System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring schedule information of a user; means for analyzing the acquired schedule information to specify a schedule type and a date and time; means for selecting an optimal insurance plan based on the specified schedule; means for notifying the user of the selected insurance plan; means for applying an insurance plan approved by the user to an insurance contract; means for collecting location information and behavior data of the user; and means for analyzing the collected behavior data to specify a situation with a high loss risk.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] This invention relates to a system that prevents users from forgetting to take out insurance due to unexpected plans or business trips, and provides the optimal insurance plan at the optimal time. Specifically, the objective is to provide a system that reduces the user's risk of loss and provides a sense of security by acquiring and analyzing schedule information, selecting and notifying the optimal insurance plan, and collecting and analyzing location information and behavioral data. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means. A system is provided that includes a means for acquiring a user's schedule information and a means for analyzing the acquired schedule information to identify the type and date and time of the schedule. The system further includes a means for selecting an optimal insurance plan based on the identified schedule, a means for notifying the user of the selected insurance plan, and a means for applying the approved insurance plan to an insurance contract. The system also includes a means for collecting the user's location information and behavioral data, and for identifying situations with a high risk of loss by analyzing the collected behavioral data. This system provides the optimal insurance plan at the optimal time, reducing the user's risk of loss.

[0006] Specifically, it can include a means for creatively proposing recommended insurance plans to users in places and situations where there is a high risk of loss based on the analyzed schedule information and behavioral data. Furthermore, it is possible to add a means for acquiring the user's schedule information through a calendar API and analyzing the schedule contents using a natural language processing algorithm. This provides an environment where users can concentrate on their daily lives and work with peace of mind.

[0007] "User" refers to an individual or corporation that uses this system and is the entity that provides schedule information and location information.

[0008] "Schedule information" refers to information about events or appointments that a user has entered into a calendar or schedule management tool, including the content, date, time, location, and so on.

[0009] "Analysis" refers to the process of processing the acquired schedule information and behavioral data, extracting specific information from it, and classifying it.

[0010] "Insurance Plan" refers to the types and conditions of insurance provided according to the user's needs and risks, including the contents of the insurance contract covering the risk of loss.

[0011] "Notification" refers to the means by which the server communicates information to the user, including mobile apps, emails, push notifications, etc.

[0012] "Location information" refers to data about a user's current location or past locations, including information obtained from GPS and Wi-Fi access points.

[0013] "Behavioral data" refers to information about a user's daily behavior and movement patterns, and includes location information, time, visited destinations, and the like.

[0014] "Loss risk" refers to the likelihood that a user will lose an item, and is assessed based on the frequency, location, and circumstances.

[0015] "Creative proposal" refers to the process of predicting user behavior patterns and risks based on AI and data analysis, and proposing appropriate insurance plans.

[0016] "Calendar API" refers to an application programming interface (API) for retrieving schedule information in conjunction with calendar services (e.g., Google Calendar, Outlook Calendar). [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system that provides optimal insurance plans based on a user's schedule and location information. This system works in conjunction with the user's device, server, and calendar API. Specifically, it acquires the user's schedule information, analyzes it, selects the optimal insurance plan, notifies the user, and applies it. It also collects location and behavioral data to predict risks and make creative proposals.

[0039] A natural language description of what the program does

[0040] Retrieving and parsing calendar information

[0041] 1. The user installs a calendar app on their device, enters schedule information, and configures the device to access the calendar API.

[0042] 2. The server periodically connects to the calendar API to retrieve the user's schedule information. At that time, data on the user's various schedules (meetings, business trips, private events, etc.) is retrieved via the calendar API.

[0043] 3. The server analyzes the acquired schedule information and uses a natural language processing algorithm to understand the content of the event and identify the type (e.g., business trip, meeting), date, time, and location.

[0044] Insurance plan recommendations and coverage

[0045] 4. The server selects the optimal insurance plan based on the analysis results, taking into account the user's past behavioral patterns and existing insurance contract status.

[0046] 5. The server notifies the user of the selected insurance plan. For example, a message such as "You have an unexpected business trip next Tuesday, which increases your risk of loss. We recommend this insurance plan" is sent via a mobile app or email.

[0047] 6. The user reviews the notification and approves the recommended insurance plan. After approval, the device sends the information to the server.

[0048] 7. The server receives the user's approval and updates the insurance contract with the new insurance coverage.

[0049] Location and behavioral data collection

[0050] 8. The user installs the provided Tracker app on their smartphone and sets permission to share location information.

[0051] 9. The device (smartphone) periodically collects location data and other behavioral information and sends it to a server, including GPS data, Bluetooth device detection, Wi-Fi login information, etc.

[0052] 10. The server analyzes the collected behavioral data and applies learning algorithms to identify locations and situations where the user is at high risk of loss.

[0053] Creative suggestions and support

[0054] 11. Based on the learning results and real-time behavioral data, the server predicts locations and situations with a high risk of loss and suggests advance notifications and insurance plans to the user. For example, a message such as, "You often spend time in cafes on your next business trip, so there is a high risk of loss there. We will apply insurance for this location."

[0055] 12. Once the user approves the proposal, the server immediately applies the insurance plan and reflects it in the user's contract.

[0056] Specific examples

[0057] For example, suppose a user has an unexpected business trip scheduled for next Tuesday entered into their calendar. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user approves, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information and behavioral data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[0058] In this way, users can automatically apply the most suitable insurance plan for unexpected events and can also handle situations where the risk of loss is high. This system prevents users from forgetting to take out insurance and effectively reduces the risk of loss that may occur in their daily lives.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The server periodically sends a request to the user's calendar API to obtain the latest schedule information, and the user's schedule data is collected on the server.

[0062] Step 2:

[0063] The server then uses natural language processing (NLP) algorithms to analyze the calendar information, identifying the type of event (e.g., business trip, meeting, private event), date, time, and location.

[0064] Step 3:

[0065] The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[0066] Step 4:

[0067] The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and email. The notification includes the recommended insurance plan and the reason for the recommendation.

[0068] Step 5:

[0069] The user checks the notification from the server and decides whether to approve the recommended insurance plan. After approval, the user's device sends the information to the server.

[0070] Step 6:

[0071] The server updates the insurance contract based on the received approval information, allowing the new insurance plan to be applied in real time.

[0072] Step 7:

[0073] The user installs the provided Tracker app on their smartphone and sets up location sharing.

[0074] Step 8:

[0075] The device (smartphone) periodically collects the user's location information and behavioral data and sends it to a server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0076] Step 9:

[0077] The server analyzes the collected behavioral data using machine learning algorithms to identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[0078] Step 10:

[0079] The server predicts locations and situations with a high risk of loss, notifies the user, and proposes a specific insurance plan. For example, a notification could be sent saying, "Since you will be spending a long time at the next cafe, we will apply for insurance that covers the risk of loss there."

[0080] Step 11:

[0081] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan, and this information is also updated in real time.

[0082] In this way, the most suitable insurance plan is automatically selected and applied based on the user's schedule information and behavioral data, reducing the user's risk of loss.

[0083] Example 1

[0084] 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."

[0085] Conventional insurance systems require users to purchase insurance themselves, making it difficult to respond immediately to sudden changes in plans. Furthermore, they lack the functionality to predict risks based on users' daily activities and location information and then propose appropriate insurance plans based on those risks. This leads to problems such as users forgetting to purchase insurance or being unable to respond appropriately to high-risk situations.

[0086] 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.

[0087] In this invention, the server includes means for acquiring a user's schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means operating in real time to notify the user of the selected insurance plan, means for predicting risks based on the collected location information and behavioral data, and means for notifying the user of risks in advance and creatively proposing insurance plans. This allows the server to automatically select and notify an appropriate insurance plan based on the user's behavior and schedule, making it possible to quickly respond to sudden schedule changes and high-risk situations.

[0088] "User's schedule information" refers to schedules such as meetings, business trips, and private events that a user has entered into a calendar app or schedule management tool.

[0089] "Analysis" refers to the process of using natural language processing algorithms and learning algorithms to understand the content of the acquired user's schedule information and behavioral data, and to identify specific information (e.g., date, time, location, type).

[0090] "Insurance plan" refers to the content of the insurance contract that is applied based on the user's actions and plans, and includes, for example, short-term business trip insurance and plans covering loss risks.

[0091] "Selecting" refers to choosing the most suitable insurance plan from multiple candidates based on analyzed data.

[0092] "Notifying" refers to informing the user about the selected insurance plan via email, mobile app, etc.

[0093] "Approve" means that the user consents to the notified insurance plan and expresses his / her intention to accept its application.

[0094] "Location Information" refers to a user's current location and movement data obtained through GPS, Wi-Fi login, Bluetooth device detection, etc.

[0095] "Behavioral Data" refers to data that includes information about a user's daily activities, travel patterns, and location.

[0096] "Predicting risks" refers to predicting the risk of loss in specific situations or locations based on collected location information and behavioral data.

[0097] "Creative proposals" refers to presenting users with new insurance plan ideas and improvement methods that are not bound by conventional patterns, based on risk predictions and analysis results.

[0098] MODE FOR CARRYING OUT THE INVENTION

[0099] This invention is a system that provides optimal insurance plans based on a user's schedule information and location information. This system operates in conjunction with the user's device, a server, and a calendar API.

[0100] Specifically, a user first installs a calendar app on their device and inputs their schedule information. The server then periodically connects to the calendar API to retrieve the user's schedule information. This can be done using a widely used calendar API such as the Google Calendar API.

[0101] The server analyzes the acquired schedule information and uses natural language processing (NLP) algorithms to identify the type of event (e.g., business trip, meeting), date, time, and location. Based on this information, the server selects the most suitable insurance plan. This selection process also takes into account past behavioral data and existing insurance policy information. The server then notifies the user of the selected insurance plan via email or in-app notification.

[0102] The user then approves the recommended insurance plan through the mobile app, and once approval is complete, the device sends the information to the server, which then updates the insurance contract with the new coverage.

[0103] The system also has the ability to collect user location and behavioral data. When a user installs the Tracker app on their smartphone and sets location sharing permission, the device periodically collects location data and other behavioral information and sends it to a server. This includes GPS data, Bluetooth device detection, Wi-Fi login information, and more.

[0104] The server analyzes the collected behavioral data and applies a learning algorithm to identify locations and situations where the user is at high risk of losing their device. Based on the learning results and real-time behavioral data, it makes risk predictions and proposes advance notifications and insurance plans to the user.

[0105] As a concrete example, if a user has an unexpected business trip scheduled for next Tuesday entered in their calendar, the server will obtain and analyze that information, select an insurance plan for short-term business trips, and notify the user. If the user approves the plan, the insurance contract will be updated immediately. Similarly, for a user who spends much of their weekdays in a cafe, the server will determine from their location and behavioral data that there is a high risk of loss at the cafe, and will suggest an insurance plan that is appropriate for that time period.

[0106] An example prompt is:

[0107] "Please explain in detail how to recommend the best insurance plan for the user based on their schedule and location."

[0108] "Please explain in detail how to select the right insurance plan for users who plan to travel."

[0109] "How can I use a user's location information to predict risks in a specific location and suggest insurance plans?"

[0110] In this way, users can quickly respond to sudden changes in plans or high-risk situations and prevent forgetting to take out insurance. This system can effectively reduce risks that may occur in users' lives.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1:

[0113] A user installs a calendar app on their device and enters event information. The entered event information includes the type of event, date, time, and location. Specifically, the user enters an event such as "Business trip to Tokyo next Tuesday." This event information is saved in cloud storage via the calendar API.

[0114] Step 2:

[0115] The server periodically connects to the calendar API to retrieve the user's schedule information. The retrieved data includes the type of event, date, time, and location from the user's calendar. For example, the Google Calendar API is used to retrieve "User A's schedule" and store it in the server's database.

[0116] Step 3:

[0117] The server analyzes the schedule information it has acquired and uses a natural language processing algorithm (NLP) to understand the content of the event. The input data is the schedule content, and the output after analysis is the type, date, time, and location of the identified event. For example, from the content "Business trip in Tokyo next Tuesday," it extracts "Business trip," "October 10, 2023," and "Tokyo."

[0118] Step 4:

[0119] The server selects the optimal insurance plan based on the analysis results. This selection also takes into account the user's past behavioral data and existing insurance contract status. The input data is the identified event information and the user's insurance history, and the output data is the recommended insurance plan. For example, a "short-term insurance plan tailored to business trips" may be selected.

[0120] Step 5:

[0121] The server notifies the user of the selected insurance plan. The notification is sent via a mobile app or email. The input data is the selected insurance plan, and the output data is the notification message sent to the user. Specifically, the message sent reads, "You have an unexpected business trip next Tuesday, which increases the risk of loss. We recommend this insurance plan."

[0122] Step 6:

[0123] The user reviews the notification and approves the recommended insurance plan by pressing a button in the mobile app. The input is the notification message, and the output is the user's approval data.

[0124] Step 7:

[0125] The terminal sends the user's authorization information to the server, where the input data is the user's authorization data and the output data is the server's updated insurance policy information.

[0126] Step 8:

[0127] The server updates the insurance contract with the new insurance details upon receiving the user's approval. The input data is the approval information, and the output data is the updated insurance contract details.

[0128] Step 9:

[0129] A user installs the Tracker app on their smartphone and sets location sharing permission. The input data is the location sharing setting, and the output data is the location sharing permission status.

[0130] Step 10:

[0131] The device periodically collects location data and behavioral information and sends it to a server. The input data is GPS data, Bluetooth device detection, Wi-Fi login information, etc., and the output data is the behavioral data sent to the server.

[0132] Step 11:

[0133] The server analyzes the collected behavioral data and applies a learning algorithm to identify locations and situations where the user is at high risk of losing their belongings. The input data is behavioral data, and the output data is the identified risk information. For example, it identifies a pattern such as "often spending time at cafes during the day on weekdays."

[0134] Step 12:

[0135] The server predicts risks based on the learning results and real-time behavioral data, and then notifies the user and suggests insurance plans. The input data is the identified risk information, and the output data is the notification message sent to the user. For example, a notification may be sent stating, "Since you often spend time in cafes at your next business trip destination, there is a high risk of loss at that location. We will apply insurance for this location."

[0136] Step 13:

[0137] The user approves the proposal, and the server immediately applies the insurance plan and reflects it in the user's contract. The input data is the user's approval data, and the output data is the updated insurance plan and contract information.

[0138] (Application example 1)

[0139] 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."

[0140] In modern brick-and-mortar stores, the increased risk of theft and loss when purchasing expensive items is a serious problem. Conventional insurance systems assume fixed conditions and situations and are unable to propose optimal insurance plans that reflect the user's behavior and location information in real time. This poses a challenge, making it difficult to quickly apply an appropriate insurance plan when a user purchases a product.

[0141] 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.

[0142] In this invention, the server includes means for scanning a product code when a user selects a product in a physical store, means for analyzing the product code and assessing the risk associated with the product, and means for proposing an optimal insurance plan in real time based on the assessed risk. This makes it possible to perform risk assessment on the spot when a user purchases a product in a physical store, and to immediately propose and apply an optimal insurance plan.

[0143] "User" refers to a person who uses this system.

[0144] "Schedule information" refers to data on events and schedules that are set in advance by the user.

[0145] "Means of acquisition" refers to the server's ability to gather information from calendar APIs and other data sources.

[0146] "Means of analysis" refers to the function of processing the acquired data and identifying details such as category, time, and location.

[0147] "Means for identifying" refers to the function of extracting specific information from the analyzed data and clarifying its content.

[0148] "Insurance Plan" means a contract that provides insurance coverage against a specific risk.

[0149] "Means of selection" refers to the function by which the server selects the most suitable insurance plan based on the analyzed data.

[0150] "Means for notifying" refers to a function for informing the user of the selected insurance plan.

[0151] "Means of application" refers to the function of officially reflecting the insurance plan approved by the user in the contract.

[0152] "Location information" refers to data indicating a user's current location and movement history.

[0153] "Behavioral data" refers to data that indicates a user's past behavioral patterns and history.

[0154] "Means of collection" refers to functions for collecting location information and behavioral data.

[0155] "Product code" refers to the identification information of a product sold in a physical store.

[0156] "Means of scanning" refers to the ability to read product codes using smart glasses, smartphones, etc.

[0157] "Risks relating to products" refers to the possible losses or dangers associated with a particular product.

[0158] "Means for assessing risk" refers to the function of analyzing the risks associated with a product based on collected data.

[0159] "Real-time recommendations" refers to the ability to instantly show users insurance plans based on current information.

[0160] MODE FOR CARRYING OUT THE INVENTION

[0161] The embodiment of this invention is a system that provides optimal insurance plans in real time based on a user's schedule information and location information. This system operates in cooperation with the user's terminal, a server, and a calendar API. The details of the system are described below.

[0162] System Configuration

[0163] The system requires the user's smart glasses or smartphone and a server as the main hardware. The software includes a calendar API, location API, and insurance plan API. The system collects and analyzes data related to the user's schedule, location, and product code to propose the optimal insurance plan.

[0164] Data collection and analysis

[0165] The server obtains the user's schedule information through the calendar API. This schedule information is then analyzed using a natural language processing algorithm. The analyzed schedule information is then categorized into detailed data such as the schedule type, date, and location. Location and behavior data are also collected through the user's smart glasses or smartphone.

[0166] Based on the analysis data, the server tracks users' behavior in physical stores by scanning product codes. When a user scans an item, the risk associated with that item is instantly assessed and corresponding insurance plans are proposed in real time.

[0167] Insurance plan proposal and application

[0168] The server selects the optimal insurance plan based on the analysis results and notifies the user. If the user approves the proposed insurance plan, the information is sent to the server and the insurance contract is updated. This allows the user to immediately apply the optimal insurance plan for the risks of the products purchased in the physical store.

[0169] Specific examples

[0170] For example, if a user is about to purchase an expensive camera in a brick-and-mortar store, they can scan the product code on the camera with smart glasses or a smartphone. The system will then immediately assess the theft risk of the product and send a notification saying, "This camera has a high theft risk, so we recommend a dedicated theft insurance plan." If the user approves the insurance plan, the plan will be applied as a new insurance policy.

[0171] Prompt Sentence Examples

[0172] The following prompts can be used to instruct the generative AI model on how the system should behave:

[0173] Develop an application that predicts the items a user is likely to purchase that day based on their location and calendar information, assesses the risks associated with those items, and displays appropriate insurance plans in real time. For example, if a user is planning to purchase an expensive camera, implement a function that evaluates the risk of theft and suggests corresponding insurance plans by scanning the camera's product code.

[0174] In this way, the present invention can quickly assess the risks a user faces while shopping in a physical store and provide the most appropriate insurance plan, thereby improving the user's peace of mind and convenience.

[0175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0176] Step 1:

[0177] A user installs a calendar app on their device (smart glasses or smartphone) and inputs schedule information. At this time, the device is set up to be able to access the calendar API.

[0178] Input: Schedule information entered by the user

[0179] Output: Calendar information for which access to the Calendar API has been granted

[0180] Specific operation: The user enters event information into the calendar app and sets up synchronization.

[0181] Step 2:

[0182] The server periodically connects to the calendar API to obtain the user's schedule information. The calendar API obtains data on the user's various schedules (meetings, business trips, private events, etc.).

[0183] Input: Schedule information obtained from the calendar API

[0184] Output: Raw schedule information data

[0185] Specific operation: The server sends an API request and receives schedule information in JSON format.

[0186] Step 3:

[0187] The server analyzes the acquired schedule information and uses natural language processing algorithms to understand the contents of the event, specifically identifying the type of schedule, date, time, and location.

[0188] Input: Raw schedule information data

[0189] Output: Parsed schedule information (e.g., business trips, meetings, etc.)

[0190] Specific operation: The server applies natural language processing algorithms to perform text analysis.

[0191] Step 4:

[0192] The server selects the optimal insurance plan based on the analysis results, taking into account the user's past behavioral patterns and existing insurance contract status.

[0193] Input: Analyzed schedule information, past behavior patterns, existing insurance contract status

[0194] Output: Optimal insurance plan

[0195] Specific operation: The server performs multivariate analysis, comparing multiple factors to determine the optimal insurance plan.

[0196] Step 5:

[0197] The server notifies the user of the selected insurance plan, for example, via a mobile app or email.

[0198] Input: Best insurance plan information

[0199] Output: A message to inform the user

[0200] Specific operation: The server sends a push notification to the user's device.

[0201] Step 6:

[0202] The user checks the notification and approves the recommended insurance plan. The approved insurance plan information is sent from the device to the server.

[0203] Input: User authorization information

[0204] Output: Insurance plan approval notice

[0205] Specific behavior: The user taps the application notification and clicks the approve button.

[0206] Step 7:

[0207] The server updates the insurance contract with the user's approval and applies the new insurance details.

[0208] Input: User approval notification

[0209] Output: Updated insurance contract information

[0210] Specific operation: The server updates the contract database.

[0211] Step 8:

[0212] The user installs the provided Tracker app on their smartphone and sets permission to share location information.

[0213] Input: User's location sharing permission

[0214] Output: Location sharing enabled

[0215] Specific behavior: The user enables location sharing in the app's settings screen.

[0216] Step 9:

[0217] The device (smartphone) periodically collects location data and other behavioral information and sends it to a server.

[0218] Input: location data, behavioral information

[0219] Output: Location data and behavioral information sent to the server

[0220] Specific operation: The smartphone periodically acquires GPS data and sends it to the server.

[0221] Step 10:

[0222] The server analyzes the collected behavioral data and applies learning algorithms to identify locations and situations where users are at high risk of loss.

[0223] Input: location data, behavioral information

[0224] Output: Identification of locations and situations with high risk of loss

[0225] Specific operation: The server performs data analysis using machine learning algorithms.

[0226] Step 11:

[0227] Based on the learning results and real-time behavioral data, the server predicts locations and situations with a high risk of loss, and provides advance notification and offers insurance plans to users.

[0228] Input: Learning results, real-time behavioral data

[0229] Output: Notification of recommended insurance plan

[0230] Specific operation: The server sends a push notification to the user based on the risk assessment result.

[0231] Step 12:

[0232] If the user approves the proposal, the server immediately applies the insurance plan and reflects it in the user's contract.

[0233] Input: User approval notification

[0234] Output: Insurance plan applied

[0235] Specific operation: The server updates the contract database again.

[0236] 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.

[0237] This invention is a system that integrates a user's schedule information, location information, and emotional data to provide the optimal insurance plan. This system operates in conjunction with the user's device, server, emotion engine, and calendar API. Specifically, it acquires the user's schedule information, analyzes it, and selects, notifies, and applies the optimal insurance plan. It also collects location information and emotional data to make risk predictions and creative proposals.

[0238] A natural language description of what the program does

[0239] Retrieving and parsing calendar information

[0240] 1. The user installs a calendar app on their device, enters schedule information, and configures the device to access the calendar API.

[0241] 2. The server periodically connects to the calendar API to retrieve the user's schedule information. Data on the user's various schedules (meetings, business trips, private events, etc.) is retrieved through the calendar API.

[0242] 3. The server analyzes the acquired schedule information using natural language processing (NLP) algorithms to identify the type of schedule (e.g., business trip, meeting), date, time, and location.

[0243] Insurance plan recommendations and coverage

[0244] 4. The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[0245] 5. The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and emails. The notification includes the recommended insurance plan and the reason for the recommendation.

[0246] 6. The user reviews the notification and decides whether to approve the recommended insurance plan. After approval, the device sends the information to the server.

[0247] 7. The server receives the user's approval and updates the insurance policy, so that the new insurance plan is applied in real time.

[0248] Location and behavioral data collection

[0249] 8. The user installs the provided Tracker app on their smartphone and sets up location sharing.

[0250] 9. The device (smartphone) periodically collects the user's location and behavior data and sends it to the server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0251] 10. The server uses machine learning algorithms to analyze the collected behavioral data and identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[0252] Emotion data collection and analysis

[0253] 11. Emotion data is acquired using the emotion engine provided by the user. The emotion engine analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[0254] 12. The device transmits emotional data to a server that analyzes the data to determine the user's emotional state, including stress levels and signs of attention loss.

[0255] 13. The server analyzes the emotional data in combination with the behavioral data to identify the risk of loss and an appropriate insurance plan based on the user's emotional state.

[0256] Creative suggestions and support

[0257] 14. Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification may be sent saying, "Since you will be spending a long time at the next cafe, we will apply for specific insurance to cover the risk of loss there."

[0258] 15. Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan. This information is also updated in real time.

[0259] Specific examples

[0260] For example, a user enters an unexpected business trip into their calendar for next Tuesday. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user accepts this, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information, behavioral data, and emotional data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[0261] In this way, this system comprehensively analyzes the user's schedule information, location information, behavioral data, and emotional data, and provides the most appropriate insurance plan, thereby increasing the user's sense of security.

[0262] The processing flow will be explained below.

[0263] Step 1:

[0264] The server periodically sends a request to the user's calendar API to obtain the latest schedule information, and the user's schedule data is aggregated on the server.

[0265] Step 2:

[0266] The server then uses natural language processing (NLP) algorithms to analyze the calendar information, identifying the type of event (e.g., business trip, meeting, private event), date, time, and location.

[0267] Step 3:

[0268] The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[0269] Step 4:

[0270] The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and email. The notification includes the recommended insurance plan and the reason for the recommendation.

[0271] Step 5:

[0272] The user checks the notification from the server and decides whether to approve the recommended insurance plan. After approval, the user's device sends the information to the server.

[0273] Step 6:

[0274] The server updates the insurance contract based on the received approval information, allowing the new insurance plan to be applied in real time.

[0275] Step 7:

[0276] The user installs the provided Tracker app on their smartphone and sets up location sharing.

[0277] Step 8:

[0278] The device (smartphone) periodically collects the user's location information and behavioral data and sends it to a server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0279] Step 9:

[0280] The server analyzes the collected behavioral data using machine learning algorithms to identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[0281] Step 10:

[0282] Emotion data is acquired using the emotion engine provided by the user, which analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[0283] Step 11:

[0284] The device sends emotional data to a server that analyzes it to determine the user's emotional state, including stress levels and signs of attention loss.

[0285] Step 12:

[0286] The server analyzes the emotional data in combination with the behavioral data to identify the risk of loss and an appropriate insurance plan based on the user's emotional state.

[0287] Step 13:

[0288] Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification could be sent saying, "Since you will be spending a long time at the next cafe, we will apply for a specific insurance policy that covers the risk of loss there."

[0289] Step 14:

[0290] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan, and this information is also updated in real time.

[0291] Example 2

[0292] 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."

[0293] There is a need for a system that quickly and efficiently provides appropriate insurance plans for risks that arise from users' schedules and actions in their daily lives. However, conventional systems have difficulty comprehensively analyzing a user's schedule information, location information, and emotional data to provide appropriate insurance plans. Risk assessment that takes emotional state into account is also insufficient. Therefore, a system capable of more comprehensive and accurate risk assessment is needed.

[0294] 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.

[0295] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means for acquiring and analyzing emotional data, and means for integrating and analyzing the emotional data and behavioral data to perform risk assessment. This makes it possible to comprehensively analyze a variety of user data, provide a comprehensive risk assessment, and provide an optimal insurance plan.

[0296] "Schedule information" refers to detailed information about an event or activity that a user enters into a calendar application, including the date, time, location, and content.

[0297] A "Calendar API" is a programming interface for accessing, reading, and writing data in a calendar application.

[0298] "Natural language processing algorithms" are technologies and computer programs that allow computers to analyze, understand, and generate human language.

[0299] An "insurance plan" is a package containing the terms and conditions of an insurance policy against a particular risk.

[0300] "Location Information" means a user's geographic location data as determined by GPS or a network.

[0301] "Behavioral data" is data that indicates a user's movement patterns and activity history.

[0302] "Emotion data" is data that represents the user's emotional state and is obtained through facial recognition, voice analysis, touch input, etc.

[0303] "Risk assessment" is the process of analyzing multiple data sets to identify the level of risk in each individual situation.

[0304] "Notifications" are messages sent by the system to inform users of important information or suggestions, and include push notifications and emails.

[0305] "Analysis" is the process of processing acquired data and extracting useful information.

[0306] A "profile database" is a database that stores information such as a user's past activities and contract status.

[0307] The present invention is a system that integrates a user's schedule information, location information, and emotion data to provide the optimal insurance plan. This system operates in cooperation with the user's device, a server, an emotion engine, and a calendar API. A specific embodiment of this system is described below.

[0308] Retrieving and parsing calendar information

[0309] A user installs a calendar application on their device and inputs their schedule information. The device is then configured to be able to access a calendar API (e.g., Google Calendar API). Through this API, the user's schedule information is periodically sent to a server.

[0310] The server connects to the calendar API to retrieve the user's event information, which is then parsed using a Python natural language processing (NLP) library (e.g., NLTK or spaCy) to determine the event type (e.g., business trip, meeting), date, time, and location.

[0311] Insurance plan recommendations and coverage

[0312] Based on the acquired schedule information, the server refers to the user's profile database (e.g., PostgreSQL) and selects the most appropriate insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the schedule includes a business trip, it will recommend business trip insurance.

[0313] Information about the selected insurance plan is sent to the user via notification methods (e.g., push notification by Firebase Cloud Messaging, email by SendGrid). The user checks the notification and approves the recommended insurance plan on their device. After approval, the device sends the information to the server via a REST API.

[0314] The server, upon receiving the user's approval, updates the insurance contract details in the database and applies the new insurance plan in real time.

[0315] Location and behavioral data collection

[0316] Users install the provided tracker application (e.g., MyTracks or Google Fit) on their smartphones and set up location sharing. The device uses its GPS module to collect location information and periodically transmits it to a server. This collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0317] The server analyzes the collected behavioral data using machine learning algorithms such as Scikit-learn to identify locations and situations with a high risk of loss.

[0318] Emotion data collection and analysis

[0319] Users acquire emotion data using an emotion engine (e.g., Emotion SDK), which analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[0320] The device sends the analyzed emotional data to a server, which then analyzes it using a data analysis library such as Pandas to identify the user's emotional state (e.g., stress level, signs of attention loss).

[0321] The server integrates and analyzes the emotional data and behavioral data to identify the user's risk assessment and appropriate insurance plan. Based on the analysis results, creative insurance plans are proposed for specific behaviors.

[0322] Creative suggestions and support

[0323] Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification may be sent saying, "Since you will be spending a long time at the next cafe, we will apply for a specific insurance policy that covers the risk of loss there."

[0324] Once the user has reviewed and approved these proposals, the server will instantly update the insurance contract and the new insurance plan will be applied in real time.

[0325] Specific examples

[0326] For example, a user enters an unexpected business trip into their calendar for next Tuesday. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user accepts this, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information, behavioral data, and emotional data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[0327] Example prompt:

[0328] "A user has a business trip scheduled for next Tuesday on their calendar. How can we use this information to recommend and notify them of the best insurance plan?"

[0329] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0330] Step 1:

[0331] A user installs a calendar application on their device and inputs appointment information. The input data includes the appointment title, date and time, location, and notes. The device is set up to be able to access a calendar API (e.g., Google Calendar API). At this point, the input is the appointment information manually entered by the user. The output is the appointment information registered in the calendar.

[0332] Step 2:

[0333] The server periodically connects to the calendar API to obtain the user's schedule information. The server accesses the API using an HTTP request. The input is the schedule information obtained from the calendar API. The output is JSON format data of the retrieved schedule information.

[0334] Step 3:

[0335] The server analyzes the acquired schedule information using a natural language processing (NLP) algorithm. A Python NLP library (NLTK or spaCy) is used for the analysis. The input is the schedule information in JSON format. The NLP algorithm identifies the schedule type (meeting, business trip, etc.), date, time, and location. The output is data containing the analyzed schedule type, date, time, and location.

[0336] Step 4:

[0337] The server references the user's profile database (e.g., PostgreSQL) and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. The input is the parsed schedule information and the user's profile data. An SQL query is used to search for suitable insurance plans. The output is a list of selected insurance plans.

[0338] Step 5:

[0339] The server sends a notification to the user based on the selected insurance plan. Notification methods include Firebase Cloud Messaging and SendGrid. The input is information about the selected insurance plan. The output is a push notification or email notification to the user's device.

[0340] Step 6:

[0341] The user checks the notification on the device and chooses whether to approve or reject the recommended insurance plan. The input is the notification content from the server. The user's operation executes the action of approval or rejection. The output is the result of approval or rejection.

[0342] Step 7:

[0343] The terminal sends the user's approval result to the server. The input is the user's approval or denial result. The data is sent to the server using an HTTP POST request. The output is the approval result sent to the server.

[0344] Step 8:

[0345] The server receives the user's approval and updates the insurance contract details in the database. It accesses the database using an ORM such as SQLAlchemy. The input is the user's approval result and the selected insurance plan. The output is the updated insurance contract data.

[0346] Step 9:

[0347] The user installs the provided tracker application on their smartphone and configures location sharing. The input is the application installation and configuration information. The output is the device state after the location sharing configuration is complete.

[0348] Step 10:

[0349] The device periodically collects location information and behavioral data using a GPS module and sends it to a server. The inputs are the GPS data collected by the device, Bluetooth device detection information, and Wi-Fi login information. The output is the location information and behavioral data sent to the server.

[0350] Step 11:

[0351] The server analyzes the collected behavioral data using machine learning algorithms (such as Scikit-learn) to identify locations and situations where the user is at high risk of losing their device. The input is location information and behavioral data. The output is the identified high-risk locations and situations.

[0352] Step 12:

[0353] The user acquires emotion data using the emotion engine. The emotion engine uses a facial recognition camera, voice analysis, and touch input. The inputs are camera footage, voice data, and touch information. The output is emotion data analyzed in real time.

[0354] Step 13:

[0355] The device sends emotional data to the server. The input is the emotional data analyzed by the device. It is encrypted and sent using HTTPS. The output is the emotional data sent to the server.

[0356] Step 14:

[0357] The server analyzes the emotion data using a data analysis library such as Pandas to identify the user's emotional state (stress level, signs of attention loss). The input is the emotion data. The output is the analyzed emotional state data.

[0358] Step 15:

[0359] The server integrates and analyzes the emotional and behavioral data to perform a risk assessment of the user. The input is the emotional and behavioral data. The output is a comprehensive risk assessment result and the identification of an appropriate insurance plan.

[0360] Step 16:

[0361] The server proposes and notifies the user of a creative insurance plan based on the risk assessment results. The input is the risk assessment results. The output is a proposal notification sent to the user terminal.

[0362] Step 17:

[0363] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract to include the new insurance plan. The input is the user's approval. The output is the updated insurance contract information.

[0364] (Application example 2)

[0365] 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."

[0366] Today's busy users frequently move around and have diverse schedules, making it difficult to properly predict and address the associated risks. Furthermore, while emotional states are an important factor in risk assessment, there is no efficient way to grasp and utilize them. As a result, optimal insurance plans and security measures are often not applied in high-risk situations, reducing users' sense of security. To address these issues, a system is needed that comprehensively analyzes users' schedule information, location information, behavioral data, and emotional data to provide optimal insurance plans and security measures.

[0367] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means for acquiring and analyzing user emotion data, means for combining the emotion data with the behavioral data to perform risk assessment, and means for recommending an appropriate insurance plan to the user based on the identified risk assessment. This makes it possible to perform risk assessment based on the user's various schedules, movements, and emotional state, and automatically present and apply optimal insurance plans and security measures.

[0368] "User" refers to any individual or organization that uses this system.

[0369] "Schedule information" is information about events and activities that a user has scheduled, including the date, time, location, and content.

[0370] "Analysis" refers to breaking down acquired data or information and processing it to understand its structure and meaning.

[0371] An "insurance plan" refers to the content and conditions of the insurance contracted by a user, and provides coverage according to risk.

[0372] "Notification" refers to the act of informing a user of information from a system, and means of notification include push notifications and emails.

[0373] "Location information" refers to geographical data about a user's current location and travel route.

[0374] "Behavioral data" refers to data about a user's daily movements and movement patterns, including GPS data and Wi-Fi login information.

[0375] "Emotional data" is data collected to analyze a user's emotional state, and is information obtained from facial expressions, tone of voice, touch input, etc.

[0376] "Risk assessment" refers to the use of acquired and analyzed data to assess the degree of risk a user may face.

[0377] "Recommendation" refers to the system suggesting the best options or solutions to the user.

[0378] "Acquisition" refers to the act of the system gathering the necessary data or information.

[0379] "NLP algorithm" is an abbreviation for natural language processing algorithm, a technology for analyzing human language and understanding its meaning.

[0380] The present invention relates to a system that integrates a user's schedule information, location information, behavioral data, and emotional data to provide optimal insurance plans and security measures. This system operates in conjunction with the user's smartphone (terminal), a server, an emotional analysis engine, and a calendar API.

[0381] System Overview

[0382] The system consists of the following main components:

[0383] 1. User's smartphone (device)

[0384] Calendar API: Used to obtain the user's schedule information.

[0385] GPS function: Used to collect user location information.

[0386] Camera and microphone: Used to capture user emotion data.

[0387] Notification function: Used to notify users of selected insurance plans and risk alerts.

[0388] 2. Server

[0389] Natural language processing (NLP) algorithm: Analyzes the acquired schedule information and identifies the type of schedule and date and time.

[0390] Machine learning algorithms: Analyze behavioral and emotional data to make risk assessments.

[0391] Database: Stores user profile data and past behavioral data.

[0392] 3. Sentiment Analysis Engine

[0393] Facial recognition algorithm: Obtains emotional data from the user's facial expressions.

[0394] Voice analysis algorithm: Analyzes emotional data from the tone and patterns of the user's voice.

[0395] Processing flow

[0396] 1. Acquisition and analysis of schedule information

[0397] When a user enters appointment information into a smartphone calendar app, the information is sent to the server via the calendar API, and the server uses a natural language processing algorithm to analyze the information and identify the type of appointment and the date and time.

[0398] Example: For an event such as "Drinking party in Shinjuku at 10pm next Friday," the keywords "drinking party," "late night," and "Shinjuku" are extracted and the risk is assessed.

[0399] 2. Location and behavioral data collection

[0400] The user's smartphone uses its GPS function and Wi-Fi login information to periodically send its current location and route to the server.

[0401] Example: Tracking travel routes within a 15-minute walk from Shinjuku Station and assessing public safety.

[0402] 3. Emotional Data Collection and Analysis

[0403] The smartphone's camera and microphone are used to analyze the user's facial expressions and voice in real time to obtain emotional data. The emotion analysis engine analyzes facial expressions and voice and sends the data to a server.

[0404] Example: Facial recognition and voice analysis algorithms identify when a user is under stress.

[0405] 4. Risk Assessment and Notification

[0406] The server integrates behavioral and emotional data to assess high-risk situations in real time, and if necessary, sends risk alerts and optimal insurance plans via push notifications to smartphones.

[0407] Example: Sending a notification saying, "You will be spending a long time at the next cafe, so we will apply for specific insurance to cover the risk of loss there."

[0408] Example prompts to input to the generative AI model

[0409] "Please suggest the best insurance plan for my next appointment."

[0410] "Predict risks based on your current emotional state and recommend appropriate actions."

[0411] As described above, by centrally understanding and analyzing a user's schedule, location information, behavioral data, and emotional data, it is possible to provide optimal insurance plans and security measures in real time so that users can live their daily lives with peace of mind.

[0412] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0413] Step 1:

[0414] A user enters appointment information into a calendar app on their smartphone. This becomes the input data. This appointment information is sent to a server via a calendar API. The server receives the appointment information and analyzes the contents of the appointment using a natural language processing (NLP) algorithm. This analysis identifies information such as the type of appointment, date, time, and location. The output is structured data of the analyzed appointment information.

[0415] Step 2:

[0416] The server uses the structured schedule information to refer to the user's profile database and selects the optimal insurance plan based on past behavioral patterns and existing insurance contract status. The input is the analyzed schedule information and the user's profile data. In this step, a machine learning algorithm is used to determine the insurance plan that is most suitable for the user. The output is the selected insurance plan.

[0417] Step 3:

[0418] The server generates a notification of the selected insurance plan and sends it to the device. The device then notifies the user via push notification or email. The input is the selected insurance plan, and the output is the notification sent to the user. Specifically, the notification includes the recommended insurance plan and the reason for its selection.

[0419] Step 4:

[0420] The user checks the notification on the device and decides whether to approve the recommended insurance plan. When the user approves, the device sends the information to the server. The input is the user's approval, and the output is the approval data sent to the server.

[0421] Step 5:

[0422] The server updates the insurance contract details with the user's approval. This allows the new insurance plan to be applied in real time. The input is the user's approval data, and the output is the updated insurance contract data. Specifically, an API that changes the contract details in conjunction with the insurance company's system is called.

[0423] Step 6:

[0424] The user's smartphone periodically sends their current location and route to the server using GPS and Wi-Fi login information. The input is the user's location information, and the output is the location information stored in the server's behavior database.

[0425] Step 7:

[0426] The server analyzes the collected behavioral data using a machine learning algorithm to identify locations and situations where the user is at high risk of losing their item. The input is location information and behavioral data, and the output is risk assessment data. Specifically, the server calculates a risk score based on past data patterns.

[0427] Step 8:

[0428] The user acquires emotional data using the smartphone's camera and microphone. The device analyzes the user's emotional state using facial recognition and voice analysis algorithms and sends the data to a server. The input is the user's facial expression and voice data, and the output is the analyzed emotional data.

[0429] Step 9:

[0430] The server analyzes the emotional data in combination with the behavioral data to assess the risk of loss and crime. The input is the emotional data and the behavioral data, and the output is a comprehensive risk assessment. Specific operations include determining whether stress or reduced attention contributes to the risk.

[0431] Step 10:

[0432] Based on the risk assessment results, the server generates and sends notifications to users recommending appropriate insurance plans and security measures. The input is risk assessment data, and the output is a push notification to the user. Specifically, if the user is in a high-risk area, a message recommending a specific insurance plan is sent.

[0433] This makes it possible to perform risk assessments based on the user's diverse schedules, movements, and emotional state, and automatically present and apply optimal insurance plans and security measures.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Second embodiment]

[0438] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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."

[0450] This invention is a system that provides optimal insurance plans based on a user's schedule and location information. This system works in conjunction with the user's device, server, and calendar API. Specifically, it acquires the user's schedule information, analyzes it, selects the optimal insurance plan, notifies the user, and applies it. It also collects location and behavioral data to predict risks and make creative proposals.

[0451] A natural language description of what the program does

[0452] Retrieving and parsing calendar information

[0453] 1. The user installs a calendar app on their device, enters schedule information, and configures the device to access the calendar API.

[0454] 2. The server periodically connects to the calendar API to retrieve the user's schedule information. At that time, data on the user's various schedules (meetings, business trips, private events, etc.) is retrieved via the calendar API.

[0455] 3. The server analyzes the acquired schedule information and uses a natural language processing algorithm to understand the content of the event and identify the type (e.g., business trip, meeting), date, time, and location.

[0456] Insurance plan recommendations and coverage

[0457] 4. The server selects the optimal insurance plan based on the analysis results, taking into account the user's past behavioral patterns and existing insurance contract status.

[0458] 5. The server notifies the user of the selected insurance plan. For example, a message such as "You have an unexpected business trip next Tuesday, which increases your risk of loss. We recommend this insurance plan" is sent via a mobile app or email.

[0459] 6. The user reviews the notification and approves the recommended insurance plan. After approval, the device sends the information to the server.

[0460] 7. The server receives the user's approval and updates the insurance contract with the new insurance coverage.

[0461] Location and behavioral data collection

[0462] 8. The user installs the provided Tracker app on their smartphone and sets permission to share location information.

[0463] 9. The device (smartphone) periodically collects location data and other behavioral information and sends it to a server, including GPS data, Bluetooth device detection, Wi-Fi login information, etc.

[0464] 10. The server analyzes the collected behavioral data and applies learning algorithms to identify locations and situations where the user is at high risk of loss.

[0465] Creative suggestions and support

[0466] 11. Based on the learning results and real-time behavioral data, the server predicts locations and situations with a high risk of loss and suggests advance notifications and insurance plans to the user. For example, a message such as, "You often spend time in cafes on your next business trip, so there is a high risk of loss there. We will apply insurance for this location."

[0467] 12. Once the user approves the proposal, the server immediately applies the insurance plan and reflects it in the user's contract.

[0468] Specific examples

[0469] For example, suppose a user has an unexpected business trip scheduled for next Tuesday entered into their calendar. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user approves, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information and behavioral data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[0470] In this way, users can automatically apply the most suitable insurance plan for unexpected events and can also handle situations where the risk of loss is high. This system prevents users from forgetting to take out insurance and effectively reduces the risk of loss that may occur in their daily lives.

[0471] The processing flow will be explained below.

[0472] Step 1:

[0473] The server periodically sends a request to the user's calendar API to obtain the latest schedule information, and the user's schedule data is collected on the server.

[0474] Step 2:

[0475] The server then uses natural language processing (NLP) algorithms to analyze the calendar information, identifying the type of event (e.g., business trip, meeting, private event), date, time, and location.

[0476] Step 3:

[0477] The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[0478] Step 4:

[0479] The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and email. The notification includes the recommended insurance plan and the reason for the recommendation.

[0480] Step 5:

[0481] The user checks the notification from the server and decides whether to approve the recommended insurance plan. After approval, the user's device sends the information to the server.

[0482] Step 6:

[0483] The server updates the insurance contract based on the received approval information, allowing the new insurance plan to be applied in real time.

[0484] Step 7:

[0485] The user installs the provided Tracker app on their smartphone and sets up location sharing.

[0486] Step 8:

[0487] The device (smartphone) periodically collects the user's location information and behavioral data and sends it to a server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0488] Step 9:

[0489] The server analyzes the collected behavioral data using machine learning algorithms to identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[0490] Step 10:

[0491] The server predicts locations and situations with a high risk of loss, notifies the user, and proposes a specific insurance plan. For example, a notification could be sent saying, "Since you will be spending a long time at the next cafe, we will apply for insurance that covers the risk of loss there."

[0492] Step 11:

[0493] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan, and this information is also updated in real time.

[0494] In this way, the most suitable insurance plan is automatically selected and applied based on the user's schedule information and behavioral data, reducing the user's risk of loss.

[0495] Example 1

[0496] 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."

[0497] Conventional insurance systems require users to purchase insurance themselves, making it difficult to respond immediately to sudden changes in plans. Furthermore, they lack the functionality to predict risks based on users' daily activities and location information and then propose appropriate insurance plans based on those risks. This leads to problems such as users forgetting to purchase insurance or being unable to respond appropriately to high-risk situations.

[0498] 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.

[0499] In this invention, the server includes means for acquiring a user's schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means operating in real time to notify the user of the selected insurance plan, means for predicting risks based on the collected location information and behavioral data, and means for notifying the user of risks in advance and creatively proposing insurance plans. This allows the server to automatically select and notify an appropriate insurance plan based on the user's behavior and schedule, making it possible to quickly respond to sudden schedule changes and high-risk situations.

[0500] "User's schedule information" refers to schedules such as meetings, business trips, and private events that a user has entered into a calendar app or schedule management tool.

[0501] "Analysis" refers to the process of using natural language processing algorithms and learning algorithms to understand the content of the acquired user's schedule information and behavioral data, and to identify specific information (e.g., date, time, location, type).

[0502] "Insurance plan" refers to the content of the insurance contract that is applied based on the user's actions and plans, and includes, for example, short-term business trip insurance and plans covering loss risks.

[0503] "Selecting" refers to choosing the most suitable insurance plan from multiple candidates based on analyzed data.

[0504] "Notifying" refers to informing the user about the selected insurance plan via email, mobile app, etc.

[0505] "Approve" means that the user consents to the notified insurance plan and expresses his / her intention to accept its application.

[0506] "Location Information" refers to a user's current location and movement data obtained through GPS, Wi-Fi login, Bluetooth device detection, etc.

[0507] "Behavioral Data" refers to data that includes information about a user's daily activities, travel patterns, and location.

[0508] "Predicting risks" refers to predicting the risk of loss in specific situations or locations based on collected location information and behavioral data.

[0509] "Creative proposals" refers to presenting users with new insurance plan ideas and improvement methods that are not bound by conventional patterns, based on risk predictions and analysis results.

[0510] MODE FOR CARRYING OUT THE INVENTION

[0511] This invention is a system that provides optimal insurance plans based on a user's schedule information and location information. This system operates in conjunction with the user's device, a server, and a calendar API.

[0512] Specifically, a user first installs a calendar app on their device and inputs their schedule information. The server then periodically connects to the calendar API to retrieve the user's schedule information. This can be done using a widely used calendar API such as the Google Calendar API.

[0513] The server analyzes the acquired schedule information and uses natural language processing (NLP) algorithms to identify the type of event (e.g., business trip, meeting), date, time, and location. Based on this information, the server selects the most suitable insurance plan. This selection process also takes into account past behavioral data and existing insurance policy information. The server then notifies the user of the selected insurance plan via email or in-app notification.

[0514] The user then approves the recommended insurance plan through the mobile app, and once approval is complete, the device sends the information to the server, which then updates the insurance contract with the new coverage.

[0515] The system also has the ability to collect user location and behavioral data. When a user installs the Tracker app on their smartphone and sets location sharing permission, the device periodically collects location data and other behavioral information and sends it to a server. This includes GPS data, Bluetooth device detection, Wi-Fi login information, and more.

[0516] The server analyzes the collected behavioral data and applies a learning algorithm to identify locations and situations where the user is at high risk of losing their device. Based on the learning results and real-time behavioral data, it makes risk predictions and proposes advance notifications and insurance plans to the user.

[0517] As a concrete example, if a user has an unexpected business trip scheduled for next Tuesday entered in their calendar, the server will obtain and analyze that information, select an insurance plan for short-term business trips, and notify the user. If the user approves the plan, the insurance contract will be updated immediately. Similarly, for a user who spends much of their weekdays in a cafe, the server will determine from their location and behavioral data that there is a high risk of loss at the cafe, and will suggest an insurance plan that is appropriate for that time period.

[0518] An example prompt is:

[0519] "Please explain in detail how to recommend the best insurance plan for the user based on their schedule and location."

[0520] "Please explain in detail how to select the right insurance plan for users who plan to travel."

[0521] "How can I use a user's location information to predict risks in a specific location and suggest insurance plans?"

[0522] In this way, users can quickly respond to sudden changes in plans or high-risk situations and prevent forgetting to take out insurance. This system can effectively reduce risks that may occur in users' lives.

[0523] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0524] Step 1:

[0525] A user installs a calendar app on their device and enters event information. The entered event information includes the type of event, date, time, and location. Specifically, the user enters an event such as "Business trip to Tokyo next Tuesday." This event information is saved in cloud storage via the calendar API.

[0526] Step 2:

[0527] The server periodically connects to the calendar API to retrieve the user's schedule information. The retrieved data includes the type of event, date, time, and location from the user's calendar. For example, the Google Calendar API is used to retrieve "User A's schedule" and store it in the server's database.

[0528] Step 3:

[0529] The server analyzes the schedule information it has acquired and uses a natural language processing algorithm (NLP) to understand the content of the event. The input data is the schedule content, and the output after analysis is the type, date, time, and location of the identified event. For example, from the content "Business trip in Tokyo next Tuesday," it extracts "Business trip," "October 10, 2023," and "Tokyo."

[0530] Step 4:

[0531] The server selects the optimal insurance plan based on the analysis results. This selection also takes into account the user's past behavioral data and existing insurance contract status. The input data is the identified event information and the user's insurance history, and the output data is the recommended insurance plan. For example, a "short-term insurance plan tailored to business trips" may be selected.

[0532] Step 5:

[0533] The server notifies the user of the selected insurance plan. The notification is sent via a mobile app or email. The input data is the selected insurance plan, and the output data is the notification message sent to the user. Specifically, the message sent reads, "You have an unexpected business trip next Tuesday, which increases the risk of loss. We recommend this insurance plan."

[0534] Step 6:

[0535] The user reviews the notification and approves the recommended insurance plan by pressing a button in the mobile app. The input is the notification message, and the output is the user's approval data.

[0536] Step 7:

[0537] The terminal sends the user's authorization information to the server, where the input data is the user's authorization data and the output data is the server's updated insurance policy information.

[0538] Step 8:

[0539] The server updates the insurance contract with the new insurance details upon receiving the user's approval. The input data is the approval information, and the output data is the updated insurance contract details.

[0540] Step 9:

[0541] A user installs the Tracker app on their smartphone and sets location sharing permission. The input data is the location sharing setting, and the output data is the location sharing permission status.

[0542] Step 10:

[0543] The device periodically collects location data and behavioral information and sends it to a server. The input data is GPS data, Bluetooth device detection, Wi-Fi login information, etc., and the output data is the behavioral data sent to the server.

[0544] Step 11:

[0545] The server analyzes the collected behavioral data and applies a learning algorithm to identify locations and situations where the user is at high risk of losing their belongings. The input data is behavioral data, and the output data is the identified risk information. For example, it identifies a pattern such as "often spending time at cafes during the day on weekdays."

[0546] Step 12:

[0547] The server predicts risks based on the learning results and real-time behavioral data, and then notifies the user and suggests insurance plans. The input data is the identified risk information, and the output data is the notification message sent to the user. For example, a notification may be sent stating, "Since you often spend time in cafes at your next business trip destination, there is a high risk of loss at that location. We will apply insurance for this location."

[0548] Step 13:

[0549] The user approves the proposal, and the server immediately applies the insurance plan and reflects it in the user's contract. The input data is the user's approval data, and the output data is the updated insurance plan and contract information.

[0550] (Application example 1)

[0551] 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."

[0552] In modern brick-and-mortar stores, the increased risk of theft and loss when purchasing expensive items is a serious problem. Conventional insurance systems assume fixed conditions and situations and are unable to propose optimal insurance plans that reflect the user's behavior and location information in real time. This poses a challenge, making it difficult to quickly apply an appropriate insurance plan when a user purchases a product.

[0553] 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.

[0554] In this invention, the server includes means for scanning a product code when a user selects a product in a physical store, means for analyzing the product code and assessing the risk associated with the product, and means for proposing an optimal insurance plan in real time based on the assessed risk. This makes it possible to perform risk assessment on the spot when a user purchases a product in a physical store, and to immediately propose and apply an optimal insurance plan.

[0555] "User" refers to a person who uses this system.

[0556] "Schedule information" refers to data on events and schedules that are set in advance by the user.

[0557] "Means of acquisition" refers to the server's ability to gather information from calendar APIs and other data sources.

[0558] "Means of analysis" refers to the function of processing the acquired data and identifying details such as category, time, and location.

[0559] "Means for identifying" refers to the function of extracting specific information from the analyzed data and clarifying its content.

[0560] "Insurance Plan" means a contract that provides insurance coverage against a specific risk.

[0561] "Means of selection" refers to the function by which the server selects the most suitable insurance plan based on the analyzed data.

[0562] "Means for notifying" refers to a function for informing the user of the selected insurance plan.

[0563] "Means of application" refers to the function of officially reflecting the insurance plan approved by the user in the contract.

[0564] "Location information" refers to data indicating a user's current location and movement history.

[0565] "Behavioral data" refers to data that indicates a user's past behavioral patterns and history.

[0566] "Means of collection" refers to functions for collecting location information and behavioral data.

[0567] "Product code" refers to the identification information of a product sold in a physical store.

[0568] "Means of scanning" refers to the ability to read product codes using smart glasses, smartphones, etc.

[0569] "Risks relating to products" refers to the possible losses or dangers associated with a particular product.

[0570] "Means for assessing risk" refers to the function of analyzing the risks associated with a product based on collected data.

[0571] "Real-time recommendations" refers to the ability to instantly show users insurance plans based on current information.

[0572] MODE FOR CARRYING OUT THE INVENTION

[0573] The embodiment of this invention is a system that provides optimal insurance plans in real time based on a user's schedule information and location information. This system operates in cooperation with the user's terminal, a server, and a calendar API. The details of the system are described below.

[0574] System Configuration

[0575] The system requires the user's smart glasses or smartphone and a server as the main hardware. The software includes a calendar API, location API, and insurance plan API. The system collects and analyzes data related to the user's schedule, location, and product code to propose the optimal insurance plan.

[0576] Data collection and analysis

[0577] The server obtains the user's schedule information through the calendar API. This schedule information is then analyzed using a natural language processing algorithm. The analyzed schedule information is then categorized into detailed data such as the schedule type, date, and location. Location and behavior data are also collected through the user's smart glasses or smartphone.

[0578] Based on the analysis data, the server tracks users' behavior in physical stores by scanning product codes. When a user scans an item, the risk associated with that item is instantly assessed and corresponding insurance plans are proposed in real time.

[0579] Insurance plan proposal and application

[0580] The server selects the optimal insurance plan based on the analysis results and notifies the user. If the user approves the proposed insurance plan, the information is sent to the server and the insurance contract is updated. This allows the user to immediately apply the optimal insurance plan for the risks of the products purchased in the physical store.

[0581] Specific examples

[0582] For example, if a user is about to purchase an expensive camera in a brick-and-mortar store, they can scan the product code on the camera with smart glasses or a smartphone. The system will then immediately assess the theft risk of the product and send a notification saying, "This camera has a high theft risk, so we recommend a dedicated theft insurance plan." If the user approves the insurance plan, the plan will be applied as a new insurance policy.

[0583] Prompt Sentence Examples

[0584] The following prompts can be used to instruct the generative AI model on how the system should behave:

[0585] Develop an application that predicts the items a user is likely to purchase that day based on their location and calendar information, assesses the risks associated with those items, and displays appropriate insurance plans in real time. For example, if a user is planning to purchase an expensive camera, implement a function that evaluates the risk of theft and suggests corresponding insurance plans by scanning the camera's product code.

[0586] In this way, the present invention can quickly assess the risks a user faces while shopping in a physical store and provide the most appropriate insurance plan, thereby improving the user's peace of mind and convenience.

[0587] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0588] Step 1:

[0589] A user installs a calendar app on their device (smart glasses or smartphone) and inputs schedule information. At this time, the device is set up to be able to access the calendar API.

[0590] Input: Schedule information entered by the user

[0591] Output: Calendar information for which access to the Calendar API has been granted

[0592] Specific operation: The user enters event information into the calendar app and sets up synchronization.

[0593] Step 2:

[0594] The server periodically connects to the calendar API to obtain the user's schedule information. The calendar API obtains data on the user's various schedules (meetings, business trips, private events, etc.).

[0595] Input: Schedule information obtained from the calendar API

[0596] Output: Raw schedule information data

[0597] Specific operation: The server sends an API request and receives schedule information in JSON format.

[0598] Step 3:

[0599] The server analyzes the acquired schedule information and uses natural language processing algorithms to understand the contents of the event, specifically identifying the type of schedule, date, time, and location.

[0600] Input: Raw schedule information data

[0601] Output: Parsed schedule information (e.g., business trips, meetings, etc.)

[0602] Specific operation: The server applies natural language processing algorithms to perform text analysis.

[0603] Step 4:

[0604] The server selects the optimal insurance plan based on the analysis results, taking into account the user's past behavioral patterns and existing insurance contract status.

[0605] Input: Analyzed schedule information, past behavior patterns, existing insurance contract status

[0606] Output: Optimal insurance plan

[0607] Specific operation: The server performs multivariate analysis, comparing multiple factors to determine the optimal insurance plan.

[0608] Step 5:

[0609] The server notifies the user of the selected insurance plan, for example, via a mobile app or email.

[0610] Input: Best insurance plan information

[0611] Output: A message to inform the user

[0612] Specific operation: The server sends a push notification to the user's device.

[0613] Step 6:

[0614] The user checks the notification and approves the recommended insurance plan. The approved insurance plan information is sent from the device to the server.

[0615] Input: User authorization information

[0616] Output: Insurance plan approval notice

[0617] Specific behavior: The user taps the application notification and clicks the approve button.

[0618] Step 7:

[0619] The server updates the insurance contract with the user's approval and applies the new insurance details.

[0620] Input: User approval notification

[0621] Output: Updated insurance contract information

[0622] Specific operation: The server updates the contract database.

[0623] Step 8:

[0624] The user installs the provided Tracker app on their smartphone and sets permission to share location information.

[0625] Input: User's location sharing permission

[0626] Output: Location sharing enabled

[0627] Specific behavior: The user enables location sharing in the app's settings screen.

[0628] Step 9:

[0629] The device (smartphone) periodically collects location data and other behavioral information and sends it to a server.

[0630] Input: location data, behavioral information

[0631] Output: Location data and behavioral information sent to the server

[0632] Specific operation: The smartphone periodically acquires GPS data and sends it to the server.

[0633] Step 10:

[0634] The server analyzes the collected behavioral data and applies learning algorithms to identify locations and situations where users are at high risk of loss.

[0635] Input: location data, behavioral information

[0636] Output: Identification of locations and situations with high risk of loss

[0637] Specific operation: The server performs data analysis using machine learning algorithms.

[0638] Step 11:

[0639] Based on the learning results and real-time behavioral data, the server predicts locations and situations with a high risk of loss, and provides advance notification and offers insurance plans to users.

[0640] Input: Learning results, real-time behavioral data

[0641] Output: Notification of recommended insurance plan

[0642] Specific operation: The server sends a push notification to the user based on the risk assessment result.

[0643] Step 12:

[0644] If the user approves the proposal, the server immediately applies the insurance plan and reflects it in the user's contract.

[0645] Input: User approval notification

[0646] Output: Insurance plan applied

[0647] Specific operation: The server updates the contract database again.

[0648] 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.

[0649] This invention is a system that integrates a user's schedule information, location information, and emotional data to provide the optimal insurance plan. This system operates in conjunction with the user's device, server, emotion engine, and calendar API. Specifically, it acquires the user's schedule information, analyzes it, and selects, notifies, and applies the optimal insurance plan. It also collects location information and emotional data to make risk predictions and creative proposals.

[0650] A natural language description of what the program does

[0651] Retrieving and parsing calendar information

[0652] 1. The user installs a calendar app on their device, enters schedule information, and configures the device to access the calendar API.

[0653] 2. The server periodically connects to the calendar API to retrieve the user's schedule information. Data on the user's various schedules (meetings, business trips, private events, etc.) is retrieved through the calendar API.

[0654] 3. The server analyzes the acquired schedule information using natural language processing (NLP) algorithms to identify the type of schedule (e.g., business trip, meeting), date, time, and location.

[0655] Insurance plan recommendations and coverage

[0656] 4. The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[0657] 5. The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and emails. The notification includes the recommended insurance plan and the reason for the recommendation.

[0658] 6. The user reviews the notification and decides whether to approve the recommended insurance plan. After approval, the device sends the information to the server.

[0659] 7. The server receives the user's approval and updates the insurance policy, so that the new insurance plan is applied in real time.

[0660] Location and behavioral data collection

[0661] 8. The user installs the provided Tracker app on their smartphone and sets up location sharing.

[0662] 9. The device (smartphone) periodically collects the user's location and behavior data and sends it to the server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0663] 10. The server uses machine learning algorithms to analyze the collected behavioral data and identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[0664] Emotion data collection and analysis

[0665] 11. Emotion data is acquired using the emotion engine provided by the user. The emotion engine analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[0666] 12. The device transmits emotional data to a server that analyzes the data to determine the user's emotional state, including stress levels and signs of attention loss.

[0667] 13. The server analyzes the emotional data in combination with the behavioral data to identify the risk of loss and an appropriate insurance plan based on the user's emotional state.

[0668] Creative suggestions and support

[0669] 14. Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification may be sent saying, "Since you will be spending a long time at the next cafe, we will apply for specific insurance to cover the risk of loss there."

[0670] 15. Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan. This information is also updated in real time.

[0671] Specific examples

[0672] For example, a user enters an unexpected business trip into their calendar for next Tuesday. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user accepts this, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information, behavioral data, and emotional data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[0673] In this way, this system comprehensively analyzes the user's schedule information, location information, behavioral data, and emotional data, and provides the most appropriate insurance plan, thereby increasing the user's sense of security.

[0674] The processing flow will be explained below.

[0675] Step 1:

[0676] The server periodically sends a request to the user's calendar API to obtain the latest schedule information, and the user's schedule data is aggregated on the server.

[0677] Step 2:

[0678] The server then uses natural language processing (NLP) algorithms to analyze the calendar information, identifying the type of event (e.g., business trip, meeting, private event), date, time, and location.

[0679] Step 3:

[0680] The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[0681] Step 4:

[0682] The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and email. The notification includes the recommended insurance plan and the reason for the recommendation.

[0683] Step 5:

[0684] The user checks the notification from the server and decides whether to approve the recommended insurance plan. After approval, the user's device sends the information to the server.

[0685] Step 6:

[0686] The server updates the insurance contract based on the received approval information, allowing the new insurance plan to be applied in real time.

[0687] Step 7:

[0688] The user installs the provided Tracker app on their smartphone and sets up location sharing.

[0689] Step 8:

[0690] The device (smartphone) periodically collects the user's location information and behavioral data and sends it to a server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0691] Step 9:

[0692] The server analyzes the collected behavioral data using machine learning algorithms to identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[0693] Step 10:

[0694] Emotion data is acquired using the emotion engine provided by the user, which analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[0695] Step 11:

[0696] The device sends emotional data to a server that analyzes it to determine the user's emotional state, including stress levels and signs of attention loss.

[0697] Step 12:

[0698] The server analyzes the emotional data in combination with the behavioral data to identify the risk of loss and an appropriate insurance plan based on the user's emotional state.

[0699] Step 13:

[0700] Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification could be sent saying, "Since you will be spending a long time at the next cafe, we will apply for a specific insurance policy that covers the risk of loss there."

[0701] Step 14:

[0702] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan, and this information is also updated in real time.

[0703] Example 2

[0704] 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."

[0705] There is a need for a system that quickly and efficiently provides appropriate insurance plans for risks that arise from users' schedules and actions in their daily lives. However, conventional systems have difficulty comprehensively analyzing a user's schedule information, location information, and emotional data to provide appropriate insurance plans. Risk assessment that takes emotional state into account is also insufficient. Therefore, a system capable of more comprehensive and accurate risk assessment is needed.

[0706] 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.

[0707] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means for acquiring and analyzing emotional data, and means for integrating and analyzing the emotional data and behavioral data to perform risk assessment. This makes it possible to comprehensively analyze a variety of user data, provide a comprehensive risk assessment, and provide an optimal insurance plan.

[0708] "Schedule information" refers to detailed information about an event or activity that a user enters into a calendar application, including the date, time, location, and content.

[0709] A "Calendar API" is a programming interface for accessing, reading, and writing data in a calendar application.

[0710] A "natural language processing algorithm" is a technology or computer program that allows a computer to analyze, understand, and generate human language.

[0711] An "insurance plan" is a package containing the terms and conditions of an insurance policy against a particular risk.

[0712] "Location Information" means a user's geographic location data determined by GPS or a network.

[0713] "Behavioral data" is data that indicates a user's movement patterns and activity history.

[0714] "Emotion data" is data that represents the user's emotional state and is obtained through facial recognition, voice analysis, touch input, etc.

[0715] "Risk assessment" is the process of analyzing multiple data sets to identify the level of risk in each individual situation.

[0716] "Notifications" are messages sent by the system to inform users of important information or suggestions, and include push notifications and emails.

[0717] "Analysis" is the process of processing acquired data and extracting useful information.

[0718] A "profile database" is a database that stores information such as a user's past activities and contract status.

[0719] The present invention is a system that integrates a user's schedule information, location information, and emotion data to provide the optimal insurance plan. This system operates in cooperation with the user's device, a server, an emotion engine, and a calendar API. A specific embodiment of this system is described below.

[0720] Retrieving and parsing calendar information

[0721] A user installs a calendar application on their device and inputs their schedule information. The device is then configured to be able to access a calendar API (e.g., Google Calendar API). Through this API, the user's schedule information is periodically sent to a server.

[0722] The server connects to the calendar API to retrieve the user's event information, which is then parsed using a Python natural language processing (NLP) library (e.g., NLTK or spaCy) to determine the event type (e.g., business trip, meeting), date, time, and location.

[0723] Insurance plan recommendations and coverage

[0724] Based on the acquired schedule information, the server refers to the user's profile database (e.g., PostgreSQL) and selects the most appropriate insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the schedule includes a business trip, it will recommend business trip insurance.

[0725] Information about the selected insurance plan is sent to the user via notification methods (e.g., push notification by Firebase Cloud Messaging, email by SendGrid). The user checks the notification and approves the recommended insurance plan on their device. After approval, the device sends the information to the server via a REST API.

[0726] The server, upon receiving the user's approval, updates the insurance contract details in the database and applies the new insurance plan in real time.

[0727] Location and behavioral data collection

[0728] Users install the provided tracker application (e.g., MyTracks or Google Fit) on their smartphones and set up location sharing. The device uses its GPS module to collect location information and periodically transmits it to a server. This collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0729] The server analyzes the collected behavioral data using machine learning algorithms such as Scikit-learn to identify locations and situations with a high risk of loss.

[0730] Emotion data collection and analysis

[0731] Users acquire emotion data using an emotion engine (e.g., Emotion SDK), which analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[0732] The device sends the analyzed emotional data to a server, which then analyzes it using a data analysis library such as Pandas to identify the user's emotional state (e.g., stress level, signs of attention loss).

[0733] The server integrates and analyzes the emotional data and behavioral data to identify the user's risk assessment and appropriate insurance plan. Based on the analysis results, creative insurance plans are proposed for specific behaviors.

[0734] Creative suggestions and support

[0735] Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification may be sent saying, "Since you will be spending a long time at the next cafe, we will apply for a specific insurance policy that covers the risk of loss there."

[0736] Once the user has reviewed and approved these proposals, the server will instantly update the insurance contract and the new insurance plan will be applied in real time.

[0737] Specific examples

[0738] For example, a user enters an unexpected business trip into their calendar for next Tuesday. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user accepts this, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information, behavioral data, and emotional data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[0739] Example prompt:

[0740] "A user has a business trip scheduled for next Tuesday on their calendar. How can we use this information to recommend and notify them of the best insurance plan?"

[0741] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0742] Step 1:

[0743] A user installs a calendar application on their device and inputs appointment information. The input data includes the appointment title, date and time, location, and notes. The device is set up to be able to access a calendar API (e.g., Google Calendar API). At this point, the input is the appointment information manually entered by the user. The output is the appointment information registered in the calendar.

[0744] Step 2:

[0745] The server periodically connects to the calendar API to obtain the user's schedule information. The server accesses the API using an HTTP request. The input is the schedule information obtained from the calendar API. The output is JSON format data of the retrieved schedule information.

[0746] Step 3:

[0747] The server analyzes the acquired schedule information using a natural language processing (NLP) algorithm. A Python NLP library (NLTK or spaCy) is used for the analysis. The input is the schedule information in JSON format. The NLP algorithm identifies the schedule type (meeting, business trip, etc.), date, time, and location. The output is data containing the analyzed schedule type, date, time, and location.

[0748] Step 4:

[0749] The server references the user's profile database (e.g., PostgreSQL) and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. The input is the parsed schedule information and the user's profile data. An SQL query is used to search for suitable insurance plans. The output is a list of selected insurance plans.

[0750] Step 5:

[0751] The server sends a notification to the user based on the selected insurance plan. Notification methods include Firebase Cloud Messaging and SendGrid. The input is information about the selected insurance plan. The output is a push notification or email notification to the user's device.

[0752] Step 6:

[0753] The user checks the notification on the device and chooses whether to approve or reject the recommended insurance plan. The input is the notification content from the server. The user's operation executes the action of approval or rejection. The output is the result of approval or rejection.

[0754] Step 7:

[0755] The terminal sends the user's approval result to the server. The input is the user's approval or denial result. The data is sent to the server using an HTTP POST request. The output is the approval result sent to the server.

[0756] Step 8:

[0757] The server receives the user's approval and updates the insurance contract details in the database. It accesses the database using an ORM such as SQLAlchemy. The input is the user's approval result and the selected insurance plan. The output is the updated insurance contract data.

[0758] Step 9:

[0759] The user installs the provided tracker application on their smartphone and configures location sharing. The input is the application installation and configuration information. The output is the device state after the location sharing configuration is complete.

[0760] Step 10:

[0761] The device periodically collects location information and behavioral data using a GPS module and sends it to a server. The inputs are the GPS data collected by the device, Bluetooth device detection information, and Wi-Fi login information. The output is the location information and behavioral data sent to the server.

[0762] Step 11:

[0763] The server analyzes the collected behavioral data using machine learning algorithms (such as Scikit-learn) to identify locations and situations where the user is at high risk of losing their device. The input is location information and behavioral data. The output is the identified high-risk locations and situations.

[0764] Step 12:

[0765] The user acquires emotion data using the emotion engine. The emotion engine uses a facial recognition camera, voice analysis, and touch input. The inputs are camera footage, voice data, and touch information. The output is emotion data analyzed in real time.

[0766] Step 13:

[0767] The device sends emotional data to the server. The input is the emotional data analyzed by the device. It is encrypted and sent using HTTPS. The output is the emotional data sent to the server.

[0768] Step 14:

[0769] The server analyzes the emotion data using a data analysis library such as Pandas to identify the user's emotional state (stress level, signs of attention loss). The input is the emotion data. The output is the analyzed emotional state data.

[0770] Step 15:

[0771] The server integrates and analyzes the emotional and behavioral data to perform a risk assessment of the user. The input is the emotional and behavioral data. The output is a comprehensive risk assessment result and the identification of an appropriate insurance plan.

[0772] Step 16:

[0773] The server proposes and notifies the user of a creative insurance plan based on the risk assessment results. The input is the risk assessment results. The output is a proposal notification sent to the user terminal.

[0774] Step 17:

[0775] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract to include the new insurance plan. The input is the user's approval. The output is the updated insurance contract information.

[0776] (Application example 2)

[0777] 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."

[0778] Today's busy users frequently move around and have diverse schedules, making it difficult to properly predict and address the associated risks. Furthermore, while emotional states are an important factor in risk assessment, there is no efficient way to grasp and utilize them. As a result, optimal insurance plans and security measures are often not applied in high-risk situations, reducing users' sense of security. To address these issues, a system is needed that comprehensively analyzes users' schedule information, location information, behavioral data, and emotional data to provide optimal insurance plans and security measures.

[0779] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means for acquiring and analyzing user emotion data, means for combining the emotion data with the behavioral data to perform risk assessment, and means for recommending an appropriate insurance plan to the user based on the identified risk assessment. This makes it possible to perform risk assessment based on the user's various schedules, movements, and emotional state, and automatically present and apply optimal insurance plans and security measures.

[0780] "User" refers to any individual or organization that uses this system.

[0781] "Schedule information" is information about events and activities that a user has scheduled, including the date, time, location, and content.

[0782] "Analysis" refers to breaking down acquired data or information and processing it to understand its structure and meaning.

[0783] An "insurance plan" refers to the content and conditions of the insurance contracted by a user, and provides coverage according to risk.

[0784] "Notification" refers to the act of informing a user of information from a system, and means of notification include push notifications and emails.

[0785] "Location information" refers to geographical data about a user's current location and travel route.

[0786] "Behavioral data" refers to data about a user's daily movements and movement patterns, including GPS data and Wi-Fi login information.

[0787] "Emotional data" is data collected to analyze a user's emotional state, and is information obtained from facial expressions, tone of voice, touch input, etc.

[0788] "Risk assessment" refers to the use of acquired and analyzed data to assess the degree of risk a user may face.

[0789] "Recommendation" refers to the system suggesting the best options or solutions to the user.

[0790] "Acquisition" refers to the act of the system gathering the necessary data or information.

[0791] "NLP algorithm" is an abbreviation for natural language processing algorithm, a technology for analyzing human language and understanding its meaning.

[0792] The present invention relates to a system that integrates a user's schedule information, location information, behavioral data, and emotional data to provide optimal insurance plans and security measures. This system operates in conjunction with the user's smartphone (terminal), a server, an emotional analysis engine, and a calendar API.

[0793] System Overview

[0794] The system consists of the following main components:

[0795] 1. User's smartphone (device)

[0796] Calendar API: Used to obtain the user's schedule information.

[0797] GPS function: Used to collect user location information.

[0798] Camera and microphone: Used to capture user emotion data.

[0799] Notification function: Used to notify users of selected insurance plans and risk alerts.

[0800] 2. Server

[0801] Natural language processing (NLP) algorithm: Analyzes the acquired schedule information and identifies the type of schedule and date and time.

[0802] Machine learning algorithms: Analyze behavioral and emotional data to make risk assessments.

[0803] Database: Stores user profile data and past behavioral data.

[0804] 3. Sentiment Analysis Engine

[0805] Facial recognition algorithm: Obtains emotional data from the user's facial expressions.

[0806] Voice analysis algorithm: Analyzes emotional data from the tone and patterns of the user's voice.

[0807] Processing flow

[0808] 1. Acquisition and analysis of schedule information

[0809] When a user enters appointment information into a smartphone calendar app, the information is sent to the server via the calendar API, and the server uses a natural language processing algorithm to analyze the information and identify the type of appointment and the date and time.

[0810] Example: For an event such as "Drinking party in Shinjuku at 10pm next Friday," the keywords "drinking party," "late night," and "Shinjuku" are extracted and the risk is assessed.

[0811] 2. Location and behavioral data collection

[0812] The user's smartphone uses its GPS function and Wi-Fi login information to periodically send its current location and route to the server.

[0813] Example: Tracking travel routes within a 15-minute walk from Shinjuku Station and assessing public safety.

[0814] 3. Emotional Data Collection and Analysis

[0815] The smartphone's camera and microphone are used to analyze the user's facial expressions and voice in real time to obtain emotional data. The emotion analysis engine analyzes facial expressions and voice and sends the data to a server.

[0816] Example: Facial recognition and voice analysis algorithms identify when a user is under stress.

[0817] 4. Risk Assessment and Notification

[0818] The server integrates behavioral and emotional data to assess high-risk situations in real time, and if necessary, sends risk alerts and optimal insurance plans via push notifications to smartphones.

[0819] Example: Sending a notification saying, "You will be spending a long time at the next cafe, so we will apply for specific insurance to cover the risk of loss there."

[0820] Example prompts to input to the generative AI model

[0821] "Please suggest the best insurance plan for my next appointment."

[0822] "Predict risks based on your current emotional state and recommend appropriate actions."

[0823] As described above, by centrally understanding and analyzing a user's schedule, location information, behavioral data, and emotional data, it is possible to provide optimal insurance plans and security measures in real time so that users can live their daily lives with peace of mind.

[0824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0825] Step 1:

[0826] A user enters appointment information into a calendar app on their smartphone. This becomes the input data. This appointment information is sent to a server via a calendar API. The server receives the appointment information and analyzes the contents of the appointment using a natural language processing (NLP) algorithm. This analysis identifies information such as the type of appointment, date, time, and location. The output is structured data of the analyzed appointment information.

[0827] Step 2:

[0828] The server uses the structured schedule information to refer to the user's profile database and selects the optimal insurance plan based on past behavioral patterns and existing insurance contract status. The input is the analyzed schedule information and the user's profile data. In this step, a machine learning algorithm is used to determine the insurance plan that is most suitable for the user. The output is the selected insurance plan.

[0829] Step 3:

[0830] The server generates a notification of the selected insurance plan and sends it to the device. The device then notifies the user via push notification or email. The input is the selected insurance plan, and the output is the notification sent to the user. Specifically, the notification includes the recommended insurance plan and the reason for its selection.

[0831] Step 4:

[0832] The user checks the notification on the device and decides whether to approve the recommended insurance plan. When the user approves, the device sends the information to the server. The input is the user's approval, and the output is the approval data sent to the server.

[0833] Step 5:

[0834] The server updates the insurance contract details with the user's approval. This allows the new insurance plan to be applied in real time. The input is the user's approval data, and the output is the updated insurance contract data. Specifically, an API that changes the contract details in conjunction with the insurance company's system is called.

[0835] Step 6:

[0836] The user's smartphone periodically sends their current location and route to the server using GPS and Wi-Fi login information. The input is the user's location information, and the output is the location information stored in the server's behavior database.

[0837] Step 7:

[0838] The server analyzes the collected behavioral data using a machine learning algorithm to identify locations and situations where the user is at high risk of losing their item. The input is location information and behavioral data, and the output is risk assessment data. Specifically, the server calculates a risk score based on past data patterns.

[0839] Step 8:

[0840] The user acquires emotional data using the smartphone's camera and microphone. The device analyzes the user's emotional state using facial recognition and voice analysis algorithms and sends the data to a server. The input is the user's facial expression and voice data, and the output is the analyzed emotional data.

[0841] Step 9:

[0842] The server analyzes the emotional data in combination with the behavioral data to assess the risk of loss and crime. The input is the emotional data and the behavioral data, and the output is a comprehensive risk assessment. Specific operations include determining whether stress or reduced attention contributes to the risk.

[0843] Step 10:

[0844] Based on the risk assessment results, the server generates and sends notifications to users recommending appropriate insurance plans and security measures. The input is risk assessment data, and the output is a push notification to the user. Specifically, if the user is in a high-risk area, a message recommending a specific insurance plan is sent.

[0845] This makes it possible to perform risk assessments based on the user's diverse schedules, movements, and emotional state, and automatically present and apply optimal insurance plans and security measures.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] [Third embodiment]

[0850] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0851] 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.

[0852] 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).

[0853] 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.

[0854] 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.

[0855] 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).

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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."

[0862] This invention is a system that provides optimal insurance plans based on a user's schedule and location information. This system works in conjunction with the user's device, server, and calendar API. Specifically, it acquires the user's schedule information, analyzes it, selects the optimal insurance plan, notifies the user, and applies it. It also collects location and behavioral data to predict risks and make creative proposals.

[0863] A natural language description of what the program does

[0864] Retrieving and parsing calendar information

[0865] 1. The user installs a calendar app on their device, enters schedule information, and configures the device to access the calendar API.

[0866] 2. The server periodically connects to the calendar API to retrieve the user's schedule information. At that time, data on the user's various schedules (meetings, business trips, private events, etc.) is retrieved via the calendar API.

[0867] 3. The server analyzes the acquired schedule information and uses a natural language processing algorithm to understand the content of the event and identify the type (e.g., business trip, meeting), date, time, and location.

[0868] Insurance plan recommendations and coverage

[0869] 4. The server selects the optimal insurance plan based on the analysis results, taking into account the user's past behavioral patterns and existing insurance contract status.

[0870] 5. The server notifies the user of the selected insurance plan. For example, a message such as "You have an unexpected business trip next Tuesday, which increases your risk of loss. We recommend this insurance plan" is sent via a mobile app or email.

[0871] 6. The user reviews the notification and approves the recommended insurance plan. After approval, the device sends the information to the server.

[0872] 7. The server receives the user's approval and updates the insurance contract with the new insurance coverage.

[0873] Location and behavioral data collection

[0874] 8. The user installs the provided Tracker app on their smartphone and sets permission to share location information.

[0875] 9. The device (smartphone) periodically collects location data and other behavioral information and sends it to a server, including GPS data, Bluetooth device detection, Wi-Fi login information, etc.

[0876] 10. The server analyzes the collected behavioral data and applies learning algorithms to identify locations and situations where the user is at high risk of loss.

[0877] Creative suggestions and support

[0878] 11. Based on the learning results and real-time behavioral data, the server predicts locations and situations with a high risk of loss and suggests advance notifications and insurance plans to the user. For example, a message such as, "You often spend time in cafes on your next business trip, so there is a high risk of loss there. We will apply insurance for this location."

[0879] 12. Once the user approves the proposal, the server immediately applies the insurance plan and reflects it in the user's contract.

[0880] Specific examples

[0881] For example, suppose a user has an unexpected business trip scheduled for next Tuesday entered into their calendar. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user approves, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information and behavioral data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[0882] In this way, users can automatically apply the most suitable insurance plan for unexpected events and can also handle situations where the risk of loss is high. This system prevents users from forgetting to take out insurance and effectively reduces the risk of loss that may occur in their daily lives.

[0883] The processing flow will be explained below.

[0884] Step 1:

[0885] The server periodically sends a request to the user's calendar API to obtain the latest schedule information, and the user's schedule data is collected on the server.

[0886] Step 2:

[0887] The server then uses natural language processing (NLP) algorithms to analyze the calendar information, identifying the type of event (e.g., business trip, meeting, private event), date, time, and location.

[0888] Step 3:

[0889] The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[0890] Step 4:

[0891] The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and email. The notification includes the recommended insurance plan and the reason for the recommendation.

[0892] Step 5:

[0893] The user checks the notification from the server and decides whether to approve the recommended insurance plan. After approval, the user's device sends the information to the server.

[0894] Step 6:

[0895] The server updates the insurance contract based on the received approval information, allowing the new insurance plan to be applied in real time.

[0896] Step 7:

[0897] The user installs the provided Tracker app on their smartphone and sets up location sharing.

[0898] Step 8:

[0899] The device (smartphone) periodically collects the user's location information and behavioral data and sends it to a server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[0900] Step 9:

[0901] The server analyzes the collected behavioral data using machine learning algorithms to identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[0902] Step 10:

[0903] The server predicts locations and situations with a high risk of loss, notifies the user, and proposes a specific insurance plan. For example, a notification could be sent saying, "Since you will be spending a long time at the next cafe, we will apply for insurance that covers the risk of loss there."

[0904] Step 11:

[0905] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan, and this information is also updated in real time.

[0906] In this way, the most suitable insurance plan is automatically selected and applied based on the user's schedule information and behavioral data, reducing the user's risk of loss.

[0907] Example 1

[0908] 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."

[0909] Conventional insurance systems require users to purchase insurance themselves, making it difficult to respond immediately to sudden changes in plans. Furthermore, they lack the functionality to predict risks based on users' daily activities and location information and then propose appropriate insurance plans based on those risks. This leads to problems such as users forgetting to purchase insurance or being unable to respond appropriately to high-risk situations.

[0910] 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.

[0911] In this invention, the server includes means for acquiring a user's schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means operating in real time to notify the user of the selected insurance plan, means for predicting risks based on the collected location information and behavioral data, and means for notifying the user of risks in advance and creatively proposing insurance plans. This allows the server to automatically select and notify an appropriate insurance plan based on the user's behavior and schedule, making it possible to quickly respond to sudden schedule changes and high-risk situations.

[0912] "User's schedule information" refers to schedules such as meetings, business trips, and private events that a user has entered into a calendar app or schedule management tool.

[0913] "Analysis" refers to the process of using natural language processing algorithms and learning algorithms to understand the content of the acquired user's schedule information and behavioral data, and to identify specific information (e.g., date, time, location, type).

[0914] "Insurance plan" refers to the content of the insurance contract that is applied based on the user's actions and plans, and includes, for example, short-term business trip insurance and plans covering loss risks.

[0915] "Selecting" refers to choosing the most suitable insurance plan from multiple candidates based on analyzed data.

[0916] "Notifying" refers to informing the user about the selected insurance plan via email, mobile app, etc.

[0917] "Approve" means that the user consents to the notified insurance plan and expresses his / her intention to accept its application.

[0918] "Location Information" refers to a user's current location and movement data obtained through GPS, Wi-Fi login, Bluetooth device detection, etc.

[0919] "Behavioral Data" refers to data that includes information about a user's daily activities, travel patterns, and location.

[0920] "Predicting risks" refers to predicting the risk of loss in specific situations or locations based on collected location information and behavioral data.

[0921] "Creative proposals" refers to presenting users with new insurance plan ideas and improvement methods that are not bound by conventional patterns, based on risk predictions and analysis results.

[0922] MODE FOR CARRYING OUT THE INVENTION

[0923] This invention is a system that provides optimal insurance plans based on a user's schedule information and location information. This system operates in conjunction with the user's device, a server, and a calendar API.

[0924] Specifically, a user first installs a calendar app on their device and inputs their schedule information. The server then periodically connects to the calendar API to retrieve the user's schedule information. This can be done using a widely used calendar API such as the Google Calendar API.

[0925] The server analyzes the acquired schedule information and uses natural language processing (NLP) algorithms to identify the type of event (e.g., business trip, meeting), date, time, and location. Based on this information, the server selects the most suitable insurance plan. This selection process also takes into account past behavioral data and existing insurance policy information. The server then notifies the user of the selected insurance plan via email or in-app notification.

[0926] The user then approves the recommended insurance plan through the mobile app, and once approval is complete, the device sends the information to the server, which then updates the insurance contract with the new coverage.

[0927] The system also has the ability to collect user location and behavioral data. When a user installs the Tracker app on their smartphone and sets location sharing permission, the device periodically collects location data and other behavioral information and sends it to a server. This includes GPS data, Bluetooth device detection, Wi-Fi login information, and more.

[0928] The server analyzes the collected behavioral data and applies a learning algorithm to identify locations and situations where the user is at high risk of losing their device. Based on the learning results and real-time behavioral data, it makes risk predictions and proposes advance notifications and insurance plans to the user.

[0929] As a concrete example, if a user has an unexpected business trip scheduled for next Tuesday entered in their calendar, the server will obtain and analyze that information, select an insurance plan for short-term business trips, and notify the user. If the user approves the plan, the insurance contract will be updated immediately. Similarly, for a user who spends much of their weekdays in a cafe, the server will determine from their location and behavioral data that there is a high risk of loss at the cafe, and will suggest an insurance plan that is appropriate for that time period.

[0930] An example prompt is:

[0931] "Please explain in detail how to recommend the best insurance plan for the user based on their schedule and location."

[0932] "Please explain in detail how to select the right insurance plan for users who plan to travel."

[0933] "How can I use a user's location information to predict risks in a specific location and suggest insurance plans?"

[0934] In this way, users can quickly respond to sudden changes in plans or high-risk situations and prevent forgetting to take out insurance. This system can effectively reduce risks that may occur in users' lives.

[0935] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0936] Step 1:

[0937] A user installs a calendar app on their device and enters event information. The entered event information includes the type of event, date, time, and location. Specifically, the user enters an event such as "Business trip to Tokyo next Tuesday." This event information is saved in cloud storage via the calendar API.

[0938] Step 2:

[0939] The server periodically connects to the calendar API to retrieve the user's schedule information. The retrieved data includes the type of event, date, time, and location from the user's calendar. For example, the Google Calendar API is used to retrieve "User A's schedule" and store it in the server's database.

[0940] Step 3:

[0941] The server analyzes the schedule information it has acquired and uses a natural language processing algorithm (NLP) to understand the content of the event. The input data is the schedule content, and the output after analysis is the type, date, time, and location of the identified event. For example, from the content "Business trip in Tokyo next Tuesday," it extracts "Business trip," "October 10, 2023," and "Tokyo."

[0942] Step 4:

[0943] The server selects the optimal insurance plan based on the analysis results. This selection also takes into account the user's past behavioral data and existing insurance contract status. The input data is the identified event information and the user's insurance history, and the output data is the recommended insurance plan. For example, a "short-term insurance plan tailored to business trips" may be selected.

[0944] Step 5:

[0945] The server notifies the user of the selected insurance plan. The notification is sent via a mobile app or email. The input data is the selected insurance plan, and the output data is the notification message sent to the user. Specifically, the message sent reads, "You have an unexpected business trip next Tuesday, which increases the risk of loss. We recommend this insurance plan."

[0946] Step 6:

[0947] The user reviews the notification and approves the recommended insurance plan by pressing a button in the mobile app. The input is the notification message, and the output is the user's approval data.

[0948] Step 7:

[0949] The terminal sends the user's authorization information to the server, where the input data is the user's authorization data and the output data is the server's updated insurance policy information.

[0950] Step 8:

[0951] The server updates the insurance contract with the new insurance details upon receiving the user's approval. The input data is the approval information, and the output data is the updated insurance contract details.

[0952] Step 9:

[0953] A user installs the Tracker app on their smartphone and sets location sharing permission. The input data is the location sharing setting, and the output data is the location sharing permission status.

[0954] Step 10:

[0955] The device periodically collects location data and behavioral information and sends it to a server. The input data is GPS data, Bluetooth device detection, Wi-Fi login information, etc., and the output data is the behavioral data sent to the server.

[0956] Step 11:

[0957] The server analyzes the collected behavioral data and applies a learning algorithm to identify locations and situations where the user is at high risk of losing their belongings. The input data is behavioral data, and the output data is the identified risk information. For example, it identifies a pattern such as "often spending time at cafes during the day on weekdays."

[0958] Step 12:

[0959] The server predicts risks based on the learning results and real-time behavioral data, and then notifies the user and suggests insurance plans. The input data is the identified risk information, and the output data is the notification message sent to the user. For example, a notification may be sent stating, "Since you often spend time in cafes at your next business trip destination, there is a high risk of loss at that location. We will apply insurance for this location."

[0960] Step 13:

[0961] The user approves the proposal, and the server immediately applies the insurance plan and reflects it in the user's contract. The input data is the user's approval data, and the output data is the updated insurance plan and contract information.

[0962] (Application example 1)

[0963] 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."

[0964] In modern brick-and-mortar stores, the increased risk of theft and loss when purchasing expensive items is a serious problem. Conventional insurance systems assume fixed conditions and situations and are unable to propose optimal insurance plans that reflect the user's behavior and location information in real time. This poses a challenge, making it difficult to quickly apply an appropriate insurance plan when a user purchases a product.

[0965] 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.

[0966] In this invention, the server includes means for scanning a product code when a user selects a product in a physical store, means for analyzing the product code and assessing the risk associated with the product, and means for proposing an optimal insurance plan in real time based on the assessed risk. This makes it possible to perform risk assessment on the spot when a user purchases a product in a physical store, and to immediately propose and apply an optimal insurance plan.

[0967] "User" refers to a person who uses this system.

[0968] "Schedule information" refers to data on events and schedules that are set in advance by the user.

[0969] "Means of acquisition" refers to the server's ability to gather information from calendar APIs and other data sources.

[0970] "Means of analysis" refers to the function of processing the acquired data and identifying details such as category, time, and location.

[0971] "Means for identifying" refers to the function of extracting specific information from the analyzed data and clarifying its content.

[0972] "Insurance Plan" means a contract that provides insurance coverage against a specific risk.

[0973] "Means of selection" refers to the function by which the server selects the most suitable insurance plan based on the analyzed data.

[0974] "Means for notifying" refers to a function for informing the user of the selected insurance plan.

[0975] "Means of application" refers to the function of officially reflecting the insurance plan approved by the user in the contract.

[0976] "Location information" refers to data indicating a user's current location and movement history.

[0977] "Behavioral data" refers to data that indicates a user's past behavioral patterns and history.

[0978] "Means of collection" refers to functions for collecting location information and behavioral data.

[0979] "Product code" refers to the identification information of a product sold in a physical store.

[0980] "Means of scanning" refers to the ability to read product codes using smart glasses, smartphones, etc.

[0981] "Risks relating to products" refers to the possible losses or dangers associated with a particular product.

[0982] "Means for assessing risk" refers to the function of analyzing the risks associated with a product based on collected data.

[0983] "Real-time recommendations" refers to the ability to instantly show users insurance plans based on current information.

[0984] MODE FOR CARRYING OUT THE INVENTION

[0985] The embodiment of this invention is a system that provides optimal insurance plans in real time based on a user's schedule information and location information. This system operates in cooperation with the user's terminal, a server, and a calendar API. The details of the system are described below.

[0986] System Configuration

[0987] The system requires the user's smart glasses or smartphone and a server as the main hardware. The software includes a calendar API, location API, and insurance plan API. The system collects and analyzes data related to the user's schedule, location, and product code to propose the optimal insurance plan.

[0988] Data collection and analysis

[0989] The server obtains the user's schedule information through the calendar API. This schedule information is then analyzed using a natural language processing algorithm. The analyzed schedule information is then categorized into detailed data such as the schedule type, date, and location. Location and behavior data are also collected through the user's smart glasses or smartphone.

[0990] Based on the analysis data, the server tracks users' behavior in physical stores by scanning product codes. When a user scans an item, the risk associated with that item is instantly assessed and corresponding insurance plans are proposed in real time.

[0991] Insurance plan proposal and application

[0992] The server selects the optimal insurance plan based on the analysis results and notifies the user. If the user approves the proposed insurance plan, the information is sent to the server and the insurance contract is updated. This allows the user to immediately apply the optimal insurance plan for the risks of the products purchased in the physical store.

[0993] Specific examples

[0994] For example, if a user is about to purchase an expensive camera in a brick-and-mortar store, they can scan the product code on the camera with smart glasses or a smartphone. The system will then immediately assess the theft risk of the product and send a notification saying, "This camera has a high theft risk, so we recommend a dedicated theft insurance plan." If the user approves the insurance plan, the plan will be applied as a new insurance policy.

[0995] Prompt Sentence Examples

[0996] The following prompts can be used to instruct the generative AI model on how the system should behave:

[0997] Develop an application that predicts the items a user is likely to purchase that day based on their location and calendar information, assesses the risks associated with those items, and displays appropriate insurance plans in real time. For example, if a user is planning to purchase an expensive camera, implement a function that evaluates the risk of theft and suggests corresponding insurance plans by scanning the camera's product code.

[0998] In this way, the present invention can quickly assess the risks a user faces while shopping in a physical store and provide the most appropriate insurance plan, thereby improving the user's peace of mind and convenience.

[0999] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1000] Step 1:

[1001] A user installs a calendar app on their device (smart glasses or smartphone) and inputs schedule information. At this time, the device is set up to be able to access the calendar API.

[1002] Input: Schedule information entered by the user

[1003] Output: Calendar information for which access to the Calendar API has been granted

[1004] Specific operation: The user enters event information into the calendar app and sets up synchronization.

[1005] Step 2:

[1006] The server periodically connects to the calendar API to obtain the user's schedule information. The calendar API obtains data on the user's various schedules (meetings, business trips, private events, etc.).

[1007] Input: Schedule information obtained from the calendar API

[1008] Output: Raw schedule information data

[1009] Specific operation: The server sends an API request and receives schedule information in JSON format.

[1010] Step 3:

[1011] The server analyzes the acquired schedule information and uses natural language processing algorithms to understand the contents of the event, specifically identifying the type of schedule, date, time, and location.

[1012] Input: Raw schedule information data

[1013] Output: Parsed schedule information (e.g., business trips, meetings, etc.)

[1014] Specific operation: The server applies natural language processing algorithms to perform text analysis.

[1015] Step 4:

[1016] The server selects the optimal insurance plan based on the analysis results, taking into account the user's past behavioral patterns and existing insurance contract status.

[1017] Input: Analyzed schedule information, past behavior patterns, existing insurance contract status

[1018] Output: Optimal insurance plan

[1019] Specific operation: The server performs multivariate analysis, comparing multiple factors to determine the optimal insurance plan.

[1020] Step 5:

[1021] The server notifies the user of the selected insurance plan, for example, via a mobile app or email.

[1022] Input: Best insurance plan information

[1023] Output: A message to inform the user

[1024] Specific operation: The server sends a push notification to the user's device.

[1025] Step 6:

[1026] The user checks the notification and approves the recommended insurance plan. The approved insurance plan information is sent from the device to the server.

[1027] Input: User authorization information

[1028] Output: Insurance plan approval notice

[1029] Specific behavior: The user taps the application notification and clicks the approve button.

[1030] Step 7:

[1031] The server updates the insurance contract with the user's approval and applies the new insurance details.

[1032] Input: User approval notification

[1033] Output: Updated insurance contract information

[1034] Specific operation: The server updates the contract database.

[1035] Step 8:

[1036] The user installs the provided Tracker app on their smartphone and sets permission to share location information.

[1037] Input: User's location sharing permission

[1038] Output: Location sharing enabled

[1039] Specific behavior: The user enables location sharing in the app's settings screen.

[1040] Step 9:

[1041] The device (smartphone) periodically collects location data and other behavioral information and sends it to a server.

[1042] Input: location data, behavioral information

[1043] Output: Location data and behavioral information sent to the server

[1044] Specific operation: The smartphone periodically acquires GPS data and sends it to the server.

[1045] Step 10:

[1046] The server analyzes the collected behavioral data and applies learning algorithms to identify locations and situations where users are at high risk of loss.

[1047] Input: location data, behavioral information

[1048] Output: Identification of locations and situations with high risk of loss

[1049] Specific operation: The server performs data analysis using machine learning algorithms.

[1050] Step 11:

[1051] Based on the learning results and real-time behavioral data, the server predicts locations and situations with a high risk of loss, and provides advance notification and offers insurance plans to users.

[1052] Input: Learning results, real-time behavioral data

[1053] Output: Notification of recommended insurance plan

[1054] Specific operation: The server sends a push notification to the user based on the risk assessment result.

[1055] Step 12:

[1056] If the user approves the proposal, the server immediately applies the insurance plan and reflects it in the user's contract.

[1057] Input: User approval notification

[1058] Output: Insurance plan applied

[1059] Specific operation: The server updates the contract database again.

[1060] 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.

[1061] This invention is a system that integrates a user's schedule information, location information, and emotional data to provide the optimal insurance plan. This system operates in conjunction with the user's device, server, emotion engine, and calendar API. Specifically, it acquires the user's schedule information, analyzes it, and selects, notifies, and applies the optimal insurance plan. It also collects location information and emotional data to make risk predictions and creative proposals.

[1062] A natural language description of what the program does

[1063] Retrieving and parsing calendar information

[1064] 1. The user installs a calendar app on their device, enters schedule information, and configures the device to access the calendar API.

[1065] 2. The server periodically connects to the calendar API to retrieve the user's schedule information. Data on the user's various schedules (meetings, business trips, private events, etc.) is retrieved through the calendar API.

[1066] 3. The server analyzes the acquired schedule information using natural language processing (NLP) algorithms to identify the type of schedule (e.g., business trip, meeting), date, time, and location.

[1067] Insurance plan recommendations and coverage

[1068] 4. The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[1069] 5. The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and emails. The notification includes the recommended insurance plan and the reason for the recommendation.

[1070] 6. The user reviews the notification and decides whether to approve the recommended insurance plan. After approval, the device sends the information to the server.

[1071] 7. The server receives the user's approval and updates the insurance policy, so that the new insurance plan is applied in real time.

[1072] Location and behavioral data collection

[1073] 8. The user installs the provided Tracker app on their smartphone and sets up location sharing.

[1074] 9. The device (smartphone) periodically collects the user's location and behavior data and sends it to the server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[1075] 10. The server uses machine learning algorithms to analyze the collected behavioral data and identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[1076] Emotion data collection and analysis

[1077] 11. Emotion data is acquired using the emotion engine provided by the user. The emotion engine analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[1078] 12. The device transmits emotional data to a server that analyzes the data to determine the user's emotional state, including stress levels and signs of attention loss.

[1079] 13. The server analyzes the emotional data in combination with the behavioral data to identify the risk of loss and an appropriate insurance plan based on the user's emotional state.

[1080] Creative suggestions and support

[1081] 14. Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification may be sent saying, "Since you will be spending a long time at the next cafe, we will apply for specific insurance to cover the risk of loss there."

[1082] 15. Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan. This information is also updated in real time.

[1083] Specific examples

[1084] For example, a user enters an unexpected business trip into their calendar for next Tuesday. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user accepts this, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information, behavioral data, and emotional data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[1085] In this way, this system comprehensively analyzes the user's schedule information, location information, behavioral data, and emotional data, and provides the most appropriate insurance plan, thereby increasing the user's sense of security.

[1086] The processing flow will be explained below.

[1087] Step 1:

[1088] The server periodically sends a request to the user's calendar API to obtain the latest schedule information, and the user's schedule data is aggregated on the server.

[1089] Step 2:

[1090] The server then uses natural language processing (NLP) algorithms to analyze the calendar information, identifying the type of event (e.g., business trip, meeting, private event), date, time, and location.

[1091] Step 3:

[1092] The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[1093] Step 4:

[1094] The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and email. The notification includes the recommended insurance plan and the reason for the recommendation.

[1095] Step 5:

[1096] The user checks the notification from the server and decides whether to approve the recommended insurance plan. After approval, the user's device sends the information to the server.

[1097] Step 6:

[1098] The server updates the insurance contract based on the received approval information, allowing the new insurance plan to be applied in real time.

[1099] Step 7:

[1100] The user installs the provided Tracker app on their smartphone and sets up location sharing.

[1101] Step 8:

[1102] The device (smartphone) periodically collects the user's location information and behavioral data and sends it to a server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[1103] Step 9:

[1104] The server analyzes the collected behavioral data using machine learning algorithms to identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[1105] Step 10:

[1106] Emotion data is acquired using the emotion engine provided by the user, which analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[1107] Step 11:

[1108] The device sends emotional data to a server that analyzes it to determine the user's emotional state, including stress levels and signs of attention loss.

[1109] Step 12:

[1110] The server analyzes the emotional data in combination with the behavioral data to identify the risk of loss and an appropriate insurance plan based on the user's emotional state.

[1111] Step 13:

[1112] Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification could be sent saying, "Since you will be spending a long time at the next cafe, we will apply for a specific insurance policy that covers the risk of loss there."

[1113] Step 14:

[1114] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan, and this information is also updated in real time.

[1115] Example 2

[1116] 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."

[1117] There is a need for a system that quickly and efficiently provides appropriate insurance plans for risks that arise from users' schedules and actions in their daily lives. However, conventional systems have difficulty comprehensively analyzing a user's schedule information, location information, and emotional data to provide appropriate insurance plans. Risk assessment that takes emotional state into account is also insufficient. Therefore, a system capable of more comprehensive and accurate risk assessment is needed.

[1118] 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.

[1119] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means for acquiring and analyzing emotional data, and means for integrating and analyzing the emotional data and behavioral data to perform risk assessment. This makes it possible to comprehensively analyze a variety of user data, provide a comprehensive risk assessment, and provide an optimal insurance plan.

[1120] "Schedule information" refers to detailed information about an event or activity that a user enters into a calendar application, including the date, time, location, and content.

[1121] A "Calendar API" is a programming interface for accessing, reading, and writing data in a calendar application.

[1122] "Natural language processing algorithms" are technologies and computer programs that allow computers to analyze, understand, and generate human language.

[1123] An "insurance plan" is a package containing the terms and conditions of an insurance policy against a particular risk.

[1124] "Location Information" means a user's geographic location data as determined by GPS or a network.

[1125] "Behavioral data" is data that indicates a user's movement patterns and activity history.

[1126] "Emotion data" is data that represents the user's emotional state and is obtained through facial recognition, voice analysis, touch input, etc.

[1127] "Risk assessment" is the process of analyzing multiple data sets to identify the level of risk in each individual situation.

[1128] "Notifications" are messages sent by the system to inform users of important information or suggestions, and include push notifications and emails.

[1129] "Analysis" is the process of processing acquired data and extracting useful information.

[1130] A "profile database" is a database that stores information such as a user's past activities and contract status.

[1131] The present invention is a system that integrates a user's schedule information, location information, and emotion data to provide the optimal insurance plan. This system operates in cooperation with the user's device, a server, an emotion engine, and a calendar API. A specific embodiment of this system is described below.

[1132] Retrieving and parsing calendar information

[1133] A user installs a calendar application on their device and inputs their schedule information. The device is then configured to be able to access a calendar API (e.g., Google Calendar API). Through this API, the user's schedule information is periodically sent to a server.

[1134] The server connects to the calendar API to retrieve the user's event information, which is then parsed using a Python natural language processing (NLP) library (e.g., NLTK or spaCy) to determine the event type (e.g., business trip, meeting), date, time, and location.

[1135] Insurance plan recommendations and coverage

[1136] Based on the acquired schedule information, the server refers to the user's profile database (e.g., PostgreSQL) and selects the most appropriate insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the schedule includes a business trip, it will recommend business trip insurance.

[1137] Information about the selected insurance plan is sent to the user via notification methods (e.g., push notification by Firebase Cloud Messaging, email by SendGrid). The user checks the notification and approves the recommended insurance plan on their device. After approval, the device sends the information to the server via a REST API.

[1138] The server, upon receiving the user's approval, updates the insurance contract details in the database and applies the new insurance plan in real time.

[1139] Location and behavioral data collection

[1140] Users install the provided tracker application (e.g., MyTracks or Google Fit) on their smartphones and set up location sharing. The device uses its GPS module to collect location information and periodically transmits it to a server. This collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[1141] The server analyzes the collected behavioral data using machine learning algorithms such as Scikit-learn to identify locations and situations with a high risk of loss.

[1142] Emotion data collection and analysis

[1143] Users acquire emotion data using an emotion engine (e.g., Emotion SDK), which analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[1144] The device sends the analyzed emotional data to a server, which then analyzes it using a data analysis library such as Pandas to identify the user's emotional state (e.g., stress level, signs of attention loss).

[1145] The server integrates and analyzes the emotional data and behavioral data to identify the user's risk assessment and appropriate insurance plan. Based on the analysis results, creative insurance plans are proposed for specific behaviors.

[1146] Creative suggestions and support

[1147] Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification may be sent saying, "Since you will be spending a long time at the next cafe, we will apply for specific insurance to cover the risk of loss there."

[1148] Once the user has reviewed and approved these proposals, the server will instantly update the insurance contract and the new insurance plan will be applied in real time.

[1149] Specific examples

[1150] For example, a user enters an unexpected business trip into their calendar for next Tuesday. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user accepts this, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information, behavioral data, and emotional data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[1151] Example prompt:

[1152] "A user has a business trip scheduled for next Tuesday on their calendar. How can we use this information to recommend and notify them of the best insurance plan?"

[1153] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1154] Step 1:

[1155] A user installs a calendar application on their device and inputs appointment information. The input data includes the appointment title, date and time, location, and notes. The device is set up to be able to access a calendar API (e.g., Google Calendar API). At this point, the input is the appointment information manually entered by the user. The output is the appointment information registered in the calendar.

[1156] Step 2:

[1157] The server periodically connects to the calendar API to obtain the user's schedule information. The server accesses the API using an HTTP request. The input is the schedule information obtained from the calendar API. The output is JSON format data of the retrieved schedule information.

[1158] Step 3:

[1159] The server analyzes the acquired schedule information using a natural language processing (NLP) algorithm. A Python NLP library (NLTK or spaCy) is used for the analysis. The input is the schedule information in JSON format. The NLP algorithm identifies the schedule type (meeting, business trip, etc.), date, time, and location. The output is data containing the analyzed schedule type, date, time, and location.

[1160] Step 4:

[1161] The server references the user's profile database (e.g., PostgreSQL) and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. The input is the parsed schedule information and the user's profile data. An SQL query is used to search for suitable insurance plans. The output is a list of selected insurance plans.

[1162] Step 5:

[1163] The server sends a notification to the user based on the selected insurance plan. Notification methods include Firebase Cloud Messaging and SendGrid. The input is information about the selected insurance plan. The output is a push notification or email notification to the user's device.

[1164] Step 6:

[1165] The user checks the notification on the device and chooses whether to approve or reject the recommended insurance plan. The input is the notification content from the server. The user's operation executes the action of approval or rejection. The output is the result of approval or rejection.

[1166] Step 7:

[1167] The terminal sends the user's approval result to the server. The input is the user's approval or denial result. The data is sent to the server using an HTTP POST request. The output is the approval result sent to the server.

[1168] Step 8:

[1169] The server receives the user's approval and updates the insurance contract details in the database. It accesses the database using an ORM such as SQLAlchemy. The input is the user's approval result and the selected insurance plan. The output is the updated insurance contract data.

[1170] Step 9:

[1171] The user installs the provided tracker application on their smartphone and configures location sharing. The input is the application installation and configuration information. The output is the device state after the location sharing configuration is complete.

[1172] Step 10:

[1173] The device periodically collects location information and behavioral data using a GPS module and sends it to a server. The inputs are the GPS data collected by the device, Bluetooth device detection information, and Wi-Fi login information. The output is the location information and behavioral data sent to the server.

[1174] Step 11:

[1175] The server analyzes the collected behavioral data using machine learning algorithms (such as Scikit-learn) to identify locations and situations where the user is at high risk of losing their device. The input is location information and behavioral data. The output is the identified high-risk locations and situations.

[1176] Step 12:

[1177] The user acquires emotion data using the emotion engine. The emotion engine uses a facial recognition camera, voice analysis, and touch input. The inputs are camera footage, voice data, and touch information. The output is emotion data analyzed in real time.

[1178] Step 13:

[1179] The device sends emotional data to the server. The input is the emotional data analyzed by the device. It is encrypted and sent using HTTPS. The output is the emotional data sent to the server.

[1180] Step 14:

[1181] The server analyzes the emotion data using a data analysis library such as Pandas to identify the user's emotional state (stress level, signs of attention loss). The input is the emotion data. The output is the analyzed emotional state data.

[1182] Step 15:

[1183] The server integrates and analyzes the emotional and behavioral data to perform a risk assessment of the user. The input is the emotional and behavioral data. The output is a comprehensive risk assessment result and the identification of an appropriate insurance plan.

[1184] Step 16:

[1185] The server proposes and notifies the user of a creative insurance plan based on the risk assessment results. The input is the risk assessment results. The output is a proposal notification sent to the user terminal.

[1186] Step 17:

[1187] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract to include the new insurance plan. The input is the user's approval. The output is the updated insurance contract information.

[1188] (Application example 2)

[1189] 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."

[1190] Today's busy users frequently move around and have diverse schedules, making it difficult to properly predict and address the associated risks. Furthermore, while emotional states are an important factor in risk assessment, there is no efficient way to grasp and utilize them. As a result, optimal insurance plans and security measures are often not applied in high-risk situations, reducing users' sense of security. To address these issues, a system is needed that comprehensively analyzes users' schedule information, location information, behavioral data, and emotional data to provide optimal insurance plans and security measures.

[1191] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means for acquiring and analyzing user emotion data, means for combining the emotion data with the behavioral data to perform risk assessment, and means for recommending an appropriate insurance plan to the user based on the identified risk assessment. This makes it possible to perform risk assessment based on the user's various schedules, movements, and emotional state, and automatically present and apply optimal insurance plans and security measures.

[1192] "User" refers to any individual or organization that uses this system.

[1193] "Schedule information" is information about events and activities that a user has scheduled, including the date, time, location, and content.

[1194] "Analysis" refers to breaking down acquired data or information and processing it to understand its structure and meaning.

[1195] An "insurance plan" refers to the content and conditions of the insurance contracted by a user, and provides coverage according to risk.

[1196] "Notification" refers to the act of informing a user of information from a system, and means of notification include push notifications and emails.

[1197] "Location information" refers to geographical data about a user's current location and travel route.

[1198] "Behavioral data" refers to data about a user's daily movements and movement patterns, including GPS data and Wi-Fi login information.

[1199] "Emotional data" is data collected to analyze a user's emotional state, and is information obtained from facial expressions, tone of voice, touch input, etc.

[1200] "Risk assessment" refers to the use of acquired and analyzed data to assess the degree of risk a user may face.

[1201] "Recommendation" refers to the system suggesting the best options or solutions to the user.

[1202] "Acquisition" refers to the act of the system gathering the necessary data or information.

[1203] "NLP algorithm" is an abbreviation for natural language processing algorithm, a technology for analyzing human language and understanding its meaning.

[1204] The present invention relates to a system that integrates a user's schedule information, location information, behavioral data, and emotional data to provide optimal insurance plans and security measures. This system operates in conjunction with the user's smartphone (terminal), a server, an emotional analysis engine, and a calendar API.

[1205] System Overview

[1206] The system consists of the following main components:

[1207] 1. User's smartphone (device)

[1208] Calendar API: Used to obtain the user's schedule information.

[1209] GPS function: Used to collect user location information.

[1210] Camera and microphone: Used to capture user emotion data.

[1211] Notification function: Used to notify users of selected insurance plans and risk alerts.

[1212] 2. Server

[1213] Natural language processing (NLP) algorithm: Analyzes the acquired schedule information and identifies the type of schedule and date and time.

[1214] Machine learning algorithms: Analyze behavioral and emotional data to make risk assessments.

[1215] Database: Stores user profile data and past behavioral data.

[1216] 3. Sentiment Analysis Engine

[1217] Facial recognition algorithm: Obtains emotional data from the user's facial expressions.

[1218] Voice analysis algorithm: Analyzes emotional data from the tone and patterns of the user's voice.

[1219] Processing flow

[1220] 1. Acquisition and analysis of schedule information

[1221] When a user enters appointment information into a smartphone calendar app, the information is sent to the server via the calendar API, and the server uses a natural language processing algorithm to analyze the information and identify the type of appointment and the date and time.

[1222] Example: For an event such as "Drinking party in Shinjuku at 10pm next Friday," the keywords "drinking party," "late night," and "Shinjuku" are extracted and the risk is assessed.

[1223] 2. Location and behavioral data collection

[1224] The user's smartphone uses its GPS function and Wi-Fi login information to periodically send its current location and route to the server.

[1225] Example: Tracking travel routes within a 15-minute walk from Shinjuku Station and assessing public safety.

[1226] 3. Emotional Data Collection and Analysis

[1227] The smartphone's camera and microphone are used to analyze the user's facial expressions and voice in real time to obtain emotional data. The emotion analysis engine analyzes facial expressions and voice and sends the data to a server.

[1228] Example: Facial recognition and voice analysis algorithms identify when a user is under stress.

[1229] 4. Risk Assessment and Notification

[1230] The server integrates behavioral and emotional data to assess high-risk situations in real time, and if necessary, sends risk alerts and optimal insurance plans via push notifications to smartphones.

[1231] Example: Sending a notification saying, "You will be spending a long time at the next cafe, so we will apply for specific insurance to cover the risk of loss there."

[1232] Example prompts to input to the generative AI model

[1233] "Please suggest the best insurance plan for my next appointment."

[1234] "Predict risks based on your current emotional state and recommend appropriate actions."

[1235] As described above, by centrally understanding and analyzing a user's schedule, location information, behavioral data, and emotional data, it is possible to provide optimal insurance plans and security measures in real time so that users can live their daily lives with peace of mind.

[1236] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1237] Step 1:

[1238] A user enters appointment information into a calendar app on their smartphone. This becomes the input data. This appointment information is sent to a server via a calendar API. The server receives the appointment information and analyzes the contents of the appointment using a natural language processing (NLP) algorithm. This analysis identifies information such as the type of appointment, date, time, and location. The output is structured data of the analyzed appointment information.

[1239] Step 2:

[1240] The server uses the structured schedule information to refer to the user's profile database and selects the optimal insurance plan based on past behavioral patterns and existing insurance contract status. The input is the analyzed schedule information and the user's profile data. In this step, a machine learning algorithm is used to determine the insurance plan that is most suitable for the user. The output is the selected insurance plan.

[1241] Step 3:

[1242] The server generates a notification of the selected insurance plan and sends it to the device. The device then notifies the user via push notification or email. The input is the selected insurance plan, and the output is the notification sent to the user. Specifically, the notification includes the recommended insurance plan and the reason for its selection.

[1243] Step 4:

[1244] The user checks the notification on the device and decides whether to approve the recommended insurance plan. When the user approves, the device sends the information to the server. The input is the user's approval, and the output is the approval data sent to the server.

[1245] Step 5:

[1246] The server updates the insurance contract details with the user's approval. This allows the new insurance plan to be applied in real time. The input is the user's approval data, and the output is the updated insurance contract data. Specifically, an API that changes the contract details in conjunction with the insurance company's system is called.

[1247] Step 6:

[1248] The user's smartphone periodically sends their current location and route to the server using GPS and Wi-Fi login information. The input is the user's location information, and the output is the location information stored in the server's behavior database.

[1249] Step 7:

[1250] The server analyzes the collected behavioral data using a machine learning algorithm to identify locations and situations where the user is at high risk of losing their item. The input is location information and behavioral data, and the output is risk assessment data. Specifically, the server calculates a risk score based on past data patterns.

[1251] Step 8:

[1252] The user acquires emotional data using the smartphone's camera and microphone. The device analyzes the user's emotional state using facial recognition and voice analysis algorithms and sends the data to a server. The input is the user's facial expression and voice data, and the output is the analyzed emotional data.

[1253] Step 9:

[1254] The server analyzes the emotional data in combination with the behavioral data to assess the risk of loss and crime. The input is the emotional data and the behavioral data, and the output is a comprehensive risk assessment. Specific operations include determining whether stress or reduced attention contributes to the risk.

[1255] Step 10:

[1256] Based on the risk assessment results, the server generates and sends notifications to users recommending appropriate insurance plans and security measures. The input is risk assessment data, and the output is a push notification to the user. Specifically, if the user is in a high-risk area, a message recommending a specific insurance plan is sent.

[1257] This makes it possible to perform risk assessments based on the user's diverse schedules, movements, and emotional state, and automatically present and apply optimal insurance plans and security measures.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] [Fourth embodiment]

[1262] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1263] 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.

[1264] 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).

[1265] 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.

[1266] 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.

[1267] 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).

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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."

[1275] This invention is a system that provides optimal insurance plans based on a user's schedule and location information. This system works in conjunction with the user's device, server, and calendar API. Specifically, it acquires the user's schedule information, analyzes it, selects the optimal insurance plan, notifies the user, and applies it. It also collects location and behavioral data to predict risks and make creative proposals.

[1276] A natural language description of what the program does

[1277] Retrieving and parsing calendar information

[1278] 1. The user installs a calendar app on their device, enters schedule information, and configures the device to access the calendar API.

[1279] 2. The server periodically connects to the calendar API to retrieve the user's schedule information. At that time, data on the user's various schedules (meetings, business trips, private events, etc.) is retrieved via the calendar API.

[1280] 3. The server analyzes the acquired schedule information and uses a natural language processing algorithm to understand the content of the event and identify the type (e.g., business trip, meeting), date, time, and location.

[1281] Insurance plan recommendations and coverage

[1282] 4. The server selects the optimal insurance plan based on the analysis results, taking into account the user's past behavioral patterns and existing insurance contract status.

[1283] 5. The server notifies the user of the selected insurance plan. For example, a message such as "You have an unexpected business trip next Tuesday, which increases your risk of loss. We recommend this insurance plan" is sent via a mobile app or email.

[1284] 6. The user reviews the notification and approves the recommended insurance plan. After approval, the device sends the information to the server.

[1285] 7. The server receives the user's approval and updates the insurance contract with the new insurance coverage.

[1286] Location and behavioral data collection

[1287] 8. The user installs the provided Tracker app on their smartphone and sets permission to share location information.

[1288] 9. The device (smartphone) periodically collects location data and other behavioral information and sends it to a server, including GPS data, Bluetooth device detection, Wi-Fi login information, etc.

[1289] 10. The server analyzes the collected behavioral data and applies learning algorithms to identify locations and situations where the user is at high risk of loss.

[1290] Creative suggestions and support

[1291] 11. Based on the learning results and real-time behavioral data, the server predicts locations and situations with a high risk of loss and suggests advance notifications and insurance plans to the user. For example, a message such as, "You often spend time in cafes on your next business trip, so there is a high risk of loss there. We will apply insurance for this location."

[1292] 12. Once the user approves the proposal, the server immediately applies the insurance plan and reflects it in the user's contract.

[1293] Specific examples

[1294] For example, suppose a user has an unexpected business trip scheduled for next Tuesday entered into their calendar. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user approves, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information and behavioral data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[1295] In this way, users can automatically apply the most suitable insurance plan for unexpected events and can also handle situations where the risk of loss is high. This system prevents users from forgetting to take out insurance and effectively reduces the risk of loss that may occur in their daily lives.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The server periodically sends a request to the user's calendar API to obtain the latest schedule information, and the user's schedule data is collected on the server.

[1299] Step 2:

[1300] The server then uses natural language processing (NLP) algorithms to analyze the calendar information, identifying the type of event (e.g., business trip, meeting, private event), date, time, and location.

[1301] Step 3:

[1302] The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[1303] Step 4:

[1304] The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and email. The notification includes the recommended insurance plan and the reason for the recommendation.

[1305] Step 5:

[1306] The user checks the notification from the server and decides whether to approve the recommended insurance plan. After approval, the user's device sends the information to the server.

[1307] Step 6:

[1308] The server updates the insurance contract based on the received approval information, allowing the new insurance plan to be applied in real time.

[1309] Step 7:

[1310] The user installs the provided Tracker app on their smartphone and sets up location sharing.

[1311] Step 8:

[1312] The device (smartphone) periodically collects the user's location information and behavioral data and sends it to a server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[1313] Step 9:

[1314] The server analyzes the collected behavioral data using machine learning algorithms to identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[1315] Step 10:

[1316] The server predicts locations and situations with a high risk of loss, notifies the user, and proposes a specific insurance plan. For example, a notification could be sent saying, "Since you will be spending a long time at the next cafe, we will apply for insurance that covers the risk of loss there."

[1317] Step 11:

[1318] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan, and this information is also updated in real time.

[1319] In this way, the most suitable insurance plan is automatically selected and applied based on the user's schedule information and behavioral data, reducing the user's risk of loss.

[1320] Example 1

[1321] 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."

[1322] Conventional insurance systems require users to purchase insurance themselves, making it difficult to respond immediately to sudden changes in plans. Furthermore, they lack the functionality to predict risks based on users' daily activities and location information and then propose appropriate insurance plans based on those risks. This leads to problems such as users forgetting to purchase insurance or being unable to respond appropriately to high-risk situations.

[1323] 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.

[1324] In this invention, the server includes means for acquiring a user's schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means operating in real time to notify the user of the selected insurance plan, means for predicting risks based on the collected location information and behavioral data, and means for notifying the user of risks in advance and creatively proposing insurance plans. This allows the server to automatically select and notify an appropriate insurance plan based on the user's behavior and schedule, making it possible to quickly respond to sudden schedule changes and high-risk situations.

[1325] "User's schedule information" refers to schedules such as meetings, business trips, and private events that a user has entered into a calendar app or schedule management tool.

[1326] "Analysis" refers to the process of using natural language processing algorithms and learning algorithms to understand the content of the acquired user's schedule information and behavioral data, and to identify specific information (e.g., date, time, location, type).

[1327] "Insurance plan" refers to the content of the insurance contract that is applied based on the user's actions and plans, and includes, for example, short-term business trip insurance and plans covering loss risks.

[1328] "Selecting" refers to choosing the most suitable insurance plan from multiple candidates based on analyzed data.

[1329] "Notifying" refers to informing the user about the selected insurance plan via email, mobile app, etc.

[1330] "Approve" means that the user consents to the notified insurance plan and expresses his / her intention to accept its application.

[1331] "Location Information" refers to a user's current location and movement data obtained through GPS, Wi-Fi login, Bluetooth device detection, etc.

[1332] "Behavioral Data" refers to data that includes information about a user's daily activities, travel patterns, and location.

[1333] "Predicting risks" refers to predicting the risk of loss in specific situations or locations based on collected location information and behavioral data.

[1334] "Creative proposals" refers to presenting users with new insurance plan ideas and improvement methods that are not bound by conventional patterns, based on risk predictions and analysis results.

[1335] MODE FOR CARRYING OUT THE INVENTION

[1336] This invention is a system that provides optimal insurance plans based on a user's schedule information and location information. This system operates in conjunction with the user's device, a server, and a calendar API.

[1337] Specifically, a user first installs a calendar app on their device and inputs their schedule information. The server then periodically connects to the calendar API to retrieve the user's schedule information. This can be done using a widely used calendar API such as the Google Calendar API.

[1338] The server analyzes the acquired schedule information and uses natural language processing (NLP) algorithms to identify the type of event (e.g., business trip, meeting), date, time, and location. Based on this information, the server selects the most suitable insurance plan. This selection process also takes into account past behavioral data and existing insurance policy information. The server then notifies the user of the selected insurance plan via email or in-app notification.

[1339] The user then approves the recommended insurance plan through the mobile app. Once approval is complete, the device sends the information to the server, which then updates the insurance contract with the new coverage.

[1340] The system also has the ability to collect user location and behavioral data. When a user installs the Tracker app on their smartphone and sets location sharing permission, the device periodically collects location data and other behavioral information and sends it to a server. This includes GPS data, Bluetooth device detection, Wi-Fi login information, and more.

[1341] The server analyzes the collected behavioral data and applies a learning algorithm to identify locations and situations where users are at high risk of losing their items. Based on the learning results and real-time behavioral data, it makes risk predictions and proposes advance notifications and insurance plans to users.

[1342] As a concrete example, if a user has an unexpected business trip scheduled for next Tuesday entered in their calendar, the server will obtain and analyze that information, select an insurance plan for short-term business trips, and notify the user. If the user approves the plan, the insurance contract will be updated immediately. Similarly, for a user who spends much of their weekdays in a cafe, the server will determine from their location and behavioral data that there is a high risk of loss at the cafe, and will suggest an insurance plan that is appropriate for that time period.

[1343] An example prompt is:

[1344] "Please explain in detail how to recommend the best insurance plan for the user based on their schedule and location."

[1345] "Please explain in detail how to select the right insurance plan for users who plan to travel."

[1346] "How can I use a user's location information to predict risks in a specific location and suggest insurance plans?"

[1347] In this way, users can quickly respond to sudden changes in plans or high-risk situations and prevent forgetting to take out insurance. This system can effectively reduce risks that may occur in users' lives.

[1348] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1349] Step 1:

[1350] A user installs a calendar app on their device and enters event information. The entered event information includes the type of event, date, time, and location. Specifically, the user enters an event such as "Business trip to Tokyo next Tuesday." This event information is saved in cloud storage via the calendar API.

[1351] Step 2:

[1352] The server periodically connects to the calendar API to retrieve the user's schedule information. The retrieved data includes the type of event, date, time, and location from the user's calendar. For example, the Google Calendar API is used to retrieve "User A's schedule" and store it in the server's database.

[1353] Step 3:

[1354] The server analyzes the schedule information it has acquired and uses a natural language processing algorithm (NLP) to understand the content of the event. The input data is the schedule content, and the output after analysis is the type, date, time, and location of the identified event. For example, from the content "Business trip in Tokyo next Tuesday," it extracts "Business trip," "October 10, 2023," and "Tokyo."

[1355] Step 4:

[1356] The server selects the optimal insurance plan based on the analysis results. This selection also takes into account the user's past behavioral data and existing insurance contract status. The input data is the identified event information and the user's insurance history, and the output data is the recommended insurance plan. For example, a "short-term insurance plan tailored to business trips" may be selected.

[1357] Step 5:

[1358] The server notifies the user of the selected insurance plan. The notification is sent via a mobile app or email. The input data is the selected insurance plan, and the output data is the notification message sent to the user. Specifically, the message sent reads, "You have an unexpected business trip next Tuesday, which increases the risk of loss. We recommend this insurance plan."

[1359] Step 6:

[1360] The user reviews the notification and approves the recommended insurance plan by pressing a button in the mobile app. The input is the notification message, and the output is the user's approval data.

[1361] Step 7:

[1362] The terminal sends the user's authorization information to the server, where the input data is the user's authorization data and the output data is the server's updated insurance policy information.

[1363] Step 8:

[1364] The server updates the insurance contract with the new insurance details upon receiving the user's approval. The input data is the approval information, and the output data is the updated insurance contract details.

[1365] Step 9:

[1366] A user installs the Tracker app on their smartphone and sets location sharing permission. The input data is the location sharing setting, and the output data is the location sharing permission status.

[1367] Step 10:

[1368] The device periodically collects location data and behavioral information and sends it to a server. The input data is GPS data, Bluetooth device detection, Wi-Fi login information, etc., and the output data is the behavioral data sent to the server.

[1369] Step 11:

[1370] The server analyzes the collected behavioral data and applies a learning algorithm to identify locations and situations where the user is at high risk of losing their belongings. The input data is behavioral data, and the output data is the identified risk information. For example, it identifies a pattern such as "often spending time at cafes during the day on weekdays."

[1371] Step 12:

[1372] The server predicts risks based on the learning results and real-time behavioral data, and then notifies the user and suggests insurance plans. The input data is the identified risk information, and the output data is the notification message sent to the user. For example, a notification may be sent stating, "Since you often spend time in cafes at your next business trip destination, there is a high risk of loss at that location. We will apply insurance for this location."

[1373] Step 13:

[1374] The user approves the proposal, and the server immediately applies the insurance plan and reflects it in the user's contract. The input data is the user's approval data, and the output data is the updated insurance plan and contract information.

[1375] (Application example 1)

[1376] 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."

[1377] In modern brick-and-mortar stores, the increased risk of theft and loss when purchasing expensive items is a serious problem. Conventional insurance systems assume fixed conditions and situations and are unable to propose optimal insurance plans that reflect the user's behavior and location information in real time. This poses a challenge, making it difficult to quickly apply an appropriate insurance plan when a user purchases a product.

[1378] 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.

[1379] In this invention, the server includes means for scanning a product code when a user selects a product in a physical store, means for analyzing the product code and assessing the risk associated with the product, and means for proposing an optimal insurance plan in real time based on the assessed risk. This makes it possible to perform risk assessment on the spot when a user purchases a product in a physical store, and to immediately propose and apply an optimal insurance plan.

[1380] "User" refers to a person who uses this system.

[1381] "Schedule information" refers to data on events and schedules that are set in advance by the user.

[1382] "Means of acquisition" refers to the server's ability to gather information from calendar APIs and other data sources.

[1383] "Means of analysis" refers to the function of processing the acquired data and identifying details such as category, time, and location.

[1384] "Means for identifying" refers to the function of extracting specific information from the analyzed data and clarifying its content.

[1385] "Insurance Plan" means a contract that provides insurance coverage against a specific risk.

[1386] "Means of selection" refers to the function by which the server selects the most suitable insurance plan based on the analyzed data.

[1387] "Means for notifying" refers to a function for informing the user of the selected insurance plan.

[1388] "Means of application" refers to the function of officially reflecting the insurance plan approved by the user in the contract.

[1389] "Location information" refers to data indicating a user's current location and movement history.

[1390] "Behavioral data" refers to data that indicates a user's past behavioral patterns and history.

[1391] "Means of collection" refers to functions for collecting location information and behavioral data.

[1392] "Product code" refers to the identification information of a product sold in a physical store.

[1393] "Means of scanning" refers to the ability to read product codes using smart glasses, smartphones, etc.

[1394] "Risks relating to products" refers to the possible losses or dangers associated with a particular product.

[1395] "Means for assessing risk" refers to the function of analyzing the risks associated with a product based on collected data.

[1396] "Real-time recommendations" refers to the ability to instantly show users insurance plans based on current information.

[1397] MODE FOR CARRYING OUT THE INVENTION

[1398] The embodiment of this invention is a system that provides optimal insurance plans in real time based on a user's schedule information and location information. This system operates in cooperation with the user's terminal, a server, and a calendar API. The details of the system are described below.

[1399] System Configuration

[1400] The system requires the user's smart glasses or smartphone and a server as the main hardware. The software includes a calendar API, location API, and insurance plan API. The system collects and analyzes data related to the user's schedule, location, and product code to propose the optimal insurance plan.

[1401] Data collection and analysis

[1402] The server obtains the user's schedule information through the calendar API. This schedule information is then analyzed using a natural language processing algorithm. The analyzed schedule information is then categorized into detailed data such as the schedule type, date, and location. Location and behavior data are also collected through the user's smart glasses or smartphone.

[1403] Based on the analysis data, the server tracks users' behavior in physical stores by scanning product codes. When a user scans an item, the risk associated with that item is instantly assessed and corresponding insurance plans are proposed in real time.

[1404] Insurance plan proposal and application

[1405] The server selects the optimal insurance plan based on the analysis results and notifies the user. If the user approves the proposed insurance plan, the information is sent to the server and the insurance contract is updated. This allows the user to immediately apply the optimal insurance plan for the risks of the products purchased in the physical store.

[1406] Specific examples

[1407] For example, if a user is about to purchase an expensive camera in a brick-and-mortar store, they can scan the product code on the camera with smart glasses or a smartphone. The system will then immediately assess the theft risk of the product and send a notification saying, "This camera has a high theft risk, so we recommend a dedicated theft insurance plan." If the user approves the insurance plan, the plan will be applied as a new insurance policy.

[1408] Prompt Sentence Examples

[1409] The following prompts can be used to instruct the generative AI model on how the system should behave:

[1410] Develop an application that predicts the items a user is likely to purchase that day based on their location and calendar information, assesses the risks associated with those items, and displays appropriate insurance plans in real time. For example, if a user is planning to purchase an expensive camera, implement a function that evaluates the risk of theft and suggests corresponding insurance plans by scanning the camera's product code.

[1411] In this way, the present invention can quickly assess the risks a user faces while shopping in a physical store and provide the most appropriate insurance plan, thereby improving the user's peace of mind and convenience.

[1412] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1413] Step 1:

[1414] A user installs a calendar app on their device (smart glasses or smartphone) and inputs schedule information. At this time, the device is set up to be able to access the calendar API.

[1415] Input: Schedule information entered by the user

[1416] Output: Calendar information for which access to the Calendar API has been granted

[1417] Specific operation: The user enters event information into the calendar app and sets up synchronization.

[1418] Step 2:

[1419] The server periodically connects to the calendar API to obtain the user's schedule information. The calendar API obtains data on the user's various schedules (meetings, business trips, private events, etc.).

[1420] Input: Schedule information obtained from the calendar API

[1421] Output: Raw schedule information data

[1422] Specific operation: The server sends an API request and receives schedule information in JSON format.

[1423] Step 3:

[1424] The server analyzes the acquired schedule information and uses natural language processing algorithms to understand the contents of the event, specifically identifying the type of schedule, date, time, and location.

[1425] Input: Raw schedule information data

[1426] Output: Parsed schedule information (e.g., business trips, meetings, etc.)

[1427] Specific operation: The server applies natural language processing algorithms to perform text analysis.

[1428] Step 4:

[1429] The server selects the optimal insurance plan based on the analysis results, taking into account the user's past behavioral patterns and existing insurance contract status.

[1430] Input: Analyzed schedule information, past behavior patterns, existing insurance contract status

[1431] Output: Optimal insurance plan

[1432] Specific operation: The server performs multivariate analysis, comparing multiple factors to determine the optimal insurance plan.

[1433] Step 5:

[1434] The server notifies the user of the selected insurance plan, for example, via a mobile app or email.

[1435] Input: Best insurance plan information

[1436] Output: A message to inform the user

[1437] Specific operation: The server sends a push notification to the user's device.

[1438] Step 6:

[1439] The user checks the notification and approves the recommended insurance plan. The approved insurance plan information is sent from the device to the server.

[1440] Input: User authorization information

[1441] Output: Insurance plan approval notice

[1442] Specific behavior: The user taps the application notification and clicks the approve button.

[1443] Step 7:

[1444] The server updates the insurance contract with the user's approval and applies the new insurance details.

[1445] Input: User approval notification

[1446] Output: Updated insurance contract information

[1447] Specific operation: The server updates the contract database.

[1448] Step 8:

[1449] The user installs the provided Tracker app on their smartphone and sets permission to share location information.

[1450] Input: User's location sharing permission

[1451] Output: Location sharing enabled

[1452] Specific behavior: The user enables location sharing in the app's settings screen.

[1453] Step 9:

[1454] The device (smartphone) periodically collects location data and other behavioral information and sends it to a server.

[1455] Input: location data, behavioral information

[1456] Output: Location data and behavioral information sent to the server

[1457] Specific operation: The smartphone periodically acquires GPS data and sends it to the server.

[1458] Step 10:

[1459] The server analyzes the collected behavioral data and applies learning algorithms to identify locations and situations where users are at high risk of loss.

[1460] Input: location data, behavioral information

[1461] Output: Identification of locations and situations with high risk of loss

[1462] Specific operation: The server performs data analysis using machine learning algorithms.

[1463] Step 11:

[1464] Based on the learning results and real-time behavioral data, the server predicts locations and situations with a high risk of loss, and provides advance notification and offers insurance plans to users.

[1465] Input: Learning results, real-time behavioral data

[1466] Output: Notification of recommended insurance plan

[1467] Specific operation: The server sends a push notification to the user based on the risk assessment result.

[1468] Step 12:

[1469] If the user approves the proposal, the server immediately applies the insurance plan and reflects it in the user's contract.

[1470] Input: User approval notification

[1471] Output: Insurance plan applied

[1472] Specific operation: The server updates the contract database again.

[1473] 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.

[1474] This invention is a system that integrates a user's schedule information, location information, and emotional data to provide the optimal insurance plan. This system operates in conjunction with the user's device, server, emotion engine, and calendar API. Specifically, it acquires the user's schedule information, analyzes it, and selects, notifies, and applies the optimal insurance plan. It also collects location information and emotional data to make risk predictions and creative proposals.

[1475] A natural language description of what the program does

[1476] Retrieving and parsing calendar information

[1477] 1. The user installs a calendar app on their device, enters schedule information, and configures the device to access the calendar API.

[1478] 2. The server periodically connects to the calendar API to retrieve the user's schedule information. Data on the user's various schedules (meetings, business trips, private events, etc.) is retrieved through the calendar API.

[1479] 3. The server analyzes the acquired schedule information using natural language processing (NLP) algorithms to identify the type of schedule (e.g., business trip, meeting), date, time, and location.

[1480] Insurance plan recommendations and coverage

[1481] 4. The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[1482] 5. The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and emails. The notification includes the recommended insurance plan and the reason for the recommendation.

[1483] 6. The user reviews the notification and decides whether to approve the recommended insurance plan. After approval, the device sends the information to the server.

[1484] 7. The server receives the user's approval and updates the insurance policy, so that the new insurance plan is applied in real time.

[1485] Location and behavioral data collection

[1486] 8. The user installs the provided Tracker app on their smartphone and sets up location sharing.

[1487] 9. The device (smartphone) periodically collects the user's location and behavior data and sends it to the server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[1488] 10. The server uses machine learning algorithms to analyze the collected behavioral data and identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[1489] Emotion data collection and analysis

[1490] 11. Emotion data is acquired using the emotion engine provided by the user. The emotion engine analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[1491] 12. The device transmits emotional data to a server that analyzes the data to determine the user's emotional state, including stress levels and signs of attention loss.

[1492] 13. The server analyzes the emotional data in combination with the behavioral data to identify the risk of loss and an appropriate insurance plan based on the user's emotional state.

[1493] Creative suggestions and support

[1494] 14. Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification may be sent saying, "Since you will be spending a long time at the next cafe, we will apply for specific insurance to cover the risk of loss there."

[1495] 15. Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan. This information is also updated in real time.

[1496] Specific examples

[1497] For example, a user enters an unexpected business trip into their calendar for next Tuesday. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user accepts this, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information, behavioral data, and emotional data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[1498] In this way, this system comprehensively analyzes the user's schedule information, location information, behavioral data, and emotional data, and provides the most appropriate insurance plan, thereby increasing the user's sense of security.

[1499] The processing flow will be explained below.

[1500] Step 1:

[1501] The server periodically sends a request to the user's calendar API to obtain the latest schedule information, and the user's schedule data is aggregated on the server.

[1502] Step 2:

[1503] The server then uses natural language processing (NLP) algorithms to analyze the calendar information, identifying the type of event (e.g., business trip, meeting, private event), date, time, and location.

[1504] Step 3:

[1505] The server refers to the user's profile database and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the plan includes a business trip, it will recommend business trip insurance.

[1506] Step 4:

[1507] The server sends a notification to the user based on the selected insurance plan. Notification methods include push notifications on the mobile app and email. The notification includes the recommended insurance plan and the reason for the recommendation.

[1508] Step 5:

[1509] The user checks the notification from the server and decides whether to approve the recommended insurance plan. After approval, the user's device sends the information to the server.

[1510] Step 6:

[1511] The server updates the insurance contract based on the received approval information, allowing the new insurance plan to be applied in real time.

[1512] Step 7:

[1513] The user installs the provided Tracker app on their smartphone and sets up location sharing.

[1514] Step 8:

[1515] The device (smartphone) periodically collects the user's location information and behavioral data and sends it to a server. The collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[1516] Step 9:

[1517] The server analyzes the collected behavioral data using machine learning algorithms to identify locations and situations where the user is at high risk of losing their device. Based on the risk assessment, specific actions are recommended.

[1518] Step 10:

[1519] Emotion data is acquired using the emotion engine provided by the user, which analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[1520] Step 11:

[1521] The device sends emotional data to a server that analyzes it to determine the user's emotional state, including stress levels and signs of attention loss.

[1522] Step 12:

[1523] The server analyzes the emotional data in combination with the behavioral data to identify the risk of loss and an appropriate insurance plan based on the user's emotional state.

[1524] Step 13:

[1525] Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification could be sent saying, "Since you will be spending a long time at the next cafe, we will apply for a specific insurance policy that covers the risk of loss there."

[1526] Step 14:

[1527] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract with the new plan, and this information is also updated in real time.

[1528] Example 2

[1529] 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."

[1530] There is a need for a system that quickly and efficiently provides appropriate insurance plans for risks that arise from users' schedules and actions in their daily lives. However, conventional systems have difficulty comprehensively analyzing a user's schedule information, location information, and emotional data to provide appropriate insurance plans. Risk assessment that takes emotional state into account is also insufficient. Therefore, a system capable of more comprehensive and accurate risk assessment is needed.

[1531] 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.

[1532] In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means for acquiring and analyzing emotional data, and means for integrating and analyzing the emotional data and behavioral data to perform risk assessment. This makes it possible to comprehensively analyze a variety of user data, provide a comprehensive risk assessment, and provide an optimal insurance plan.

[1533] "Schedule information" refers to detailed information about an event or activity that a user enters into a calendar application, including the date, time, location, and content.

[1534] A "Calendar API" is a programming interface for accessing, reading, and writing data in a calendar application.

[1535] "Natural language processing algorithms" are technologies and computer programs that allow computers to analyze, understand, and generate human language.

[1536] An "insurance plan" is a package containing the terms and conditions of an insurance policy against a particular risk.

[1537] "Location Information" means a user's geographic location data as determined by GPS or a network.

[1538] "Behavioral data" is data that indicates a user's movement patterns and activity history.

[1539] "Emotion data" is data that represents the user's emotional state and is obtained through facial recognition, voice analysis, touch input, etc.

[1540] "Risk assessment" is the process of analyzing multiple data sets to identify the level of risk in each individual situation.

[1541] "Notifications" are messages sent by the system to inform users of important information or suggestions, and include push notifications and emails.

[1542] "Analysis" is the process of processing acquired data and extracting useful information.

[1543] A "profile database" is a database that stores information such as a user's past activities and contract status.

[1544] The present invention is a system that integrates a user's schedule information, location information, and emotion data to provide the optimal insurance plan. This system operates in cooperation with the user's device, a server, an emotion engine, and a calendar API. A specific embodiment of this system is described below.

[1545] Retrieving and parsing calendar information

[1546] A user installs a calendar application on their device and inputs their schedule information. The device is then configured to be able to access a calendar API (e.g., Google Calendar API). Through this API, the user's schedule information is periodically sent to a server.

[1547] The server connects to the calendar API to retrieve the user's event information, which is then parsed using a Python natural language processing (NLP) library (e.g., NLTK or spaCy) to determine the event type (e.g., business trip, meeting), date, time, and location.

[1548] Insurance plan recommendations and coverage

[1549] Based on the acquired schedule information, the server refers to the user's profile database (e.g., PostgreSQL) and selects the most appropriate insurance plan based on past behavioral patterns and existing insurance contract status. For example, if the schedule includes a business trip, it will recommend business trip insurance.

[1550] Information about the selected insurance plan is sent to the user via notification methods (e.g., push notification by Firebase Cloud Messaging, email by SendGrid). The user checks the notification and approves the recommended insurance plan on their device. After approval, the device sends the information to the server via a REST API.

[1551] The server, upon receiving the user's approval, updates the insurance contract details in the database and applies the new insurance plan in real time.

[1552] Location and behavioral data collection

[1553] Users install the provided tracker application (e.g., MyTracks or Google Fit) on their smartphones and set up location sharing. The device uses its GPS module to collect location information and periodically transmits it to a server. This collected data includes GPS data, Bluetooth device detection, and Wi-Fi login information.

[1554] The server analyzes the collected behavioral data using machine learning algorithms such as Scikit-learn to identify locations and situations with a high risk of loss.

[1555] Emotion data collection and analysis

[1556] Users acquire emotion data using an emotion engine (e.g., Emotion SDK), which analyzes the user's emotional state in real time using facial recognition cameras, voice analysis, touch input, etc.

[1557] The device sends the analyzed emotional data to a server, which then analyzes it using a data analysis library such as Pandas to identify the user's emotional state (e.g., stress level, signs of attention loss).

[1558] The server integrates and analyzes the emotional data and behavioral data to identify the user's risk assessment and appropriate insurance plan. Based on the analysis results, creative insurance plans are proposed for specific behaviors.

[1559] Creative suggestions and support

[1560] Based on the analysis results and real-time behavioral and emotional data, the server predicts locations and situations with a high risk of loss and notifies the user. For example, a notification may be sent saying, "Since you will be spending a long time at the next cafe, we will apply for a specific insurance policy that covers the risk of loss there."

[1561] Once the user has reviewed and approved these proposals, the server will instantly update the insurance contract and the new insurance plan will be applied in real time.

[1562] Specific examples

[1563] For example, a user enters an unexpected business trip into their calendar for next Tuesday. The server retrieves this information and selects a short-term insurance plan tailored to the trip. The user receives a notification stating, "You have an unexpected business trip. We recommend a specific insurance plan to cover the risk of loss during your trip." If the user accepts this, the server updates and applies the insurance contract details. For example, if a user spends most of their weekdays at a cafe, the server can use location information, behavioral data, and emotional data to identify the risk of loss at the cafe and automatically apply an insurance plan appropriate for that time period.

[1564] Example prompt:

[1565] "A user has a business trip scheduled for next Tuesday on their calendar. How can we use this information to recommend and notify them of the best insurance plan?"

[1566] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1567] Step 1:

[1568] A user installs a calendar application on their device and inputs appointment information. The input data includes the appointment title, date and time, location, and notes. The device is set up to be able to access a calendar API (e.g., Google Calendar API). At this point, the input is the appointment information manually entered by the user. The output is the appointment information registered in the calendar.

[1569] Step 2:

[1570] The server periodically connects to the calendar API to obtain the user's schedule information. The server accesses the API using an HTTP request. The input is the schedule information obtained from the calendar API. The output is JSON format data of the retrieved schedule information.

[1571] Step 3:

[1572] The server analyzes the acquired schedule information using a natural language processing (NLP) algorithm. A Python NLP library (NLTK or spaCy) is used for the analysis. The input is the schedule information in JSON format. The NLP algorithm identifies the schedule type (meeting, business trip, etc.), date, time, and location. The output is data containing the analyzed schedule type, date, time, and location.

[1573] Step 4:

[1574] The server references the user's profile database (e.g., PostgreSQL) and selects the most suitable insurance plan based on past behavioral patterns and existing insurance contract status. The input is the parsed schedule information and the user's profile data. An SQL query is used to search for suitable insurance plans. The output is a list of selected insurance plans.

[1575] Step 5:

[1576] The server sends a notification to the user based on the selected insurance plan. Notification methods include Firebase Cloud Messaging and SendGrid. The input is information about the selected insurance plan. The output is a push notification or email notification to the user's device.

[1577] Step 6:

[1578] The user checks the notification on the device and chooses whether to approve or reject the recommended insurance plan. The input is the notification content from the server. The user's operation executes the action of approval or rejection. The output is the result of approval or rejection.

[1579] Step 7:

[1580] The terminal sends the user's approval result to the server. The input is the user's approval or denial result. The data is sent to the server using an HTTP POST request. The output is the approval result sent to the server.

[1581] Step 8:

[1582] The server receives the user's approval and updates the insurance contract details in the database. It accesses the database using an ORM such as SQLAlchemy. The input is the user's approval result and the selected insurance plan. The output is the updated insurance contract data.

[1583] Step 9:

[1584] The user installs the provided tracker application on their smartphone and configures location sharing. The input is the application installation and configuration information. The output is the device state after the location sharing configuration is complete.

[1585] Step 10:

[1586] The device periodically collects location information and behavioral data using a GPS module and sends it to a server. The inputs are the GPS data collected by the device, Bluetooth device detection information, and Wi-Fi login information. The output is the location information and behavioral data sent to the server.

[1587] Step 11:

[1588] The server analyzes the collected behavioral data using machine learning algorithms (such as Scikit-learn) to identify locations and situations where the user is at high risk of losing their device. The input is location information and behavioral data. The output is the identified high-risk locations and situations.

[1589] Step 12:

[1590] The user acquires emotion data using the emotion engine. The emotion engine uses a facial recognition camera, voice analysis, and touch input. The inputs are camera footage, voice data, and touch information. The output is emotion data analyzed in real time.

[1591] Step 13:

[1592] The device sends emotional data to the server. The input is the emotional data analyzed by the device. It is encrypted and sent using HTTPS. The output is the emotional data sent to the server.

[1593] Step 14:

[1594] The server analyzes the emotion data using a data analysis library such as Pandas to identify the user's emotional state (stress level, signs of attention loss). The input is the emotion data. The output is the analyzed emotional state data.

[1595] Step 15:

[1596] The server integrates and analyzes the emotional and behavioral data to perform a risk assessment of the user. The input is the emotional and behavioral data. The output is a comprehensive risk assessment result and the identification of an appropriate insurance plan.

[1597] Step 16:

[1598] The server proposes and notifies the user of a creative insurance plan based on the risk assessment results. The input is the risk assessment results. The output is a proposal notification sent to the user terminal.

[1599] Step 17:

[1600] Once the user reviews and approves the proposed insurance plan, the server immediately updates the insurance contract to include the new insurance plan. The input is the user's approval. The output is the updated insurance contract information.

[1601] (Application example 2)

[1602] 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."

[1603] Today's busy users frequently move around and have diverse schedules, making it difficult to properly predict and address the associated risks. Furthermore, while emotional states are an important factor in risk assessment, there is no efficient way to grasp and utilize them. As a result, optimal insurance plans and security measures are often not applied in high-risk situations, reducing users' sense of security. To address these issues, a system is needed that comprehensively analyzes users' schedule information, location information, behavioral data, and emotional data to provide optimal insurance plans and security measures.

[1604] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring user schedule information, means for analyzing the acquired schedule information to identify the type and date and time of the schedule, means for selecting an optimal insurance plan based on the identified schedule, means for notifying the user of the selected insurance plan, means for applying the insurance plan approved by the user to the insurance contract, means for collecting user location information and behavioral data, means for analyzing the collected behavioral data to identify situations with a high risk of loss, means for acquiring and analyzing user emotion data, means for combining the emotion data with the behavioral data to perform risk assessment, and means for recommending an appropriate insurance plan to the user based on the identified risk assessment. This makes it possible to perform risk assessment based on the user's various schedules, movements, and emotional state, and automatically present and apply optimal insurance plans and security measures.

[1605] "User" refers to any individual or organization that uses this system.

[1606] "Schedule information" is information about events and activities that a user has scheduled, including the date, time, location, and content.

[1607] "Analysis" refers to breaking down acquired data or information and processing it to understand its structure and meaning.

[1608] An "insurance plan" refers to the content and conditions of the insurance contracted by a user, and provides coverage according to risk.

[1609] "Notification" refers to the act of informing a user of information from a system, and means of notification include push notifications and emails.

[1610] "Location information" refers to geographical data about a user's current location and travel route.

[1611] "Behavioral data" refers to data about a user's daily movements and movement patterns, including GPS data and Wi-Fi login information.

[1612] "Emotional data" is data collected to analyze a user's emotional state, and is information obtained from facial expressions, tone of voice, touch input, etc.

[1613] "Risk assessment" refers to the use of acquired and analyzed data to assess the degree of risk a user may face.

[1614] "Recommendation" refers to the system suggesting the best options or solutions to the user.

[1615] "Acquisition" refers to the act of the system gathering the necessary data or information.

[1616] "NLP algorithm" is an abbreviation for natural language processing algorithm, a technology for analyzing human language and understanding its meaning.

[1617] The present invention relates to a system that integrates a user's schedule information, location information, behavioral data, and emotional data to provide optimal insurance plans and security measures. This system operates in conjunction with the user's smartphone (terminal), a server, an emotional analysis engine, and a calendar API.

[1618] System Overview

[1619] The system consists of the following main components:

[1620] 1. User's smartphone (device)

[1621] Calendar API: Used to obtain the user's schedule information.

[1622] GPS function: Used to collect user location information.

[1623] Camera and microphone: Used to capture user emotion data.

[1624] Notification function: Used to notify users of selected insurance plans and risk alerts.

[1625] 2. Server

[1626] Natural language processing (NLP) algorithm: Analyzes the acquired schedule information and identifies the type of schedule and date and time.

[1627] Machine learning algorithms: Analyze behavioral and emotional data to make risk assessments.

[1628] Database: Stores user profile data and past behavioral data.

[1629] 3. Sentiment Analysis Engine

[1630] Facial recognition algorithm: Obtains emotional data from the user's facial expressions.

[1631] Voice analysis algorithm: Analyzes emotional data from the tone and patterns of the user's voice.

[1632] Processing flow

[1633] 1. Acquisition and analysis of schedule information

[1634] When a user enters appointment information into a smartphone calendar app, the information is sent to the server via the calendar API, and the server uses a natural language processing algorithm to analyze the information and identify the type of appointment and the date and time.

[1635] Example: For an event such as "Drinking party in Shinjuku at 10pm next Friday," the keywords "drinking party," "late night," and "Shinjuku" are extracted and the risk is assessed.

[1636] 2. Location and behavioral data collection

[1637] The user's smartphone uses its GPS function and Wi-Fi login information to periodically send its current location and route to the server.

[1638] Example: Tracking travel routes within a 15-minute walk from Shinjuku Station and assessing public safety.

[1639] 3. Emotional Data Collection and Analysis

[1640] The smartphone's camera and microphone are used to analyze the user's facial expressions and voice in real time to obtain emotional data. The emotion analysis engine analyzes facial expressions and voice and sends the data to a server.

[1641] Example: Facial recognition and voice analysis algorithms identify when a user is under stress.

[1642] 4. Risk Assessment and Notification

[1643] The server integrates behavioral and emotional data to assess high-risk situations in real time, and if necessary, sends risk alerts and optimal insurance plans via push notifications to smartphones.

[1644] Example: Sending a notification saying, "You will be spending a long time at the next cafe, so we will apply for specific insurance to cover the risk of loss there."

[1645] Example prompts to input to the generative AI model

[1646] "Please suggest the best insurance plan for my next appointment."

[1647] "Predict risks based on your current emotional state and recommend appropriate actions."

[1648] As described above, by centrally understanding and analyzing a user's schedule, location information, behavioral data, and emotional data, it is possible to provide optimal insurance plans and security measures in real time so that users can live their daily lives with peace of mind.

[1649] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1650] Step 1:

[1651] A user enters appointment information into a calendar app on their smartphone. This becomes the input data. This appointment information is sent to a server via a calendar API. The server receives the appointment information and analyzes the contents of the appointment using a natural language processing (NLP) algorithm. This analysis identifies information such as the type of appointment, date, time, and location. The output is structured data of the analyzed appointment information.

[1652] Step 2:

[1653] The server uses the structured schedule information to refer to the user's profile database and selects the optimal insurance plan based on past behavioral patterns and existing insurance contract status. The input is the analyzed schedule information and the user's profile data. In this step, a machine learning algorithm is used to determine the insurance plan that is most suitable for the user. The output is the selected insurance plan.

[1654] Step 3:

[1655] The server generates a notification of the selected insurance plan and sends it to the device. The device then notifies the user via push notification or email. The input is the selected insurance plan, and the output is the notification sent to the user. Specifically, the notification includes the recommended insurance plan and the reason for its selection.

[1656] Step 4:

[1657] The user checks the notification on the device and decides whether to approve the recommended insurance plan. When the user approves, the device sends the information to the server. The input is the user's approval, and the output is the approval data sent to the server.

[1658] Step 5:

[1659] The server updates the insurance contract details with the user's approval. This allows the new insurance plan to be applied in real time. The input is the user's approval data, and the output is the updated insurance contract data. Specifically, an API that changes the contract details in conjunction with the insurance company's system is called.

[1660] Step 6:

[1661] The user's smartphone periodically sends their current location and route to the server using GPS and Wi-Fi login information. The input is the user's location information, and the output is the location information stored in the server's behavior database.

[1662] Step 7:

[1663] The server analyzes the collected behavioral data using a machine learning algorithm to identify locations and situations where the user is at high risk of losing their item. The input is location information and behavioral data, and the output is risk assessment data. Specifically, the server calculates a risk score based on past data patterns.

[1664] Step 8:

[1665] The user acquires emotional data using the smartphone's camera and microphone. The device analyzes the user's emotional state using facial recognition and voice analysis algorithms and sends the data to a server. The input is the user's facial expression and voice data, and the output is the analyzed emotional data.

[1666] Step 9:

[1667] The server analyzes the emotional data in combination with the behavioral data to assess the risk of loss and crime. The input is the emotional data and the behavioral data, and the output is a comprehensive risk assessment. Specific operations include determining whether stress or reduced attention contributes to the risk.

[1668] Step 10:

[1669] Based on the risk assessment results, the server generates and sends notifications to users recommending appropriate insurance plans and security measures. The input is risk assessment data, and the output is a push notification to the user. Specifically, if the user is in a high-risk area, a message recommending a specific insurance plan is sent.

[1670] This makes it possible to perform risk assessments based on the user's diverse schedules, movements, and emotional state, and automatically present and apply optimal insurance plans and security measures.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] 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.

[1675] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1676] 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.

[1677] 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).

[1678] 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.

[1679] 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."

[1680] 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.

[1681] 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).

[1682] 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.

[1683] 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 (Uni...

Claims

1. A means for acquiring schedule information of a user; A means for analyzing the acquired schedule information and identifying the type and date and time of the schedule; A means for selecting the most suitable insurance plan based on the identified schedule; means for notifying the user of the selected insurance plan; means for applying the user-approved insurance plan to the insurance contract; means for collecting user location and behavior data; The collected behavioral data is analyzed as a means to identify situations with high risk of loss. A system including:

2. The system of claim 1 , further comprising means for creatively suggesting recommended insurance plans to a user in locations and situations where the risk of loss is high based on the analyzed schedule information and behavioral data.

3. The system of claim 1 , further comprising means for obtaining the user's schedule information through a calendar API and analyzing the schedule content using a natural language processing algorithm.

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