system

Abstract

We provide the system. [Solution] Means for obtaining user information, Means for obtaining event information from multiple external sources, A means for creating an optimal outing plan based on the user information and the event information, A means to automate the booking of the aforementioned plan, A means for notifying the user of the completion of the aforementioned reservation procedure, A means of providing optimized suggestions by analyzing a user's past usage history and external data using machine learning algorithms, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Due to daily busyness, it is often difficult for parents to plan outings to take their children to. Therefore, there is a problem that optimal planning for parents and children to spend meaningful time is generally postponed. The burden of selecting a destination is large due to the dispersion of information, and furthermore, the reservation procedures are complicated. As a result, many families are in a situation where it is difficult to spend planned holidays.

Means for Solving the Problems

[0005] This invention provides a system that acquires a user's family structure, past visit history, and schedule information, and integrates event information obtained from external sources based on this information. This system includes a function to recommend the most suitable outing destination to the user, automatically makes reservations for the selected destination, and notifies the user of the reservation completion. This allows parents to plan efficiently and maximize the use of their family's leisure time.

[0006] "User information" refers to data about the user that the system uses, including attributes such as family structure, past visit history, and schedule information.

[0007] "External information sources" are external data providers that the system accesses to obtain information, and these include local event calendars, facility information, and weather information.

[0008] "Event information" refers to data about events and activities that occur during a specific period, and is primarily obtained from external sources.

[0009] A "recommendation method" is an algorithm or process within a system that combines user information and event information to provide the user with the most suitable options.

[0010] "Means of executing reservations" refers to a system mechanism that automatically completes the reservation process for selected destinations through an online system.

[0011] "Means of notification" refers to the communication process within the system used to inform the user that a reservation has been completed, and this includes push notifications and email notifications. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system for effectively planning how parents and children spend their holidays. This system effectively collects users' lifestyle information and, based on that information, recommends the most suitable outing destinations. The system mainly consists of a server and user terminals, each playing a specific role.

[0034] First, the device collects information about the user's family structure, places they have visited in the past, and scheduled events listed in their calendar. This information is then sent to a server via the application with the user's consent.

[0035] Next, the server integrates and collects local event information, facility information, weather data, and other information from external sources. This creates an up-to-date and relevant database to provide to users.

[0036] The server then uses the collected user information and external data to apply machine learning algorithms and design the optimal outing plan for the user. In this process, based on family structure and past preferences, it might recommend, for example, zoos or amusement parks for young children, and science museums or art museums for older children.

[0037] The selected plan is sent from the server to the terminal, where the user can review it. Once the user selects a specific destination, the server automatically initiates the booking process for that location. This booking process is completed quickly and accurately in conjunction with the online platform.

[0038] Finally, once the reservation is confirmed, the device will notify the user. This allows the user to smoothly prepare for their outing according to their plan.

[0039] This system frees parents from the burden of detailed planning, allowing them to spend more meaningful time with their families. Furthermore, the automation of information retrieval, recommendations, and booking processes enables efficient time management in daily life.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device, with the user's permission, collects information such as family composition, past visit history, and schedule. After the device has finished collecting this information through the application, it securely transmits the data to the server.

[0043] Step 2:

[0044] The server accesses external information sources to retrieve the latest local event information, facility operating information, and weather data. This data is then organized and stored in a database on the server for later processing.

[0045] Step 3:

[0046] The server uses machine learning algorithms to select appropriate destinations based on user information and external data. The resulting list of destinations is optimized to take into account the user's preferences and past activity history.

[0047] Step 4:

[0048] After the server compiles the recommendation list, it sends it to the user's terminal. The terminal then displays the received list to the user and makes it available for selection.

[0049] Step 5:

[0050] Users select their desired destination from a list of recommended options displayed on their device. Users can also enter detailed information such as dates and the number of people if needed.

[0051] Step 6:

[0052] The server receives the user's selection and makes a reservation for the chosen destination. This process is handled in conjunction with the relevant facility's online reservation system and continues until the reservation is officially completed.

[0053] Step 7:

[0054] The device notifies the user that the reservation is complete. This allows the user to confirm the reservation details and make further preparations.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] When families plan their holidays, efficiently gathering information and selecting the optimal plan can be complex and time-consuming. In particular, there is a need to automatically generate plans tailored to individual user preferences by aggregating relevant data from numerous sources. Furthermore, there is a need to reduce the burden and errors associated with manual booking procedures.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for collecting and transmitting user information to the server, means for integrating and acquiring local event data, facility data, and weather data from multiple external information sources, and means for using the user information and the external information to generate an optimal outing plan utilizing machine learning technology. This makes it possible to efficiently provide outing plans that are optimal for the user and tailored to their individual preferences, and to automate the process from planning to booking.

[0060] "User information" refers to data about individual users, including family structure data, past visit history, and schedule information.

[0061] "External information sources" refer to a collection of information that provides data on local events, facilities, and weather forecasts.

[0062] "Integration" is the process of combining and summarizing data obtained from multiple different sources.

[0063] "Machine learning techniques" are algorithms used by computers to learn data patterns and make predictions and judgments about new data.

[0064] An "outing plan" is a suggestion of travel destinations and places to visit, designed based on the user's preferences and needs, and includes specific destinations and activities.

[0065] A "reservation" is the process of securing a seat or participation slot in advance at a facility or event.

[0066] "Notification" is the act of informing a user of information or a message, whether visual or auditory.

[0067] This system is designed to effectively support holiday planning for parents and children and consists of a user terminal and a server. The terminal has a dedicated user application installed, which allows users to input user information such as family structure, past visit history, and schedule information. This data is transmitted to the server using a secure protocol. The server is programmed using general-purpose programming languages ​​such as Python and Java (registered trademark), and the database server uses MySQL (registered trademark) to store the data.

[0068] The server obtains local event information, facility details, and weather data through interfaces with external information sources. This includes using information sources such as local event APIs, open data facility information, and weather data APIs. This information is integrated with user information and stored in the server's database.

[0069] The server uses a generative AI model based on the collected information to generate the optimal outing plan. This model considers the user's family structure and past preferences to select an appropriate destination. For example, if the prompt is "If the family structure includes elementary school-aged children, please suggest activities recommended for a sunny day," a one-day plan to the zoo might be generated.

[0070] The server then sends the generated plan to the device, which notifies the user. The user can then select a suggested destination, and the server, in conjunction with the booking platform, automatically handles the booking process. This allows the user to complete the entire process from planning to booking smoothly and efficiently.

[0071] The system implemented in this way frees users from the burden of detailed information gathering and planning, allowing them to enjoy time with their families more efficiently.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The device collects user information. Through the application, users enter information such as family structure, past visit history, and calendar appointments. This data is collected from the application's forms, encrypted via a secure communication protocol, and sent to the server as "user information."

[0075] Step 2:

[0076] The server collects local event information, facility information, and weather data from external sources. It retrieves local event calendars, detailed facility information, and weather forecast data via an API interface. This data is stored in a database as "external information" and updated to reflect the latest information.

[0077] Step 3:

[0078] The server integrates collected user information with external information. Using database queries, it identifies the user's past visits and preferred activities, and matches this information with external data. This prepares the server to analyze which events and facilities are best suited to the user.

[0079] Step 4:

[0080] The server generates the optimal outing plan using a generative AI model. Using integrated data as input, it selects recommended destinations based on the user's preferences. Machine learning algorithms are utilized in this process; for example, prompts might be given such as, "If the family includes elementary school-aged children, please suggest activities suitable for a sunny day." A specific plan is then generated as output.

[0081] Step 5:

[0082] The server sends the generated plan to the device. The user can review the received notification at a low cost and select the suggested destination in a browser or in-app view. This includes detailed descriptions, images, and ratings of the destination.

[0083] Step 6:

[0084] Once the user selects a destination, the server automatically processes the reservation. Based on the selected plan, it interacts with the reservation system to secure tickets and confirm the reservation. As a result, reservation completion information is generated and recorded in the user management system.

[0085] Step 7:

[0086] The device sends a reservation confirmation notification to the user. The notification contains the completed reservation information, date and time, location, and reservation number, and the user can use this information to prepare for their trip or visit.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In modern society, it is often difficult to efficiently plan free time for families. Parents, in particular, want to spend meaningful time with their children on weekends, but the effort involved in selecting the most suitable activities from a vast amount of information and making reservations becomes a burden. This leads to complicated planning and, as a result, an inability to effectively utilize time.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for acquiring user information, means for acquiring event information from multiple external information sources, and means for analyzing the user's past usage history and external data using a machine learning algorithm to provide optimized suggestions. This enables users to smoothly plan outings based on their family structure and past preferences, and by automating online reservation procedures, it is possible to improve time efficiency and reduce the complexity of planning.

[0092] "User information" refers to data such as personal attributes, behavioral history, and schedules related to a user.

[0093] "External information sources" refer to sources of information obtained from outside the system, such as local event and facility information, and weather data.

[0094] "Event information" refers to information about activities or events that take place during a specific period and location.

[0095] An "outing plan" is an action plan that defines destinations and activities for users to spend their time meaningfully.

[0096] A "machine learning algorithm" is a computational method that learns specific patterns from accumulated data to make predictions and decisions about new data.

[0097] This invention is a system for efficiently planning parent-child holidays, and consists of a server and user terminals. The system uses user information and external information to propose the optimal outing plan.

[0098] The server first receives user information from the user's terminal. This user information includes family structure, history of places visited in the past, and schedule information. With the user's consent, this data is sent from the terminal to the server and stored in the database.

[0099] Next, the server retrieves local event information, facility information, and weather data from external sources. APIs such as Google® Maps API and OpenWeatherMap are used for this purpose. The server integrates this information and prepares it to provide users with the most up-to-date and relevant information.

[0100] The server uses machine learning algorithms (e.g., scikit-learn) to analyze collected user information and external data. This optimizes outing plans based on the user's past behavior history and preferences, and calculates suitable destinations such as zoos, amusement parks, science museums, and museums.

[0101] Recommended itineraries are sent to the user's device for review. The user then selects a specific destination, and the server automates the booking process online. Booking confirmation is sent to the user's device, allowing them to prepare for their trip according to the plan.

[0102] For example, on a rainy day, the system can suggest indoor activities. By utilizing a generative AI model, the user can be prompted with a request such as, "Please suggest indoor activities that the family can enjoy this weekend," and the AI ​​will generate optimal suggestions.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] Users use their devices to input personal information, family structure, past visit history, and schedule information. This information is transmitted to the server through the application, with consideration for privacy. The input data includes user-specific information such as family structure and visit history.

[0106] Step 2:

[0107] The server stores the received user information in a database. Simultaneously, it calls APIs to retrieve local event information, facility information, and weather data from external sources. Specifically, the server aggregates information using APIs such as Google Maps API and OpenWeatherMap. This process involves retrieving data from external APIs.

[0108] Step 3:

[0109] The server uses machine learning algorithms to analyze collected user information and external data. This process utilizes libraries such as Python's scikit-learn to process data, taking into account past usage history and user preferences. The analysis identifies recommended destinations and events for the user.

[0110] Step 4:

[0111] The server sends an optimized outing plan to the user's terminal. On the terminal, the user is shown the details of each plan and can select one. During this selection process, the user decides which plan to implement. The output at this point is the detailed information of the plan.

[0112] Step 5:

[0113] When a user selects a specific destination, the server automates the booking process for related facilities and events. Using an online booking system API, bookings are completed quickly and accurately. This process involves the calculation and transmission of data necessary for the booking procedure.

[0114] Step 6:

[0115] The server notifies the user's terminal of the reservation completion information. The terminal receives this notification, and the user begins to act according to the plan. In this final step, confirmation data of the reservation completion is transmitted to the user.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention is a system that combines an emotion engine with event and facility information provision to enable more personalized outing destination suggestions based on the user's emotional state. The aim of this system is to provide the optimal plan by taking the user's emotions into consideration.

[0118] The system is operated through an application installed on the user's device. First, the device collects information such as family structure, past visit history, and calendar schedules, just like regular user information. Furthermore, an emotion engine within the device recognizes the user's current emotional state from voice data and user interactions.

[0119] The server receives user information and sentiment data transmitted from the terminal, and based on this, retrieves local event information, facility information, and weather data from external sources. This allows the server to form a database that reflects the user's sentiments and current interests.

[0120] The server uses the received emotional information to recommend the most suitable destination. For example, if it determines that the user is seeking relaxation, it can suggest a quiet nature park or a tranquil waterside facility.

[0121] The generated recommendation list is sent from the server to the terminal, allowing the user to choose a destination. The user reviews the displayed options and selects a preferred destination. After selection, the server automatically handles the booking process for that location and notifies the user of the details.

[0122] Thus, this invention leverages user emotions to provide more personalized plans, thereby creating a satisfying outing experience for both parents and children. The overall system flow seamlessly integrates processes such as information gathering, emotion analysis, appropriate plan recommendation, booking execution, and notification, consistently supporting the user experience.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] With prior permission from the user, the device collects information such as family structure, past visit history, and schedule, and analyzes emotional information from voice data and screen operations using an emotion engine. The processed information is sent to a server to improve the user experience.

[0126] Step 2:

[0127] The server accesses external information sources to collect up-to-date data, including weather, local events, and facility information. The collected data is organized in the server's database and used for subsequent processing.

[0128] Step 3:

[0129] The server combines user information, emotional data, and external information to apply an algorithm that determines the priority of destinations influenced by emotions. In this process, the optimal location is selected according to the user's emotional state, such as wanting to relax or seeking stimulation.

[0130] Step 4:

[0131] The server sends a list of recommended destinations to the user's device. The device displays the list, allowing the user to select their desired destination.

[0132] Step 5:

[0133] The user selects a destination from the displayed options via their device and enters reservation details such as the desired date and number of people.

[0134] Step 6:

[0135] The server automatically initiates the online booking process for the selected destination. The booking information reflects the details entered by the user.

[0136] Step 7:

[0137] The device notifies the user that the reservation is complete and that all procedures for the selected destination have been finished. Based on this information, the user can proceed with planning for the day.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] Modern users demand personalized information tailored to their emotions and personal circumstances, but existing information delivery systems do not adequately address this, posing a challenge in providing individualized suggestions based on users' emotional states.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for acquiring user information, means for acquiring information from multiple external sources, and means for analyzing emotions. This enables the recommendation of an optimal location based on the user's individual emotional state and the reservation of that location.

[0143] "User information" refers to basic data about the user, including personal composition, visit history, and schedule information.

[0144] "External information sources" refer to external data sources that provide information necessary for user recommendations, such as local calendars, facility databases, and weather information.

[0145] "Methods for analyzing emotions" refer to the process of analyzing a user's voice and interactions to recognize their emotional state.

[0146] "Methods for recommending optimal locations" refer to methods for selecting and presenting the most appropriate destination to users based on collected information and analysis results.

[0147] The "means of executing a reservation" refer to a function that automatically completes the process of confirming a visit to a recommended location.

[0148] "Means of notifying reservation information" refers to methods used to inform users of confirmed reservation details, and serves the role of providing detailed information.

[0149] This invention is an information system for providing personalized suggestions based on the user's emotional state. This system consists of an application installed on the user's terminal and a server connected via a network.

[0150] The device collects the user's voice data and interactions on the device, and identifies the user's current emotional state using an emotion analysis engine. This utilizes speech recognition technology and emotion analysis algorithms. The device also retrieves the user's configuration information, past visit history, and schedule information from a database and sends this information to the server.

[0151] Based on the received user information and sentiment data, the server retrieves local calendars, facility data, and weather information from multiple external sources. In this process, the server uses a generative AI model to calculate destinations optimized for the user's state. For example, if the user desires relaxation, a quiet nature park or a tranquil waterside facility might be selected.

[0152] The user can view a list of recommendations displayed on their device and choose a destination from the options. After selection, the server automatically processes the reservation for that location and notifies the device of the reservation confirmation. An example of a prompt message to suggest destinations the user might prefer is, "Please suggest places where the user would like to relax."

[0153] Thus, the entire system has numerous features that enable it to provide personalized outing plans based on the user's emotions and preferences.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The device collects the user's personal information. It uses data from the device's sensors and calendar application as input. As output, it creates database entries containing the user's family structure, past visit history, and schedule information. This process is carried out through a database management system.

[0157] Step 2:

[0158] The device uses an emotion engine to analyze voice data and user interactions. Inputs are voice and gesture data acquired from the microphone and touch interface. Outputs are numerical or tagged information indicating the user's current emotional state. This is done using an emotion analysis algorithm.

[0159] Step 3:

[0160] The terminal transmits the collected user information and emotional state data to the server. The input is the data obtained in steps 1 and 2, and the output is sent to the server as encrypted packets. HTTPS or similar communication protocols are used.

[0161] Step 4:

[0162] The server retrieves local event information, facility information, and weather data from external sources. Input includes external data obtained via APIs. Output is a list of integrated information. Real-time information is collected via API calls.

[0163] Step 5:

[0164] The server uses a generative AI model to integrate user information, emotional state, and external information to calculate the optimal destination. The input consists of the data received in step 3 and the information collected in step 4. The output is a personalized list of recommended destinations for each user. The generative AI model processes large datasets and generates results tailored to individual needs.

[0165] Step 6:

[0166] The server sends the recommendation list to the terminal. The input is the recommendation list obtained in step 5, and the output appears as recommendation options displayed on the user's terminal. This is where the user interface rendering takes place.

[0167] Step 7:

[0168] The user makes a selection from a list of recommendations displayed on the device. The input is visual data displayed on the device, and the selected item is sent to the server as output. This selection is made via a touch interface.

[0169] Step 8:

[0170] The server processes the reservation for the selected location. The input is the user's selection data, and the output is the reservation confirmation information. The reservation is automatically confirmed using the online reservation system.

[0171] Step 9:

[0172] The server notifies the terminal of reservation confirmation information. The input is confirmation information from the reservation system, and the output is a notification message in a format accessible to the user. This process uses a notification service to ensure that the information reaches the user immediately.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] Conventional information systems failed to take into account the user's emotional state, making it difficult to suggest optimal facilities or events tailored to the user's mood and emotions. Furthermore, there was a need for a method that automatically booked outings that matched the user's mood without requiring complicated operations.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes means for acquiring user information, means for analyzing the user's emotional state from voice data and user interactions, and means for acquiring event information from multiple external information sources. This makes it possible to recommend the most suitable outing destination based on each user's emotional state and to automatically make reservations for it.

[0178] "Means of acquiring user information" refers to systems and devices that collect and utilize users' personal attributes and activity history.

[0179] "Means of obtaining event information from multiple external sources" refers to methods and devices for obtaining data on various events and facilities through the internet and various databases.

[0180] "Means for analyzing a user's emotional state from voice data and user interaction" refers to technologies and devices that analyze a user's voice and interactions with devices to understand their emotional state.

[0181] "Methods for recommending optimal outing destinations" refer to algorithms and devices that suggest outing destinations deemed optimal for the user based on acquired user information and sentiment analysis results.

[0182] "Methods for executing reservations for recommended outings" refer to technologies and devices that automatically handle the reservation process for selected facilities or events.

[0183] "Means of notifying users of reservation completion information" refers to communication methods or devices used to inform users that their reservation has been successful.

[0184] To implement this system, the user's terminal and the server must work together. The user's terminal will be a mobile information device such as a smartphone or tablet. The terminal will have software installed to collect user information, such as family structure, past visit history, and schedule information.

[0185] To analyze the user's emotional state, the system uses speech recognition software and an emotion analysis engine installed on the device. Specifically, the Google Cloud Speech-to-Text API is used for speech transcription, and the Python Emotion Recognition library is utilized for emotion analysis. This allows the system to understand the user's emotional state through voice input and interaction.

[0186] The server uses acquired user information and sentiment data to gather local event information, facility information, weather data, etc., and selects the optimal destination. This utilizes a cloud-based data processing platform such as Google Cloud BigQuery.

[0187] Information about selected destinations will be sent to users via push notifications and email. In this process, a notification function that operates on the user's device is crucial, and this function will be implemented using a mobile app that leverages the Flutter® framework.

[0188] For example, if a user's mood is analyzed as "feeling down," the server might suggest a relaxing activity, such as "a walk in a nearby nature park." Once the user approves this suggestion, the server automatically processes the reservation and notifies the user of the result.

[0189] A possible prompt format for using a generative AI model is: "Analyze the user's emotions from the voice data and find a place that will cheer them up." This allows the system to generate optimized output based on the user's emotional data.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The device retrieves user information. It reads data such as family structure, past visit history, and schedule information from a database and saves it to its internal data storage. This collects basic user information.

[0193] Step 2:

[0194] The device launches speech recognition software and acquires the user's voice data. The Google Cloud Speech-to-Text API is used to convert this voice data into text data, and the transcribed data is used as input to the sentiment analysis engine.

[0195] Step 3:

[0196] The device performs emotion analysis. The transcribed audio data is processed by the Emotion Recognition library, which analyzes the user's emotional state into categories such as "joy," "sadness," and "anger," and sends the results to the server.

[0197] Step 4:

[0198] The server retrieves event and facility information from external sources. It collects local event calendars, facility information, and weather data via BigQuery and stores this information in a database for analysis.

[0199] Step 5:

[0200] The server selects the optimal destination based on the user's emotional and event information acquired. Using a generative AI model, the prompt "Analyze the user's emotions from the voice data and find a place that will cheer them up" triggers the algorithm to generate an optimized output (recommended place).

[0201] Step 6:

[0202] The server sends recommended destinations to the device. The recommendations are sent to the device as push notifications, and the device displays the received information in a list, prompting the user to make a selection.

[0203] Step 7:

[0204] The user selects a destination from the provided list of places to go and sends their choice to the server via their device.

[0205] Step 8:

[0206] The server automatically executes the reservation for the selected destination and notifies the terminal of the result. If the reservation is successful through the reservation system, the server generates reservation completion information and sends the result to the terminal to inform the user of the reservation's success.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention is a system for effectively planning how parents and children spend their holidays. This system effectively collects users' lifestyle information and, based on that information, recommends the most suitable outing destinations. The system mainly consists of a server and user terminals, each playing a specific role.

[0224] First, the device collects information about the user's family structure, places they have visited in the past, and scheduled events listed in their calendar. This information is then sent to a server via the application with the user's consent.

[0225] Next, the server integrates and collects local event information, facility information, weather data, and other information from external sources. This creates an up-to-date and relevant database to provide to users.

[0226] The server then uses the collected user information and external data to apply machine learning algorithms and design the optimal outing plan for the user. In this process, based on family structure and past preferences, it might recommend, for example, zoos or amusement parks for young children, and science museums or art museums for older children.

[0227] The selected plan is sent from the server to the terminal, where the user can review it. Once the user selects a specific destination, the server automatically initiates the booking process for that location. This booking process is completed quickly and accurately in conjunction with the online platform.

[0228] Finally, once the reservation is confirmed, the device will notify the user. This allows the user to smoothly prepare for their outing according to their plan.

[0229] This system frees parents from the burden of detailed planning, allowing them to spend more meaningful time with their families. Furthermore, the automation of information retrieval, recommendations, and booking processes enables efficient time management in daily life.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The device, with the user's permission, collects information such as family composition, past visit history, and schedule. After the device has finished collecting this information through the application, it securely transmits the data to the server.

[0233] Step 2:

[0234] The server accesses external information sources to retrieve the latest local event information, facility operating information, and weather data. This data is then organized and stored in a database on the server for later processing.

[0235] Step 3:

[0236] The server uses machine learning algorithms to select appropriate destinations based on user information and external data. The resulting list of destinations is optimized to take into account the user's preferences and past activity history.

[0237] Step 4:

[0238] After the server compiles the recommendation list, it sends it to the user's terminal. The terminal then displays the received list to the user and makes it available for selection.

[0239] Step 5:

[0240] Users select their desired destination from a list of recommended options displayed on their device. Users can also enter detailed information such as dates and the number of people if needed.

[0241] Step 6:

[0242] The server receives the user's selection and makes a reservation for the chosen destination. This process is handled in conjunction with the relevant facility's online reservation system and continues until the reservation is officially completed.

[0243] Step 7:

[0244] The device notifies the user that the reservation is complete. This allows the user to confirm the reservation details and make further preparations.

[0245] (Example 1)

[0246] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0247] When families plan their holidays, efficiently gathering information and selecting the optimal plan can be complex and time-consuming. In particular, there is a need to automatically generate plans tailored to individual user preferences by aggregating relevant data from numerous sources. Furthermore, there is a need to reduce the burden and errors associated with manual booking procedures.

[0248] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0249] In this invention, the server includes means for collecting and transmitting user information to the server, means for integrating and acquiring local event data, facility data, and weather data from multiple external information sources, and means for using the user information and the external information to generate an optimal outing plan utilizing machine learning technology. This makes it possible to efficiently provide outing plans that are optimal for the user and tailored to their individual preferences, and to automate the process from planning to booking.

[0250] "User information" refers to data about individual users, including family structure data, past visit history, and schedule information.

[0251] "External information sources" refer to a collection of information that provides data on local events, facilities, and weather forecasts.

[0252] "Integration" is the process of combining and summarizing data obtained from multiple different sources.

[0253] "Machine learning techniques" are algorithms used by computers to learn data patterns and make predictions and judgments about new data.

[0254] An "outing plan" is a suggestion of travel destinations and places to visit, designed based on the user's preferences and needs, and includes specific destinations and activities.

[0255] A "reservation" is the process of securing a seat or participation slot in advance at a facility or event.

[0256] "Notification" is the act of informing a user of information or a message, whether visual or auditory.

[0257] This system is designed to effectively support family holiday planning and consists of a user terminal and a server. The terminal has a dedicated user application installed, which allows users to input user information such as family structure, past visit history, and schedule information. This data is transmitted to the server using a secure protocol. The server is programmed using general-purpose programming languages ​​such as Python and Java, and the database server uses MySQL or similar to store the data.

[0258] The server obtains local event information, facility details, and weather data through interfaces with external information sources. This includes using information sources such as local event APIs, open data facility information, and weather data APIs. This information is integrated with user information and stored in the server's database.

[0259] The server uses a generative AI model based on the collected information to generate the optimal outing plan. This model considers the user's family structure and past preferences to select an appropriate destination. For example, if the prompt is "If the family structure includes elementary school-aged children, please suggest activities recommended for a sunny day," a one-day plan to the zoo might be generated.

[0260] The server then sends the generated plan to the device, which notifies the user. The user can then select a suggested destination, and the server, in conjunction with the booking platform, automatically handles the booking process. This allows the user to complete the entire process from planning to booking smoothly and efficiently.

[0261] The system implemented in this way frees users from the burden of detailed information gathering and planning, allowing them to enjoy time with their families more efficiently.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The device collects user information. Through the application, users enter information such as family structure, past visit history, and calendar appointments. This data is collected from the application's forms, encrypted via a secure communication protocol, and sent to the server as "user information."

[0265] Step 2:

[0266] The server collects local event information, facility information, and weather data from external sources. It retrieves local event calendars, detailed facility information, and weather forecast data via an API interface. This data is stored in a database as "external information" and updated to reflect the latest information.

[0267] Step 3:

[0268] The server integrates collected user information with external information. Using database queries, it identifies the user's past visits and preferred activities, and matches this information with external data. This prepares the server to analyze which events and facilities are best suited to the user.

[0269] Step 4:

[0270] The server generates the optimal outing plan using a generative AI model. Using integrated data as input, it selects recommended destinations based on the user's preferences. Machine learning algorithms are utilized in this process; for example, prompts might be given such as, "If the family includes elementary school-aged children, please suggest activities suitable for a sunny day." A specific plan is then generated as output.

[0271] Step 5:

[0272] The server sends the generated plan to the device. The user can review the received notification at a low cost and select the suggested destination in a browser or in-app view. This includes detailed descriptions, images, and ratings of the destination.

[0273] Step 6:

[0274] Once the user selects a destination, the server automatically processes the reservation. Based on the selected plan, it interacts with the reservation system to secure tickets and confirm the reservation. As a result, reservation completion information is generated and recorded in the user management system.

[0275] Step 7:

[0276] The device sends a reservation confirmation notification to the user. The notification contains the completed reservation information, date and time, location, and reservation number, and the user can use this information to prepare for their trip or visit.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In modern society, it is often difficult to efficiently plan free time in family units. In particular, parents want to spend meaningful time with their children on holidays, but the task of selecting the most suitable activities from a vast amount of information and making reservation procedures has become a burden. As a result, the planning becomes complicated, and there is a problem that time cannot be effectively utilized.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for acquiring user information, means for acquiring event information from a plurality of external information sources, and means for analyzing the user's past usage history and external data using a machine learning algorithm and providing an optimized proposal. As a result, the user can smoothly make an optimal outing plan based on the family composition and past preferences, and by automating the online reservation procedure, it becomes possible to improve time efficiency and reduce the complexity of the plan.

[0282] "User information" refers to data such as personal attributes, behavior history, and schedule related to the user.

[0283] "External information source" is a source of information obtained from outside the system, such as local events, facility information, and weather data.

[0284] "Event information" refers to information about activities and events held at specific times and places.

[0285] "Outing plan" is an action plan that determines the destinations and activity contents for the user to spend meaningful time.

[0286] "Machine learning algorithm" is a calculation method for learning specific patterns based on accumulated data and making predictions and decisions for new data.

[0287] This invention is a system for efficiently planning parent-child holidays, and consists of a server and user terminals. The system uses user information and external information to propose the optimal outing plan.

[0288] The server first receives user information from the user's terminal. This user information includes family structure, history of places visited in the past, and schedule information. With the user's consent, this data is sent from the terminal to the server and stored in the database.

[0289] Next, the server retrieves local event information, facility information, and weather data from external sources. APIs such as the Google Maps API and OpenWeatherMap are used for this purpose. The server integrates this information and prepares it to provide users with the most up-to-date and relevant information.

[0290] The server uses machine learning algorithms (e.g., scikit-learn) to analyze collected user information and external data. This optimizes outing plans based on the user's past behavior history and preferences, and calculates suitable destinations such as zoos, amusement parks, science museums, and museums.

[0291] Recommended itineraries are sent to the user's device for review. The user then selects a specific destination, and the server automates the booking process online. Booking confirmation is sent to the user's device, allowing them to prepare for their trip according to the plan.

[0292] For example, on a rainy day, the system can suggest indoor activities. By utilizing a generative AI model, the user can be prompted with a request such as, "Please suggest indoor activities that the family can enjoy this weekend," and the AI ​​will generate optimal suggestions.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] Users use their devices to input personal information, family structure, past visit history, and schedule information. This information is transmitted to the server through the application, with consideration for privacy. The input data includes user-specific information such as family structure and visit history.

[0296] Step 2:

[0297] The server stores the received user information in a database. Simultaneously, it calls APIs to retrieve local event information, facility information, and weather data from external sources. Specifically, the server aggregates information using APIs such as Google Maps API and OpenWeatherMap. This process involves retrieving data from external APIs.

[0298] Step 3:

[0299] The server uses machine learning algorithms to analyze collected user information and external data. This process utilizes libraries such as Python's scikit-learn to process data, taking into account past usage history and user preferences. The analysis identifies recommended destinations and events for the user.

[0300] Step 4:

[0301] The server sends an optimized outing plan to the user's terminal. On the terminal, the user is shown the details of each plan and can select one. During this selection process, the user decides which plan to implement. The output at this point is the detailed information of the plan.

[0302] Step 5:

[0303] When the user selects a specific destination, the server automates the reservation process for related facilities and events. Using the online reservation system API, the reservation is completed quickly and accurately. Here, the data calculations and transmissions necessary for the reservation process are performed.

[0304] Step 6:

[0305] The server notifies the user terminal of the reservation completion information. The terminal receives this notification, and the user starts acting according to the plan. In this final step, the confirmation data of the reservation completion is transmitted to the user.

[0306] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0307] The present invention is a system that enables a more personalized proposal of a destination based on the user's emotional state by combining an emotion engine for providing information on events and facilities. This system aims to provide an optimal plan taking into account the user's emotion.

[0308] The system is operated via an application installed on the terminal owned by the user. First, the terminal collects family composition, past visit history, and calendar schedule information as well as normal user information. Furthermore, the emotion engine in the terminal recognizes the current emotional state from voice data and user interactions.

[0309] The server receives the user information and emotion data transmitted from the terminal and obtains regional event information, facility information, and weather data from an external information source based on this. Thereby, the server forms a database reflecting the user's emotion and current interests.

[0310] The server uses the received emotional information to recommend the most suitable destination. For example, if it determines that the user is seeking relaxation, it can suggest a quiet nature park or a tranquil waterside facility.

[0311] The generated recommendation list is sent from the server to the terminal, allowing the user to choose a destination. The user reviews the displayed options and selects a preferred destination. After selection, the server automatically handles the booking process for that location and notifies the user of the details.

[0312] Thus, this invention leverages user emotions to provide more personalized plans, thereby creating a satisfying outing experience for both parents and children. The overall system flow seamlessly integrates processes such as information gathering, emotion analysis, appropriate plan recommendation, booking execution, and notification, consistently supporting the user experience.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] With prior permission from the user, the device collects information such as family structure, past visit history, and schedule, and analyzes emotional information from voice data and screen operations using an emotion engine. The processed information is sent to a server to improve the user experience.

[0316] Step 2:

[0317] The server accesses external information sources to collect up-to-date data, including weather, local events, and facility information. The collected data is organized in the server's database and used for subsequent processing.

[0318] Step 3:

[0319] The server combines user information, emotional data, and external information to apply an algorithm that determines the priority of destinations influenced by emotions. In this process, the optimal location is selected according to the user's emotional state, such as wanting to relax or seeking stimulation.

[0320] Step 4:

[0321] The server sends a list of recommended destinations to the user's device. The device displays the list, allowing the user to select their desired destination.

[0322] Step 5:

[0323] The user selects a destination from the displayed options via their device and enters reservation details such as the desired date and number of people.

[0324] Step 6:

[0325] The server automatically initiates the online booking process for the selected destination. The booking information reflects the details entered by the user.

[0326] Step 7:

[0327] The device notifies the user that the reservation is complete and that all procedures for the selected destination have been finished. Based on this information, the user can proceed with planning for the day.

[0328] (Example 2)

[0329] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0330] Modern users demand personalized information tailored to their emotions and personal circumstances, but existing information delivery systems do not adequately address this, posing a challenge in providing individualized suggestions based on users' emotional states.

[0331] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0332] In this invention, the server includes means for acquiring user information, means for acquiring information from multiple external sources, and means for analyzing emotions. This enables the recommendation of an optimal location based on the user's individual emotional state and the reservation of that location.

[0333] "User information" refers to basic data about the user, including personal composition, visit history, and schedule information.

[0334] "External information sources" refer to external data sources that provide information necessary for user recommendations, such as local calendars, facility databases, and weather information.

[0335] "Methods for analyzing emotions" refer to the process of analyzing a user's voice and interactions to recognize their emotional state.

[0336] "Methods for recommending optimal locations" refer to methods for selecting and presenting the most appropriate destination to users based on collected information and analysis results.

[0337] The "means of executing a reservation" refer to a function that automatically completes the process of confirming a visit to a recommended location.

[0338] "Means of notifying reservation information" refers to methods used to inform users of confirmed reservation details, and serves the role of providing detailed information.

[0339] This invention is an information system for providing personalized suggestions based on the user's emotional state. This system consists of an application installed on the user's terminal and a server connected via a network.

[0340] The device collects the user's voice data and interactions on the device, and identifies the user's current emotional state using an emotion analysis engine. This utilizes speech recognition technology and emotion analysis algorithms. The device also retrieves the user's configuration information, past visit history, and schedule information from a database and sends this information to the server.

[0341] Based on the received user information and sentiment data, the server retrieves local calendars, facility data, and weather information from multiple external sources. In this process, the server uses a generative AI model to calculate destinations optimized for the user's state. For example, if the user desires relaxation, a quiet nature park or a tranquil waterside facility might be selected.

[0342] The user can view a list of recommendations displayed on their device and choose a destination from the options. After selection, the server automatically processes the reservation for that location and notifies the device of the reservation confirmation. An example of a prompt message to suggest destinations the user might prefer is, "Please suggest places where the user would like to relax."

[0343] Thus, the entire system has numerous features that enable it to provide personalized outing plans based on the user's emotions and preferences.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The device collects the user's personal information. It uses data from the device's sensors and calendar application as input. As output, it creates database entries containing the user's family structure, past visit history, and schedule information. This process is carried out through a database management system.

[0347] Step 2:

[0348] The device uses an emotion engine to analyze voice data and user interactions. Inputs are voice and gesture data acquired from the microphone and touch interface. Outputs are numerical or tagged information indicating the user's current emotional state. This is done using an emotion analysis algorithm.

[0349] Step 3:

[0350] The terminal transmits the collected user information and emotional state data to the server. The input is the data obtained in steps 1 and 2, and the output is sent to the server as encrypted packets. HTTPS or similar communication protocols are used.

[0351] Step 4:

[0352] The server retrieves local event information, facility information, and weather data from external sources. Input includes external data obtained via APIs. Output is a list of integrated information. Real-time information is collected via API calls.

[0353] Step 5:

[0354] The server uses a generative AI model to integrate user information, emotional state, and external information to calculate the optimal destination. The input consists of the data received in step 3 and the information collected in step 4. The output is a personalized list of recommended destinations for each user. The generative AI model processes large datasets and generates results tailored to individual needs.

[0355] Step 6:

[0356] The server sends the recommendation list to the terminal. The input is the recommendation list obtained in step 5, and the output appears as recommendation options displayed on the user's terminal. This is where the user interface rendering takes place.

[0357] Step 7:

[0358] The user makes a selection from a list of recommendations displayed on the device. The input is visual data displayed on the device, and the selected item is sent to the server as output. This selection is made via a touch interface.

[0359] Step 8:

[0360] The server processes the reservation for the selected location. The input is the user's selection data, and the output is the reservation confirmation information. The reservation is automatically confirmed using the online reservation system.

[0361] Step 9:

[0362] The server notifies the terminal of reservation confirmation information. The input is confirmation information from the reservation system, and the output is a notification message in a format accessible to the user. This process uses a notification service to ensure that the information reaches the user immediately.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] Conventional information systems failed to take into account the user's emotional state, making it difficult to suggest optimal facilities or events tailored to the user's mood and emotions. Furthermore, there was a need for a method that automatically booked outings that matched the user's mood without requiring complicated operations.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes means for acquiring user information, means for analyzing the user's emotional state from voice data and user interactions, and means for acquiring event information from multiple external information sources. This makes it possible to recommend the most suitable outing destination based on each user's emotional state and to automatically make reservations for it.

[0368] "Means of acquiring user information" refers to systems and devices that collect and utilize users' personal attributes and activity history.

[0369] "Means of obtaining event information from multiple external sources" refers to methods and devices for obtaining data on various events and facilities through the internet and various databases.

[0370] "Means for analyzing a user's emotional state from voice data and user interaction" refers to technologies and devices that analyze a user's voice and interactions with devices to understand their emotional state.

[0371] "Methods for recommending optimal outing destinations" refer to algorithms and devices that suggest outing destinations deemed optimal for the user based on acquired user information and sentiment analysis results.

[0372] "Methods for executing reservations for recommended outings" refer to technologies and devices that automatically handle the reservation process for selected facilities or events.

[0373] "Means of notifying users of reservation completion information" refers to communication methods or devices used to inform users that their reservation has been successful.

[0374] To implement this system, the user's terminal and the server must work together. The user's terminal will be a mobile information device such as a smartphone or tablet. The terminal will have software installed to collect user information, such as family structure, past visit history, and schedule information.

[0375] To analyze the user's emotional state, the system uses speech recognition software and an emotion analysis engine installed on the device. Specifically, the Google Cloud Speech-to-Text API is used for speech transcription, and the Python Emotion Recognition library is utilized for emotion analysis. This allows the system to understand the user's emotional state through voice input and interaction.

[0376] The server uses acquired user information and sentiment data to gather local event information, facility information, weather data, etc., and selects the optimal destination. This utilizes a cloud-based data processing platform such as Google Cloud BigQuery.

[0377] Information about selected destinations will be sent to users via push notifications and email. In this process, a notification function that operates on the user's device is crucial, and this function will be implemented using a mobile app powered by the Flutter framework.

[0378] For example, if a user's mood is analyzed as "feeling down," the server might suggest a relaxing activity, such as "a walk in a nearby nature park." Once the user approves this suggestion, the server automatically processes the reservation and notifies the user of the result.

[0379] A possible prompt format for using a generative AI model is: "Analyze the user's emotions from the voice data and find a place that will cheer them up." This allows the system to generate optimized output based on the user's emotional data.

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The device retrieves user information. It reads data such as family structure, past visit history, and schedule information from a database and saves it to its internal data storage. This collects basic user information.

[0383] Step 2:

[0384] The device launches speech recognition software and acquires the user's voice data. The Google Cloud Speech-to-Text API is used to convert this voice data into text data, and the transcribed data is used as input to the sentiment analysis engine.

[0385] Step 3:

[0386] The device performs emotion analysis. The transcribed audio data is processed by the Emotion Recognition library, which analyzes the user's emotional state into categories such as "joy," "sadness," and "anger," and sends the results to the server.

[0387] Step 4:

[0388] The server retrieves event and facility information from external sources. It collects local event calendars, facility information, and weather data via BigQuery and stores this information in a database for analysis.

[0389] Step 5:

[0390] The server selects the optimal destination based on the user's emotional and event information acquired. Using a generative AI model, the prompt "Analyze the user's emotions from the voice data and find a place that will cheer them up" triggers the algorithm to generate an optimized output (recommended place).

[0391] Step 6:

[0392] The server sends recommended destinations to the device. The recommendations are sent to the device as push notifications, and the device displays the received information in a list, prompting the user to make a selection.

[0393] Step 7:

[0394] The user selects a destination from the provided list of places to go and sends their choice to the server via their device.

[0395] Step 8:

[0396] The server automatically executes the reservation for the selected destination and notifies the terminal of the result. If the reservation is successful through the reservation system, the server generates reservation completion information and sends the result to the terminal to inform the user of the reservation's success.

[0397] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0408] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0409] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0410] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] This invention is a system for effectively planning how parents and children spend their holidays. This system effectively collects users' lifestyle information and, based on that information, recommends the most suitable outing destinations. The system mainly consists of a server and user terminals, each playing a specific role.

[0414] First, the device collects information about the user's family structure, places they have visited in the past, and scheduled events listed in their calendar. This information is then sent to a server via the application with the user's consent.

[0415] Next, the server integrates and collects local event information, facility information, weather data, and other information from external sources. This creates an up-to-date and relevant database to provide to users.

[0416] The server then uses the collected user information and external data to apply machine learning algorithms and design the optimal outing plan for the user. In this process, based on family structure and past preferences, it might recommend, for example, zoos or amusement parks for young children, and science museums or art museums for older children.

[0417] The selected plan is sent from the server to the terminal, where the user can review it. Once the user selects a specific destination, the server automatically initiates the booking process for that location. This booking process is completed quickly and accurately in conjunction with the online platform.

[0418] Finally, once the reservation is confirmed, the device will notify the user. This allows the user to smoothly prepare for their outing according to their plan.

[0419] This system frees parents from the burden of detailed planning, allowing them to spend more meaningful time with their families. Furthermore, the automation of information retrieval, recommendations, and booking processes enables efficient time management in daily life.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The device, with the user's permission, collects information such as family composition, past visit history, and schedule. After the device has finished collecting this information through the application, it securely transmits the data to the server.

[0423] Step 2:

[0424] The server accesses external information sources to retrieve the latest local event information, facility operating information, and weather data. This data is then organized and stored in a database on the server for later processing.

[0425] Step 3:

[0426] The server uses machine learning algorithms to select appropriate destinations based on user information and external data. The resulting list of destinations is optimized to take into account the user's preferences and past activity history.

[0427] Step 4:

[0428] After the server compiles the recommendation list, it sends it to the user's terminal. The terminal then displays the received list to the user and makes it available for selection.

[0429] Step 5:

[0430] Users select their desired destination from a list of recommended options displayed on their device. Users can also enter detailed information such as dates and the number of people if needed.

[0431] Step 6:

[0432] The server receives the user's selection and makes a reservation for the chosen destination. This process is handled in conjunction with the relevant facility's online reservation system and continues until the reservation is officially completed.

[0433] Step 7:

[0434] The device notifies the user that the reservation is complete. This allows the user to confirm the reservation details and make further preparations.

[0435] (Example 1)

[0436] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0437] When families plan their holidays, efficiently gathering information and selecting the optimal plan can be complex and time-consuming. In particular, there is a need to automatically generate plans tailored to individual user preferences by aggregating relevant data from numerous sources. Furthermore, there is a need to reduce the burden and errors associated with manual booking procedures.

[0438] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0439] In this invention, the server includes means for collecting and transmitting user information to the server, means for integrating and acquiring local event data, facility data, and weather data from multiple external information sources, and means for using the user information and the external information to generate an optimal outing plan utilizing machine learning technology. This makes it possible to efficiently provide outing plans that are optimal for the user and tailored to their individual preferences, and to automate the process from planning to booking.

[0440] "User information" refers to data about individual users, including family structure data, past visit history, and schedule information.

[0441] "External information sources" refer to a collection of information that provides data on local events, facilities, and weather forecasts.

[0442] "Integration" is the process of combining and summarizing data obtained from multiple different sources.

[0443] "Machine learning techniques" are algorithms used by computers to learn data patterns and make predictions and judgments about new data.

[0444] An "outing plan" is a suggestion of travel destinations and places to visit, designed based on the user's preferences and needs, and includes specific destinations and activities.

[0445] A "reservation" is the process of securing a seat or participation slot in advance at a facility or event.

[0446] "Notification" is the act of informing a user of information or a message, whether visual or auditory.

[0447] This system is designed to effectively support family holiday planning and consists of a user terminal and a server. The terminal has a dedicated user application installed, which allows users to input user information such as family structure, past visit history, and schedule information. This data is transmitted to the server using a secure protocol. The server is programmed using general-purpose programming languages ​​such as Python and Java, and the database server uses MySQL or similar to store the data.

[0448] The server obtains local event information, facility details, and weather data through interfaces with external information sources. This includes using information sources such as local event APIs, open data facility information, and weather data APIs. This information is integrated with user information and stored in the server's database.

[0449] The server uses a generative AI model based on the collected information to generate the optimal outing plan. This model considers the user's family structure and past preferences to select an appropriate destination. For example, if the prompt is "If the family structure includes elementary school-aged children, please suggest activities recommended for a sunny day," a one-day plan to the zoo might be generated.

[0450] The server then sends the generated plan to the device, which notifies the user. The user can then select a suggested destination, and the server, in conjunction with the booking platform, automatically handles the booking process. This allows the user to complete the entire process from planning to booking smoothly and efficiently.

[0451] The system implemented in this way frees users from the burden of detailed information gathering and planning, allowing them to enjoy time with their families more efficiently.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The device collects user information. Through the application, users enter information such as family structure, past visit history, and calendar appointments. This data is collected from the application's forms, encrypted via a secure communication protocol, and sent to the server as "user information."

[0455] Step 2:

[0456] The server collects local event information, facility information, and weather data from external sources. It retrieves local event calendars, detailed facility information, and weather forecast data via an API interface. This data is stored in a database as "external information" and updated to reflect the latest information.

[0457] Step 3:

[0458] The server integrates collected user information with external information. Using database queries, it identifies the user's past visits and preferred activities, and matches this information with external data. This prepares the server to analyze which events and facilities are best suited to the user.

[0459] Step 4:

[0460] The server generates the optimal outing plan using a generative AI model. Using integrated data as input, it selects recommended destinations based on the user's preferences. Machine learning algorithms are utilized in this process; for example, prompts might be given such as, "If the family includes elementary school-aged children, please suggest activities suitable for a sunny day." A specific plan is then generated as output.

[0461] Step 5:

[0462] The server sends the generated plan to the device. The user can review the received notification at a low cost and select the suggested destination in a browser or in-app view. This includes detailed descriptions, images, and ratings of the destination.

[0463] Step 6:

[0464] Once the user selects a destination, the server automatically processes the reservation. Based on the selected plan, it interacts with the reservation system to secure tickets and confirm the reservation. As a result, reservation completion information is generated and recorded in the user management system.

[0465] Step 7:

[0466] The device sends a reservation confirmation notification to the user. The notification contains the completed reservation information, date and time, location, and reservation number, and the user can use this information to prepare for their trip or visit.

[0467] (Application Example 1)

[0468] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0469] In modern society, it is often difficult to efficiently plan free time for families. Parents, in particular, want to spend meaningful time with their children on weekends, but the effort involved in selecting the most suitable activities from a vast amount of information and making reservations becomes a burden. This leads to complicated planning and, as a result, an inability to effectively utilize time.

[0470] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0471] In this invention, the server includes means for acquiring user information, means for acquiring event information from multiple external information sources, and means for analyzing the user's past usage history and external data using a machine learning algorithm to provide optimized suggestions. This enables users to smoothly plan outings based on their family structure and past preferences, and by automating online reservation procedures, it is possible to improve time efficiency and reduce the complexity of planning.

[0472] "User information" refers to data such as personal attributes, behavioral history, and schedules related to a user.

[0473] "External information sources" refer to sources of information obtained from outside the system, such as local event and facility information, and weather data.

[0474] "Event information" refers to information about activities or events that take place during a specific period and location.

[0475] An "outing plan" is an action plan that defines destinations and activities for users to spend their time meaningfully.

[0476] A "machine learning algorithm" is a computational method that learns specific patterns from accumulated data to make predictions and decisions about new data.

[0477] This invention is a system for efficiently planning parent-child holidays, and consists of a server and user terminals. The system uses user information and external information to propose the optimal outing plan.

[0478] The server first receives user information from the user's terminal. This user information includes family structure, history of places visited in the past, and schedule information. With the user's consent, this data is sent from the terminal to the server and stored in the database.

[0479] Next, the server retrieves local event information, facility information, and weather data from external sources. APIs such as the Google Maps API and OpenWeatherMap are used for this purpose. The server integrates this information and prepares it to provide users with the most up-to-date and relevant information.

[0480] The server uses machine learning algorithms (e.g., scikit-learn) to analyze collected user information and external data. This optimizes outing plans based on the user's past behavior history and preferences, and calculates suitable destinations such as zoos, amusement parks, science museums, and museums.

[0481] Recommended itineraries are sent to the user's device for review. The user then selects a specific destination, and the server automates the booking process online. Booking confirmation is sent to the user's device, allowing them to prepare for their trip according to the plan.

[0482] For example, on a rainy day, the system can suggest indoor activities. By utilizing a generative AI model, the user can be prompted with a request such as, "Please suggest indoor activities that the family can enjoy this weekend," and the AI ​​will generate optimal suggestions.

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] Users use their devices to input personal information, family structure, past visit history, and schedule information. This information is transmitted to the server through the application, with consideration for privacy. The input data includes user-specific information such as family structure and visit history.

[0486] Step 2:

[0487] The server stores the received user information in a database. Simultaneously, it calls APIs to retrieve local event information, facility information, and weather data from external sources. Specifically, the server aggregates information using APIs such as Google Maps API and OpenWeatherMap. This process involves retrieving data from external APIs.

[0488] Step 3:

[0489] The server uses machine learning algorithms to analyze collected user information and external data. This process utilizes libraries such as Python's scikit-learn to process data, taking into account past usage history and user preferences. The analysis identifies recommended destinations and events for the user.

[0490] Step 4:

[0491] The server sends an optimized outing plan to the user's terminal. On the terminal, the user is shown the details of each plan and can select one. During this selection process, the user decides which plan to implement. The output at this point is the detailed information of the plan.

[0492] Step 5:

[0493] When a user selects a specific destination, the server automates the booking process for related facilities and events. Using an online booking system API, bookings are completed quickly and accurately. This process involves the calculation and transmission of data necessary for the booking procedure.

[0494] Step 6:

[0495] The server notifies the user's terminal of the reservation completion information. The terminal receives this notification, and the user begins to act according to the plan. In this final step, confirmation data of the reservation completion is transmitted to the user.

[0496] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0497] This invention is a system that combines an emotion engine with event and facility information provision to enable more personalized outing destination suggestions based on the user's emotional state. The aim of this system is to provide the optimal plan by taking the user's emotions into consideration.

[0498] The system is operated through an application installed on the user's device. First, the device collects information such as family structure, past visit history, and calendar schedules, just like regular user information. Furthermore, an emotion engine within the device recognizes the user's current emotional state from voice data and user interactions.

[0499] The server receives user information and sentiment data transmitted from the terminal, and based on this, retrieves local event information, facility information, and weather data from external sources. This allows the server to form a database that reflects the user's sentiments and current interests.

[0500] The server uses the received emotional information to recommend the most suitable destination. For example, if it determines that the user is seeking relaxation, it can suggest a quiet nature park or a tranquil waterside facility.

[0501] The generated recommendation list is sent from the server to the terminal, allowing the user to choose a destination. The user reviews the displayed options and selects a preferred destination. After selection, the server automatically handles the booking process for that location and notifies the user of the details.

[0502] Thus, this invention leverages user emotions to provide more personalized plans, thereby creating a satisfying outing experience for both parents and children. The overall system flow seamlessly integrates processes such as information gathering, emotion analysis, appropriate plan recommendation, booking execution, and notification, consistently supporting the user experience.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] With prior permission from the user, the device collects information such as family structure, past visit history, and schedule, and analyzes emotional information from voice data and screen operations using an emotion engine. The processed information is sent to a server to improve the user experience.

[0506] Step 2:

[0507] The server accesses external information sources to collect up-to-date data, including weather, local events, and facility information. The collected data is organized in the server's database and used for subsequent processing.

[0508] Step 3:

[0509] The server combines user information, emotional data, and external information to apply an algorithm that determines the priority of destinations influenced by emotions. In this process, the optimal location is selected according to the user's emotional state, such as wanting to relax or seeking stimulation.

[0510] Step 4:

[0511] The server sends a list of recommended destinations to the user's device. The device displays the list, allowing the user to select their desired destination.

[0512] Step 5:

[0513] The user selects a destination from the displayed options via their device and enters reservation details such as the desired date and number of people.

[0514] Step 6:

[0515] The server automatically initiates the online booking process for the selected destination. The booking information reflects the details entered by the user.

[0516] Step 7:

[0517] The device notifies the user that the reservation is complete and that all procedures for the selected destination have been finished. Based on this information, the user can proceed with planning for the day.

[0518] (Example 2)

[0519] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0520] Modern users demand personalized information tailored to their emotions and personal circumstances, but existing information delivery systems do not adequately address this, posing a challenge in providing individualized suggestions based on users' emotional states.

[0521] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0522] In this invention, the server includes means for acquiring user information, means for acquiring information from multiple external sources, and means for analyzing emotions. This enables the recommendation of an optimal location based on the user's individual emotional state and the reservation of that location.

[0523] "User information" refers to basic data about the user, including personal composition, visit history, and schedule information.

[0524] "External information sources" refer to external data sources that provide information necessary for user recommendations, such as local calendars, facility databases, and weather information.

[0525] "Methods for analyzing emotions" refer to the process of analyzing a user's voice and interactions to recognize their emotional state.

[0526] "Methods for recommending optimal locations" refer to methods for selecting and presenting the most appropriate destination to users based on collected information and analysis results.

[0527] The "means of executing a reservation" refer to a function that automatically completes the process of confirming a visit to a recommended location.

[0528] "Means of notifying reservation information" refers to methods used to inform users of confirmed reservation details, and serves the role of providing detailed information.

[0529] This invention is an information system for providing personalized suggestions based on the user's emotional state. This system consists of an application installed on the user's terminal and a server connected via a network.

[0530] The device collects the user's voice data and interactions on the device, and identifies the user's current emotional state using an emotion analysis engine. This utilizes speech recognition technology and emotion analysis algorithms. The device also retrieves the user's configuration information, past visit history, and schedule information from a database and sends this information to the server.

[0531] Based on the received user information and sentiment data, the server retrieves local calendars, facility data, and weather information from multiple external sources. In this process, the server uses a generative AI model to calculate destinations optimized for the user's state. For example, if the user desires relaxation, a quiet nature park or a tranquil waterside facility might be selected.

[0532] The user can view a list of recommendations displayed on their device and choose a destination from the options. After selection, the server automatically processes the reservation for that location and notifies the device of the reservation confirmation. An example of a prompt message to suggest destinations the user might prefer is, "Please suggest places where the user would like to relax."

[0533] Thus, the entire system has numerous features that enable it to provide personalized outing plans based on the user's emotions and preferences.

[0534] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0535] Step 1:

[0536] The device collects the user's personal information. It uses data from the device's sensors and calendar application as input. As output, it creates database entries containing the user's family structure, past visit history, and schedule information. This process is carried out through a database management system.

[0537] Step 2:

[0538] The device uses an emotion engine to analyze voice data and user interactions. Inputs are voice and gesture data acquired from the microphone and touch interface. Outputs are numerical or tagged information indicating the user's current emotional state. This is done using an emotion analysis algorithm.

[0539] Step 3:

[0540] The terminal transmits the collected user information and emotional state data to the server. The input is the data obtained in steps 1 and 2, and the output is sent to the server as encrypted packets. HTTPS or similar communication protocols are used.

[0541] Step 4:

[0542] The server retrieves local event information, facility information, and weather data from external sources. Input includes external data obtained via APIs. Output is a list of integrated information. Real-time information is collected via API calls.

[0543] Step 5:

[0544] The server uses a generative AI model to integrate user information, emotional state, and external information to calculate the optimal destination. The input consists of the data received in step 3 and the information collected in step 4. The output is a personalized list of recommended destinations for each user. The generative AI model processes large datasets and generates results tailored to individual needs.

[0545] Step 6:

[0546] The server sends the recommendation list to the terminal. The input is the recommendation list obtained in step 5, and the output appears as recommendation options displayed on the user's terminal. This is where the user interface rendering takes place.

[0547] Step 7:

[0548] The user makes a selection from a list of recommendations displayed on the device. The input is visual data displayed on the device, and the selected item is sent to the server as output. This selection is made via a touch interface.

[0549] Step 8:

[0550] The server processes the reservation for the selected location. The input is the user's selection data, and the output is the reservation confirmation information. The reservation is automatically confirmed using the online reservation system.

[0551] Step 9:

[0552] The server notifies the terminal of reservation confirmation information. The input is confirmation information from the reservation system, and the output is a notification message in a format accessible to the user. This process uses a notification service to ensure that the information reaches the user immediately.

[0553] (Application Example 2)

[0554] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] Conventional information systems failed to take into account the user's emotional state, making it difficult to suggest optimal facilities or events tailored to the user's mood and emotions. Furthermore, there was a need for a method that automatically booked outings that matched the user's mood without requiring complicated operations.

[0556] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0557] In this invention, the server includes means for acquiring user information, means for analyzing the user's emotional state from voice data and user interactions, and means for acquiring event information from multiple external information sources. This makes it possible to recommend the most suitable outing destination based on each user's emotional state and to automatically make reservations for it.

[0558] "Means of acquiring user information" refers to systems and devices that collect and utilize users' personal attributes and activity history.

[0559] "Means of obtaining event information from multiple external sources" refers to methods and devices for obtaining data on various events and facilities through the internet and various databases.

[0560] "Means for analyzing a user's emotional state from voice data and user interaction" refers to technologies and devices that analyze a user's voice and interactions with devices to understand their emotional state.

[0561] "Methods for recommending optimal outing destinations" refer to algorithms and devices that suggest outing destinations deemed optimal for the user based on acquired user information and sentiment analysis results.

[0562] "Methods for executing reservations for recommended outings" refer to technologies and devices that automatically handle the reservation process for selected facilities or events.

[0563] "Means of notifying users of reservation completion information" refers to communication methods or devices used to inform users that their reservation has been successful.

[0564] To implement this system, the user's terminal and the server must work together. The user's terminal will be a mobile information device such as a smartphone or tablet. The terminal will have software installed to collect user information, such as family structure, past visit history, and schedule information.

[0565] To analyze the user's emotional state, the system uses speech recognition software and an emotion analysis engine installed on the device. Specifically, the Google Cloud Speech-to-Text API is used for speech transcription, and the Python Emotion Recognition library is utilized for emotion analysis. This allows the system to understand the user's emotional state through voice input and interaction.

[0566] The server uses acquired user information and sentiment data to gather local event information, facility information, weather data, etc., and selects the optimal destination. This utilizes a cloud-based data processing platform such as Google Cloud BigQuery.

[0567] Information about selected destinations will be sent to users via push notifications and email. In this process, a notification function that operates on the user's device is crucial, and this function will be implemented using a mobile app powered by the Flutter framework.

[0568] For example, if a user's mood is analyzed as "feeling down," the server might suggest a relaxing activity, such as "a walk in a nearby nature park." Once the user approves this suggestion, the server automatically processes the reservation and notifies the user of the result.

[0569] A possible prompt format for using a generative AI model is: "Analyze the user's emotions from the voice data and find a place that will cheer them up." This allows the system to generate optimized output based on the user's emotional data.

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The device retrieves user information. It reads data such as family structure, past visit history, and schedule information from a database and saves it to its internal data storage. This collects basic user information.

[0573] Step 2:

[0574] The device launches speech recognition software and acquires the user's voice data. The Google Cloud Speech-to-Text API is used to convert this voice data into text data, and the transcribed data is used as input to the sentiment analysis engine.

[0575] Step 3:

[0576] The device performs emotion analysis. The transcribed audio data is processed by the Emotion Recognition library, which analyzes the user's emotional state into categories such as "joy," "sadness," and "anger," and sends the results to the server.

[0577] Step 4:

[0578] The server retrieves event and facility information from external sources. It collects local event calendars, facility information, and weather data via BigQuery and stores this information in a database for analysis.

[0579] Step 5:

[0580] The server selects the optimal destination based on the user's emotional and event information acquired. Using a generative AI model, the prompt "Analyze the user's emotions from the voice data and find a place that will cheer them up" triggers the algorithm to generate an optimized output (recommended place).

[0581] Step 6:

[0582] The server sends recommended destinations to the device. The recommendations are sent to the device as push notifications, and the device displays the received information in a list, prompting the user to make a selection.

[0583] Step 7:

[0584] The user selects a destination from the provided list of places to go and sends their choice to the server via their device.

[0585] Step 8:

[0586] The server automatically executes the reservation for the selected destination and notifies the terminal of the result. If the reservation is successful through the reservation system, the server generates reservation completion information and sends the result to the terminal to inform the user of the reservation's success.

[0587] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0588] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0589] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0593] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0594] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0595] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0596] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0597] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0598] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0599] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0600] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0601] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0602] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0603] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0604] This invention is a system for effectively planning how parents and children spend their holidays. This system effectively collects users' lifestyle information and, based on that information, recommends the most suitable outing destinations. The system mainly consists of a server and user terminals, each playing a specific role.

[0605] First, the device collects information about the user's family structure, places they have visited in the past, and scheduled events listed in their calendar. This information is then sent to a server via the application with the user's consent.

[0606] Next, the server integrates and collects local event information, facility information, weather data, and other information from external sources. This creates an up-to-date and relevant database to provide to users.

[0607] The server then uses the collected user information and external data to apply machine learning algorithms and design the optimal outing plan for the user. In this process, based on family structure and past preferences, it might recommend, for example, zoos or amusement parks for young children, and science museums or art museums for older children.

[0608] The selected plan is sent from the server to the terminal, where the user can review it. Once the user selects a specific destination, the server automatically initiates the booking process for that location. This booking process is completed quickly and accurately in conjunction with the online platform.

[0609] Finally, once the reservation is confirmed, the device will notify the user. This allows the user to smoothly prepare for their outing according to their plan.

[0610] This system frees parents from the burden of detailed planning, allowing them to spend more meaningful time with their families. Furthermore, the automation of information retrieval, recommendations, and booking processes enables efficient time management in daily life.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The device, with the user's permission, collects information such as family composition, past visit history, and schedule. After the device has finished collecting this information through the application, it securely transmits the data to the server.

[0614] Step 2:

[0615] The server accesses external information sources to retrieve the latest local event information, facility operating information, and weather data. This data is then organized and stored in a database on the server for later processing.

[0616] Step 3:

[0617] The server uses machine learning algorithms to select appropriate destinations based on user information and external data. The resulting list of destinations is optimized to take into account the user's preferences and past activity history.

[0618] Step 4:

[0619] After the server compiles the recommendation list, it sends it to the user's terminal. The terminal then displays the received list to the user and makes it available for selection.

[0620] Step 5:

[0621] Users select their desired destination from a list of recommended options displayed on their device. Users can also enter detailed information such as dates and the number of people if needed.

[0622] Step 6:

[0623] The server receives the user's selection and makes a reservation for the chosen destination. This process is handled in conjunction with the relevant facility's online reservation system and continues until the reservation is officially completed.

[0624] Step 7:

[0625] The device notifies the user that the reservation is complete. This allows the user to confirm the reservation details and make further preparations.

[0626] (Example 1)

[0627] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] When families plan their holidays, efficiently gathering information and selecting the optimal plan can be complex and time-consuming. In particular, there is a need to automatically generate plans tailored to individual user preferences by aggregating relevant data from numerous sources. Furthermore, there is a need to reduce the burden and errors associated with manual booking procedures.

[0629] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0630] In this invention, the server includes means for collecting and transmitting user information to the server, means for integrating and acquiring local event data, facility data, and weather data from multiple external information sources, and means for using the user information and the external information to generate an optimal outing plan utilizing machine learning technology. This makes it possible to efficiently provide outing plans that are optimal for the user and tailored to their individual preferences, and to automate the process from planning to booking.

[0631] "User information" refers to data about individual users, including family structure data, past visit history, and schedule information.

[0632] "External information sources" refer to a collection of information that provides data on local events, facilities, and weather forecasts.

[0633] "Integration" is the process of combining and summarizing data obtained from multiple different sources.

[0634] "Machine learning techniques" are algorithms used by computers to learn data patterns and make predictions and judgments about new data.

[0635] An "outing plan" is a suggestion of travel destinations and places to visit, designed based on the user's preferences and needs, and includes specific destinations and activities.

[0636] A "reservation" is the process of securing a seat or participation slot in advance at a facility or event.

[0637] "Notification" is the act of informing a user of information or a message, whether visual or auditory.

[0638] This system is designed to effectively support family holiday planning and consists of a user terminal and a server. The terminal has a dedicated user application installed, which allows users to input user information such as family structure, past visit history, and schedule information. This data is transmitted to the server using a secure protocol. The server is programmed using general-purpose programming languages ​​such as Python and Java, and the database server uses MySQL or similar to store the data.

[0639] The server obtains local event information, facility details, and weather data through interfaces with external information sources. This includes using information sources such as local event APIs, open data facility information, and weather data APIs. This information is integrated with user information and stored in the server's database.

[0640] The server uses a generative AI model based on the collected information to generate the optimal outing plan. This model considers the user's family structure and past preferences to select an appropriate destination. For example, if the prompt is "If the family structure includes elementary school-aged children, please suggest activities recommended for a sunny day," a one-day plan to the zoo might be generated.

[0641] The server then sends the generated plan to the device, which notifies the user. The user can then select a suggested destination, and the server, in conjunction with the booking platform, automatically handles the booking process. This allows the user to complete the entire process from planning to booking smoothly and efficiently.

[0642] The system implemented in this way frees users from the burden of detailed information gathering and planning, allowing them to enjoy time with their families more efficiently.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] The device collects user information. Through the application, users enter information such as family structure, past visit history, and calendar appointments. This data is collected from the application's forms, encrypted via a secure communication protocol, and sent to the server as "user information."

[0646] Step 2:

[0647] The server collects local event information, facility information, and weather data from external sources. It retrieves local event calendars, detailed facility information, and weather forecast data via an API interface. This data is stored in a database as "external information" and updated to reflect the latest information.

[0648] Step 3:

[0649] The server integrates collected user information with external information. Using database queries, it identifies the user's past visits and preferred activities, and matches this information with external data. This prepares the server to analyze which events and facilities are best suited to the user.

[0650] Step 4:

[0651] The server generates the optimal outing plan using a generative AI model. Using integrated data as input, it selects recommended destinations based on the user's preferences. Machine learning algorithms are utilized in this process; for example, prompts might be given such as, "If the family includes elementary school-aged children, please suggest activities suitable for a sunny day." A specific plan is then generated as output.

[0652] Step 5:

[0653] The server sends the generated plan to the device. The user can review the received notification at a low cost and select the suggested destination in a browser or in-app view. This includes detailed descriptions, images, and ratings of the destination.

[0654] Step 6:

[0655] Once the user selects a destination, the server automatically processes the reservation. Based on the selected plan, it interacts with the reservation system to secure tickets and confirm the reservation. As a result, reservation completion information is generated and recorded in the user management system.

[0656] Step 7:

[0657] The device sends a reservation confirmation notification to the user. The notification contains the completed reservation information, date and time, location, and reservation number, and the user can use this information to prepare for their trip or visit.

[0658] (Application Example 1)

[0659] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] In modern society, it is often difficult to efficiently plan free time for families. Parents, in particular, want to spend meaningful time with their children on weekends, but the effort involved in selecting the most suitable activities from a vast amount of information and making reservations becomes a burden. This leads to complicated planning and, as a result, an inability to effectively utilize time.

[0661] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0662] In this invention, the server includes means for acquiring user information, means for acquiring event information from multiple external information sources, and means for analyzing the user's past usage history and external data using a machine learning algorithm to provide optimized suggestions. This enables users to smoothly plan outings based on their family structure and past preferences, and by automating online reservation procedures, it is possible to improve time efficiency and reduce the complexity of planning.

[0663] "User information" refers to data such as personal attributes, behavioral history, and schedules related to a user.

[0664] "External information sources" refer to sources of information obtained from outside the system, such as local event and facility information, and weather data.

[0665] "Event information" refers to information about activities or events that take place during a specific period and location.

[0666] An "outing plan" is an action plan that defines destinations and activities for users to spend their time meaningfully.

[0667] A "machine learning algorithm" is a computational method that learns specific patterns from accumulated data to make predictions and decisions about new data.

[0668] This invention is a system for efficiently planning parent-child holidays, and consists of a server and user terminals. The system uses user information and external information to propose the optimal outing plan.

[0669] The server first receives user information from the user's terminal. This user information includes family structure, history of places visited in the past, and schedule information. With the user's consent, this data is sent from the terminal to the server and stored in the database.

[0670] Next, the server retrieves local event information, facility information, and weather data from external sources. APIs such as the Google Maps API and OpenWeatherMap are used for this purpose. The server integrates this information and prepares it to provide users with the most up-to-date and relevant information.

[0671] The server uses machine learning algorithms (e.g., scikit-learn) to analyze collected user information and external data. This optimizes outing plans based on the user's past behavior history and preferences, and calculates suitable destinations such as zoos, amusement parks, science museums, and museums.

[0672] Recommended itineraries are sent to the user's device for review. The user then selects a specific destination, and the server automates the booking process online. Booking confirmation is sent to the user's device, allowing them to prepare for their trip according to the plan.

[0673] For example, on a rainy day, the system can suggest indoor activities. By utilizing a generative AI model, the user can be prompted with a request such as, "Please suggest indoor activities that the family can enjoy this weekend," and the AI ​​will generate optimal suggestions.

[0674] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0675] Step 1:

[0676] Users use their devices to input personal information, family structure, past visit history, and schedule information. This information is transmitted to the server through the application, with consideration for privacy. The input data includes user-specific information such as family structure and visit history.

[0677] Step 2:

[0678] The server stores the received user information in a database. Simultaneously, it calls APIs to retrieve local event information, facility information, and weather data from external sources. Specifically, the server aggregates information using APIs such as Google Maps API and OpenWeatherMap. This process involves retrieving data from external APIs.

[0679] Step 3:

[0680] The server uses machine learning algorithms to analyze collected user information and external data. This process utilizes libraries such as Python's scikit-learn to process data, taking into account past usage history and user preferences. The analysis identifies recommended destinations and events for the user.

[0681] Step 4:

[0682] The server sends an optimized outing plan to the user's terminal. On the terminal, the user is shown the details of each plan and can select one. During this selection process, the user decides which plan to implement. The output at this point is the detailed information of the plan.

[0683] Step 5:

[0684] When a user selects a specific destination, the server automates the booking process for related facilities and events. Using an online booking system API, bookings are completed quickly and accurately. This process involves the calculation and transmission of data necessary for the booking procedure.

[0685] Step 6:

[0686] The server notifies the user's terminal of the reservation completion information. The terminal receives this notification, and the user begins to act according to the plan. In this final step, confirmation data of the reservation completion is transmitted to the user.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] This invention is a system that combines an emotion engine with event and facility information provision to enable more personalized outing destination suggestions based on the user's emotional state. The aim of this system is to provide the optimal plan by taking the user's emotions into consideration.

[0689] The system is operated through an application installed on the user's device. First, the device collects information such as family structure, past visit history, and calendar schedules, just like regular user information. Furthermore, an emotion engine within the device recognizes the user's current emotional state from voice data and user interactions.

[0690] The server receives user information and sentiment data transmitted from the terminal, and based on this, retrieves local event information, facility information, and weather data from external sources. This allows the server to form a database that reflects the user's sentiments and current interests.

[0691] The server uses the received emotional information to recommend the most suitable destination. For example, if it determines that the user is seeking relaxation, it can suggest a quiet nature park or a tranquil waterside facility.

[0692] The generated recommendation list is sent from the server to the terminal, allowing the user to choose a destination. The user reviews the displayed options and selects a preferred destination. After selection, the server automatically handles the booking process for that location and notifies the user of the details.

[0693] Thus, this invention leverages user emotions to provide more personalized plans, thereby creating a satisfying outing experience for both parents and children. The overall system flow seamlessly integrates processes such as information gathering, emotion analysis, appropriate plan recommendation, booking execution, and notification, consistently supporting the user experience.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] With prior permission from the user, the device collects information such as family structure, past visit history, and schedule, and analyzes emotional information from voice data and screen operations using an emotion engine. The processed information is sent to a server to improve the user experience.

[0697] Step 2:

[0698] The server accesses external information sources to collect up-to-date data, including weather, local events, and facility information. The collected data is organized in the server's database and used for subsequent processing.

[0699] Step 3:

[0700] The server combines user information, emotional data, and external information to apply an algorithm that determines the priority of destinations influenced by emotions. In this process, the optimal location is selected according to the user's emotional state, such as wanting to relax or seeking stimulation.

[0701] Step 4:

[0702] The server sends a list of recommended destinations to the user's device. The device displays the list, allowing the user to select their desired destination.

[0703] Step 5:

[0704] The user selects a destination from the displayed options via their device and enters reservation details such as the desired date and number of people.

[0705] Step 6:

[0706] The server automatically initiates the online booking process for the selected destination. The booking information reflects the details entered by the user.

[0707] Step 7:

[0708] The device notifies the user that the reservation is complete and that all procedures for the selected destination have been finished. Based on this information, the user can proceed with planning for the day.

[0709] (Example 2)

[0710] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0711] Modern users demand personalized information tailored to their emotions and personal circumstances, but existing information delivery systems do not adequately address this, posing a challenge in providing individualized suggestions based on users' emotional states.

[0712] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0713] In this invention, the server includes means for acquiring user information, means for acquiring information from multiple external sources, and means for analyzing emotions. This enables the recommendation of an optimal location based on the user's individual emotional state and the reservation of that location.

[0714] "User information" refers to basic data about the user, including personal composition, visit history, and schedule information.

[0715] "External information sources" refer to external data sources that provide information necessary for user recommendations, such as local calendars, facility databases, and weather information.

[0716] "Methods for analyzing emotions" refer to the process of analyzing a user's voice and interactions to recognize their emotional state.

[0717] "Methods for recommending optimal locations" refer to methods for selecting and presenting the most appropriate destination to users based on collected information and analysis results.

[0718] The "means of executing a reservation" refer to a function that automatically completes the process of confirming a visit to a recommended location.

[0719] "Means of notifying reservation information" refers to methods used to inform users of confirmed reservation details, and serves the role of providing detailed information.

[0720] This invention is an information system for providing personalized suggestions based on the user's emotional state. This system consists of an application installed on the user's terminal and a server connected via a network.

[0721] The device collects the user's voice data and interactions on the device, and identifies the user's current emotional state using an emotion analysis engine. This utilizes speech recognition technology and emotion analysis algorithms. The device also retrieves the user's configuration information, past visit history, and schedule information from a database and sends this information to the server.

[0722] Based on the received user information and sentiment data, the server retrieves local calendars, facility data, and weather information from multiple external sources. In this process, the server uses a generative AI model to calculate destinations optimized for the user's state. For example, if the user desires relaxation, a quiet nature park or a tranquil waterside facility might be selected.

[0723] The user can view a list of recommendations displayed on their device and choose a destination from the options. After selection, the server automatically processes the reservation for that location and notifies the device of the reservation confirmation. An example of a prompt message to suggest destinations the user might prefer is, "Please suggest places where the user would like to relax."

[0724] Thus, the entire system has numerous features that enable it to provide personalized outing plans based on the user's emotions and preferences.

[0725] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0726] Step 1:

[0727] The device collects the user's personal information. It uses data from the device's sensors and calendar application as input. As output, it creates database entries containing the user's family structure, past visit history, and schedule information. This process is carried out through a database management system.

[0728] Step 2:

[0729] The device uses an emotion engine to analyze voice data and user interactions. Inputs are voice and gesture data acquired from the microphone and touch interface. Outputs are numerical or tagged information indicating the user's current emotional state. This is done using an emotion analysis algorithm.

[0730] Step 3:

[0731] The terminal transmits the collected user information and emotional state data to the server. The input is the data obtained in steps 1 and 2, and the output is sent to the server as encrypted packets. HTTPS or similar communication protocols are used.

[0732] Step 4:

[0733] The server retrieves local event information, facility information, and weather data from external sources. Input includes external data obtained via APIs. Output is a list of integrated information. Real-time information is collected via API calls.

[0734] Step 5:

[0735] The server uses a generative AI model to integrate user information, emotional state, and external information to calculate the optimal destination. The input consists of the data received in step 3 and the information collected in step 4. The output is a personalized list of recommended destinations for each user. The generative AI model processes large datasets and generates results tailored to individual needs.

[0736] Step 6:

[0737] The server sends the recommendation list to the terminal. The input is the recommendation list obtained in step 5, and the output appears as recommendation options displayed on the user's terminal. This is where the user interface rendering takes place.

[0738] Step 7:

[0739] The user makes a selection from a list of recommendations displayed on the device. The input is visual data displayed on the device, and the selected item is sent to the server as output. This selection is made via a touch interface.

[0740] Step 8:

[0741] The server processes the reservation for the selected location. The input is the user's selection data, and the output is the reservation confirmation information. The reservation is automatically confirmed using the online reservation system.

[0742] Step 9:

[0743] The server notifies the terminal of reservation confirmation information. The input is confirmation information from the reservation system, and the output is a notification message in a format accessible to the user. This process uses a notification service to ensure that the information reaches the user immediately.

[0744] (Application Example 2)

[0745] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] Conventional information systems failed to take into account the user's emotional state, making it difficult to suggest optimal facilities or events tailored to the user's mood and emotions. Furthermore, there was a need for a method that automatically booked outings that matched the user's mood without requiring complicated operations.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0748] In this invention, the server includes means for acquiring user information, means for analyzing the user's emotional state from voice data and user interactions, and means for acquiring event information from multiple external information sources. This makes it possible to recommend the most suitable outing destination based on each user's emotional state and to automatically make reservations for it.

[0749] "Means of acquiring user information" refers to systems and devices that collect and utilize users' personal attributes and activity history.

[0750] "Means of obtaining event information from multiple external sources" refers to methods and devices for obtaining data on various events and facilities through the internet and various databases.

[0751] "Means for analyzing a user's emotional state from voice data and user interaction" refers to technologies and devices that analyze a user's voice and interactions with devices to understand their emotional state.

[0752] "Methods for recommending optimal outing destinations" refer to algorithms and devices that suggest outing destinations deemed optimal for the user based on acquired user information and sentiment analysis results.

[0753] "Methods for executing reservations for recommended outings" refer to technologies and devices that automatically handle the reservation process for selected facilities or events.

[0754] "Means of notifying users of reservation completion information" refers to communication methods or devices used to inform users that their reservation has been successful.

[0755] To implement this system, the user's terminal and the server must work together. The user's terminal will be a mobile information device such as a smartphone or tablet. The terminal will have software installed to collect user information, such as family structure, past visit history, and schedule information.

[0756] To analyze the user's emotional state, the system uses speech recognition software and an emotion analysis engine installed on the device. Specifically, the Google Cloud Speech-to-Text API is used for speech transcription, and the Python Emotion Recognition library is utilized for emotion analysis. This allows the system to understand the user's emotional state through voice input and interaction.

[0757] The server uses acquired user information and sentiment data to gather local event information, facility information, weather data, etc., and selects the optimal destination. This utilizes a cloud-based data processing platform such as Google Cloud BigQuery.

[0758] Information about selected destinations will be sent to users via push notifications and email. In this process, a notification function that operates on the user's device is crucial, and this function will be implemented using a mobile app powered by the Flutter framework.

[0759] For example, if a user's mood is analyzed as "feeling down," the server might suggest a relaxing activity, such as "a walk in a nearby nature park." Once the user approves this suggestion, the server automatically processes the reservation and notifies the user of the result.

[0760] A possible prompt format for using a generative AI model is: "Analyze the user's emotions from the voice data and find a place that will cheer them up." This allows the system to generate optimized output based on the user's emotional data.

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The device retrieves user information. It reads data such as family structure, past visit history, and schedule information from a database and saves it to its internal data storage. This collects basic user information.

[0764] Step 2:

[0765] The device launches speech recognition software and acquires the user's voice data. The Google Cloud Speech-to-Text API is used to convert this voice data into text data, and the transcribed data is used as input to the sentiment analysis engine.

[0766] Step 3:

[0767] The device performs emotion analysis. The transcribed audio data is processed by the Emotion Recognition library, which analyzes the user's emotional state into categories such as "joy," "sadness," and "anger," and sends the results to the server.

[0768] Step 4:

[0769] The server retrieves event and facility information from external sources. It collects local event calendars, facility information, and weather data via BigQuery and stores this information in a database for analysis.

[0770] Step 5:

[0771] The server selects the optimal destination based on the user's emotional and event information acquired. Using a generative AI model, the prompt "Analyze the user's emotions from the voice data and find a place that will cheer them up" triggers the algorithm to generate an optimized output (recommended place).

[0772] Step 6:

[0773] The server sends recommended destinations to the device. The recommendations are sent to the device as push notifications, and the device displays the received information in a list, prompting the user to make a selection.

[0774] Step 7:

[0775] The user selects a destination from the provided list of places to go and sends their choice to the server via their device.

[0776] Step 8:

[0777] The server automatically executes the reservation for the selected destination and notifies the terminal of the result. If the reservation is successful through the reservation system, the server generates reservation completion information and sends the result to the terminal to inform the user of the reservation's success.

[0778] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0779] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0781] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0782] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0783] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0784] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0785] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0786] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0787] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0788] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0789] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0790] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0791] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0792] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0793] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0794] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0795] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0796] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0797] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0798] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] Means for obtaining user information,

[0802] Means for obtaining event information from multiple external sources,

[0803] A means for recommending the most suitable destination based on the user information and event information,

[0804] The means of making a reservation for the recommended destination mentioned above,

[0805] A means for notifying the user of the reservation completion information,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which obtains the aforementioned user information from family structure, past visit history, and schedule information.

[0809] (Claim 3)

[0810] The system according to claim 1, which obtains the aforementioned event information from a local event calendar, facility information, and weather information.

[0811] "Example 1"

[0812] (Claim 1)

[0813] A means of collecting user information and sending it to the server,

[0814] A means of integrating and acquiring local event data, facility data, and weather data from multiple external sources,

[0815] A means for generating an optimal outing plan using machine learning technology with the user information and the external information,

[0816] A means for automating destination reservations based on the generated plan,

[0817] A means for notifying the user terminal of the completion status of the aforementioned reservation,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, wherein the user information is obtained from the user's family structure data, visit history information, and schedule information.

[0821] (Claim 3)

[0822] The system according to claim 1, which obtains the aforementioned external information from local event schedules, facility details, and weather forecasts.

[0823] "Application Example 1"

[0824] (Claim 1)

[0825] Means for obtaining user information,

[0826] Means for obtaining event information from multiple external sources,

[0827] A means for creating an optimal outing plan based on the user information and the event information,

[0828] A means to automate the booking of the aforementioned plan,

[0829] A means for notifying the user of the completion of the aforementioned reservation procedure,

[0830] A means of providing optimized suggestions by analyzing a user's past usage history and external data using machine learning algorithms,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, which obtains the aforementioned user information from family structure attributes, previously visited location information, and schedule management information.

[0834] (Claim 3)

[0835] The system according to claim 1, which obtains the aforementioned event information from a local activity calendar, facility-related information, and weather data.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] Means for obtaining user information,

[0839] Means of obtaining information from multiple external sources,

[0840] A means of analyzing emotions,

[0841] A means for recommending the optimal location based on the user information, the external information, and the sentiment analysis,

[0842] A means for making a reservation at the aforementioned recommended location,

[0843] A means for notifying the user of the aforementioned reservation information,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, which obtains the aforementioned user information from user configuration, past history, and schedule information.

[0847] (Claim 3)

[0848] The system according to claim 1, wherein the aforementioned external information is obtained from a local calendar, facility data, and weather information.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] Means for obtaining user information,

[0852] Means for obtaining event information from multiple external sources,

[0853] A means for analyzing a user's emotional state from voice data and user interactions,

[0854] A means for recommending the most suitable outing destination based on the user information, the sentiment information, and the event information,

[0855] The means of making a reservation for the recommended destination mentioned above,

[0856] A means for notifying the user of the reservation completion information,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the user information is obtained from family information, past visit history, and schedule information.

[0860] (Claim 3)

[0861] The system according to claim 1, which obtains the aforementioned event information from regional information, facility information, and weather information. [Explanation of symbols]

[0862] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for obtaining user information, Means for obtaining event information from multiple external sources, A means for creating an optimal outing plan based on the user information and the event information, A means to automate the booking of the aforementioned plan, A means for notifying the user of the completion of the aforementioned reservation procedure, A means of providing optimized suggestions by analyzing a user's past usage history and external data using machine learning algorithms, A system that includes this.

2. The system according to claim 1, wherein the user information is obtained from family structure attributes, previously visited location information, and schedule management information.

3. The system according to claim 1, which obtains the aforementioned event information from a local activity calendar, facility-related information, and weather data.

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