selected communication plan and wireless network connection point, thereby enabling the user to automatically secure an optimal communication environment in real time and optimize their communication plan.

[0928] A "user" is a person who uses this system and is the entity that provides the schedule of activities and past communication usage status.

[0929] The "activity schedule" refers to information about activities that a user has planned for a specific period or location, and includes details of trips, business trips, etc.

[0930] "Communication usage status" is data showing past communication usage records, and specifically includes information such as communication speed, connection frequency, and number of disconnections.

[0931] "Data collection means" refers to methods and devices for acquiring and storing schedules and past communication usage information.

[0932] "Data analysis means" refers to processes and algorithms for analyzing collected data, and specifically includes analytical methods using machine learning algorithms.

[0933] "Communication plan" refers to various plans offered by communication service providers, including terms such as data volume, price, and service content.

[0934] "Wireless network connection point" refers to a location or service where Internet connection is possible, such as Wi-Fi or a mobile network.

[0935] "Notification means" refers to a method or device for informing users of the selected communication plan and wireless network connection point information.

[0936] "Means for automatically switching communication plans" refers to the function or process by which the system automatically changes to the most suitable communication plan after obtaining the user's permission.

[0937] The present invention is a system that collects and analyzes a user's planned activities and past communication usage status, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.

[0938] This system is mainly composed of three entities: a server, a terminal, and a user.

[0939] server:

[0940] The server collects the user's planned activities and past communication usage data. The server then analyzes the collected data using machine learning algorithms to select the optimal communication plan and wireless network connection point. The server then sends the results of these selections to the device and notifies the user.

[0941] Device:

[0942] The device saves the user's planned activities and collects past communication usage information. The device also acquires the user's current location information in real time and sends it to the server. The device notifies the user of the proposal received from the server, and if the user approves, the device automatically switches communication plans.

[0943] User:

[0944] The user opens the application and enters their planned activities, for example, "I'm going on a business trip to London from October 15th to October 20th." The device then records this information and sends it to the server.

[0945] Specifically, the invention is carried out in the following steps.

[0946] 1. Data Collection:

[0947] When a user enters their planned activities into the app, the device stores this information. At the same time, the device collects past communication usage data and transmits real-time location information to the server.

[0948] 2. Data Analysis:

[0949] The server uses machine learning algorithms to analyze the collected data. For example, it can identify patterns based on past data, such as "In London, Cafe B has stable Wi-Fi." The server then compares the data with a database to select the optimal communication plan and wireless network connection point.

[0950] 3. Proposal generation:

[0951] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi hotspots and data roaming plans that fit the user's schedule. The server then sends these recommendations to the device, which then notifies the user. For example, the recommendation might be, "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0952] 4. Notices and Advice:

[0953] After receiving the suggestion, the device will notify the user, for example, by displaying a message such as, "You can save on data charges by using the Wi-Fi at Cafe B while you're in London. To connect, use the PASS displayed inside the store."

[0954] 5. Automatic switching:

[0955] The server references the stability data of local telecommunications companies and selects the optimal communication plan. For example, it may make a decision based on information such as "Telecommunications Company A's plan is stable in 90% of the London area." The server then sends this information to the device, which then notifies the user of the automatic switchover proposal. If the user selects "Yes," the device will automatically change the communication plan.

[0956] This system allows users to use the most suitable wireless network connection point and automatically switch to an efficient communication plan, thereby reducing communication charges and providing an optimal communication environment.

[0957] Examples:

[0958] Example prompt sentence:

[0959] A user enters into the app that he or she will be traveling to London from October 15th to October 20th.

[0960] The server analyzes the situation and suggests that "high-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[0961] The device will notify the user that "You can save on data charges by using Cafe B's WiFi while you are in London."

[0962] The user selects "Yes" and the device automatically changes the communication plan.

[0963] This invention allows users to instantly secure an optimal communication environment, enabling comfortable and economical communication use.

[0964] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0965] Step 1:

[0966] Data collection

[0967] Input: User's planned activities, past communication usage, current location information

[0968] Operation:

[0969] The user enters their planned activities into the app, such as "Business trip to London from October 15th to October 20th."

[0970] The device stores this information and collects past communication data (e.g., "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times").

[0971] The device uses GPS to obtain real-time location information.

[0972] Output: Sends action plans, past communication data, and location information to the server.

[0973] Step 2:

[0974] Data analysis

[0975] Input: Action schedule sent to the server, past communication data, location information

[0976] Operation:

[0977] The server stores the schedule, past communication data, and location information in a database for centralized management.

[0978] The server uses machine learning algorithms to analyze the collected data and identify patterns, such as "in London, Cafe B has the most reliable Wi-Fi."

[0979] The server checks the database and selects the optimal communication plan and wireless network connection point.

[0980] Output: Proposals for optimal communication plans and wireless network connection points.

[0981] Step 3:

[0982] Proposal generation

[0983] Input: Proposals for optimal communication plans and wireless network connection points based on data analysis

[0984] Operation:

[0985] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[0986] The server sends the generated proposal to the terminal.

[0987] Output: A proposal to notify the user.

[0988] Step 4:

[0989] Notices and Advice

[0990] Input: Proposal sent from the server

[0991] Operation:

[0992] The device receives the suggestion and notifies the user. For example, "You can save on communication charges by using the WiFi at Cafe B while you're in London. To connect, use the PASS displayed inside the store."

[0993] The device will display a confirmation message to the user asking, "Do you want to use this WiFi spot? [Yes] [No]."

[0994] The user reviews the notification and selects "Yes" or "No."

[0995] Output: The user's choice ("Yes" or "No").

[0996] Step 5:

[0997] Automatic Switching

[0998] Input: User's choice ("Yes" or "No"), stability data of local carriers

[0999] Operation:

[1000] The server references the stability data of local carriers and recommends the best plan for a specific area. For example, "Plans from carrier A are stable in 90% of the London area."

[1001] The device sends the user a notification of automatic switching, displaying the message "Telecommunications company A's plan is the best for the area around Cafe B. Would you like to automatically switch? [Yes] [No]."

[1002] If the user selects "Yes," the device will automatically change the communication plan.

[1003] Output: Automatically switch to the best communication plan.

[1004] (Application example 1)

[1005] 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."

[1006] In today's food delivery services, users often face challenges due to unstable communication environments, which can hinder smooth ordering and delivery. High communication charges and the difficulty of finding optimal communication spots are also inconvenient for users. There is a need to develop a system that can resolve these issues and provide users with a comfortable and efficient food delivery experience.

[1007] 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.

[1008] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data and selecting an optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for proposing an optimal communication environment based on the user's planned activities and past order history, and means for optimizing food delivery orders using the proposed communication environment. This enables optimal food delivery orders based on the communication environment, allowing users to enjoy reduced communication charges and a smooth ordering process.

[1009] "User's planned activities" refers to the activities and schedule that the user plans to carry out in the future.

[1010] "Past communication usage" refers to historical data on how a user has previously used communications.

[1011] An "optimal communication plan" refers to a pricing plan that best suits the user's needs and situation and enables highly efficient communication.

[1012] "Wireless Network Access Point" means a specific location where Wi-Fi or other wireless communications are available.

[1013] "Means for analyzing data" refers to methods and tools for analyzing collected data and extracting useful information.

[1014] "Means for suggesting a communication environment" refers to a method or system for recommending the optimal communication environment to a user based on the analysis results.

[1015] "Means for automatically switching communication plans" refers to a function that automatically changes to the most suitable communication plan with the user's permission.

[1016] "Food delivery" refers to a service that delivers meals from restaurants and eateries to designated locations.

[1017] This invention is a system that aims to optimize the communication environment in food delivery services, selecting the optimal communication plan and wireless network connection point based on the user's planned activities and past communication usage, and proposing and automatically switching between them.

[1018] Hardware and software used

[1019] Smartphone: This is the main interface device for this system, and obtains the current location using a GPS module.

[1020] Server: The central processing center for data analysis and proposal generation. It runs machine learning algorithms using Google Cloud Machine Learning and Amazon SageMaker.

[1021] Firebase Cloud Messaging (FCM): A messaging service for sending notifications to users.

[1022] Generative AI model: Generates optimal suggestions based on the user's planned activities and communication usage status.

[1023] Data collection

[1024] The device (smartphone) stores the itinerary entered by the user, including information such as the duration of the business trip and the location of the trip. The smartphone's GPS module also obtains the user's current location in real time and sends it to the server along with past communication usage information.

[1025] Data analysis

[1026] The server analyzes the collected data and selects the optimal communication plan and wireless network connection point based on past communication usage and current location information. Machine learning algorithms are used to compare various data and identify the optimal communication environment for a specific area and time period.

[1027] Proposal Generation and Notification

[1028] Based on the results of the analysis on the server, the system generates recommendations for the optimal communication environment for the user. The generated recommendations are sent to the user's device via Firebase Cloud Messaging (FCM). For example, the notification might say, "If you order food delivery from your office between 12:00 and 1:00 PM, this WiFi spot is the best choice."

[1029] Automatic Switching

[1030] If the user confirms and approves the proposal, the device will automatically switch to the most suitable communication plan, optimizing the communication environment and ensuring smooth food delivery orders and deliveries.

[1031] Specific examples

[1032] For example, if a user inputs that they will be staying in London from October 15th to October 20th, the server will identify the most reliable Wi-Fi spot in London based on their past communication usage and current location data. Based on the analysis results, a notification will be sent to their smartphone saying, "The best Wi-Fi spot is Cafe B. It is recommended that you use it between 12:00 and 13:00."

[1033] Prompt Sentence Examples

[1034] Below are some example prompts to input to the generative AI model:

[1035] "Please suggest the most stable WiFi spot and data plan for a user staying in London from October 15th to October 20th."

[1036] This invention allows users to enjoy an optimal communication environment and ensures stable communication even when using food delivery services, which is expected to reduce communication charges and streamline ordering procedures, thereby improving user satisfaction.

[1037] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1038] Step 1: Data collection

[1039] The device (smartphone) stores the itinerary entered by the user. For example, if the user enters "Business trip to London from October 15th to October 20th," that information is saved on the device. The smartphone's GPS module is also used to obtain real-time location information. Furthermore, past communication usage information (e.g., connection speeds and frequency of disconnections at Wi-Fi hotspots used in the past) is also collected, and all this data is sent to the server.

[1040] input:

[1041] User-entered itinerary (e.g., duration of stay, location of stay)

[1042] Current location information (GPS data)

[1043] Past communication usage

[1044] output:

[1045] Composite data sent to the server (planned activities, location information, past communication data)

[1046] Step 2: Data analysis

[1047] The server analyzes data based on the device's planned activities, current location, and past network usage. It uses machine learning algorithms from Google Cloud Machine Learning and Amazon SageMaker to identify the optimal network environment for a specific area and time of day. For example, it identifies the most reliable Wi-Fi hotspot in a specific area of ​​London.

[1048] input:

[1049] Planned activities, current location information, past communication usage

[1050] output:

[1051] Optimal communication plan and wireless network connection point

[1052] Step 3: Proposal Generation

[1053] The server generates recommendations for the optimal communication environment for the user based on the results of the data analysis. Specifically, it generates recommendations such as "The WiFi at Cafe B is the best option while you're in London." These recommendations are then sent to the user's device using Firebase Cloud Messaging (FCM).

[1054] input:

[1055] Data analysis results (optimal communication plan and network connection points)

[1056] output:

[1057] Notification message (optimal communication environment suggestions)

[1058] Step 4: Notification and confirmation

[1059] The device notifies the user of the suggestion sent from the server. The notification might say, for example, "You can save on data charges by using Cafe B's Wi-Fi while you're in London. Would you like to use it? [Yes] [No]," and ask for the user's confirmation.

[1060] input:

[1061] Proposal content (information on the optimal communication environment)

[1062] output:

[1063] User notification message

[1064] User response (yes or no)

[1065] Step 5: Automatic Switching

[1066] The server then sends instructions to automatically switch to the optimal communication plan for the device if the user confirms and approves the proposal. For example, if the user selects "Yes," the device will automatically switch to the selected communication plan, allowing the user to use the optimal communication environment.

[1067] input:

[1068] User response ("Yes")

[1069] output:

[1070] Switching your device's data plan

[1071] Through these steps, users are provided with an optimal communication environment and communication stability is ensured even when using food delivery services.

[1072] 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.

[1073] The present invention is a system that recognizes a user's planned activities, past communication usage, and emotional state, and proposes and automatically switches optimal communication plans and wireless network connection points. The present invention also includes a system that combines an emotion engine, and adjusts the proposal content and communication plan based on the user's emotional state.

[1074] Explanation of program processing

[1075] 1. Data Collection

[1076] The user enters their planned activities into the app. Example: "Business trip to London from October 15th to October 20th."

[1077] The device collects the user's past communication usage data, obtains real-time location information, and sends it to the server. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[1078] 2. Recognizing emotional states

[1079] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. Example: "The user is feeling stressed."

[1080] 3. Data Analysis

[1081] The server analyzes the collected itinerary, past communication data, current location information, and the user's emotional state, and uses machine learning algorithms to identify patterns such as "In London, Cafe B has stable Wi-Fi."

[1082] Based on the evaluation results of the emotion engine, the optimal communication plan and wireless network connection point are selected to reduce the user's stress level.

[1083] 4. Proposal generation

[1084] Based on the analysis results and the user's emotional state, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule, and sends them to the device. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[1085] 5. Notices and Advice

[1086] The device receives the suggestion and notifies the user. For example, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store."

[1087] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[1088] 6. Automatic Switching

[1089] The server refers to the stability data of local carriers and recommends the best communication plan for a specific area. Example: "Plans from carrier A are stable in 90% of areas in London."

[1090] The device will send an automatic switch notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[1091] If the user selects "Yes," the device will automatically change the communication plan.

[1092] Specific examples

[1093] 1. Data Collection

[1094] A user enters information about a business trip to London from October 15 to October 20 into the app. The device saves this information and sends past communication data and current location to the server.

[1095] 2. Recognizing emotional states

[1096] The device collects the user's voice and facial expression data in real time and sends it to the emotion engine, which analyzes it and determines that the user is experiencing stress.

[1097] 3. Data Analysis

[1098] The server analyzes past traffic data to identify the best Wi-Fi spots in a specific area of ​​London. For example, Cafe B may provide a stable speed of 45Mbps.

[1099] Based on the evaluation results of the emotion engine, Cafe B is selected to reduce the user's stress level.

[1100] 4. Proposal generation

[1101] Based on the user's planned activities and emotional state, the server generates a suggestion such as "Use Cafe B's WiFi on October 16th" and sends it to the terminal.

[1102] 5. Notices and Advice

[1103] The device notifies the user, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The user confirms the suggestion and is prompted with a confirmation message: "Do you want to use this WiFi spot? [Yes] [No]."

[1104] 6. Automatic Switching

[1105] The server references the stability data of telecommunications companies in London and determines that "telecommunications company A is the best option." It then sends a notification to the device proposing an automatic switch, and if the user selects "Yes," the device automatically switches to a different communication plan.

[1106] In this way, the system of the present invention provides an optimal communication environment in real time based on the user's emotional state, reducing the user's communication charges and alleviating stress.

[1107] The processing flow will be explained below.

[1108] Step 1:

[1109] The user enters their planned activities into the app. The user's planned location and duration are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[1110] Step 2:

[1111] The device collects the user's past communication usage data, obtains real-time location information, and sends it to the server. The collected data includes information on previously connected WiFi spots, their communication quality, and data usage. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[1112] Step 3:

[1113] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. Example: "The user is feeling stressed."

[1114] Step 4:

[1115] The server analyzes the collected schedule, past communication data, current location information, and the user's emotional state. It uses a machine learning algorithm to identify patterns such as "In London, Cafe B has stable Wi-Fi." Based on the evaluation results of the emotion engine, it selects the optimal communication plan and wireless network connection point to reduce the user's stress level.

[1116] Step 5:

[1117] The server generates suggestions based on the analysis results and the user's emotional state and sends them to the user's device. Example: "High-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[1118] Step 6:

[1119] The device receives the suggestion and notifies the user. The notification includes details of the suggested WiFi spot and communication plan, as well as how to connect. For example, "Using the WiFi at Cafe B while you're in London will save you money and reduce stress. To connect, use the PASS displayed in the store."

[1120] Step 7:

[1121] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[1122] Step 8:

[1123] The server refers to the reliability data of the carriers and recommends the best plan for a specific area. Example: "Plans from carrier A are stable in 90% of the area of ​​London."

[1124] Step 9:

[1125] The device sends an automatic switching notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[1126] Step 10:

[1127] If the user selects "Yes," the device will automatically change the communication plan.

[1128] Step 11:

[1129] The server continuously collects new communication data and updates the analysis results. Based on the analysis results, the recommendation content is updated as appropriate and notified again. Example: "A new WiFi spot has been found. Please try Cafe C (speed 50Mbps)."

[1130] Example 2

[1131] 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."

[1132] Conventional communication plans and wireless network connection points often cannot always be optimally selected based solely on the user's schedule and past communication usage. Furthermore, since they provide a communication environment without taking the user's emotional state into consideration, they do not contribute to user satisfaction or stress reduction. Therefore, there is a need for a system that takes into account not only the user's schedule and past communication usage, but also the user's emotional state.

[1133] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1134] In this invention, the server includes means for collecting the user's planned activities and past communication usage status, means for analyzing the collected data and selecting the optimal communication plan and wireless network connection point, and means for analyzing the user's emotional state and adjusting the content of the proposal based on this. This makes it possible to provide the optimal communication environment by comprehensively analyzing the user's planned activities, past communication data, real-time location information, and emotional state.

[1135] "User's planned activities" is information about the activities and movements that the user plans to carry out in the future.

[1136] "Communication usage status" is information indicating what communication means and communication volume the user has used in the past, as well as the connection status at that time.

[1137] "Current location information" refers to the geographical location information of the user's terminal.

[1138] "Emotional state" refers to the user's psychological or emotional state, and is the result of analysis of voice and facial expression data.

[1139] A "communication plan" is a contractual provision that includes the terms of use, fees, speed, etc. of the communication service selected or proposed to the user.

[1140] A "wireless network access point" is a specific access point or hotspot used by a user to connect to the Internet.

[1141] An "emotion engine" is software or hardware that analyzes voice and facial expression data to identify a user's emotional state.

[1142] "Data analysis" is a set of processes used to analyze collected data and find patterns and relationships.

[1143] A "machine learning algorithm" is a type of artificial intelligence technology used in data analysis, which learns from past data and makes future predictions and classifications.

[1144] The "proposal content" is detailed information about the optimal communication plan and wireless network connection points that are presented to the user based on the analysis results.

[1145] "Automatically switching communication plans" refers to the system automatically changing to the most suitable communication plan after obtaining the user's permission.

[1146] This invention is a system that comprehensively analyzes a user's schedule, past communication usage, and emotional state, and then proposes and automatically switches to the optimal communication plan and wireless network connection point.The purpose of this invention is to provide a comfortable communication environment for users and reduce stress.

[1147] The system consists of a server, a terminal, and an emotion engine including a generative AI model. The specific features of each hardware and software are described below.

[1148] Hardware and software used

[1149] Server: A server with high-performance data processing capabilities, such as AWS (Amazon Web Services) or Google Cloud Platform, is used. The server applies machine learning algorithms to analyze large amounts of data in real time.

[1150] Device: A mobile device used by a user, such as a smartphone or tablet, that is equipped with GPS, a camera, and a microphone and collects data about their schedule and emotional state.

[1151] Emotion engine: A generative AI model that analyzes voice and facial expression data to identify a user's emotional state. For example, it uses existing AI engines such as Google's Cloud Vision API or IBM's Watson.

[1152] Explanation of program processing

[1153] 1. Data Collection

[1154] A user inputs a schedule into a smartphone application. For example, the user inputs "Business trip to London from October 15th to October 20th."

[1155] The device saves data on planned activities and collects past communication usage data. It also obtains real-time location information and sends this data to the server. For example, it collects information such as "Connected to Hotel A's WiFi on October 10, 2023, the speed was 20Mbps, and the disconnection frequency was 3 times."

[1156] 2. Recognizing emotional states

[1157] The device collects the user's voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. For example, it may recognize from voice data that the user is "feeling stressed."

[1158] 3. Data Analysis

[1159] The server aggregates the collected data on your planned activities, past communication data, current location, and emotional state. Based on this data, it applies machine learning algorithms to identify the optimal communication plan and wireless network connection point. For example, it might discover a pattern that "in London, Cafe B has stable Wi-Fi."

[1160] Based on the results of the analysis of the user's emotional state, the system selects the optimal communication plan and wireless network connection point to reduce the user's stress level.

[1161] 4. Proposal generation

[1162] Based on the analysis results and the user's emotional state, the server generates a recommendation for the optimal communication plan and wireless network connection point that matches the user's planned activities. For example, it may suggest that "high-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[1163] The proposal is sent to the terminal.

[1164] 5. Notices and Advice

[1165] The device then notifies the user of the suggestions received from the server. For example, it displays a message such as, "If you use the Wi-Fi at Cafe B during your stay in London, you can save on communication costs and reduce stress. To connect, use the PASS displayed inside the store."

[1166] The user reviews the suggestion and selects "Do you want to use this WiFi spot? [Yes] [No]."

[1167] 6. Automatic Switching

[1168] The server refers to the stability data of telecommunications companies in London and recommends the most suitable telecommunications company. For example, it may determine that "telecommunications company A is the best."

[1169] The device will send a notification to the user about the automatic switch. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[1170] If the user selects "Yes," the device will automatically change the communication plan.

[1171] Specific examples

[1172] The following is an example of a scenario in which the system of the present invention actually works.

[1173] 1. Data collection: The user enters "Business trip to London from October 15th to October 20th" into a smartphone app. The device stores this information and sends past communication data, such as "Connected to Hotel A's WiFi on October 10th, 2023, speed was 20Mbps, and disconnection frequency was 3 times," along with current location information to the server.

[1174] 2. Emotional state recognition: The device captures the user's voice and facial expressions and sends them to the emotion engine, which recognizes that the user is feeling stressed.

[1175] 3. Data Analysis: The server analyzes all the data and determines that Cafe B is the best WiFi spot. It also determines that "Cafe B is suitable for reducing user stress."

[1176] 4. Proposal generation: The server generates a proposal such as "It would be good to use Cafe B's WiFi on October 16th" and sends it to the device.

[1177] 5. Notification and Advice: The device will display the suggestion to the user and prompt them to choose "Do you want to use this WiFi hotspot? [Yes] [No]."

[1178] 6. Automatic switching: The server selects the optimal carrier and notifies the device. If the user approves, the device automatically changes its communication plan.

[1179] The present invention allows users to enjoy an optimal communication environment while reducing stress.

[1180] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1181] Step 1: Data collection

[1182] A user inputs their schedule into a smartphone application. Example input data: "Business trip to London from October 15th to October 20th."

[1183] The device stores the planned activity data entered by the user. It also collects past communication usage data (e.g., "Connected to Hotel A's WiFi on October 10, 2023, speed 20Mbps, disconnection frequency 3 times") and real-time location information. Location information is obtained using the GPS function.

[1184] The device sends the collected data to the server. The input is the planned activity, past communication data, and location information, and the output is a data packet containing this information.

[1185] Step 2: Recognizing your emotional state

[1186] The device collects the user's voice and facial expression data in real time. Voice data is acquired using a microphone, and facial expression data is captured using a camera.

[1187] The device sends the collected voice and facial expression data to the emotion engine. The input is voice data and facial expression data, and the output is the analysis result by the emotion engine.

[1188] The emotion engine identifies the user's emotional state through voice and facial expression analysis, for example, determining that the user is feeling stressed.

[1189] Step 3: Data analysis

[1190] The server centrally collects the action schedule, past communication data, current location information, and emotional state sent from the device. These data are the input.

[1191] The server applies machine learning algorithms to analyze the data and identify usage patterns, concluding, for example, that "in London, Cafe B has the best WiFi." The output is the optimal data plan or wireless network connection point.

[1192] Based on the analysis of the user's emotional state, the server selects the optimal communication plan and Wi-Fi hotspot to reduce the user's stress level.

[1193] Step 4: Proposal Generation

[1194] The server generates recommendations for optimal communication plans and wireless network connection points based on the user's planned activities based on the results of data analysis and the user's emotional state. For example, it generates a recommendation that "high-speed WiFi (45Mbps) is available at Cafe B on October 16th." The inputs are the analysis results and the user's emotional state, and the output is the recommendations.

[1195] The server transmits the generated proposal to the terminal.

[1196] Step 5: Inform and advise

[1197] The device notifies the user of the suggestion received from the server. For example, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The input is the suggestion, and the output is the notification to the user.

[1198] The device prompts the user to choose, "Do you want to use this WiFi hotspot? [Yes] [No]." The input is the suggestion, and the output is the user's choice.

[1199] Step 6: Automatic Switching

[1200] The server refers to the stability data of telecommunications companies in London and recommends the most suitable telecommunications company. For example, it determines that "telecommunications company A is the most suitable." The input is the telecommunications company stability data, and the output is the recommended telecommunications company.

[1201] The device sends a notification of automatic switching to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]." The input is the recommended carrier, and the output is the notification to the user.

[1202] If the user selects "Yes," the device will automatically change the communication plan. The input is the user's selection, and the output is the change in communication plan.

[1203] In this way, the system of the present invention comprehensively analyzes the user's planned activities, past communication usage, current location information, and emotional state, and provides the optimal communication environment, thereby reducing the user's stress and saving on communication charges.

[1204] (Application example 2)

[1205] 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."

[1206] Conventional communication plan proposal systems optimize plans based on the user's planned activities and past communication usage, but are unable to consider the user's emotional state or stress level. As a result, the optimal communication environment for the user may not be provided. Furthermore, food delivery apps are also unable to make proposals that take into account the user's planned activities and emotional state, making it difficult to improve user satisfaction. These issues can lead to a decline in user convenience and satisfaction.

[1207] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1208] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data and selecting an optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for recognizing the user's emotional state and adjusting the content of suggestions and communication plans based on the analysis results, and means for collecting past order history and planned activities and suggesting optimal foods and promotions. This makes it possible to provide an optimal communication environment that takes into account the user's emotional state in addition to their planned activities and past communication usage status, and also enables suggestions that improve user satisfaction in food delivery apps.

[1209] "Planned activities" refers to information about activities and plans that the user plans to carry out in the future.

[1210] "Communication usage status" refers to data on past communications made by a user, and refers to a detailed history of communications such as communication volume, communication speed, and number of disconnections.

[1211] "Communication plan" refers to the pricing plans and contract terms for internet and data communications provided by telecommunications companies.

[1212] "Wireless Network Access Point" refers to a location where you can connect to public or corporate Wi-Fi or other wireless communication networks.

[1213] "Emotional state" refers to the user's current feelings and state of mind, and includes emotional states such as stress, happiness, and fatigue.

[1214] "Suggestion content" refers to information such as recommended items, services, plans, and promotions that are generated based on the analysis results and the user's status.

[1215] "Past order history" refers to the history of orders and purchases made by the user up to now, and includes information such as the number of purchases and trends of specific products.

[1216] "Promotion" refers to advertising activities such as offering special offers, discounts, and events to users, and is a means of increasing product sales and user satisfaction.

[1217] An "emotion engine" refers to a software module that analyzes audio and image data to identify a user's emotional state.

[1218] A "machine learning algorithm" is a computational method for analyzing large amounts of data and finding statistical patterns, enabling predictions and classifications.

[1219] This system analyzes a user's schedule, past communication usage, and emotional state to propose optimal communication plans and wireless network connection points, and automatically switches communication plans. In addition, in the case of a food delivery app, it can suggest optimal foods and promotions based on past order history and schedule.

[1220] The system mainly consists of the following elements:

[1221] Server: Collects data, analyzes, generates proposals, notifies, and automatically switches.

[1222] Terminal: Collects input data from the user, recognizes emotional states in real time, communicates with the server, makes suggestions, and stores data.

[1223] User: Provides information such as planned activities, past communication usage, and order history.

[1224] The server includes means for collecting the user's planned activities and past communication usage status, means for analyzing the collected data and selecting the optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for recognizing the user's emotional state and adjusting the content of suggestions and communication plans based on the analysis results, and means for collecting the user's past order history and planned activities and suggesting optimal foods and promotions.

[1225] Hardware and software used

[1226] Hardware: Smartphone, built-in camera, built-in microphone

[1227] Software: Python, TensorFlow (emotion engine), Firebase (cloud database), Flutter (UI development)

[1228] Data processing and calculation

[1229] The server receives the user's planned activities, past communication usage, and emotional state, including voice and facial expression data. This data is analyzed using TensorFlow to identify the user's emotional state. Based on the analysis results, machine learning algorithms select the optimal communication plan and wireless network connection point. Based on past ordering history, the optimal food and promotions for food delivery are also selected. Based on this, recommendations are generated and notified to the user. The notifications are displayed on the smartphone using Flutter and stored in Firebase.

[1230] Specific examples

[1231] For example, if a user plans to go on a business trip to London from October 15th to October 20th, the server receives this information and analyzes their past communication usage and order history. In addition, it can identify whether the user is feeling stressed from voice and facial expression data. Based on this, the server can suggest the optimal communication plan and wireless network connection points at cafes, and notify the user to use Cafe B's WiFi during their stay in London. The food delivery app can also suggest a relaxing tea and pizza set to reduce the user's stress.

[1232] Prompt Sentence Examples

[1233] For example, the following prompt sentence can be input to a generative AI model:

[1234] plaintext

[1235] User ID: 12345

[1236] Planned action: Work from home at 4pm on October 12th

[1237] Past orders: 3 pizzas, 1 sushi, 2 salads

[1238] Current emotional state: Tired

[1239] Generated AI prompt:

[1240] 1. Proposing the best meal set

[1241] 2. Describe the reasons for your recommendation based on your past order history and your current emotional state

[1242] In this way, the present invention can optimize the communication environment and improve user satisfaction with food delivery by making optimal suggestions taking into account the user's planned activities and emotional state.

[1243] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1244] Step 1:

[1245] The user inputs their itinerary into the app. For example, this information might be "Business trip to London from October 15th to October 20th." The input data is saved on the device and later sent to the server. The itinerary is the input data, and the saved itinerary is generated as the output data.

[1246] Step 2:

[1247] The device collects past communication usage data and obtains real-time location information. The collected data is stored within the system and sent to the server. Specifically, it includes data such as "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times." The input data is past communication usage and current location, and the output data is generated and sent to the server.

[1248] Step 3:

[1249] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine uses TensorFlow to analyze this data and identify the user's emotional state. Specifically, emotional states such as "stress" and "fatigue" are identified from the user's voice and facial expressions. The collected voice and facial expression data are used as input data, and the analyzed emotional state is generated as output data.

[1250] Step 4:

[1251] The server analyzes the collected itinerary, past communication data, current location information, and the user's emotional state. A machine learning algorithm is then used to identify patterns, such as "In London, Cafe B has stable Wi-Fi." The input data are the itinerary, communication usage, current location, and emotional state, and the analysis results are generated as output data.

[1252] Step 5:

[1253] Based on the emotion engine's evaluation results, the server selects the optimal communication plan or wireless network connection point to reduce the user's stress level. The server evaluates options based on specific parameters and determines the most appropriate choice for the user. Specifically, it may select something like "Telecommunications Company A's plan is stable in 90% of London." The input data are the analysis results and the user's emotional state, and the output data is the optimal communication plan or connection point.

[1254] Step 6:

[1255] Based on the analysis results and the user's emotional state, the server generates recommendations for the optimal WiFi spot or data roaming plan that matches the user's schedule and sends them to the device. For example, a recommendation might be generated such as "We recommend using the WiFi at Cafe B on October 16th." The optimal communication plan or connection point is the input data, and the recommendations are generated as output data.

[1256] Step 7:

[1257] The device receives the suggestions and notifies the user. For example, a notification might say, "If you use the WiFi at Cafe B while you're in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The suggestions are input data, and a notification to the user is generated as output data.

[1258] Step 8:

[1259] The user checks the suggestion and chooses whether to use it. For example, a confirmation message such as "Do you want to use this WiFi spot? [Yes] [No]" is displayed. The user's choice is the input data, and the user's response is generated as the output data.

[1260] Step 9:

[1261] The server references the stability data of local telecommunications companies and recommends the optimal communication plan for a specific area. For example, a proposal such as "Telecommunications Company A's plan is optimal for 90% of the London area" is generated. The input data is regional information and telecommunications company data, and the output data is the optimal communication plan.

[1262] Step 10:

[1263] The device sends a notification of automatic switching to the user. For example, a notification such as "Telecommunications company A's plan is optimal for the area around Cafe B. Would you like to perform automatic switching? [Yes] [No]" is sent. If the user selects "Yes," the device automatically changes the communication plan. The input data are the optimal communication plan and the user's selection, and the output data is generated indicating the execution of automatic switching.

[1264] 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.

[1265] 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.

[1266] 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.

[1267] [Fourth embodiment]

[1268] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1269] 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.

[1270] 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).

[1271] 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.

[1272] 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.

[1273] 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).

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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.

[1280] 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."

[1281] The present invention is a system that collects and analyzes a user's planned activities and past communication usage status, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.

[1282] Explanation of program processing

[1283] 1. Data Collection

[1284] When a user enters their planned activities into the app, the location and duration of their stay are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[1285] The device collects past communication usage data. For example, "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[1286] The device obtains the current location information in real time and sends it to the server.

[1287] 2. Data Analysis

[1288] The server analyzes the collected data, learning from users' schedules and past usage patterns, and using machine learning algorithms to identify patterns such as "In London, Cafe B has stable Wi-Fi."

[1289] By comparing the collected data with the database, the optimal communication plan and wireless network connection point are selected.

[1290] 3. Proposal generation

[1291] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[1292] The content of this proposal is transmitted to the terminal.

[1293] 4. Notice and Advice

[1294] The device receives the suggestion and notifies the user. Example: "You can save on communication charges by using the WiFi at Cafe B while you are in London. To connect, use the PASS displayed inside the store."

[1295] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[1296] 5. Automatic switching

[1297] The server refers to the stability data of local carriers and recommends the best communication plan for a specific area. Example: "Plans from carrier A are stable in 90% of areas in London."

[1298] The device will send a notification to the user about the automatic switch. For example, "Plan from carrier A is the best option for the area around cafe B. Would you like to switch automatically? [Yes] [No]"

[1299] If the user selects "Yes," the device will automatically change the communication plan.

[1300] Specific examples

[1301] 1. Data Collection

[1302] A user enters information about a business trip to London from October 15 to October 20 into the app. The device saves this information and sends past communication data and current location to the server.

[1303] 2. Data Analysis

[1304] The server analyzes past traffic data to identify the best Wi-Fi spots in a particular area of ​​London. For example, it might find that Cafe B offers a stable speed of 45Mbps.

[1305] 3. Proposal generation

[1306] Based on the user's planned activities, the server generates a suggestion such as "It would be a good idea to use Cafe B's WiFi on October 16th" and sends it to the terminal.

[1307] 4. Notice and Advice

[1308] The device notifies the user, "You can save on communication charges by using the WiFi at Cafe B while you are in London. To connect, use the PASS displayed inside the store." The user confirms the suggestion and is prompted with a confirmation message: "Do you want to use this WiFi spot? [Yes] [No]."

[1309] 5. Automatic switching

[1310] The server references the stability data of telecommunications companies in London and determines that "telecommunications company A is the best option." It then sends a notification to the device proposing an automatic switch, and if the user selects "Yes," the device automatically switches to a different communication plan.

[1311] In this way, the present invention can provide an optimal communication environment in real time and reduce the user's communication charges.

[1312] The processing flow will be explained below.

[1313] Step 1:

[1314] The user enters their planned activities into the app. The user's planned location and duration are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[1315] Step 2:

[1316] The device collects data on the user's past communication usage. This data includes the WiFi spots connected to, communication quality, data usage, etc. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[1317] Step 3:

[1318] The device acquires real-time location information and transmits the current location information to the server.

[1319] Step 4:

[1320] The server analyzes the collected itinerary, past communication data, and current location information. It uses machine learning algorithms to identify communication quality and cost patterns. For example, it identifies a pattern that Cafe B in London has stable Wi-Fi.

[1321] Step 5:

[1322] The server checks the database of public Wi-Fi spots and data roaming plans to select the optimal communication plan and wireless network connection point. For example, select Cafe B or a new Wi-Fi spot (Cafe C, speed 45Mbps).

[1323] Step 6:

[1324] The server generates a proposal based on the analysis results and sends it to the user's device. Example: A notification is generated saying, "High-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[1325] Step 7:

[1326] The device receives the suggestion from the server and notifies the user. The user confirms the suggestion and decides whether to use it or not. Example: "Do you want to use this WiFi spot? [Yes] [No]"

[1327] Step 8:

[1328] The server refers to the stability data of the telecommunications companies and recommends the best communication plan for a specific area. Example: Showing that Telecommunications Company A's plan is stable in 90% of the area of ​​London.

[1329] Step 9:

[1330] The device sends an automatic switching notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[1331] Step 10:

[1332] If the user selects "Yes," the device will automatically change the communication plan.

[1333] Step 11:

[1334] The server continuously collects new communication data and updates the analysis results. Based on the analysis results, the recommendation content is updated as appropriate and notified again. Example: "A new WiFi spot has been found. Please try Cafe C (speed 50Mbps)."

[1335] Example 1

[1336] 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."

[1337] In today's communications environment, it is difficult for users to secure an optimal communication environment in various locations. Furthermore, optimizing a communication plan requires complex manual operations, which takes time and effort. The present invention aims to solve these problems and provide a system that automatically provides users with the optimal communication environment and communication plan.

[1338] 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.

[1339] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data using a machine learning algorithm to select an optimal communication plan and wireless network connection point, and means for notifying the user of the selected communication plan and wireless network connection point, thereby enabling the user to automatically secure an optimal communication environment in real time and optimize their communication plan.

[1340] A "user" is a person who uses this system and is the entity that provides the schedule of activities and past communication usage status.

[1341] The "activity schedule" refers to information about activities that a user has planned for a specific period or location, and includes details of trips, business trips, etc.

[1342] "Communication usage status" is data showing past communication usage records, and specifically includes information such as communication speed, connection frequency, and number of disconnections.

[1343] "Data collection means" refers to methods and devices for acquiring and storing schedules and past communication usage information.

[1344] "Data analysis means" refers to processes and algorithms for analyzing collected data, and specifically includes analytical methods using machine learning algorithms.

[1345] "Communication plan" refers to various plans offered by communication service providers, including terms such as data volume, price, and service content.

[1346] "Wireless network connection point" refers to a location or service where Internet connection is possible, such as Wi-Fi or a mobile network.

[1347] "Notification means" refers to a method or device for informing users of the selected communication plan and wireless network connection point information.

[1348] "Means for automatically switching communication plans" refers to the function or process by which the system automatically changes to the most suitable communication plan after obtaining the user's permission.

[1349] The present invention is a system that collects and analyzes a user's planned activities and past communication usage status, selects the optimal communication plan and wireless network connection point, notifies the user, and automatically switches between them.

[1350] This system is mainly composed of three entities: a server, a terminal, and a user.

[1351] server:

[1352] The server collects the user's planned activities and past communication usage data. The server then analyzes the collected data using machine learning algorithms to select the optimal communication plan and wireless network connection point. The server then sends the results of these selections to the device and notifies the user.

[1353] Device:

[1354] The device saves the user's planned activities and collects past communication usage information. The device also acquires the user's current location information in real time and sends it to the server. The device notifies the user of the proposal received from the server, and if the user approves, the device automatically switches communication plans.

[1355] User:

[1356] The user opens the application and enters their planned activities, for example, "I'm going on a business trip to London from October 15th to October 20th." The device then records this information and sends it to the server.

[1357] Specifically, the invention is carried out in the following steps.

[1358] 1. Data Collection:

[1359] When a user enters their planned activities into the app, the device stores this information. At the same time, the device collects past communication usage data and transmits real-time location information to the server.

[1360] 2. Data Analysis:

[1361] The server uses machine learning algorithms to analyze the collected data. For example, it can identify patterns based on past data, such as "In London, Cafe B has stable Wi-Fi." The server then compares the data with a database to select the optimal communication plan and wireless network connection point.

[1362] 3. Proposal generation:

[1363] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi hotspots and data roaming plans that fit the user's schedule. The server then sends these recommendations to the device, which then notifies the user. For example, the recommendation might be, "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[1364] 4. Notices and Advice:

[1365] After receiving the suggestion, the device will notify the user, for example, by displaying a message such as, "You can save on data charges by using the Wi-Fi at Cafe B while you're in London. To connect, use the PASS displayed inside the store."

[1366] 5. Automatic switching:

[1367] The server references the stability data of local telecommunications companies and selects the optimal communication plan. For example, it may make a decision based on information such as "Telecommunications Company A's plan is stable in 90% of the London area." The server then sends this information to the device, which then notifies the user of the automatic switchover proposal. If the user selects "Yes," the device will automatically change the communication plan.

[1368] This system allows users to use the most suitable wireless network connection point and automatically switch to an efficient communication plan, thereby reducing communication charges and providing an optimal communication environment.

[1369] Examples:

[1370] Example prompt sentence:

[1371] A user enters into the app that he or she will be traveling to London from October 15th to October 20th.

[1372] The server analyzes the situation and suggests that "high-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[1373] The device will notify the user that "You can save on data charges by using Cafe B's WiFi while you are in London."

[1374] The user selects "Yes" and the device automatically changes the communication plan.

[1375] This invention allows users to instantly secure an optimal communication environment, enabling comfortable and economical communication use.

[1376] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1377] Step 1:

[1378] Data collection

[1379] Input: User's planned activities, past communication usage, current location information

[1380] Operation:

[1381] The user enters their planned activities into the app, such as "Business trip to London from October 15th to October 20th."

[1382] The device stores this information and collects past communication data (e.g., "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times").

[1383] The device uses GPS to obtain real-time location information.

[1384] Output: Sends action plans, past communication data, and location information to the server.

[1385] Step 2:

[1386] Data analysis

[1387] Input: Action schedule sent to the server, past communication data, location information

[1388] Operation:

[1389] The server stores the schedule, past communication data, and location information in a database for centralized management.

[1390] The server uses machine learning algorithms to analyze the collected data and identify patterns, such as "in London, Cafe B has the most reliable Wi-Fi."

[1391] The server checks the database and selects the optimal communication plan and wireless network connection point.

[1392] Output: Proposals for optimal communication plans and wireless network connection points.

[1393] Step 3:

[1394] Proposal generation

[1395] Input: Proposals for optimal communication plans and wireless network connection points based on data analysis

[1396] Operation:

[1397] Based on the analysis results, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[1398] The server sends the generated proposal to the terminal.

[1399] Output: A proposal to notify the user.

[1400] Step 4:

[1401] Notices and Advice

[1402] Input: Proposal sent from the server

[1403] Operation:

[1404] The device receives the suggestion and notifies the user. For example, "You can save on communication charges by using the WiFi at Cafe B while you're in London. To connect, use the PASS displayed inside the store."

[1405] The device will display a confirmation message to the user asking, "Do you want to use this WiFi spot? [Yes] [No]."

[1406] The user reviews the notification and selects "Yes" or "No."

[1407] Output: The user's choice ("Yes" or "No").

[1408] Step 5:

[1409] Automatic Switching

[1410] Input: User's choice ("Yes" or "No"), stability data of local carriers

[1411] Operation:

[1412] The server references the stability data of local carriers and recommends the best plan for a specific area. For example, "Plans from carrier A are stable in 90% of the London area."

[1413] The device sends the user a notification of automatic switching, displaying the message "Telecommunications company A's plan is the best for the area around Cafe B. Would you like to automatically switch? [Yes] [No]."

[1414] If the user selects "Yes," the device will automatically change the communication plan.

[1415] Output: Automatically switch to the best communication plan.

[1416] (Application example 1)

[1417] 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."

[1418] In today's food delivery services, users often face challenges due to unstable communication environments, which can hinder smooth ordering and delivery. High communication charges and the difficulty of finding optimal communication spots are also inconvenient for users. There is a need to develop a system that can resolve these issues and provide users with a comfortable and efficient food delivery experience.

[1419] 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.

[1420] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data and selecting an optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for proposing an optimal communication environment based on the user's planned activities and past order history, and means for optimizing food delivery orders using the proposed communication environment. This enables optimal food delivery orders based on the communication environment, allowing users to enjoy reduced communication charges and a smooth ordering process.

[1421] "User's planned activities" refers to the activities and schedule that the user plans to carry out in the future.

[1422] "Past communication usage" refers to historical data on how a user has previously used communications.

[1423] An "optimal communication plan" refers to a pricing plan that best suits the user's needs and situation and enables highly efficient communication.

[1424] "Wireless Network Access Point" means a specific location where Wi-Fi or other wireless communications are available.

[1425] "Means for analyzing data" refers to methods and tools for analyzing collected data and extracting useful information.

[1426] "Means for suggesting a communication environment" refers to a method or system for recommending the optimal communication environment to a user based on the analysis results.

[1427] "Means for automatically switching communication plans" refers to a function that automatically changes to the most suitable communication plan with the user's permission.

[1428] "Food delivery" refers to a service that delivers meals from restaurants and eateries to designated locations.

[1429] This invention is a system that aims to optimize the communication environment in food delivery services, selecting the optimal communication plan and wireless network connection point based on the user's planned activities and past communication usage, and proposing and automatically switching between them.

[1430] Hardware and software used

[1431] Smartphone: This is the main interface device for this system, and obtains the current location using a GPS module.

[1432] Server: The central processing center for data analysis and proposal generation. It runs machine learning algorithms using Google Cloud Machine Learning and Amazon SageMaker.

[1433] Firebase Cloud Messaging (FCM): A messaging service for sending notifications to users.

[1434] Generative AI model: Generates optimal suggestions based on the user's planned activities and communication usage status.

[1435] Data collection

[1436] The device (smartphone) stores the itinerary entered by the user, including information such as the duration of the business trip and the location of the trip. The smartphone's GPS module also obtains the user's current location in real time and sends it to the server along with past communication usage information.

[1437] Data analysis

[1438] The server analyzes the collected data and selects the optimal communication plan and wireless network connection point based on past communication usage and current location information. Machine learning algorithms are used to compare various data and identify the optimal communication environment for a specific area and time period.

[1439] Proposal Generation and Notification

[1440] Based on the results of the analysis on the server, the system generates recommendations for the optimal communication environment for the user. The generated recommendations are sent to the user's device via Firebase Cloud Messaging (FCM). For example, the notification might say, "If you order food delivery from your office between 12:00 and 1:00 PM, this WiFi spot is the best choice."

[1441] Automatic Switching

[1442] If the user confirms and approves the proposal, the device will automatically switch to the most suitable communication plan, optimizing the communication environment and ensuring smooth food delivery orders and deliveries.

[1443] Specific examples

[1444] For example, if a user inputs that they will be staying in London from October 15th to October 20th, the server will identify the most reliable Wi-Fi spot in London based on their past communication usage and current location data. Based on the analysis results, a notification will be sent to their smartphone saying, "The best Wi-Fi spot is Cafe B. It is recommended that you use it between 12:00 and 13:00."

[1445] Prompt Sentence Examples

[1446] Below are some example prompts to input to the generative AI model:

[1447] "Please suggest the most stable WiFi spot and data plan for a user staying in London from October 15th to October 20th."

[1448] This invention allows users to enjoy an optimal communication environment and ensures stable communication even when using food delivery services, which is expected to reduce communication charges and streamline ordering procedures, thereby improving user satisfaction.

[1449] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1450] Step 1: Data collection

[1451] The device (smartphone) stores the itinerary entered by the user. For example, if the user enters "Business trip to London from October 15th to October 20th," that information is saved on the device. The smartphone's GPS module is also used to obtain real-time location information. Furthermore, past communication usage information (e.g., connection speeds and frequency of disconnections at Wi-Fi hotspots used in the past) is also collected, and all this data is sent to the server.

[1452] input:

[1453] User-entered itinerary (e.g., duration of stay, location of stay)

[1454] Current location information (GPS data)

[1455] Past communication usage

[1456] output:

[1457] Composite data sent to the server (planned activities, location information, past communication data)

[1458] Step 2: Data analysis

[1459] The server analyzes data based on the device's planned activities, current location, and past network usage. It uses machine learning algorithms from Google Cloud Machine Learning and Amazon SageMaker to identify the optimal network environment for a specific area and time of day. For example, it identifies the most reliable Wi-Fi hotspot in a specific area of ​​London.

[1460] input:

[1461] Planned activities, current location information, past communication usage

[1462] output:

[1463] Optimal communication plan and wireless network connection point

[1464] Step 3: Proposal Generation

[1465] The server generates recommendations for the optimal communication environment for the user based on the results of the data analysis. Specifically, it generates recommendations such as "The WiFi at Cafe B is the best option while you're in London." These recommendations are then sent to the user's device using Firebase Cloud Messaging (FCM).

[1466] input:

[1467] Data analysis results (optimal communication plan and network connection points)

[1468] output:

[1469] Notification message (optimal communication environment suggestions)

[1470] Step 4: Notification and confirmation

[1471] The device notifies the user of the suggestion sent from the server. The notification might say, for example, "You can save on data charges by using Cafe B's Wi-Fi while you're in London. Would you like to use it? [Yes] [No]," and ask for the user's confirmation.

[1472] input:

[1473] Proposal content (information on the optimal communication environment)

[1474] output:

[1475] User notification message

[1476] User response (yes or no)

[1477] Step 5: Automatic Switching

[1478] The server then sends instructions to automatically switch to the optimal communication plan for the device if the user confirms and approves the proposal. For example, if the user selects "Yes," the device will automatically switch to the selected communication plan, allowing the user to use the optimal communication environment.

[1479] input:

[1480] User response ("Yes")

[1481] output:

[1482] Switching your device's data plan

[1483] Through these steps, users are provided with an optimal communication environment and communication stability is ensured even when using food delivery services.

[1484] 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.

[1485] The present invention is a system that recognizes a user's planned activities, past communication usage, and emotional state, and proposes and automatically switches optimal communication plans and wireless network connection points. The present invention also includes a system that combines an emotion engine, and adjusts the proposal content and communication plan based on the user's emotional state.

[1486] Explanation of program processing

[1487] 1. Data Collection

[1488] The user enters their planned activities into the app. Example: "Business trip to London from October 15th to October 20th."

[1489] The device collects the user's past communication usage data, obtains real-time location information, and sends it to the server. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[1490] 2. Recognizing emotional states

[1491] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. Example: "The user is feeling stressed."

[1492] 3. Data Analysis

[1493] The server analyzes the collected itinerary, past communication data, current location information, and the user's emotional state, and uses machine learning algorithms to identify patterns such as "In London, Cafe B has stable Wi-Fi."

[1494] Based on the evaluation results of the emotion engine, the optimal communication plan and wireless network connection point are selected to reduce the user's stress level.

[1495] 4. Proposal generation

[1496] Based on the analysis results and the user's emotional state, the server generates recommendations for the optimal Wi-Fi spots and data roaming plans that fit the user's schedule, and sends them to the device. Example: "High-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[1497] 5. Notices and Advice

[1498] The device receives the suggestion and notifies the user. For example, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store."

[1499] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[1500] 6. Automatic Switching

[1501] The server refers to the stability data of local carriers and recommends the best communication plan for a specific area. Example: "Plans from carrier A are stable in 90% of areas in London."

[1502] The device will send an automatic switch notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[1503] If the user selects "Yes," the device will automatically change the communication plan.

[1504] Specific examples

[1505] 1. Data Collection

[1506] A user enters information about a business trip to London from October 15 to October 20 into the app. The device saves this information and sends past communication data and current location to the server.

[1507] 2. Recognizing emotional states

[1508] The device collects the user's voice and facial expression data in real time and sends it to the emotion engine, which analyzes it and determines that the user is experiencing stress.

[1509] 3. Data Analysis

[1510] The server analyzes past traffic data to identify the best Wi-Fi spots in a specific area of ​​London. For example, Cafe B may provide a stable speed of 45Mbps.

[1511] Based on the evaluation results of the emotion engine, Cafe B is selected to reduce the user's stress level.

[1512] 4. Proposal generation

[1513] Based on the user's planned activities and emotional state, the server generates a suggestion such as "Use Cafe B's WiFi on October 16th" and sends it to the terminal.

[1514] 5. Notices and Advice

[1515] The device notifies the user, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The user confirms the suggestion and is prompted with a confirmation message: "Do you want to use this WiFi spot? [Yes] [No]."

[1516] 6. Automatic Switching

[1517] The server references the stability data of telecommunications companies in London and determines that "telecommunications company A is the best option." It then sends a notification to the device proposing an automatic switch, and if the user selects "Yes," the device automatically switches to a different communication plan.

[1518] In this way, the system of the present invention provides an optimal communication environment in real time based on the user's emotional state, reducing the user's communication charges and alleviating stress.

[1519] The processing flow will be explained below.

[1520] Step 1:

[1521] The user enters their planned activities into the app. The user's planned location and duration are recorded on the device. Example: "Business trip to London from October 15th to October 20th."

[1522] Step 2:

[1523] The device collects the user's past communication usage data, obtains real-time location information, and sends it to the server. The collected data includes information on previously connected WiFi spots, their communication quality, and data usage. Example: "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times."

[1524] Step 3:

[1525] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. Example: "The user is feeling stressed."

[1526] Step 4:

[1527] The server analyzes the collected schedule, past communication data, current location information, and the user's emotional state. It uses a machine learning algorithm to identify patterns such as "In London, Cafe B has stable Wi-Fi." Based on the evaluation results of the emotion engine, it selects the optimal communication plan and wireless network connection point to reduce the user's stress level.

[1528] Step 5:

[1529] The server generates suggestions based on the analysis results and the user's emotional state and sends them to the user's device. Example: "High-speed WiFi (45Mbps) is available at Cafe B on October 16th."

[1530] Step 6:

[1531] The device receives the suggestion and notifies the user. The notification includes details of the suggested WiFi spot and communication plan, as well as how to connect. For example, "Using the WiFi at Cafe B while you're in London will save you money and reduce stress. To connect, use the PASS displayed in the store."

[1532] Step 7:

[1533] The user checks the suggestion and chooses whether to use it. Example: "Do you want to use this Wi-Fi hotspot? [Yes] [No]"

[1534] Step 8:

[1535] The server refers to the reliability data of the carriers and recommends the best plan for a specific area. Example: "Plans from carrier A are stable in 90% of the area of ​​London."

[1536] Step 9:

[1537] The device sends an automatic switching notification to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[1538] Step 10:

[1539] If the user selects "Yes," the device will automatically change the communication plan.

[1540] Step 11:

[1541] The server continuously collects new communication data and updates the analysis results. Based on the analysis results, the recommendation content is updated as appropriate and notified again. Example: "A new WiFi spot has been found. Please try Cafe C (speed 50Mbps)."

[1542] Example 2

[1543] 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."

[1544] Conventional communication plans and wireless network connection points often cannot always be optimally selected based solely on the user's schedule and past communication usage. Furthermore, since they provide a communication environment without taking the user's emotional state into consideration, they do not contribute to user satisfaction or stress reduction. Therefore, there is a need for a system that takes into account not only the user's schedule and past communication usage, but also the user's emotional state.

[1545] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1546] In this invention, the server includes means for collecting the user's planned activities and past communication usage status, means for analyzing the collected data and selecting the optimal communication plan and wireless network connection point, and means for analyzing the user's emotional state and adjusting the content of the proposal based on this. This makes it possible to provide the optimal communication environment by comprehensively analyzing the user's planned activities, past communication data, real-time location information, and emotional state.

[1547] "User's planned activities" is information about the activities and movements that the user plans to carry out in the future.

[1548] "Communication usage status" is information indicating what communication means and communication volume the user has used in the past, as well as the connection status at that time.

[1549] "Current location information" refers to the geographical location information of the user's terminal.

[1550] "Emotional state" refers to the user's psychological or emotional state, and is the result of analysis of voice and facial expression data.

[1551] A "communication plan" is a contractual provision that includes the terms of use, fees, speed, etc. of the communication service selected or proposed to the user.

[1552] A "wireless network access point" is a specific access point or hotspot used by a user to connect to the Internet.

[1553] An "emotion engine" is software or hardware that analyzes voice and facial expression data to identify a user's emotional state.

[1554] "Data analysis" is a set of processes used to analyze collected data and find patterns and relationships.

[1555] A "machine learning algorithm" is a type of artificial intelligence technology used in data analysis, which learns from past data and makes future predictions and classifications.

[1556] The "proposal content" is detailed information about the optimal communication plan and wireless network connection points that are presented to the user based on the analysis results.

[1557] "Automatically switching communication plans" refers to the system automatically changing to the most suitable communication plan after obtaining the user's permission.

[1558] This invention is a system that comprehensively analyzes a user's schedule, past communication usage, and emotional state, and then proposes and automatically switches to the optimal communication plan and wireless network connection point.The purpose of this invention is to provide a comfortable communication environment for users and reduce stress.

[1559] The system consists of a server, a terminal, and an emotion engine including a generative AI model. The specific features of each hardware and software are described below.

[1560] Hardware and software used

[1561] Server: A server with high-performance data processing capabilities, such as AWS (Amazon Web Services) or Google Cloud Platform, is used. The server applies machine learning algorithms to analyze large amounts of data in real time.

[1562] Device: A mobile device used by a user, such as a smartphone or tablet, that is equipped with GPS, a camera, and a microphone and collects data about their schedule and emotional state.

[1563] Emotion engine: A generative AI model that analyzes voice and facial expression data to identify a user's emotional state. For example, it uses existing AI engines such as Google's Cloud Vision API or IBM's Watson.

[1564] Explanation of program processing

[1565] 1. Data Collection

[1566] A user inputs a schedule into a smartphone application. For example, the user inputs "Business trip to London from October 15th to October 20th."

[1567] The device saves data on planned activities and collects past communication usage data. It also obtains real-time location information and sends this data to the server. For example, it collects information such as "Connected to Hotel A's WiFi on October 10, 2023, the speed was 20Mbps, and the disconnection frequency was 3 times."

[1568] 2. Recognizing emotional states

[1569] The device collects the user's voice and facial expression data in real time and sends it to the emotion engine. The emotion engine analyzes this data and identifies the user's emotional state. For example, it may recognize from voice data that the user is "feeling stressed."

[1570] 3. Data Analysis

[1571] The server aggregates the collected data on your planned activities, past communication data, current location, and emotional state. Based on this data, it applies machine learning algorithms to identify the optimal communication plan and wireless network connection point. For example, it might discover a pattern that "in London, Cafe B has stable Wi-Fi."

[1572] Based on the results of the analysis of the user's emotional state, the system selects the optimal communication plan and wireless network connection point to reduce the user's stress level.

[1573] 4. Proposal generation

[1574] Based on the analysis results and the user's emotional state, the server generates a recommendation for the optimal communication plan and wireless network connection point that matches the user's planned activities. For example, it may suggest that "high-speed Wi-Fi (45Mbps) is available at Cafe B on October 16th."

[1575] The proposal is sent to the terminal.

[1576] 5. Notices and Advice

[1577] The device then notifies the user of the suggestions received from the server. For example, it displays a message such as, "If you use the Wi-Fi at Cafe B during your stay in London, you can save on communication costs and reduce stress. To connect, use the PASS displayed inside the store."

[1578] The user reviews the suggestion and selects "Do you want to use this WiFi spot? [Yes] [No]."

[1579] 6. Automatic Switching

[1580] The server refers to the stability data of telecommunications companies in London and recommends the most suitable telecommunications company. For example, it may determine that "telecommunications company A is the best."

[1581] The device will send a notification to the user about the automatic switch. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]"

[1582] If the user selects "Yes," the device will automatically change the communication plan.

[1583] Specific examples

[1584] The following is an example of a scenario in which the system of the present invention actually works.

[1585] 1. Data collection: The user enters "Business trip to London from October 15th to October 20th" into a smartphone app. The device stores this information and sends past communication data, such as "Connected to Hotel A's WiFi on October 10th, 2023, speed was 20Mbps, and disconnection frequency was 3 times," along with current location information to the server.

[1586] 2. Emotional state recognition: The device captures the user's voice and facial expressions and sends them to the emotion engine, which recognizes that the user is feeling stressed.

[1587] 3. Data Analysis: The server analyzes all the data and determines that Cafe B is the best WiFi spot. It also determines that "Cafe B is suitable for reducing user stress."

[1588] 4. Proposal generation: The server generates a proposal such as "It would be good to use Cafe B's WiFi on October 16th" and sends it to the device.

[1589] 5. Notification and Advice: The device will display the suggestion to the user and prompt them to choose "Do you want to use this WiFi hotspot? [Yes] [No]."

[1590] 6. Automatic switching: The server selects the optimal carrier and notifies the device. If the user approves, the device automatically changes its communication plan.

[1591] The present invention allows users to enjoy an optimal communication environment while reducing stress.

[1592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1593] Step 1: Data collection

[1594] A user inputs their schedule into a smartphone application. Example input data: "Business trip to London from October 15th to October 20th."

[1595] The device stores the planned activity data entered by the user. It also collects past communication usage data (e.g., "Connected to Hotel A's WiFi on October 10, 2023, speed 20Mbps, disconnection frequency 3 times") and real-time location information. Location information is obtained using the GPS function.

[1596] The device sends the collected data to the server. The input is the planned activity, past communication data, and location information, and the output is a data packet containing this information.

[1597] Step 2: Recognizing your emotional state

[1598] The device collects the user's voice and facial expression data in real time. Voice data is acquired using a microphone, and facial expression data is captured using a camera.

[1599] The device sends the collected voice and facial expression data to the emotion engine. The input is voice data and facial expression data, and the output is the analysis result by the emotion engine.

[1600] The emotion engine identifies the user's emotional state through voice and facial expression analysis, for example, determining that the user is feeling stressed.

[1601] Step 3: Data analysis

[1602] The server centrally collects the action schedule, past communication data, current location information, and emotional state sent from the device. These data are the input.

[1603] The server applies machine learning algorithms to analyze the data and identify usage patterns, concluding, for example, that "in London, Cafe B has the best WiFi." The output is the optimal data plan or wireless network connection point.

[1604] Based on the analysis of the user's emotional state, the server selects the optimal communication plan and Wi-Fi hotspot to reduce the user's stress level.

[1605] Step 4: Proposal Generation

[1606] The server generates recommendations for optimal communication plans and wireless network connection points based on the user's planned activities based on the results of data analysis and the user's emotional state. For example, it generates a recommendation that "high-speed WiFi (45Mbps) is available at Cafe B on October 16th." The inputs are the analysis results and the user's emotional state, and the output is the recommendations.

[1607] The server transmits the generated proposal to the terminal.

[1608] Step 5: Inform and advise

[1609] The device notifies the user of the suggestion received from the server. For example, "If you use the WiFi at Cafe B during your stay in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The input is the suggestion, and the output is the notification to the user.

[1610] The device prompts the user to choose, "Do you want to use this WiFi hotspot? [Yes] [No]." The input is the suggestion, and the output is the user's choice.

[1611] Step 6: Automatic Switching

[1612] The server refers to the stability data of telecommunications companies in London and recommends the most suitable telecommunications company. For example, it determines that "telecommunications company A is the most suitable." The input is the telecommunications company stability data, and the output is the recommended telecommunications company.

[1613] The device sends a notification of automatic switching to the user. For example, "Plan from carrier A is the best option for the area around cafe B. Do you want to switch automatically? [Yes] [No]." The input is the recommended carrier, and the output is the notification to the user.

[1614] If the user selects "Yes," the device will automatically change the communication plan. The input is the user's selection, and the output is the change in communication plan.

[1615] In this way, the system of the present invention comprehensively analyzes the user's planned activities, past communication usage, current location information, and emotional state, and provides the optimal communication environment, thereby reducing the user's stress and saving on communication charges.

[1616] (Application example 2)

[1617] 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."

[1618] Conventional communication plan proposal systems optimize plans based on the user's planned activities and past communication usage, but are unable to consider the user's emotional state or stress level. As a result, the optimal communication environment for the user may not be provided. Furthermore, food delivery apps are also unable to make proposals that take into account the user's planned activities and emotional state, making it difficult to improve user satisfaction. These issues can lead to a decline in user convenience and satisfaction.

[1619] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1620] In this invention, the server includes means for collecting a user's planned activities and past communication usage status, means for analyzing the collected data and selecting an optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for recognizing the user's emotional state and adjusting the content of suggestions and communication plans based on the analysis results, and means for collecting past order history and planned activities and suggesting optimal foods and promotions. This makes it possible to provide an optimal communication environment that takes into account the user's emotional state in addition to their planned activities and past communication usage status, and also enables suggestions that improve user satisfaction in food delivery apps.

[1621] "Planned activities" refers to information about activities and plans that the user plans to carry out in the future.

[1622] "Communication usage status" refers to data on past communications made by a user, and refers to a detailed history of communications such as communication volume, communication speed, and number of disconnections.

[1623] "Communication plan" refers to the pricing plans and contract terms for internet and data communications provided by telecommunications companies.

[1624] "Wireless Network Access Point" refers to a location where you can connect to public or corporate Wi-Fi or other wireless communication networks.

[1625] "Emotional state" refers to the user's current feelings and state of mind, and includes emotional states such as stress, happiness, and fatigue.

[1626] "Suggestion content" refers to information such as recommended items, services, plans, and promotions that are generated based on the analysis results and the user's status.

[1627] "Past order history" refers to the history of orders and purchases made by the user up to now, and includes information such as the number of purchases and trends of specific products.

[1628] "Promotion" refers to advertising activities such as offering special offers, discounts, and events to users, and is a means of increasing product sales and user satisfaction.

[1629] An "emotion engine" refers to a software module that analyzes audio and image data to identify a user's emotional state.

[1630] A "machine learning algorithm" is a computational method for analyzing large amounts of data and finding statistical patterns, enabling predictions and classifications.

[1631] This system analyzes a user's schedule, past communication usage, and emotional state to propose optimal communication plans and wireless network connection points, and automatically switches communication plans. In addition, in the case of a food delivery app, it can suggest optimal foods and promotions based on past order history and schedule.

[1632] The system mainly consists of the following elements:

[1633] Server: Collects data, analyzes, generates proposals, notifies, and automatically switches.

[1634] Terminal: Collects input data from the user, recognizes emotional states in real time, communicates with the server, makes suggestions, and stores data.

[1635] User: Provides information such as planned activities, past communication usage, and order history.

[1636] The server includes means for collecting the user's planned activities and past communication usage status, means for analyzing the collected data and selecting the optimal communication plan and wireless network connection point, means for notifying the user of the selected communication plan and wireless network connection point, means for automatically switching communication plans with the user's permission, means for recognizing the user's emotional state and adjusting the content of suggestions and communication plans based on the analysis results, and means for collecting the user's past order history and planned activities and suggesting optimal foods and promotions.

[1637] Hardware and software used

[1638] Hardware: Smartphone, built-in camera, built-in microphone

[1639] Software: Python, TensorFlow (emotion engine), Firebase (cloud database), Flutter (UI development)

[1640] Data processing and calculation

[1641] The server receives the user's planned activities, past communication usage, and emotional state, including voice and facial expression data. This data is analyzed using TensorFlow to identify the user's emotional state. Based on the analysis results, machine learning algorithms select the optimal communication plan and wireless network connection point. Based on past ordering history, the optimal food and promotions for food delivery are also selected. Based on this, recommendations are generated and notified to the user. The notifications are displayed on the smartphone using Flutter and stored in Firebase.

[1642] Specific examples

[1643] For example, if a user plans to go on a business trip to London from October 15th to October 20th, the server receives this information and analyzes their past communication usage and order history. In addition, it can identify whether the user is feeling stressed from voice and facial expression data. Based on this, the server can suggest the optimal communication plan and wireless network connection points at cafes, and notify the user to use Cafe B's WiFi during their stay in London. The food delivery app can also suggest a relaxing tea and pizza set to reduce the user's stress.

[1644] Prompt Sentence Examples

[1645] For example, the following prompt sentence can be input to a generative AI model:

[1646] plaintext

[1647] User ID: 12345

[1648] Planned action: Work from home at 4pm on October 12th

[1649] Past orders: 3 pizzas, 1 sushi, 2 salads

[1650] Current emotional state: Tired

[1651] Generated AI prompt:

[1652] 1. Proposing the best meal set

[1653] 2. Describe the reasons for your recommendation based on your past order history and your current emotional state

[1654] In this way, the present invention can optimize the communication environment and improve user satisfaction with food delivery by making optimal suggestions taking into account the user's planned activities and emotional state.

[1655] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1656] Step 1:

[1657] The user inputs their itinerary into the app. For example, this information might be "Business trip to London from October 15th to October 20th." The input data is saved on the device and later sent to the server. The itinerary is the input data, and the saved itinerary is generated as the output data.

[1658] Step 2:

[1659] The device collects past communication usage data and obtains real-time location information. The collected data is stored within the system and sent to the server. Specifically, it includes data such as "October 10, 2023, Hotel A WiFi connection, speed 20Mbps, disconnection frequency 3 times." The input data is past communication usage and current location, and the output data is generated and sent to the server.

[1660] Step 3:

[1661] The device collects voice and facial expression data in real time and sends it to the emotion engine. The emotion engine uses TensorFlow to analyze this data and identify the user's emotional state. Specifically, emotional states such as "stress" and "fatigue" are identified from the user's voice and facial expressions. The collected voice and facial expression data are used as input data, and the analyzed emotional state is generated as output data.

[1662] Step 4:

[1663] The server analyzes the collected itinerary, past communication data, current location information, and the user's emotional state. A machine learning algorithm is then used to identify patterns, such as "In London, Cafe B has stable Wi-Fi." The input data are the itinerary, communication usage, current location, and emotional state, and the analysis results are generated as output data.

[1664] Step 5:

[1665] Based on the emotion engine's evaluation results, the server selects the optimal communication plan or wireless network connection point to reduce the user's stress level. The server evaluates options based on specific parameters and determines the most appropriate choice for the user. Specifically, it may select something like "Telecommunications Company A's plan is stable in 90% of London." The input data are the analysis results and the user's emotional state, and the output data is the optimal communication plan or connection point.

[1666] Step 6:

[1667] Based on the analysis results and the user's emotional state, the server generates recommendations for the optimal WiFi spot or data roaming plan that matches the user's schedule and sends them to the device. For example, a recommendation might be generated such as "We recommend using the WiFi at Cafe B on October 16th." The optimal communication plan or connection point is the input data, and the recommendations are generated as output data.

[1668] Step 7:

[1669] The device receives the suggestions and notifies the user. For example, a notification might say, "If you use the WiFi at Cafe B while you're in London, you can save on communication charges and reduce stress. To connect, use the PASS displayed inside the store." The suggestions are input data, and a notification to the user is generated as output data.

[1670] Step 8:

[1671] The user checks the suggestion and chooses whether to use it. For example, a confirmation message such as "Do you want to use this WiFi spot? [Yes] [No]" is displayed. The user's choice is the input data, and the user's response is generated as the output data.

[1672] Step 9:

[1673] The server references the stability data of local telecommunications companies and recommends the optimal communication plan for a specific area. For example, a proposal such as "Telecommunications Company A's plan is optimal for 90% of the London area" is generated. The input data is regional information and telecommunications company data, and the output data is the optimal communication plan.

[1674] Step 10:

[1675] The device sends a notification of automatic switching to the user. For example, a notification such as "Telecommunications company A's plan is optimal for the area around Cafe B. Would you like to perform automatic switching? [Yes] [No]" is sent. If the user selects "Yes," the device automatically changes the communication plan. The input data are the optimal communication plan and the user's selection, and the output data is generated indicating the execution of automatic switching.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1681] 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.

[1682] 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).

[1683] 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.

[1684] 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."

[1685] 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.

[1686] 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).

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] The following is further disclosed regarding the above embodiment.

[1698] (Claim 1)

[1699] A means of collecting users' schedules and past communication usage status;

[1700] A means of analyzing the collected data and selecting the optimal communication plan and wireless network connection point,

[1701] A means for notifying the user of the selected communication plan and wireless network connection point;

[1702] A means to automatically switch communication plans with user permission;

[1703] A system including:

[1704] (Claim 2)

[1705] A means for inputting and saving a user's itinerary;

[1706] A means for obtaining current location information;

[1707] 2. The system of claim 1, further comprising: said means for collecting said planned activity and current location information.

[1708] (Claim 3)

[1709] A means of analyzing communication quality and data usage using machine learning algorithms,

[1710] 2. The system according to claim 1, further comprising means for proposing an optimal communication plan and wireless network connection points based on the analysis results.

[1711] "Example 1"

[1712] (Claim 1)

[1713] A means of collecting users' schedules and past communication usage status;

[1714] A means of analyzing the collected data and using machine learning algorithms to select the optimal communication plan and wireless network connection point;

[1715] A means for notifying the user of the selected communication plan and wireless network connection point;

[1716] A means to automatically switch communication plans with user permission;

[1717] A system including:

[1718] (Claim 2)

[1719] A means for inputting and saving a user's itinerary;

[1720] A means for obtaining current location information;

[1721] A means for collecting past communication data and current location information and transmitting the collected data to a server;

[1722] 2. The system of claim 1, further comprising: said means for collecting said planned activity and current location information.

[1723] (Claim 3)

[1724] A means of analyzing communication quality and data usage using machine learning algorithms,

[1725] A method to propose optimal communication plans and wireless network connection points based on the analysis results and notify users.

[1726] A means for the user to check the notified proposal content and select whether or not to use it;

[1727] 10. The system of claim 1, comprising:

[1728] "Application Example 1"

[1729] (Claim 1)

[1730] A means of collecting users' schedules and past communication usage status;

[1731] A means of analyzing the collected data and selecting the optimal communication plan and wireless network connection point,

[1732] A means for notifying the user of the selected communication plan and wireless network connection point;

[1733] A means to automatically switch communication plans with user permission;

[1734] A means to propose the optimal communication environment based on the user's planned activities and past order history;

[1735] A means for optimizing food delivery orders utilizing the proposed communications environment; and

[1736] A system including:

[1737] (Claim 2)

[1738] A means for inputting and saving a user's itinerary;

[1739] A means for obtaining current location information;

[1740] means for collecting the information on the action plan and current location;

[1741] A means for performing data analysis to provide an optimal communication environment to a user;

[1742] 2. The system of claim 1, further comprising: said means for collecting said planned activity and current location information.

[1743] (Claim 3)

[1744] A means of analyzing communication quality and data usage using machine learning algorithms,

[1745] A method to propose optimal communication plans and wireless network connection points based on the analysis results,

[1746] The system according to claim 1, further comprising a means for improving the efficiency of food delivery based on the analysis results.

[1747] "Example 2: Combining Emotion Engines"

[1748] (Claim 1)

[1749] A means of collecting users' schedules and past communication usage status;

[1750] A means of analyzing the collected data and selecting the optimal communication plan and wireless network connection point,

[1751] A means for notifying the user of the selected communication plan and wireless network connection point;

[1752] A means to automatically switch communication plans with user permission;

[1753] a means for collecting voice and facial expression data and identifying emotional states;

[1754] a means for adjusting the suggestions based on the emotional state;

[1755] A system including:

[1756] (Claim 2)

[1757] A means for inputting and saving a user's itinerary;

[1758] A means for obtaining current location information;

[1759] a means of collecting voice and facial expression data;

[1760] 2. The system according to claim 1, further comprising the means for collecting the action schedule, current location information, and voice and facial expression data.

[1761] (Claim 3)

[1762] A means of analyzing communication quality and data usage using machine learning algorithms,

[1763] A method to propose optimal communication plans and wireless network connection points based on the analysis results,

[1764] 10. The system of claim 1, further comprising means for adjusting communication plans and wireless network connection points based on emotional state.

[1765] "Application example 2 when combining emotion engines"

[1766] (Claim 1)

[1767] A means of collecting users' schedules and past communication usage status;

[1768] A means of analyzing the collected data and selecting the optimal communication plan and wireless network connection point,

[1769] Notify users of the selected communication plan and wireless network connection point,

[1770] A means to automatically switch communication plans with user permission;

[1771] A means of recognizing the user's emotional state and adjusting the content of proposals and communication plans based on the analysis results;

[1772] A means of collecting past order history and planned activities to suggest the most suitable foods and promotions;

[1773] A system including:

[1774] (Claim 2)

[1775] A means for inputting and saving a user's itinerary;

[1776] A means for obtaining current location information;

[1777] The means for collecting the action schedule and current location information is provided.

[1778] 10. The system of claim 1.

[1779] (Claim 3)

[1780] A means of analyzing communication quality and data usage using machine learning algorithms,

[1781] A method to propose optimal communication plans and wireless network connection points based on the analysis results,

[1782] The system is equipped with an emotion engine that analyzes the emotional state and a means for suggesting optimal foods and promotions based on past order history.

[1783] 10. The system of claim 1. [Explanation of symbols]

[1784] 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 of collecting users' schedules and past communication usage status; A means of analyzing the collected data and selecting the optimal communication plan and wireless network connection point, A means for notifying the user of the selected communication plan and wireless network connection point; A means to automatically switch communication plans with user permission; A system including:

2. A means for inputting and saving a user's itinerary; A means for obtaining current location information; 2. The system of claim 1, further comprising: said means for collecting said planned activities and current location information.

3. A means of analyzing communication quality and data usage using machine learning algorithms, 2. The system according to claim 1, further comprising means for proposing an optimum communication plan and wireless network connection points based on the analysis results.

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