system

Abstract

We provide the system. [Solution] A means of inputting information from users and generating profile data related to the group's overall activity plan, A method for collecting conversation data from group chats in real time and analyzing keywords and sentiments using natural language processing, A means for generating activity suggestions based on the aforementioned analysis results and profile data, and for sending the suggested content to the user, A means of receiving user feedback and adjusting the proposed content, The final activity plan after adjustments will be presented, and the means for booking and settling that plan will be provided. A means of sending notifications before an activity and collecting feedback after it is completed, 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 in 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] There is a need for a method that enables busy and indecisive working people to smoothly determine an activity plan in a group. It is difficult to fully consider the interests and feelings of individual users when selecting an activity, and as a result, an activity that satisfies everyone may not be selected. In addition, the reservation and settlement of activities are troublesome, and the burden until the actual activity is also a problem.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a means for generating profile data based on user information and further analyzing group chat conversation data collected in real time using natural language processing technology. Next, optimal activity suggestions are generated based on the analysis results and profile data and sent to the user. The suggestions are adjusted to reflect user feedback, and the final activity plan is created. Furthermore, activity reservations and payments are performed through an online platform, and notifications are sent before the activity. Feedback is collected after the activity, making it possible to continuously incorporate it into future suggestions. This ensures that everyone can make satisfactory activity choices.

[0006] "User information" refers to data provided by individuals using the system, including their name, email address, hobbies, and interests.

[0007] "Profile data" refers to data generated from user information that indicates an individual's preferences and tendencies.

[0008] A "group chat" is a form of digital communication in which multiple users exchange text messages in real time.

[0009] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0010] An "activity suggestion" is a list of potential activities presented to the user based on the results of the system's analysis.

[0011] "Feedback" refers to the opinions and impressions that users provide regarding proposed or decided activities.

[0012] An "online platform" is a digital environment where activities can be booked and payments made via the internet.

[0013] A "notification" is a message or alert sent to a user to inform them of specific information. [Brief explanation of the drawing]

[0014] [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.

Embodiments for Carrying out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered 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.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered 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.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] As an embodiment of the present invention, a system is first constructed to enable busy working professionals to smoothly decide on activity plans as a group. This system proposes appropriate activities based on user information and supports the booking and payment of those activities. The specific processing is described below.

[0036] To start using the system, users access the platform via their terminal and enter their basic information and preferences. This generates profile data about the user. This profile data plays an important role in suggesting future activities. For example, if a user is interested in visiting cafes and watching sports, this information will be registered in the system.

[0037] When a group starts planning an activity, data from the group chat is sent from the terminal to the server. The server collects this data in real time and analyzes the text using natural language processing. From the analysis results, it identifies each user's interests and mood at the time, and generates activity suggestions based on that information. For example, if all members say they "want to relax," the server might suggest a "hot spring trip."

[0038] The proposed activities are displayed on each user's device, and users can provide feedback on these suggestions. The server analyzes the collected feedback and adjusts the suggestions as needed. After the final activity plan is decided, the server handles the booking and payment of the proposed activities through the online platform.

[0039] The day before the activity, the server sends a reminder message to the user's device to help them remember. After the activity is completed, the server collects feedback from the user again and uses it as data to improve future proposals.

[0040] This allows the system to constantly optimize activity plans to match the user's interests and preferences, supporting smooth and satisfying decision-making.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users access the system through their devices and enter their basic information and interests. This allows the user's profile data to be sent from the device to the server and stored there.

[0044] Step 2:

[0045] When planning a group activity, users initiate a group chat in real time. The device sends the chat conversation data to the server.

[0046] Step 3:

[0047] To analyze the chat data received by the server, natural language processing techniques are used to analyze the text. Keywords and emotions are extracted to identify the participants' current mood and interests.

[0048] Step 4:

[0049] Based on the analysis results, the server generates optimal activity suggestions by combining them with the user's profile data. These suggestions include the activity content, time, location, and budget.

[0050] Step 5:

[0051] The proposed activities are displayed on the user's device, and the user enters their opinions and feedback on each proposal using the device. The device then sends the feedback to the server.

[0052] Step 6:

[0053] The server analyzes the overall feedback and fine-tunes the proposals as needed. The server then determines the final action plan and notifies the user again.

[0054] Step 7:

[0055] The user reviews and agrees to the final plan via their device. The server uses the online platform to book and process payments for the activity.

[0056] Step 8:

[0057] The day before the activity, the server sends a reminder message to the user's device.

[0058] Step 9:

[0059] After the activity ends, the server will collect user feedback again and update the data to use for future suggestions.

[0060] (Example 1)

[0061] 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."

[0062] In modern society, it is difficult for busy users to efficiently and quickly plan activities as a group. Furthermore, the process from the initial planning stages to final decisions, reservations, and payments is complex, and there is a need to provide optimal suggestions tailored to individual interests and circumstances. Current methods struggle to efficiently meet all these requirements, and therefore improvement is necessary.

[0063] 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.

[0064] In this invention, the server includes means for inputting information from the user and generating data related to the user's activity plan; means for collecting communication data in real time and analyzing keywords and status using information processing technology; and means for generating suggestions based on the analysis results and generated data and transmitting the content to the user. This makes it possible to quickly and accurately provide activity suggestions to busy users that are tailored to their interests and circumstances, and to smoothly proceed with the process from planning to booking and payment.

[0065] "Means of inputting information" refers to methods of receiving basic information and data related to interests from users and converting it into a format usable within the system.

[0066] "Means of generating data" refers to a system that creates profile data related to activity plans based on information entered by the user.

[0067] "Means of collecting communication data" refers to technologies that acquire the content of conversations and messages between multiple users in real time.

[0068] "Methods of analysis using information processing technology" refer to methods that extract keywords and emotions from collected data and use them to determine the user's interests and state of mind.

[0069] "Methods for generating suggestions" refer to methods for deriving optimal activity plans and options for users based on analyzed data.

[0070] "Means of sending content to the user" refers to technologies that display or notify the user of generated suggestions or information on their device.

[0071] "Means for receiving and adjusting responses" refers to a system that updates or modifies proposals based on user feedback.

[0072] "Means of presenting the final proposal" refers to a method of clearly showing the adjusted proposal to the user and assisting them in making a decision.

[0073] "Means of making reservations and payments" refers to technology that allows users to complete the necessary reservation procedures and payment processing online for a chosen activity.

[0074] "Means of sending notifications" refers to methods used to inform users in advance about the timing of activities and to draw their attention to them.

[0075] "Methods for collecting feedback" refer to a system that obtains user experience and satisfaction levels after an activity has ended and uses this information to inform future proposals.

[0076] This invention is a system that efficiently provides activity plans tailored to the interests and circumstances of individual users. To achieve this, users, terminals, and servers must work in close coordination.

[0077] First, users access the platform using their devices and enter basic information and interests. This input is done through an interface on the user's device, and the collected data is sent from the device to the server. The server generates profile data based on this information and stores it in a database. A relational database, in general terms, is suitable for this specific database system.

[0078] Next, the server collects communication data in real time and processes the information. For natural language processing, software such as general machine learning frameworks may be used. During analysis, keywords and sentiments are extracted from the text, and based on this, the user's interests and state are determined.

[0079] The server uses an AI model based on the analysis results to automatically generate optimal activity suggestions for the user. Examples of prompts used in this process include specific instructions such as, "Based on the user's recent chat history, please suggest ideal activities for the weekend."

[0080] The proposed activity is displayed on the user's device, and the user provides feedback on this proposal. The server receives the user's response and readjusts the proposal as needed. It then presents the final activity plan and allows the user to book and pay for the activity online. It is recommended to use a secure and reliable online service API for this booking and payment process.

[0081] Finally, the server sends a notification to the user's device the day before the activity, providing any necessary reminders. After the activity is completed, feedback from the user can be collected again and stored in a database to be used to improve future proposals.

[0082] Thus, the system of the present invention can improve the user experience by efficiently generating activity suggestions that match the needs of individual users and automating all processes.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users access the platform through their devices and enter basic information such as their name, interests, and preferences. This information is sent to the server as input data via the web interface. The server generates profile data based on the received data and stores it in a relational database. Specifically, this involves initializing profile items and creating database entries.

[0086] Step 2:

[0087] When planning group activities, the terminal sends communication data—the content of the conversation—to the server in real time. The server, upon receiving this data, analyzes the conversation using natural language processing to extract keywords and emotions. Machine learning algorithms are used for this analysis, and the resulting analysis data reflects each user's interests and state. Based on these analysis results, the server adds and updates the information to the original database.

[0088] Step 3:

[0089] The server uses a generative AI model to generate activity suggestions based on analysis results and profile data. Specific prompts (e.g., "Based on the user's recent chat history, please suggest ideal activities for the weekend") are used during the generation process. The server processes the input data by converting it into a format suitable for the generative AI model, obtaining the model's output. This output is the suggestion data and is sent to the user's terminal.

[0090] Step 4:

[0091] On the device, the user reviews the suggested activities and submits feedback. This feedback is input data based on the user's selections and is sent back to the server. The server analyzes this feedback data and adjusts the generated suggestions. Specifically, suggestions are ranked and new suggestions are generated based on the content of the feedback.

[0092] Step 5:

[0093] The server finalizes the adjusted activity proposal and executes the booking and payment procedures. This activity proposal is confirmed via the online platform using the booking system API. Specific actions include creating booking information and processing payment information. The final output is a notification to the user of the confirmed activity proposal.

[0094] Step 6:

[0095] The day before an activity, the server sends a reminder message to the user's device. This message is notification data to help the user remember. Furthermore, after the activity is completed, the server collects feedback from the user again and stores it in a database to be used for future suggestions. This process optimizes the system so that it can always provide suggestions that meet the user's needs.

[0096] (Application Example 1)

[0097] 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."

[0098] For modern people leading busy lives, planning group activities efficiently and satisfactorily is crucial. However, selecting appropriate activities based on individual interests and schedules, and smoothly handling booking and transaction procedures, is a challenging task. In particular, there is a need for means to reduce the time and effort spent on scheduling and activity proposals, and to eliminate inefficiencies. Current technology has the problem of not being able to comprehensively meet these requirements.

[0099] 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.

[0100] In this invention, the server includes means for inputting information from the user and generating attribute data related to the activity plan of the entire group; means for analyzing conversational data using natural language processing and extracting keywords and emotions; and means for the robot to communicate the final adjusted activity plan to the user by voice. This makes it possible to quickly suggest activities that are suitable for the user's interests and schedule, and to automate reservation and transaction procedures.

[0101] "User information" refers to data related to an individual's interests and preferences that the user provides to the system.

[0102] A "group activity plan" is a detailed plan for an activity in which multiple people participate together.

[0103] "Attribute data" refers to a dataset that represents the preferences and past activity history of individual users.

[0104] "Natural language processing" is a computational technique for analyzing human language and understanding its meaning.

[0105] "Conversation data" refers to text information exchanged between users in real time.

[0106] "Keywords and emotions" refer to information that indicates important words and emotional states extracted from the user's statements.

[0107] The "adjusted final activity plan" is the final activity plan optimized based on feedback from users.

[0108] "Means of communication by robots to users via voice" refers to methods by which autonomous machines use voice functions to provide information to humans.

[0109] "Reservations and transactions" refer to situations that include confirming appointments and related payment procedures.

[0110] The system for implementing this invention consists of a process that collects user information and generates suggestions based on that information. The server plays a central role in this system and performs information processing. Users access the system through a terminal and input attribute data. This data reflects the user's interests and preferences and is used to analyze conversational data collected in real time.

[0111] The server uses specific software for natural language processing, such as an NLP library like "spaCy," to extract keywords and emotions from conversational data. This analysis is essential for identifying individual user interests and generating optimal activity suggestions. The final, adjusted activity suggestions are then communicated to the user via voice by the robot.

[0112] Furthermore, the servers automate the booking and transaction processes, working in conjunction with e-commerce platforms to execute them. This allows users to plan and complete their activities without any hassle.

[0113] As a concrete example, the server receives a prompt such as "Tell me some recommended events for next Saturday," and then, based on the user's past interest data, suggests the most suitable leisure activity and notifies the user via voice. This process makes the user's daily life more convenient.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The user's device displays an information input screen, and the user enters attribute data related to their interests and preferences. This input data is sent to the server as a profile that will form the basis for subsequent suggestion generation.

[0117] Step 2:

[0118] Group chat data is transmitted to the server in real time via the device. The server receives this data and uses natural language processing (NLP) software to analyze keywords and emotions within the conversation data. This analysis extracts each user's interests and mood at that time.

[0119] Step 3:

[0120] Based on the attribute data collected in Step 1 and the information analyzed in Step 2, the server uses a generative AI model to generate activity suggestions tailored to each user. This model determines the priority of activities based on the received prompt examples and sends them to the user's terminal.

[0121] Step 4:

[0122] Users review suggested activities on their devices and submit feedback. The server collects this feedback and adjusts the activity suggestions as needed. The feedback is received as text data and analyzed again to improve the accuracy of the activity suggestions.

[0123] Step 5:

[0124] The server determines the final adjusted activity plan and communicates it to the user via voice through the robot. In this process, the activity plan is converted into voice data using speech synthesis software and communicated to the user.

[0125] Step 6:

[0126] The server integrates with the e-commerce platform based on the final activity plan, automatically scheduling activities and processing transactions. This allows users to complete the process easily.

[0127] Step 7:

[0128] The server sends a reminder to the user's device the day before or immediately before the activity to alert them. This notification is incorporated into the user's schedule using their calendar app.

[0129] Step 8:

[0130] After the activity concludes, the server collects feedback again and incorporates it into future proposals, thereby enabling the system to improve itself. This feedback serves as important data for increasing the accuracy of future activity proposals.

[0131] 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.

[0132] This invention provides a system that combines emotion recognition technology to enable busy professionals to effectively plan group activities. Specifically, it implements a mechanism that analyzes the user's emotions in real time and utilizes the results to propose activities.

[0133] The system begins with the user accessing it through their device. The user enters their basic information and interests to create profile data. After sending this data to the server, it is used to suggest activities. This profile data includes, for example, whether the user is interested in "outdoor activities" or "relaxing at a cafe."

[0134] During group activity planning, the device sends group chat conversation data to the server. The server uses natural language processing and an emotion engine to extract keywords from the conversation and classify the user's emotions. For example, if the words "stressed" and "tired" appear frequently in the chat, the emotion engine will determine that the user is "seeking relaxation."

[0135] Using these analysis results, the server generates activity suggestions tailored to each user. For example, it might suggest "hot spring trips" or "spas" to users who prioritize relaxation, and "hiking" or "sports games" to users who desire active activities. These suggestions are sent to users via their devices, and each user can provide feedback on them.

[0136] The server receives feedback and adjusts the final activity plan while considering the results of the emotion engine. Activities are then booked and paid for via an online platform. A reminder message is sent the day before the activity, and feedback is collected after its completion. This feedback is used to improve future proposals and also helps to refine the emotion engine.

[0137] This system allows users to receive suggestions based on their emotions and interests, enabling them to make highly satisfying activity choices. By combining this with emotion recognition technology, it becomes possible to provide a more personalized experience.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] Users access the system through their device and input their basic information and interests. The device sends this data to the server, and profile data is generated. This profile data records the user's hobbies and activity preferences.

[0141] Step 2:

[0142] When planning group activities, users initiate a group chat in real time. Their devices then send the chat conversation data to the server.

[0143] Step 3:

[0144] The server analyzes the received chat data using natural language processing techniques to extract key keywords. The emotion engine uses this data to classify each user's emotional state based on their statements. For example, it identifies emotions from words such as "excited" or "tired."

[0145] Step 4:

[0146] The server integrates the results of the sentiment analysis with profile data to generate activity suggestions best suited to the participant. For example, if it determines that relaxation is needed, it will suggest activities such as "hot springs" or "watching a movie."

[0147] Step 5:

[0148] Activity suggestions are sent to the user's device, and the user provides feedback on the suggested activity. This feedback includes opinions on how well the suggested activity aligns with the user's interests.

[0149] Step 6:

[0150] The server analyzes the collected feedback and modifies the proposed activities as needed. The results of the emotion engine analysis are also considered to form the final activity plan.

[0151] Step 7:

[0152] After the user agrees to the final plan via their device, the server handles the activity booking and payment through the online platform. The booking status and payment confirmation are then completed.

[0153] Step 8:

[0154] The server sends a reminder message to the user's device the day before the activity.

[0155] Step 9:

[0156] After the activity ends, the server collects feedback from users. This feedback is used as training data for the emotion engine and will be reflected in future suggestions.

[0157] (Example 2)

[0158] 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".

[0159] In modern society, efficiently generating personalized action suggestions based on the emotions and interests of individual users is a challenging task. Because it requires methods to seamlessly collect and interpret appropriate information from group dialogues involving diverse participants, conventional methods have not provided systems with sufficient flexibility and accuracy.

[0160] 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.

[0161] In this invention, the server includes means for inputting information from users and generating information data related to the group's overall action plan; means for collecting conversational information from group dialogue in real time and analyzing words and emotions through linguistic analysis; and means for generating action suggestions based on the analysis results and information data, and transmitting the suggested content to the user. This makes it possible to generate personalized action suggestions based on each user's emotions and interests.

[0162] "Information data" refers to data that forms the basis for shaping an action plan for the entire group, based on user input and their interests.

[0163] "Group dialogue" is a term that refers to the process of conversation and communication among multiple participants, and is used as a platform for collecting useful information in real time.

[0164] "Linguistic analysis" is a technical method that uses natural language processing techniques to extract and analyze important words, phrases, and emotions from text data.

[0165] "Emotion" refers to the emotional state a user displays during conversations and activity suggestions, and is analyzed as a criterion for suggesting actions.

[0166] "Action suggestions" are specific activity or behavioral suggestions that are generated based on analyzed information data and emotions and provided to the user.

[0167] "Instructions" refers to the process of putting a generated action proposal into concrete action, or the process of determining the details of an activity.

[0168] "Transaction" refers to the economic procedures necessary for implementing a proposed action, namely settlement and contract confirmation.

[0169] This invention is a system for providing personalized action suggestions based on the user's emotions and interests. The system utilizes a networked environment including servers and terminals.

[0170] First, the user inputs their basic information and data related to their interests through their device. The device then compiles this information and sends it to the server in an appropriate format. The devices used here are common computing devices such as smartphones and PCs.

[0171] Next, the server collects conversational information from the group dialogue in real time. The collected data is analyzed using natural language processing software on the server (for example, Google Cloud Natural Language API). This analysis extracts keywords and emotions from the conversation.

[0172] Furthermore, the server uses these analysis results and profile data to create personalized action suggestions for each user, leveraging a generative AI model. This includes data processing steps to determine how to present the action suggestions.

[0173] For example, if a user is thinking, "I want to relieve my recent fatigue," the server can sense this information through real-time analysis and suggest relaxation plans such as a hot spring trip or spa treatment. This suggestion process is expressed as a prompt message like this: "User A said in a group chat, 'I've been feeling really tired lately.' What kind of relaxation activity would you suggest?"

[0174] Subsequently, the user uses a terminal to send feedback on the suggestion to the server. This feedback is used to improve the accuracy of future suggestions. Activity instructions and transactions are conducted through an online payment platform, and the terminal provides reminders the day before and collects feedback after the activity. Through this entire process, users are offered individually optimized activities, and their selection is made accordingly.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] Users input their basic information and data related to their interests through a terminal. The input information is formatted by the terminal and prepared for transmission to the server. Specifically, the terminal converts this data into a common data format such as JSON. Input includes "interested activity categories" and "past events attended," and output is profile data in a format that the server can receive.

[0178] Step 2:

[0179] The terminal collects conversational information from group discussions in real time and sends it to the server. The conversation is input as text data, which is then divided according to certain criteria for batch processing and formatted for transmission to the server. The output is conversational information formatted for analysis.

[0180] Step 3:

[0181] The server analyzes the received conversational information using natural language processing software. Specifically, it uses the Google Cloud Natural Language API to extract keywords and sentiments from the text. Once this analysis is complete, a list of keywords and sentiment scores are generated. The input is the collected conversational data, and the output is the identified keywords and sentiment states.

[0182] Step 4:

[0183] The server uses a generative AI model to combine analysis results with user profile data to generate action suggestions. This model predicts the optimal action plan based on past data. Specifically, it takes generated keywords and emotion scores as input and outputs action suggestions (e.g., a relaxation plan).

[0184] Step 5:

[0185] The server sends the suggested action to the terminal and presents it to the user. After receiving it, the user can input feedback on the suggestion. Specifically, the user evaluates the attractiveness of the suggestion through the interface on the terminal, and this data is sent to the server as feedback data. The input is evaluation information for the suggested action, and the output is the collected feedback data.

[0186] Step 6:

[0187] The server analyzes the feedback data and makes adjustments to improve the accuracy of future suggestions. Specifically, it uses this feedback to update the parameters of the emotion engine and suggestion model, allowing them to learn to make suggestions that are closer to the user's preferences. The input is the collected feedback data, and the output is the adjusted suggestion model.

[0188] Step 7:

[0189] The server, in conjunction with the online payment platform, confirms the reservation and payment for the final decided action. The terminal also sends a reminder message to the user the day before the activity and provides an interface for collecting feedback after the activity. This entire process ensures smooth reservation confirmation and feedback collection. The input is reservation information, and the output is the confirmed reservation status and newly collected feedback.

[0190] (Application Example 2)

[0191] 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".

[0192] In modern households, there is a lack of activity suggestions based on the individual feelings and interests of each family member, making it difficult to find relaxation and activity options within the home, especially in busy daily lives. There is a need to address this problem and provide mechanisms to promote more personalized family activities.

[0193] 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.

[0194] This invention includes a server that inputs information from users and generates profile data related to the group's overall activity plan; a server that collects information from group conversations in real time and analyzes keywords and emotions using natural language processing; and a server that recognizes the emotions of family members and cooperates with smart home appliances that suggest household activities. This makes it possible to suggest appropriate household activities based on the emotions of each family member.

[0195] "User information" refers to data about a user's personal interests and activities that they themselves input.

[0196] "Profile data" refers to data generated from information collected from users, representing individual characteristics and preferences related to their activity plans.

[0197] "Gathering information from group conversations" refers to the process of acquiring the content of conversations taking place within a group in real time and using it as data for analysis.

[0198] "Natural language processing" refers to the technology that allows computers to understand and analyze human language, and is used to extract emotions and keywords.

[0199] "Family members" refers to the individual members of a household who share a living space together.

[0200] "Smart home appliances" refer to electronic devices in the home that can utilize emotion recognition data to provide integrated services.

[0201] An "electronic platform" refers to a digital infrastructure for booking and processing services and activities via the internet.

[0202] In the system for implementing this invention, the server handles the main processes. The server receives information from the user and generates individual profile data. This utilizes initial data about the user's interests and activities. Subsequently, the server collects group conversation data in real time and analyzes the conversation content using natural language processing. This process employs advanced sentiment recognition technology, and the system extracts keywords and emotional states. Software such as the Google Cloud Natural Language API is used for sentiment recognition, and the extracted data is used in the next step: activity suggestions.

[0203] The device plays a crucial role in sending suggested activities to the user and receiving feedback. User feedback is returned to the server, and the system uses this to refine its suggestions. This process is designed to provide more personalized suggestions using a generative AI model.

[0204] Furthermore, to enable activity suggestions within the home, emotional data of family members is linked with smart home appliances. These smart appliances suggest appropriate activities based on the user's emotions, such as adjusting lighting settings for relaxation mode or playing music.

[0205] For example, if the word "tired" is frequently used in a conversation at home, the system may instruct the smart lighting in the living space to be set to a softer color and play calming music to create a more relaxing atmosphere.

[0206] An example of a prompt would be, "Whenever I sense fatigue in my family, please tell me how to help them relax." This serves as a guideline for the generated AI model to provide an experience optimized for the user.

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The server receives information from the user. The user inputs data about their interests and activities through their terminal, and this data is sent to the server. The input data is stored in the system as profile data and used for future activity suggestions.

[0210] Step 2:

[0211] The server collects group conversation data in real time. Chat and message information is sent from the terminal to the server. The input conversation data is analyzed using natural language processing technology, and keywords and emotions are extracted. The output obtained from this process is data that indicates the user's emotional state.

[0212] Step 3:

[0213] The server uses profile data and sentiment analysis results to generate optimal activities. A generative AI model receives this data as input and proposes activities based on the user's current emotional state. These suggestions are sent to the terminal and presented to the user as output.

[0214] Step 4:

[0215] The user reviews the suggested activities on their device and provides feedback. The user's response is sent to the server and recorded as input in the system's database. The server uses this feedback to make adjustments to improve the accuracy of the suggestions.

[0216] Step 5:

[0217] The server works in conjunction with smart home appliances to perform its functions. The appliances receive instructions from the server and adjust their settings to the optimal level based on the user's emotional state. For example, actions such as changing the color of the lighting might be output.

[0218] Step 6:

[0219] After an activity is completed, the server collects detailed feedback from the user. This provides the data needed to suggest future activities and adjust the generated AI model. The feedback is collected and stored on the server.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] As an embodiment of the present invention, a system is first constructed to enable busy working professionals to smoothly decide on activity plans as a group. This system proposes appropriate activities based on user information and supports the booking and payment of those activities. The specific processing is described below.

[0237] To start using the system, users access the platform via their terminal and enter their basic information and preferences. This generates profile data about the user. This profile data plays an important role in suggesting future activities. For example, if a user is interested in visiting cafes and watching sports, this information will be registered in the system.

[0238] When a group starts planning an activity, data from the group chat is sent from the terminal to the server. The server collects this data in real time and analyzes the text using natural language processing. From the analysis results, it identifies each user's interests and mood at the time, and generates activity suggestions based on that information. For example, if all members say they "want to relax," the server might suggest a "hot spring trip."

[0239] The proposed activities are displayed on each user's device, and users can provide feedback on these suggestions. The server analyzes the collected feedback and adjusts the suggestions as needed. After the final activity plan is decided, the server handles the booking and payment of the proposed activities through the online platform.

[0240] The day before the activity, the server sends a reminder message to the user's device to help them remember. After the activity is completed, the server collects feedback from the user again and uses it as data to improve future proposals.

[0241] This allows the system to constantly optimize activity plans to match the user's interests and preferences, supporting smooth and satisfying decision-making.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] Users access the system through their devices and enter their basic information and interests. This allows the user's profile data to be sent from the device to the server and stored there.

[0245] Step 2:

[0246] When planning a group activity, users initiate a group chat in real time. The device then sends the chat conversation data to the server.

[0247] Step 3:

[0248] To analyze the chat data received by the server, natural language processing techniques are used to analyze the text. Keywords and emotions are extracted to identify the participants' current mood and interests.

[0249] Step 4:

[0250] Based on the analysis results, the server generates optimal activity suggestions by combining them with the user's profile data. These suggestions include the activity content, time, location, and budget.

[0251] Step 5:

[0252] The proposed activities are displayed on the user's device, and the user enters their opinions and feedback on each proposal using the device. The device then sends the feedback to the server.

[0253] Step 6:

[0254] The server analyzes the overall feedback and fine-tunes the proposals as needed. The server then determines the final action plan and notifies the user again.

[0255] Step 7:

[0256] The user reviews and agrees to the final plan via their device. The server uses the online platform to book and process payments for the activity.

[0257] Step 8:

[0258] The day before the activity, the server sends a reminder message to the user's device.

[0259] Step 9:

[0260] After the activity ends, the server will collect user feedback again and update the data to use for future suggestions.

[0261] (Example 1)

[0262] 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."

[0263] In modern society, it is difficult for busy users to efficiently and quickly plan activities as a group. Furthermore, the process from the initial planning stages to final decisions, reservations, and payments is complex, and there is a need to provide optimal suggestions tailored to individual interests and circumstances. Current methods struggle to efficiently meet all these requirements, and therefore improvement is necessary.

[0264] 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.

[0265] In this invention, the server includes means for inputting information from the user and generating data related to the user's activity plan; means for collecting communication data in real time and analyzing keywords and status using information processing technology; and means for generating suggestions based on the analysis results and generated data and transmitting the content to the user. This makes it possible to quickly and accurately provide activity suggestions to busy users that are tailored to their interests and circumstances, and to smoothly proceed with the process from planning to booking and payment.

[0266] "Means of inputting information" refers to methods of receiving basic information and data related to interests from users and converting it into a format usable within the system.

[0267] "Means of generating data" refers to a system that creates profile data related to activity plans based on information entered by the user.

[0268] "Means of collecting communication data" refers to technologies that acquire the content of conversations and messages between multiple users in real time.

[0269] "Methods of analysis using information processing technology" refer to methods that extract keywords and emotions from collected data and use them to determine the user's interests and state of mind.

[0270] "Methods for generating suggestions" refer to methods for deriving optimal activity plans and options for users based on analyzed data.

[0271] "Means of sending content to the user" refers to technologies that display or notify the user of generated suggestions or information on their device.

[0272] "Means for receiving and adjusting responses" refers to a system that updates or modifies proposals based on user feedback.

[0273] "Means of presenting the final proposal" refers to a method of clearly showing the adjusted proposal to the user and assisting them in making a decision.

[0274] "Means of making reservations and payments" refers to technology that allows users to complete the necessary reservation procedures and payment processing online for a chosen activity.

[0275] "Means of sending notifications" refers to methods used to inform users in advance about the timing of activities and to draw their attention to them.

[0276] "Methods for collecting feedback" refer to a system that obtains user experience and satisfaction levels after an activity has ended and uses this information to inform future proposals.

[0277] This invention is a system that efficiently provides activity plans tailored to the interests and circumstances of individual users. To achieve this, users, terminals, and servers must work in close coordination.

[0278] First, users access the platform using their devices and enter basic information and interests. This input is done through an interface on the user's device, and the collected data is sent from the device to the server. The server generates profile data based on this information and stores it in a database. A relational database, in general terms, is suitable for this specific database system.

[0279] Next, the server collects communication data in real time and processes the information. For natural language processing, software such as general machine learning frameworks may be used. During analysis, keywords and sentiments are extracted from the text, and based on this, the user's interests and state are determined.

[0280] The server uses an AI model based on the analysis results to automatically generate optimal activity suggestions for the user. Examples of prompts used in this process include specific instructions such as, "Based on the user's recent chat history, please suggest ideal activities for the weekend."

[0281] The proposed activity is displayed on the user's device, and the user provides feedback on this proposal. The server receives the user's response and readjusts the proposal as needed. It then presents the final activity plan and allows the user to book and pay for the activity online. It is recommended to use a secure and reliable online service API for this booking and payment process.

[0282] Finally, on the day before the event, the server sends notifications to the user's terminal to provide the necessary reminders. Also, after the event, feedback from the user can be received again and accumulated in the database to be utilized in the next proposal.

[0283] In this way, the system of the present invention can efficiently generate activity proposals that match the needs of individual users and improve the user experience by automating all processes.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The user accesses the platform through the terminal and inputs basic information such as name, interests, and preferences. This information is sent to the server as input data entered via the web interface. The server generates profile data based on the received data and saves it in a relational database. Specifically, initialization of profile items and creation of database entries are performed.

[0287] Step 2:

[0288] When the terminal plans group activities, it sends the communication data, which is the conversation content, to the server in real time. The server that receives this data analyzes the conversation content using natural language processing and extracts keywords and emotions. A machine learning algorithm is utilized for this analysis, and the resulting analysis data reflects the interests and states of each user. Based on this analysis result, the server adds and updates the information in the original database.

[0289] Step 3:

[0290] The server uses a generative AI model to generate activity suggestions based on analysis results and profile data. Specific prompts (e.g., "Based on the user's recent chat history, please suggest ideal activities for the weekend") are used during the generation process. The server processes the input data by converting it into a format suitable for the generative AI model, obtaining the model's output. This output is the suggestion data and is sent to the user's terminal.

[0291] Step 4:

[0292] On the device, the user reviews the suggested activities and submits feedback. This feedback is input data based on the user's selections and is sent back to the server. The server analyzes this feedback data and adjusts the generated suggestions. Specifically, suggestions are ranked and new suggestions are generated based on the content of the feedback.

[0293] Step 5:

[0294] The server finalizes the adjusted activity proposal and executes the booking and payment procedures. This activity proposal is confirmed via the online platform using the booking system API. Specific actions include creating booking information and processing payment information. The final output is a notification to the user of the confirmed activity proposal.

[0295] Step 6:

[0296] The day before an activity, the server sends a reminder message to the user's device. This message is notification data to help the user remember. Furthermore, after the activity is completed, the server collects feedback from the user again and stores it in a database to be used for future suggestions. This process optimizes the system so that it can always provide suggestions that meet the user's needs.

[0297] (Application Example 1)

[0298] 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."

[0299] For modern people leading busy lives, planning group activities efficiently and satisfactorily is crucial. However, selecting appropriate activities based on individual interests and schedules, and smoothly handling booking and transaction procedures, is a challenging task. In particular, there is a need for means to reduce the time and effort spent on scheduling and activity proposals, and to eliminate inefficiencies. Current technology has the problem of not being able to comprehensively meet these requirements.

[0300] 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.

[0301] In this invention, the server includes means for inputting information from the user and generating attribute data related to the activity plan of the entire group; means for analyzing conversational data using natural language processing and extracting keywords and emotions; and means for the robot to communicate the final adjusted activity plan to the user by voice. This makes it possible to quickly suggest activities that are suitable for the user's interests and schedule, and to automate reservation and transaction procedures.

[0302] "User information" refers to data related to an individual's interests and preferences that the user provides to the system.

[0303] A "group activity plan" is a detailed plan for an activity in which multiple people participate together.

[0304] "Attribute data" refers to a dataset that represents the preferences and past activity history of individual users.

[0305] "Natural language processing" is a computational technique for analyzing human language and understanding its meaning.

[0306] "Conversation data" refers to the text information exchanged in real time by users.

[0307] "Keywords and emotions" refer to information indicating important words and emotional states extracted from the user's speech.

[0308] "Final activity plan after adjustment" refers to the final activity plan optimized based on feedback from the user.

[0309] "Means by which the robot conveys to the user in voice" refers to the method by which an autonomous machine provides information to humans using the voice function.

[0310] "Reservations and transactions" refer to cases including the confirmation of schedules and related payment procedures.

[0311] The system for implementing this invention is composed of a process that collects user information and generates proposals based on it. The server plays a central role in this system and performs information processing. The user accesses the system through a terminal and inputs attribute data. This data reflects the user's interests and preferences and is used for the analysis of real-time collected conversation data.

[0312] The server uses specific software for natural language processing, such as the NLP library "spaCy", to extract keywords and emotions in the conversation data. The results of this analysis are essential for identifying the interests of individual users and generating optimal activity proposals. Also, the final activity plan after adjustment is conveyed to the user by the robot through voice.

[0313] Furthermore, the server automates the process of reservations and transactions and executes it in cooperation with an e-commerce trading platform. As a result, the user can complete the planning and procedures of activities without much effort.

[0314] As a concrete example, the server receives a prompt such as "Tell me some recommended events for next Saturday," and then, based on the user's past interest data, suggests the most suitable leisure activity and notifies the user via voice. This process makes the user's daily life more convenient.

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The user's device displays an information input screen, and the user enters attribute data related to their interests and preferences. This input data is sent to the server as a profile that will later form the basis for suggestion generation.

[0318] Step 2:

[0319] Group chat data is transmitted to the server in real time via the device. The server receives this data and uses natural language processing (NLP) software to analyze keywords and emotions within the conversation data. This analysis extracts each user's interests and mood at that time.

[0320] Step 3:

[0321] Based on the attribute data collected in Step 1 and the information analyzed in Step 2, the server uses a generative AI model to generate activity suggestions tailored to each user. This model determines the priority of activities based on the received prompt examples and sends them to the user's terminal.

[0322] Step 4:

[0323] Users review suggested activities on their devices and submit feedback. The server collects this feedback and adjusts the activity suggestions as needed. The feedback is received as text data and analyzed again to improve the accuracy of the activity suggestions.

[0324] Step 5:

[0325] The server determines the final adjusted activity plan and communicates it to the user via voice through the robot. In this process, the activity plan is converted into voice data using speech synthesis software and communicated to the user.

[0326] Step 6:

[0327] The server integrates with the e-commerce platform based on the final activity plan, automatically scheduling activities and processing transactions. This allows users to complete the process easily.

[0328] Step 7:

[0329] The server sends a reminder to the user's device the day before or immediately before the activity to alert them. This notification is incorporated into the user's schedule using their calendar app.

[0330] Step 8:

[0331] After the activity concludes, the server collects feedback again and incorporates it into future proposals, allowing the system to improve itself. This feedback serves as important data for increasing the accuracy of future activity proposals.

[0332] 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.

[0333] This invention provides a system that combines emotion recognition technology to enable busy professionals to effectively plan group activities. Specifically, it implements a mechanism that analyzes the user's emotions in real time and utilizes the results to propose activities.

[0334] The system begins with the user accessing it through their device. The user enters their basic information and interests to create profile data. After sending this data to the server, it is used to suggest activities. This profile data includes, for example, whether the user is interested in "outdoor activities" or "relaxing at a cafe."

[0335] During group activity planning, the device sends group chat conversation data to the server. The server uses natural language processing and an emotion engine to extract keywords from the conversation and classify the user's emotions. For example, if the words "stressed" and "tired" appear frequently in the chat, the emotion engine will determine that the user is "seeking relaxation."

[0336] Using these analysis results, the server generates activity suggestions tailored to each user. For example, it might suggest "hot spring trips" or "spas" to users who prioritize relaxation, and "hiking" or "sports games" to users who desire active activities. These suggestions are sent to users via their devices, and each user can provide feedback on them.

[0337] The server receives feedback and adjusts the final activity plan while considering the results of the emotion engine. Activities are then booked and paid for via an online platform. A reminder message is sent the day before the activity, and feedback is collected after its completion. This feedback is used to improve future proposals and also helps to refine the emotion engine.

[0338] This system allows users to receive suggestions based on their emotions and interests, enabling them to make highly satisfying activity choices. By combining this with emotion recognition technology, it becomes possible to provide a more personalized experience.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] Users access the system through their device and input their basic information and interests. The device sends this data to the server, and profile data is generated. This profile data records the user's hobbies and activity preferences.

[0342] Step 2:

[0343] When planning group activities, users initiate a group chat in real time. Their devices then send the chat conversation data to the server.

[0344] Step 3:

[0345] The server analyzes the received chat data using natural language processing techniques to extract key keywords. The emotion engine uses this data to classify each user's emotional state based on their statements. For example, it identifies emotions from words such as "excited" or "tired."

[0346] Step 4:

[0347] The server integrates the results of the emotion analysis with profile data to generate activity suggestions best suited to the participant. For example, if it determines that relaxation is needed, it will suggest activities such as "hot springs" or "watching a movie."

[0348] Step 5:

[0349] Activity suggestions are sent to the user's device, and the user provides feedback on the suggested activity. This feedback includes opinions on how well the suggested activity aligns with the user's interests.

[0350] Step 6:

[0351] The server analyzes the collected feedback and modifies the proposed activities as needed. The results of the emotion engine analysis are also considered to form the final activity plan.

[0352] Step 7:

[0353] After the user agrees to the final plan via their device, the server handles the activity booking and payment through the online platform. The booking status and payment confirmation are then completed.

[0354] Step 8:

[0355] The server sends a reminder message to the user's device the day before the activity.

[0356] Step 9:

[0357] After the activity ends, the server collects feedback from users. This feedback is used as training data for the emotion engine and will be reflected in future suggestions.

[0358] (Example 2)

[0359] 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".

[0360] In modern society, efficiently generating personalized action suggestions based on the emotions and interests of individual users is a challenging task. Because it requires methods to seamlessly collect and interpret appropriate information from group dialogues involving diverse participants, conventional methods have not provided systems with sufficient flexibility and accuracy.

[0361] 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.

[0362] In this invention, the server includes means for inputting information from users and generating information data related to the group's overall action plan; means for collecting conversational information from group dialogue in real time and analyzing words and emotions through linguistic analysis; and means for generating action suggestions based on the analysis results and information data, and transmitting the suggested content to the user. This makes it possible to generate personalized action suggestions based on each user's emotions and interests.

[0363] "Information data" refers to data that forms the basis for shaping an action plan for the entire group, based on user input and their interests.

[0364] "Group dialogue" is a term that refers to the process of conversation and communication among multiple participants, and is used as a platform for collecting useful information in real time.

[0365] "Linguistic analysis" is a technical method that uses natural language processing techniques to extract and analyze important words, phrases, and emotions from text data.

[0366] "Emotion" refers to the emotional state a user displays during conversations and activity suggestions, and is analyzed as a criterion for suggesting actions.

[0367] "Action suggestions" are specific activity or behavioral suggestions that are generated based on analyzed information data and emotions and provided to the user.

[0368] "Instructions" refers to the process of putting a generated action proposal into concrete action, or the process of determining the details of an activity.

[0369] "Transaction" refers to the economic procedures necessary for implementing a proposed action, namely settlement and contract confirmation.

[0370] This invention is a system for providing personalized action suggestions based on the user's emotions and interests. The system utilizes a networked environment including servers and terminals.

[0371] First, the user inputs their basic information and data related to their interests through their device. The device then compiles this information and sends it to the server in an appropriate format. The devices used here are common computing devices such as smartphones and PCs.

[0372] Next, the server collects conversational information from the group dialogue in real time. The collected data is analyzed using natural language processing software on the server (for example, Google Cloud Natural Language API). This analysis extracts keywords and emotions from the conversation.

[0373] Furthermore, the server uses these analysis results and profile data to create personalized action suggestions for each user, leveraging a generative AI model. This includes data processing steps to determine how to present these action suggestions.

[0374] For example, if a user is thinking, "I want to relieve my recent fatigue," the server can sense this information through real-time analysis and suggest relaxation plans such as a hot spring trip or spa treatment. This suggestion process is expressed as a prompt message like this: "User A said in a group chat, 'I've been feeling really tired lately.' What kind of relaxation activity would you suggest?"

[0375] Subsequently, the user uses a terminal to send feedback on the suggestion to the server. This feedback is used to improve the accuracy of future suggestions. Activity instructions and transactions are conducted through an online payment platform, and the terminal provides reminders the day before and collects feedback after the activity. Through this entire process, users are offered individually optimized activities, and their selection is made accordingly.

[0376] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0377] Step 1:

[0378] Users input their basic information and data related to their interests through a terminal. The input information is formatted by the terminal and prepared for transmission to the server. Specifically, the terminal converts this data into a common data format such as JSON. Input includes "interested activity categories" and "past events attended," and output is profile data in a format that the server can receive.

[0379] Step 2:

[0380] The terminal collects conversational information from group discussions in real time and sends it to the server. The conversation is input as text data, which is then divided according to certain criteria for batch processing and formatted for transmission to the server. The output is conversational information formatted for analysis.

[0381] Step 3:

[0382] The server analyzes the received conversational information using natural language processing software. Specifically, it uses the Google Cloud Natural Language API to extract keywords and sentiments from the text. Once this analysis is complete, a list of keywords and sentiment scores are generated. The input is the collected conversational data, and the output is the identified keywords and sentiment states.

[0383] Step 4:

[0384] The server uses a generative AI model to combine analysis results with user profile data to generate action suggestions. This model predicts the optimal action plan based on past data. Specifically, it takes generated keywords and emotion scores as input and outputs action suggestions (e.g., a relaxation plan).

[0385] Step 5:

[0386] The server sends the suggested action to the terminal and presents it to the user. After receiving it, the user can input feedback on the suggestion. Specifically, the user evaluates the attractiveness of the suggestion through the interface on the terminal, and this data is sent to the server as feedback data. The input is evaluation information for the suggested action, and the output is the collected feedback data.

[0387] Step 6:

[0388] The server analyzes the feedback data and makes adjustments to improve the accuracy of future suggestions. Specifically, it uses this feedback to update the parameters of the emotion engine and suggestion model, allowing them to learn to make suggestions that are closer to the user's preferences. The input is the collected feedback data, and the output is the adjusted suggestion model.

[0389] Step 7:

[0390] The server, in conjunction with the online payment platform, confirms the reservation and payment for the final decided action. The terminal also sends a reminder message to the user the day before the activity and provides an interface for collecting feedback after the activity. This entire process ensures smooth reservation confirmation and feedback collection. The input is reservation information, and the output is the confirmed reservation status and newly collected feedback.

[0391] (Application Example 2)

[0392] 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."

[0393] In modern households, there is a lack of activity suggestions based on the individual feelings and interests of each family member, making it difficult to find relaxation and activity options within the home, especially in busy daily lives. There is a need to address this problem and provide mechanisms to promote more personalized family activities.

[0394] 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.

[0395] This invention includes a server that inputs information from users and generates profile data related to the group's overall activity plan; a server that collects information from group conversations in real time and analyzes keywords and emotions using natural language processing; and a server that recognizes the emotions of family members and cooperates with smart home appliances that suggest household activities. This makes it possible to suggest appropriate household activities based on the emotions of each family member.

[0396] "User information" refers to data about a user's personal interests and activities that they themselves input.

[0397] "Profile data" refers to data generated from information collected from users, representing individual characteristics and preferences related to their activity plans.

[0398] "Gathering information from group conversations" refers to the process of acquiring the content of conversations taking place within a group in real time and using it as data for analysis.

[0399] "Natural language processing" refers to the technology that allows computers to understand and analyze human language, and is used to extract emotions and keywords.

[0400] "Family members" refers to the individual members of a household who share a living space together.

[0401] "Smart home appliances" refer to electronic devices in the home that can utilize emotion recognition data to provide integrated services.

[0402] An "electronic platform" refers to a digital infrastructure for booking and processing services and activities via the internet.

[0403] In the system for implementing this invention, the server handles the main processes. The server receives information from the user and generates individual profile data. This utilizes initial data about the user's interests and activities. Subsequently, the server collects group conversation data in real time and analyzes the conversation content using natural language processing. This process employs advanced sentiment recognition technology, and the system extracts keywords and emotional states. Software such as the Google Cloud Natural Language API is used for sentiment recognition, and the extracted data is used in the next step: activity suggestions.

[0404] The device plays a crucial role in sending suggested activities to the user and receiving feedback. User feedback is returned to the server, and the system uses this to refine its suggestions. This process is designed to provide more personalized suggestions using a generative AI model.

[0405] Furthermore, to enable activity suggestions within the home, emotional data of family members is linked with smart home appliances. These smart appliances suggest appropriate activities based on the user's emotions, such as adjusting lighting settings for relaxation mode or playing music.

[0406] For example, if the word "tired" comes up frequently in a conversation at home, the system might instruct the smart lighting in the living space to be set to a softer color and play calming music to create a more relaxing atmosphere.

[0407] An example of a prompt would be, "Whenever I sense fatigue in my family, please tell me how to help them relax." This serves as a guideline for the generated AI model to provide an experience optimized for the user.

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] The server receives information from the user. The user inputs data about their interests and activities through their terminal, and this data is sent to the server. The input data is stored in the system as profile data and used for future activity suggestions.

[0411] Step 2:

[0412] The server collects group conversation data in real time. Chat and message information is sent from the terminal to the server. The input conversation data is analyzed using natural language processing technology, and keywords and emotions are extracted. The output obtained from this process is data that indicates the user's emotional state.

[0413] Step 3:

[0414] The server uses profile data and sentiment analysis results to generate optimal activities. A generative AI model receives this data as input and proposes activities based on the user's current emotional state. These suggestions are sent to the terminal and presented to the user as output.

[0415] Step 4:

[0416] The user reviews the suggested activities on their device and provides feedback. The user's response is sent to the server and recorded as input in the system's database. The server uses this feedback to make adjustments to improve the accuracy of the suggestions.

[0417] Step 5:

[0418] The server works in conjunction with smart home appliances to perform its functions. The appliances receive instructions from the server and adjust their settings to the optimal level based on the user's emotional state. For example, actions such as changing the color of the lighting might be output.

[0419] Step 6:

[0420] After an activity is completed, the server collects detailed feedback from the user. This provides the data needed to suggest future activities and adjust the generated AI model. The feedback is collected and stored on the server.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] As an embodiment of the present invention, a system is first constructed to enable busy working professionals to smoothly decide on activity plans as a group. This system proposes appropriate activities based on user information and supports the booking and payment of those activities. The specific processing is described below.

[0438] To start using the system, users access the platform via their terminal and enter their basic information and preferences. This generates profile data about the user. This profile data plays an important role in suggesting future activities. For example, if a user is interested in visiting cafes and watching sports, this information will be registered in the system.

[0439] When a group starts planning an activity, data from the group chat is sent from the terminal to the server. The server collects this data in real time and analyzes the text using natural language processing. From the analysis results, it identifies each user's interests and mood at the time, and generates activity suggestions based on that information. For example, if all members say they "want to relax," the server might suggest a "hot spring trip."

[0440] The proposed activities are displayed on each user's device, and users can provide feedback on these suggestions. The server analyzes the collected feedback and adjusts the suggestions as needed. After the final activity plan is decided, the server handles the booking and payment of the proposed activities through the online platform.

[0441] The day before the activity, the server sends a reminder message to the user's device to help them remember. After the activity is completed, the server collects feedback from the user again and uses it as data to improve future proposals.

[0442] This allows the system to constantly optimize activity plans to match the user's interests and preferences, supporting smooth and satisfying decision-making.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] Users access the system through their devices and enter their basic information and interests. This allows the user's profile data to be sent from the device to the server and stored there.

[0446] Step 2:

[0447] When planning a group activity, users initiate a group chat in real time. The device then sends the chat conversation data to the server.

[0448] Step 3:

[0449] To analyze the chat data received by the server, natural language processing techniques are used to analyze the text. Keywords and emotions are extracted to identify the participants' current mood and interests.

[0450] Step 4:

[0451] Based on the analysis results, the server generates optimal activity suggestions by combining them with the user's profile data. These suggestions include the activity content, time, location, and budget.

[0452] Step 5:

[0453] The proposed activities are displayed on the user's device, and the user enters their opinions and feedback on each proposal using the device. The device then sends the feedback to the server.

[0454] Step 6:

[0455] The server analyzes the overall feedback and fine-tunes the proposals as needed. The server then determines the final action plan and notifies the user again.

[0456] Step 7:

[0457] The user reviews and agrees to the final plan via their device. The server uses the online platform to book and process payments for the activity.

[0458] Step 8:

[0459] The day before the activity, the server sends a reminder message to the user's device.

[0460] Step 9:

[0461] After the activity ends, the server will collect user feedback again and update the data to use for future suggestions.

[0462] (Example 1)

[0463] 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."

[0464] In modern society, it is difficult for busy users to efficiently and quickly plan activities as a group. Furthermore, the process from the initial planning stages to final decisions, reservations, and payments is complex, and there is a need to provide optimal suggestions tailored to individual interests and circumstances. Current methods struggle to efficiently meet all these requirements, and therefore improvement is necessary.

[0465] 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.

[0466] In this invention, the server includes means for inputting information from the user and generating data related to the user's activity plan; means for collecting communication data in real time and analyzing keywords and status using information processing technology; and means for generating suggestions based on the analysis results and generated data and transmitting the content to the user. This makes it possible to quickly and accurately provide activity suggestions to busy users that are tailored to their interests and circumstances, and to smoothly proceed with the process from planning to booking and payment.

[0467] "Means of inputting information" refers to methods of receiving basic information and data related to interests from users and converting it into a format usable within the system.

[0468] "Means of generating data" refers to a system that creates profile data related to activity plans based on information entered by the user.

[0469] "Means of collecting communication data" refers to technologies that acquire the content of conversations and messages between multiple users in real time.

[0470] "Methods of analysis using information processing technology" refer to methods that extract keywords and emotions from collected data and use them to determine the user's interests and state of mind.

[0471] "Methods for generating suggestions" refer to methods for deriving optimal activity plans and options for users based on analyzed data.

[0472] "Means of sending content to the user" refers to technologies that display or notify the user of generated suggestions or information on their device.

[0473] "Means for receiving and adjusting responses" refers to a system that updates or modifies proposals based on user feedback.

[0474] "Means of presenting the final proposal" refers to a method of clearly showing the adjusted proposal to the user and assisting them in making a decision.

[0475] "Means of making reservations and payments" refers to technology that allows users to complete the necessary reservation procedures and payment processing online for a chosen activity.

[0476] "Means of sending notifications" refers to methods used to inform users in advance about the timing of activities and to draw their attention to them.

[0477] "Methods for collecting feedback" refer to a system that obtains user experience and satisfaction levels after an activity has ended and uses this information to inform future proposals.

[0478] This invention is a system that efficiently provides activity plans tailored to the interests and circumstances of individual users. To achieve this, users, terminals, and servers must work in close coordination.

[0479] First, users access the platform using their devices and enter basic information and interests. This input is done through an interface on the user's device, and the collected data is sent from the device to the server. The server generates profile data based on this information and stores it in a database. A relational database, in general terms, is suitable for this specific database system.

[0480] Next, the server collects communication data in real time and processes the information. For natural language processing, software such as general machine learning frameworks may be used. During analysis, keywords and sentiments are extracted from the text, and based on this, the user's interests and state are determined.

[0481] The server uses an AI model based on the analysis results to automatically generate optimal activity suggestions for the user. Examples of prompts used in this process include specific instructions such as, "Based on the user's recent chat history, please suggest ideal activities for the weekend."

[0482] The proposed activity is displayed on the user's device, and the user provides feedback on this proposal. The server receives the user's response and readjusts the proposal as needed. It then presents the final activity plan and allows the user to book and pay for the activity online. It is recommended to use a secure and reliable online service API for this booking and payment process.

[0483] Finally, the server sends a notification to the user's device the day before the activity, providing any necessary reminders. After the activity is completed, feedback from the user can be collected again and stored in a database to be used to improve future proposals.

[0484] Thus, the system of the present invention can improve the user experience by efficiently generating activity suggestions that match the needs of individual users and automating all processes.

[0485] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0486] Step 1:

[0487] Users access the platform through their devices and enter basic information such as their name, interests, and preferences. This information is sent to the server as input data via the web interface. The server generates profile data based on the received data and stores it in a relational database. Specifically, this involves initializing profile items and creating database entries.

[0488] Step 2:

[0489] When planning group activities, the terminal sends communication data—the content of the conversation—to the server in real time. The server, upon receiving this data, analyzes the conversation using natural language processing to extract keywords and emotions. Machine learning algorithms are used for this analysis, and the resulting analysis data reflects each user's interests and state. Based on these analysis results, the server adds and updates the information to the original database.

[0490] Step 3:

[0491] The server uses a generative AI model to generate activity suggestions based on analysis results and profile data. Specific prompts (e.g., "Based on the user's recent chat history, please suggest ideal activities for the weekend") are used during the generation process. The server processes the input data by converting it into a format suitable for the generative AI model, obtaining the model's output. This output is the suggestion data and is sent to the user's terminal.

[0492] Step 4:

[0493] On the device, the user reviews the suggested activities and submits feedback. This feedback is input data based on the user's selections and is sent back to the server. The server analyzes this feedback data and adjusts the generated suggestions. Specifically, suggestions are ranked and new suggestions are generated based on the content of the feedback.

[0494] Step 5:

[0495] The server finalizes the adjusted activity proposal and executes the booking and payment procedures. This activity proposal is confirmed via the online platform using the booking system API. Specific actions include creating booking information and processing payment information. The final output is a notification to the user of the confirmed activity proposal.

[0496] Step 6:

[0497] The day before an activity, the server sends a reminder message to the user's device. This message is notification data to help the user remember. Furthermore, after the activity is completed, the server collects feedback from the user again and stores it in a database to be used for future suggestions. This process optimizes the system so that it can always provide suggestions that meet the user's needs.

[0498] (Application Example 1)

[0499] 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."

[0500] For modern people leading busy lives, planning group activities efficiently and satisfactorily is crucial. However, selecting appropriate activities based on individual interests and schedules, and smoothly handling booking and transaction procedures, is a challenging task. In particular, there is a need for means to reduce the time and effort spent on scheduling and activity proposals, and to eliminate inefficiencies. Current technology has the problem of not being able to comprehensively meet these requirements.

[0501] 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.

[0502] In this invention, the server includes means for inputting information from the user and generating attribute data related to the activity plan of the entire group; means for analyzing conversational data using natural language processing and extracting keywords and emotions; and means for the robot to communicate the final adjusted activity plan to the user by voice. This makes it possible to quickly suggest activities that are suitable for the user's interests and schedule, and to automate reservation and transaction procedures.

[0503] "User information" refers to data related to an individual's interests and preferences that the user provides to the system.

[0504] A "group activity plan" is a detailed plan for an activity in which multiple people participate together.

[0505] "Attribute data" refers to a dataset that represents the preferences and past activity history of individual users.

[0506] "Natural language processing" is a computational technique for analyzing human language and understanding its meaning.

[0507] "Conversation data" refers to text information exchanged between users in real time.

[0508] "Keywords and emotions" refer to information that indicates important words and emotional states extracted from the user's statements.

[0509] The "adjusted final activity plan" is the final activity plan optimized based on feedback from users.

[0510] "Means of communication by robots to users via voice" refers to methods by which autonomous machines use voice functions to provide information to humans.

[0511] "Reservations and transactions" refer to situations that include confirming appointments and related payment procedures.

[0512] The system for implementing this invention consists of a process that collects user information and generates suggestions based on that information. The server plays a central role in this system and performs information processing. Users access the system through a terminal and input attribute data. This data reflects the user's interests and preferences and is used to analyze conversational data collected in real time.

[0513] The server uses specific software for natural language processing, such as an NLP library like "spaCy," to extract keywords and emotions from conversational data. This analysis is essential for identifying individual user interests and generating optimal activity suggestions. The final, adjusted activity suggestions are then communicated to the user via voice by the robot.

[0514] Furthermore, the servers automate the booking and transaction processes, working in conjunction with e-commerce platforms to execute them. This allows users to plan and complete their activities without any hassle.

[0515] As a concrete example, the server receives a prompt such as "Tell me some recommended events for next Saturday," and then, based on the user's past interest data, suggests the most suitable leisure activity and notifies the user via voice. This process makes the user's daily life more convenient.

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The user's device displays an information input screen, and the user enters attribute data related to their interests and preferences. This input data is sent to the server as a profile that will later form the basis for suggestion generation.

[0519] Step 2:

[0520] Group chat data is transmitted to the server in real time via the device. The server receives this data and uses natural language processing (NLP) software to analyze keywords and emotions within the conversation data. This analysis extracts each user's interests and mood at that time.

[0521] Step 3:

[0522] Based on the attribute data collected in Step 1 and the information analyzed in Step 2, the server uses a generative AI model to generate activity suggestions tailored to each user. This model determines the priority of activities based on the received prompt examples and sends them to the user's terminal.

[0523] Step 4:

[0524] Users review suggested activities on their devices and submit feedback. The server collects this feedback and adjusts the activity suggestions as needed. The feedback is received as text data and analyzed again to improve the accuracy of the activity suggestions.

[0525] Step 5:

[0526] The server determines the final adjusted activity plan and communicates it to the user via voice through the robot. In this process, the activity plan is converted into voice data using speech synthesis software and communicated to the user.

[0527] Step 6:

[0528] The server integrates with the e-commerce platform based on the final activity plan, automatically scheduling activities and processing transactions. This allows users to complete the process easily.

[0529] Step 7:

[0530] The server sends a reminder to the user's device the day before or immediately before the activity to alert them. This notification is incorporated into the user's schedule using their calendar app.

[0531] Step 8:

[0532] After the activity concludes, the server collects feedback again and incorporates it into future proposals, allowing the system to improve itself. This feedback serves as important data for increasing the accuracy of future activity proposals.

[0533] 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.

[0534] This invention provides a system that combines emotion recognition technology to enable busy professionals to effectively plan group activities. Specifically, it implements a mechanism that analyzes the user's emotions in real time and utilizes the results to propose activities.

[0535] The system begins with the user accessing it through their device. The user enters their basic information and interests to create profile data. After sending this data to the server, it is used to suggest activities. This profile data includes, for example, whether the user is interested in "outdoor activities" or "relaxing at a cafe."

[0536] During group activity planning, the device sends group chat conversation data to the server. The server uses natural language processing and an emotion engine to extract keywords from the conversation and classify the user's emotions. For example, if the words "stressed" and "tired" appear frequently in the chat, the emotion engine will determine that the user is "seeking relaxation."

[0537] Using these analysis results, the server generates activity suggestions tailored to each user. For example, it might suggest "hot spring trips" or "spas" to users who prioritize relaxation, and "hiking" or "sports games" to users who desire active activities. These suggestions are sent to users via their devices, and each user can provide feedback on them.

[0538] The server receives feedback and adjusts the final activity plan while considering the results of the emotion engine. Activities are then booked and paid for via an online platform. A reminder message is sent the day before the activity, and feedback is collected after its completion. This feedback is used to improve future proposals and also helps to refine the emotion engine.

[0539] This system allows users to receive suggestions based on their emotions and interests, enabling them to make highly satisfying activity choices. By combining this with emotion recognition technology, it becomes possible to provide a more personalized experience.

[0540] The following describes the processing flow.

[0541] Step 1:

[0542] Users access the system through their device and input their basic information and interests. The device sends this data to the server, and profile data is generated. This profile data records the user's hobbies and activity preferences.

[0543] Step 2:

[0544] When planning group activities, users initiate a group chat in real time. Their devices then send the chat conversation data to the server.

[0545] Step 3:

[0546] The server analyzes the received chat data using natural language processing techniques to extract key keywords. The emotion engine uses this data to classify each user's emotional state based on their statements. For example, it identifies emotions from words such as "excited" or "tired."

[0547] Step 4:

[0548] The server integrates the results of the emotion analysis with profile data to generate activity suggestions best suited to the participant. For example, if it determines that relaxation is needed, it will suggest activities such as "hot springs" or "watching a movie."

[0549] Step 5:

[0550] Activity suggestions are sent to the user's device, and the user provides feedback on the suggested activity. This feedback includes opinions on how well the suggested activity aligns with the user's interests.

[0551] Step 6:

[0552] The server analyzes the collected feedback and modifies the proposed activities as needed. The results of the emotion engine analysis are also considered to form the final activity plan.

[0553] Step 7:

[0554] After the user agrees to the final plan via their device, the server handles the activity booking and payment through the online platform. The booking status and payment confirmation are then completed.

[0555] Step 8:

[0556] The server sends a reminder message to the user's device the day before the activity.

[0557] Step 9:

[0558] After the activity ends, the server collects feedback from users. This feedback is used as training data for the emotion engine and will be reflected in future suggestions.

[0559] (Example 2)

[0560] 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."

[0561] In modern society, efficiently generating personalized action suggestions based on the emotions and interests of individual users is a challenging task. Because it requires methods to seamlessly collect and interpret appropriate information from group dialogues involving diverse participants, conventional methods have not provided systems with sufficient flexibility and accuracy.

[0562] 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.

[0563] In this invention, the server includes means for inputting information from users and generating information data related to the group's overall action plan; means for collecting conversational information from group dialogue in real time and analyzing words and emotions through linguistic analysis; and means for generating action suggestions based on the analysis results and information data, and transmitting the suggested content to the user. This makes it possible to generate personalized action suggestions based on each user's emotions and interests.

[0564] "Information data" refers to data that forms the basis for shaping an action plan for the entire group, based on user input and their interests.

[0565] "Group dialogue" is a term that refers to the process of conversation and communication among multiple participants, and is used as a platform for collecting useful information in real time.

[0566] "Linguistic analysis" is a technical method that uses natural language processing techniques to extract and analyze important words, phrases, and emotions from text data.

[0567] "Emotion" refers to the emotional state a user displays during conversations and activity suggestions, and is analyzed as a criterion for suggesting actions.

[0568] "Action suggestions" are specific activity or behavioral suggestions that are generated based on analyzed information data and emotions and provided to the user.

[0569] "Instructions" refers to the process of putting a generated action proposal into concrete action, or the process of determining the details of an activity.

[0570] "Transaction" refers to the economic procedures necessary for implementing a proposed action, namely settlement and contract confirmation.

[0571] This invention is a system for providing personalized action suggestions based on the user's emotions and interests. The system utilizes a networked environment including servers and terminals.

[0572] First, the user inputs their basic information and data related to their interests through their device. The device then compiles this information and sends it to the server in an appropriate format. The devices used here are common computing devices such as smartphones and PCs.

[0573] Next, the server collects conversational information from the group dialogue in real time. The collected data is analyzed using natural language processing software on the server (for example, Google Cloud Natural Language API). This analysis extracts keywords and emotions from the conversation.

[0574] Furthermore, the server uses these analysis results and profile data to create personalized action suggestions for each user, leveraging a generative AI model. This includes data processing steps to determine how to present these action suggestions.

[0575] For example, if a user is thinking, "I want to relieve my recent fatigue," the server can sense this information through real-time analysis and suggest relaxation plans such as a hot spring trip or spa treatment. This suggestion process is expressed as a prompt message like this: "User A said in a group chat, 'I've been feeling really tired lately.' What kind of relaxation activity would you suggest?"

[0576] Subsequently, the user uses a terminal to send feedback on the suggestion to the server. This feedback is used to improve the accuracy of future suggestions. Activity instructions and transactions are conducted through an online payment platform, and the terminal provides reminders the day before and collects feedback after the activity. Through this entire process, users are offered individually optimized activities, and their selection is made accordingly.

[0577] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0578] Step 1:

[0579] Users input their basic information and data related to their interests through a terminal. The input information is formatted by the terminal and prepared for transmission to the server. Specifically, the terminal converts this data into a common data format such as JSON. Input includes "interested activity categories" and "past events attended," and output is profile data in a format that the server can receive.

[0580] Step 2:

[0581] The terminal collects conversational information from group discussions in real time and sends it to the server. The conversation is input as text data, which is then divided according to certain criteria for batch processing and formatted for transmission to the server. The output is conversational information formatted for analysis.

[0582] Step 3:

[0583] The server analyzes the received conversational information using natural language processing software. Specifically, it uses the Google Cloud Natural Language API to extract keywords and sentiments from the text. Once this analysis is complete, a list of keywords and sentiment scores are generated. The input is the collected conversational data, and the output is the identified keywords and sentiment states.

[0584] Step 4:

[0585] The server uses a generative AI model to combine analysis results with user profile data to generate action suggestions. This model predicts the optimal action plan based on past data. Specifically, it takes generated keywords and emotion scores as input and outputs action suggestions (e.g., a relaxation plan).

[0586] Step 5:

[0587] The server sends the suggested action to the terminal and presents it to the user. After receiving it, the user can input feedback on the suggestion. Specifically, the user evaluates the attractiveness of the suggestion through the interface on the terminal, and this data is sent to the server as feedback data. The input is evaluation information for the suggested action, and the output is the collected feedback data.

[0588] Step 6:

[0589] The server analyzes the feedback data and makes adjustments to improve the accuracy of future suggestions. Specifically, it uses this feedback to update the parameters of the emotion engine and suggestion model, allowing them to learn to make suggestions that are closer to the user's preferences. The input is the collected feedback data, and the output is the adjusted suggestion model.

[0590] Step 7:

[0591] The server, in conjunction with the online payment platform, confirms the reservation and payment for the final decided action. The terminal also sends a reminder message to the user the day before the activity and provides an interface for collecting feedback after the activity. This entire process ensures smooth reservation confirmation and feedback collection. The input is reservation information, and the output is the confirmed reservation status and newly collected feedback.

[0592] (Application Example 2)

[0593] 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."

[0594] In modern households, there is a lack of activity suggestions based on the individual feelings and interests of each family member, making it difficult to find relaxation and activity options within the home, especially in busy daily lives. There is a need to address this problem and provide mechanisms to promote more personalized family activities.

[0595] 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.

[0596] This invention includes a server that inputs information from users and generates profile data related to the group's overall activity plan; a server that collects information from group conversations in real time and analyzes keywords and emotions using natural language processing; and a server that recognizes the emotions of family members and cooperates with smart home appliances that suggest household activities. This makes it possible to suggest appropriate household activities based on the emotions of each family member.

[0597] "User information" refers to data about a user's personal interests and activities that they themselves input.

[0598] "Profile data" refers to data generated from information collected from users, representing individual characteristics and preferences related to their activity plans.

[0599] "Gathering information from group conversations" refers to the process of acquiring the content of conversations taking place within a group in real time and using it as data for analysis.

[0600] "Natural language processing" refers to the technology that allows computers to understand and analyze human language, and is used to extract emotions and keywords.

[0601] "Family members" refers to the individual members of a household who share a living space together.

[0602] "Smart home appliances" refer to electronic devices in the home that can utilize emotion recognition data to provide integrated services.

[0603] An "electronic platform" refers to a digital infrastructure for booking and processing services and activities via the internet.

[0604] In the system for implementing this invention, the server handles the main processes. The server receives information from the user and generates individual profile data. This utilizes initial data about the user's interests and activities. Subsequently, the server collects group conversation data in real time and analyzes the conversation content using natural language processing. This process employs advanced sentiment recognition technology, and the system extracts keywords and emotional states. Software such as the Google Cloud Natural Language API is used for sentiment recognition, and the extracted data is used in the next step: activity suggestions.

[0605] The device plays a crucial role in sending suggested activities to the user and receiving feedback. User feedback is returned to the server, and the system uses this to refine its suggestions. This process is designed to provide more personalized suggestions using a generative AI model.

[0606] Furthermore, to enable activity suggestions within the home, emotional data of family members is linked with smart home appliances. These smart appliances suggest appropriate activities based on the user's emotions, such as adjusting lighting settings for relaxation mode or playing music.

[0607] For example, if the word "tired" comes up frequently in a conversation at home, the system might instruct the smart lighting in the living space to be set to a softer color and play calming music to create a more relaxing atmosphere.

[0608] An example of a prompt would be, "Whenever I sense fatigue in my family, please tell me how to help them relax." This serves as a guideline for the generated AI model to provide an experience optimized for the user.

[0609] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0610] Step 1:

[0611] The server receives information from the user. The user inputs data about their interests and activities through their terminal, and this data is sent to the server. The input data is stored in the system as profile data and used for future activity suggestions.

[0612] Step 2:

[0613] The server collects group conversation data in real time. Chat and message information is sent from the terminal to the server. The input conversation data is analyzed using natural language processing technology, and keywords and emotions are extracted. The output obtained from this process is data that indicates the user's emotional state.

[0614] Step 3:

[0615] The server uses profile data and sentiment analysis results to generate optimal activities. A generative AI model receives this data as input and proposes activities based on the user's current emotional state. These suggestions are sent to the terminal and presented to the user as output.

[0616] Step 4:

[0617] The user reviews the suggested activities on their device and provides feedback. The user's response is sent to the server and recorded as input in the system's database. The server uses this feedback to make adjustments to improve the accuracy of the suggestions.

[0618] Step 5:

[0619] The server works in conjunction with smart home appliances to perform its functions. The appliances receive instructions from the server and adjust their settings to the optimal level based on the user's emotional state. For example, actions such as changing the color of the lighting might be output.

[0620] Step 6:

[0621] After an activity is completed, the server collects detailed feedback from the user. This provides the data needed to suggest future activities and adjust the generated AI model. The feedback is collected and stored on the server.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] [Fourth Embodiment]

[0626] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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".

[0639] As an embodiment of the present invention, a system is first constructed to enable busy working professionals to smoothly decide on activity plans as a group. This system proposes appropriate activities based on user information and supports the booking and payment of those activities. The specific processing is described below.

[0640] To start using the system, users access the platform via their terminal and enter their basic information and preferences. This generates profile data about the user. This profile data plays an important role in suggesting future activities. For example, if a user is interested in visiting cafes and watching sports, this information will be registered in the system.

[0641] When a group starts planning an activity, data from the group chat is sent from the terminal to the server. The server collects this data in real time and analyzes the text using natural language processing. From the analysis results, it identifies each user's interests and mood at the time, and generates activity suggestions based on that information. For example, if all members say they "want to relax," the server might suggest a "hot spring trip."

[0642] The proposed activities are displayed on each user's device, and users can provide feedback on these suggestions. The server analyzes the collected feedback and adjusts the suggestions as needed. After the final activity plan is decided, the server handles the booking and payment of the proposed activities through the online platform.

[0643] The day before the activity, the server sends a reminder message to the user's device to help them remember. After the activity is completed, the server collects feedback from the user again and uses it as data to improve future proposals.

[0644] This allows the system to constantly optimize activity plans to match the user's interests and preferences, supporting smooth and satisfying decision-making.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] Users access the system through their devices and enter their basic information and interests. This allows the user's profile data to be sent from the device to the server and stored there.

[0648] Step 2:

[0649] When planning a group activity, users initiate a group chat in real time. The device then sends the chat conversation data to the server.

[0650] Step 3:

[0651] To analyze the chat data received by the server, natural language processing techniques are used to analyze the text. Keywords and emotions are extracted to identify the participants' current mood and interests.

[0652] Step 4:

[0653] Based on the analysis results, the server generates optimal activity suggestions by combining them with the user's profile data. These suggestions include the activity content, time, location, and budget.

[0654] Step 5:

[0655] The proposed activities are displayed on the user's device, and the user enters their opinions and feedback on each proposal using the device. The device then sends the feedback to the server.

[0656] Step 6:

[0657] The server analyzes the overall feedback and fine-tunes the proposals as needed. The server then determines the final action plan and notifies the user again.

[0658] Step 7:

[0659] The user reviews and agrees to the final plan via their device. The server uses the online platform to book and process payments for the activity.

[0660] Step 8:

[0661] The day before the activity, the server sends a reminder message to the user's device.

[0662] Step 9:

[0663] After the activity ends, the server will collect user feedback again and update the data to use for future suggestions.

[0664] (Example 1)

[0665] 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".

[0666] In modern society, it is difficult for busy users to efficiently and quickly plan activities as a group. Furthermore, the process from the initial planning stages to final decisions, reservations, and payments is complex, and there is a need to provide optimal suggestions tailored to individual interests and circumstances. Current methods struggle to efficiently meet all these requirements, and therefore improvement is necessary.

[0667] 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.

[0668] In this invention, the server includes means for inputting information from the user and generating data related to the user's activity plan; means for collecting communication data in real time and analyzing keywords and status using information processing technology; and means for generating suggestions based on the analysis results and generated data and transmitting the content to the user. This makes it possible to quickly and accurately provide activity suggestions to busy users that are tailored to their interests and circumstances, and to smoothly proceed with the process from planning to booking and payment.

[0669] "Means of inputting information" refers to methods of receiving basic information and data related to interests from users and converting it into a format usable within the system.

[0670] "Means of generating data" refers to a system that creates profile data related to activity plans based on information entered by the user.

[0671] "Means of collecting communication data" refers to technologies that acquire the content of conversations and messages between multiple users in real time.

[0672] "Methods of analysis using information processing technology" refer to methods that extract keywords and emotions from collected data and use them to determine the user's interests and state of mind.

[0673] "Methods for generating suggestions" refer to methods for deriving optimal activity plans and options for users based on analyzed data.

[0674] "Means of sending content to the user" refers to technologies that display or notify the user of generated suggestions or information on their device.

[0675] "Means for receiving and adjusting responses" refers to a system that updates or modifies proposals based on user feedback.

[0676] "Means of presenting the final proposal" refers to a method of clearly showing the adjusted proposal to the user and assisting them in making a decision.

[0677] "Means of making reservations and payments" refers to technology that allows users to complete the necessary reservation procedures and payment processing online for a chosen activity.

[0678] "Means of sending notifications" refers to methods used to inform users in advance about the timing of activities and to draw their attention to them.

[0679] "Methods for collecting feedback" refer to a system that obtains user experience and satisfaction levels after an activity has ended and uses this information to inform future proposals.

[0680] This invention is a system that efficiently provides activity plans tailored to the interests and circumstances of individual users. To achieve this, users, terminals, and servers must work in close coordination.

[0681] First, users access the platform using their devices and enter basic information and interests. This input is done through an interface on the user's device, and the collected data is sent from the device to the server. The server generates profile data based on this information and stores it in a database. A relational database, in general terms, is suitable for this specific database system.

[0682] Next, the server collects communication data in real time and processes the information. For natural language processing, software such as general machine learning frameworks may be used. During analysis, keywords and sentiments are extracted from the text, and based on this, the user's interests and state are determined.

[0683] The server uses an AI model based on the analysis results to automatically generate optimal activity suggestions for the user. Examples of prompts used in this process include specific instructions such as, "Based on the user's recent chat history, please suggest ideal activities for the weekend."

[0684] The proposed activity is displayed on the user's device, and the user provides feedback on this proposal. The server receives the user's response and readjusts the proposal as needed. It then presents the final activity plan and allows the user to book and pay for the activity online. It is recommended to use a secure and reliable online service API for this booking and payment process.

[0685] Finally, the server sends a notification to the user's device the day before the activity, providing any necessary reminders. After the activity is completed, feedback from the user can be collected again and stored in a database to be used to improve future proposals.

[0686] Thus, the system of the present invention can improve the user experience by efficiently generating activity suggestions that match the needs of individual users and automating all processes.

[0687] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0688] Step 1:

[0689] Users access the platform through their devices and enter basic information such as their name, interests, and preferences. This information is sent to the server as input data via the web interface. The server generates profile data based on the received data and stores it in a relational database. Specifically, this involves initializing profile items and creating database entries.

[0690] Step 2:

[0691] When planning group activities, the terminal sends communication data—the content of the conversation—to the server in real time. The server, upon receiving this data, analyzes the conversation using natural language processing to extract keywords and emotions. Machine learning algorithms are used for this analysis, and the resulting analysis data reflects each user's interests and state. Based on these analysis results, the server adds and updates the information to the original database.

[0692] Step 3:

[0693] The server uses a generative AI model to generate activity suggestions based on analysis results and profile data. Specific prompts (e.g., "Based on the user's recent chat history, please suggest ideal activities for the weekend") are used during the generation process. The server processes the input data by converting it into a format suitable for the generative AI model, obtaining the model's output. This output is the suggestion data and is sent to the user's terminal.

[0694] Step 4:

[0695] On the device, the user reviews the suggested activities and submits feedback. This feedback is input data based on the user's selections and is sent back to the server. The server analyzes this feedback data and adjusts the generated suggestions. Specifically, suggestions are ranked and new suggestions are generated based on the content of the feedback.

[0696] Step 5:

[0697] The server finalizes the adjusted activity proposal and executes the booking and payment procedures. This activity proposal is confirmed via the online platform using the booking system API. Specific actions include creating booking information and processing payment information. The final output is a notification to the user of the confirmed activity proposal.

[0698] Step 6:

[0699] The day before an activity, the server sends a reminder message to the user's device. This message is notification data to help the user remember. Furthermore, after the activity is completed, the server collects feedback from the user again and stores it in a database to be used for future suggestions. This process optimizes the system so that it can always provide suggestions that meet the user's needs.

[0700] (Application Example 1)

[0701] 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".

[0702] For modern people leading busy lives, planning group activities efficiently and satisfactorily is crucial. However, selecting appropriate activities based on individual interests and schedules, and smoothly handling booking and transaction procedures, is a challenging task. In particular, there is a need for means to reduce the time and effort spent on scheduling and activity proposals, and to eliminate inefficiencies. Current technology has the problem of not being able to comprehensively meet these requirements.

[0703] 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.

[0704] In this invention, the server includes means for inputting information from the user and generating attribute data related to the activity plan of the entire group; means for analyzing conversational data using natural language processing and extracting keywords and emotions; and means for the robot to communicate the final adjusted activity plan to the user by voice. This makes it possible to quickly suggest activities that are suitable for the user's interests and schedule, and to automate reservation and transaction procedures.

[0705] "User information" refers to data related to an individual's interests and preferences that the user provides to the system.

[0706] A "group activity plan" is a detailed plan for an activity in which multiple people participate together.

[0707] "Attribute data" refers to a dataset that represents the preferences and past activity history of individual users.

[0708] "Natural language processing" is a computational technique for analyzing human language and understanding its meaning.

[0709] "Conversation data" refers to text information exchanged between users in real time.

[0710] "Keywords and emotions" refer to information that indicates important words and emotional states extracted from the user's statements.

[0711] The "adjusted final activity plan" is the final activity plan optimized based on feedback from users.

[0712] "Means of communication by robots to users via voice" refers to methods by which autonomous machines use voice functions to provide information to humans.

[0713] "Reservations and transactions" refer to situations that include confirming appointments and related payment procedures.

[0714] The system for implementing this invention consists of a process that collects user information and generates suggestions based on that information. The server plays a central role in this system and performs information processing. Users access the system through a terminal and input attribute data. This data reflects the user's interests and preferences and is used to analyze conversational data collected in real time.

[0715] The server uses specific software for natural language processing, such as an NLP library like "spaCy," to extract keywords and emotions from conversational data. This analysis is essential for identifying individual user interests and generating optimal activity suggestions. The final, adjusted activity suggestions are then communicated to the user via voice by the robot.

[0716] Furthermore, the servers automate the booking and transaction processes, working in conjunction with e-commerce platforms to execute them. This allows users to plan and complete their activities without any hassle.

[0717] As a concrete example, the server receives a prompt such as "Tell me some recommended events for next Saturday," and then, based on the user's past interest data, suggests the most suitable leisure activity and notifies the user via voice. This process makes the user's daily life more convenient.

[0718] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0719] Step 1:

[0720] The user's device displays an information input screen, and the user enters attribute data related to their interests and preferences. This input data is sent to the server as a profile that will later form the basis for suggestion generation.

[0721] Step 2:

[0722] Group chat data is transmitted to the server in real time via the device. The server receives this data and uses natural language processing (NLP) software to analyze keywords and emotions within the conversation data. This analysis extracts each user's interests and mood at that time.

[0723] Step 3:

[0724] Based on the attribute data collected in Step 1 and the information analyzed in Step 2, the server uses a generative AI model to generate activity suggestions tailored to each user. This model determines the priority of activities based on the received prompt examples and sends them to the user's terminal.

[0725] Step 4:

[0726] Users review suggested activities on their devices and submit feedback. The server collects this feedback and adjusts the activity suggestions as needed. The feedback is received as text data and analyzed again to improve the accuracy of the activity suggestions.

[0727] Step 5:

[0728] The server determines the final adjusted activity plan and communicates it to the user via voice through the robot. In this process, the activity plan is converted into voice data using speech synthesis software and communicated to the user.

[0729] Step 6:

[0730] The server integrates with the e-commerce platform based on the final activity plan, automatically scheduling activities and processing transactions. This allows users to complete the process easily.

[0731] Step 7:

[0732] The server sends a reminder to the user's device the day before or immediately before the activity to alert them. This notification is incorporated into the user's schedule using their calendar app.

[0733] Step 8:

[0734] After the activity concludes, the server collects feedback again and incorporates it into future proposals, allowing the system to improve itself. This feedback serves as important data for increasing the accuracy of future activity proposals.

[0735] 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.

[0736] This invention provides a system that combines emotion recognition technology to enable busy professionals to effectively plan group activities. Specifically, it implements a mechanism that analyzes the user's emotions in real time and utilizes the results to propose activities.

[0737] The system begins with the user accessing it through their device. The user enters their basic information and interests to create profile data. After sending this data to the server, it is used to suggest activities. This profile data includes, for example, whether the user is interested in "outdoor activities" or "relaxing at a cafe."

[0738] During group activity planning, the device sends group chat conversation data to the server. The server uses natural language processing and an emotion engine to extract keywords from the conversation and classify the user's emotions. For example, if the words "stressed" and "tired" appear frequently in the chat, the emotion engine will determine that the user is "seeking relaxation."

[0739] Using these analysis results, the server generates activity suggestions tailored to each user. For example, it might suggest "hot spring trips" or "spas" to users who prioritize relaxation, and "hiking" or "sports games" to users who desire active activities. These suggestions are sent to users via their devices, and each user can provide feedback on them.

[0740] The server receives feedback and adjusts the final activity plan while considering the results of the emotion engine. Activities are then booked and paid for via an online platform. A reminder message is sent the day before the activity, and feedback is collected after its completion. This feedback is used to improve future proposals and also helps to refine the emotion engine.

[0741] This system allows users to receive suggestions based on their emotions and interests, enabling them to make highly satisfying activity choices. By combining this with emotion recognition technology, it becomes possible to provide a more personalized experience.

[0742] The following describes the processing flow.

[0743] Step 1:

[0744] Users access the system through their device and input their basic information and interests. The device sends this data to the server, and profile data is generated. This profile data records the user's hobbies and activity preferences.

[0745] Step 2:

[0746] When planning group activities, users initiate a group chat in real time. Their devices then send the chat conversation data to the server.

[0747] Step 3:

[0748] The server analyzes the received chat data using natural language processing techniques to extract key keywords. The emotion engine uses this data to classify each user's emotional state based on their statements. For example, it identifies emotions from words such as "excited" or "tired."

[0749] Step 4:

[0750] The server integrates the results of the emotion analysis with profile data to generate activity suggestions best suited to the participant. For example, if it determines that relaxation is needed, it will suggest activities such as "hot springs" or "watching a movie."

[0751] Step 5:

[0752] Activity suggestions are sent to the user's device, and the user provides feedback on the suggested activity. This feedback includes opinions on how well the suggested activity aligns with the user's interests.

[0753] Step 6:

[0754] The server analyzes the collected feedback and modifies the proposed activities as needed. The results of the emotion engine analysis are also considered to form the final activity plan.

[0755] Step 7:

[0756] After the user agrees to the final plan via their device, the server handles the activity booking and payment through the online platform. The booking status and payment confirmation are then completed.

[0757] Step 8:

[0758] The server sends a reminder message to the user's device the day before the activity.

[0759] Step 9:

[0760] After the activity ends, the server collects feedback from users. This feedback is used as training data for the emotion engine and will be reflected in future suggestions.

[0761] (Example 2)

[0762] 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".

[0763] In modern society, efficiently generating personalized action suggestions based on the emotions and interests of individual users is a challenging task. Because it requires methods to seamlessly collect and interpret appropriate information from group dialogues involving diverse participants, conventional methods have not provided systems with sufficient flexibility and accuracy.

[0764] 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.

[0765] In this invention, the server includes means for inputting information from users and generating information data related to the group's overall action plan; means for collecting conversational information from group dialogue in real time and analyzing words and emotions through linguistic analysis; and means for generating action suggestions based on the analysis results and information data, and transmitting the suggested content to the user. This makes it possible to generate personalized action suggestions based on each user's emotions and interests.

[0766] "Information data" refers to data that forms the basis for shaping an action plan for the entire group, based on user input and their interests.

[0767] "Group dialogue" is a term that refers to the process of conversation and communication among multiple participants, and is used as a platform for collecting useful information in real time.

[0768] "Linguistic analysis" is a technical method that uses natural language processing techniques to extract and analyze important words, phrases, and emotions from text data.

[0769] "Emotion" refers to the emotional state a user displays during conversations and activity suggestions, and is analyzed as a criterion for suggesting actions.

[0770] "Action suggestions" are specific activity or behavioral suggestions that are generated based on analyzed information data and emotions and provided to the user.

[0771] "Instructions" refers to the process of putting a generated action proposal into concrete action, or the process of determining the details of an activity.

[0772] "Transaction" refers to the economic procedures necessary for implementing a proposed action, namely settlement and contract confirmation.

[0773] This invention is a system for providing personalized action suggestions based on the user's emotions and interests. The system utilizes a networked environment including servers and terminals.

[0774] First, the user inputs their basic information and data related to their interests through their device. The device then compiles this information and sends it to the server in an appropriate format. The devices used here are common computing devices such as smartphones and PCs.

[0775] Next, the server collects conversational information from the group dialogue in real time. The collected data is analyzed using natural language processing software on the server (for example, Google Cloud Natural Language API). This analysis extracts keywords and emotions from the conversation.

[0776] Furthermore, the server uses these analysis results and profile data to create personalized action suggestions for each user, leveraging a generative AI model. This includes data processing steps to determine how to present these action suggestions.

[0777] For example, if a user is thinking, "I want to relieve my recent fatigue," the server can sense this information through real-time analysis and suggest relaxation plans such as a hot spring trip or spa treatment. This suggestion process is expressed as a prompt message like this: "User A said in a group chat, 'I've been feeling really tired lately.' What kind of relaxation activity would you suggest?"

[0778] Subsequently, the user uses a terminal to send feedback on the suggestion to the server. This feedback is used to improve the accuracy of future suggestions. Activity instructions and transactions are conducted through an online payment platform, and the terminal provides reminders the day before and collects feedback after the activity. Through this entire process, users are offered individually optimized activities, and their selection is made accordingly.

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] Users input their basic information and data related to their interests through a terminal. The input information is formatted by the terminal and prepared for transmission to the server. Specifically, the terminal converts this data into a common data format such as JSON. Input includes "interested activity categories" and "past events attended," and output is profile data in a format that the server can receive.

[0782] Step 2:

[0783] The terminal collects conversational information from group discussions in real time and sends it to the server. The conversation is input as text data, which is then divided according to certain criteria for batch processing and formatted for transmission to the server. The output is conversational information formatted for analysis.

[0784] Step 3:

[0785] The server analyzes the received conversational information using natural language processing software. Specifically, it uses the Google Cloud Natural Language API to extract keywords and sentiments from the text. Once this analysis is complete, a list of keywords and sentiment scores are generated. The input is the collected conversational data, and the output is the identified keywords and sentiment states.

[0786] Step 4:

[0787] The server uses a generative AI model to combine analysis results with user profile data to generate action suggestions. This model predicts the optimal action plan based on past data. Specifically, it takes generated keywords and emotion scores as input and outputs action suggestions (e.g., a relaxation plan).

[0788] Step 5:

[0789] The server sends the suggested action to the terminal and presents it to the user. After receiving it, the user can input feedback on the suggestion. Specifically, the user evaluates the attractiveness of the suggestion through the interface on the terminal, and this data is sent to the server as feedback data. The input is evaluation information for the suggested action, and the output is the collected feedback data.

[0790] Step 6:

[0791] The server analyzes the feedback data and makes adjustments to improve the accuracy of future suggestions. Specifically, it uses this feedback to update the parameters of the emotion engine and suggestion model, allowing them to learn to make suggestions that are closer to the user's preferences. The input is the collected feedback data, and the output is the adjusted suggestion model.

[0792] Step 7:

[0793] The server, in conjunction with the online payment platform, confirms the reservation and payment for the final decided action. The terminal also sends a reminder message to the user the day before the activity and provides an interface for collecting feedback after the activity. This entire process ensures smooth reservation confirmation and feedback collection. The input is reservation information, and the output is the confirmed reservation status and newly collected feedback.

[0794] (Application Example 2)

[0795] 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".

[0796] In modern households, there is a lack of activity suggestions based on the individual feelings and interests of each family member, making it difficult to find relaxation and activity options within the home, especially in busy daily lives. There is a need to address this problem and provide mechanisms to promote more personalized family activities.

[0797] 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.

[0798] This invention includes a server that inputs information from users and generates profile data related to the group's overall activity plan; a server that collects information from group conversations in real time and analyzes keywords and emotions using natural language processing; and a server that recognizes the emotions of family members and cooperates with smart home appliances that suggest household activities. This makes it possible to suggest appropriate household activities based on the emotions of each family member.

[0799] "User information" refers to data about a user's personal interests and activities that they themselves input.

[0800] "Profile data" refers to data generated from information collected from users, representing individual characteristics and preferences related to their activity plans.

[0801] "Gathering information from group conversations" refers to the process of acquiring the content of conversations taking place within a group in real time and using it as data for analysis.

[0802] "Natural language processing" refers to the technology that allows computers to understand and analyze human language, and is used to extract emotions and keywords.

[0803] "Family members" refers to the individual members of a household who share a living space together.

[0804] "Smart home appliances" refer to electronic devices in the home that can utilize emotion recognition data to provide integrated services.

[0805] An "electronic platform" refers to a digital infrastructure for booking and processing services and activities via the internet.

[0806] In the system for implementing this invention, the server handles the main processes. The server receives information from the user and generates individual profile data. This utilizes initial data about the user's interests and activities. Subsequently, the server collects group conversation data in real time and analyzes the conversation content using natural language processing. This process employs advanced sentiment recognition technology, and the system extracts keywords and emotional states. Software such as the Google Cloud Natural Language API is used for sentiment recognition, and the extracted data is used in the next step: activity suggestions.

[0807] The device plays a crucial role in sending suggested activities to the user and receiving feedback. User feedback is returned to the server, and the system uses this to refine its suggestions. This process is designed to provide more personalized suggestions using a generative AI model.

[0808] Furthermore, to enable activity suggestions within the home, emotional data of family members is linked with smart home appliances. These smart appliances suggest appropriate activities based on the user's emotions, such as adjusting lighting settings for relaxation mode or playing music.

[0809] For example, if the word "tired" comes up frequently in a conversation at home, the system might instruct the smart lighting in the living space to be set to a softer color and play calming music to create a more relaxing atmosphere.

[0810] An example of a prompt would be, "Whenever I sense fatigue in my family, please tell me how to help them relax." This serves as a guideline for the generated AI model to provide an experience optimized for the user.

[0811] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0812] Step 1:

[0813] The server receives information from the user. The user inputs data about their interests and activities through their terminal, and this data is sent to the server. The input data is stored in the system as profile data and used for future activity suggestions.

[0814] Step 2:

[0815] The server collects group conversation data in real time. Chat and message information is sent from the terminal to the server. The input conversation data is analyzed using natural language processing technology, and keywords and emotions are extracted. The output obtained from this process is data that indicates the user's emotional state.

[0816] Step 3:

[0817] The server uses profile data and sentiment analysis results to generate optimal activities. A generative AI model receives this data as input and proposes activities based on the user's current emotional state. These suggestions are sent to the terminal and presented to the user as output.

[0818] Step 4:

[0819] The user reviews the suggested activities on their device and provides feedback. The user's response is sent to the server and recorded as input in the system's database. The server uses this feedback to make adjustments to improve the accuracy of the suggestions.

[0820] Step 5:

[0821] The server works in conjunction with smart home appliances to perform its functions. The appliances receive instructions from the server and adjust their settings to the optimal level based on the user's emotional state. For example, actions such as changing the color of the lighting might be output.

[0822] Step 6:

[0823] After an activity is completed, the server collects detailed feedback from the user. This provides the data needed to suggest future activities and adjust the generated AI model. The feedback is collected and stored on the server.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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."

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] The following is further disclosed regarding the embodiments described above.

[0846] (Claim 1)

[0847] A means of inputting information from users and generating profile data related to the group's overall activity plan,

[0848] A method for collecting conversation data from group chats in real time and analyzing keywords and sentiments using natural language processing,

[0849] A means for generating activity suggestions based on the aforementioned analysis results and profile data, and for sending the suggested content to the user,

[0850] A means of receiving user feedback and adjusting the proposed content,

[0851] The final activity plan after adjustments will be presented, and the means for booking and settling that plan will be provided.

[0852] A means of sending notifications before an activity and collecting feedback after it is completed,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the analysis by natural language processing includes a method for identifying the mood and interests of each participant.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the booking and payment of the final activity plan are performed through an online platform.

[0858] "Example 1"

[0859] (Claim 1)

[0860] A means of inputting information from the user and generating data related to the user's activity plan,

[0861] A means of collecting communication data in real time and analyzing keywords and states using information processing technology,

[0862] A means for generating a proposal based on the aforementioned analysis results and generated data, and for sending the content to the user,

[0863] A means of receiving user feedback and adjusting the proposed content,

[0864] The means of presenting the final proposal after adjustments, and carrying out the reservation and payment of that proposal,

[0865] A means of sending notifications before an action is taken and collecting responses after it is completed,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the analysis using the aforementioned information processing technology includes a method for identifying the status and interests of each participant.

[0869] (Claim 3)

[0870] The system according to claim 1, wherein the reservation and payment of the aforementioned final proposal are performed through an electronic platform.

[0871] "Application Example 1"

[0872] (Claim 1)

[0873] A means of inputting information from users and generating attribute data related to the activity plan of the entire group,

[0874] A method for collecting conversational data in real time and analyzing keywords and emotions using natural language processing,

[0875] A means for generating activity suggestions based on the aforementioned analysis results and attribute data, and for sending the suggested content to the user,

[0876] A means of receiving user feedback and adjusting the proposed content,

[0877] The final revised activity plan will be presented, along with the means to book and execute the transaction for that plan.

[0878] A method to send notifications before the activity, collect feedback after it is completed, and incorporate it into future proposals,

[0879] A means by which the robot communicates the proposed content to the user by voice,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, wherein the analysis by natural language processing includes a method for identifying the mood and interests of each participant.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the booking and transaction of the aforementioned final activity plan are carried out through an e-commerce platform.

[0885] "Example 2 of combining an emotion engine"

[0886] (Claim 1)

[0887] A means for inputting information from users and generating information data related to the action plan for the entire group,

[0888] A method for collecting conversational information from group dialogues in real time and analyzing words and emotions through linguistic analysis,

[0889] A means for generating action suggestions based on the aforementioned analysis results and information data, and for sending the suggested content to the user,

[0890] A means of receiving responses from users and adjusting the proposed content,

[0891] Present the final action plan after adjustments, and provide the means to give instructions and execute the transaction according to that plan.

[0892] A means of sending a notification before an action is taken and collecting responses after it is completed,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, wherein the analysis by language analysis includes a method for identifying the psychological state and interests of each participant.

[0896] (Claim 3)

[0897] The system according to claim 1, wherein the instructions and transactions of the final action plan are executed via remote information processing means.

[0898] "Application example 2 when combining with an emotional engine"

[0899] (Claim 1)

[0900] A means of inputting information from users and generating profile data related to the group's overall activity plan,

[0901] A method for collecting information from group conversations in real time and analyzing keywords and sentiments using natural language processing,

[0902] A means for generating activity suggestions based on the aforementioned analysis results and profile data, and for sending the suggested content to the user,

[0903] A means of receiving user feedback and adjusting the proposed content,

[0904] A means of integrating with smart home appliances that recognize the emotions of family members and suggest household activities,

[0905] The means of presenting the final adjusted activity plan and executing the reservation and processing of that plan,

[0906] A means of sending out notifications before the activity and collecting feedback after it is completed,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, further comprising means for the analysis by natural language processing to identify the mood and interests of each participant and to propose activities in the living space based on the results.

[0910] (Claim 3)

[0911] The system according to claim 1, wherein the reservation and processing of the aforementioned final activity plan are performed through an electronic platform. [Explanation of symbols]

[0912] 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. A means of inputting information from users and generating profile data related to the group's overall activity plan, A method for collecting conversation data from group chats in real time and analyzing keywords and sentiments using natural language processing, A means for generating activity suggestions based on the aforementioned analysis results and profile data, and for sending the suggested content to the user, A means of receiving user feedback and adjusting the proposed content, The final activity plan after adjustments will be presented, and the means for booking and settling that plan will be provided. A means of sending notifications before an activity and collecting feedback after it is completed, A system that includes this.

2. The system according to claim 1, wherein the analysis by natural language processing includes a method for identifying the mood and interests of each participant.

3. The system according to claim 1, wherein the reservation and payment of the final activity plan are performed through an online platform.

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