system

Abstract

We provide the system. [Solution] An input method for entering user needs information, An analytical means that analyzes the aforementioned needs information and proposes appropriate support candidates, A means of communication for communicating with the aforementioned support candidate, A recording means for recording the agreement of both parties based on the aforementioned communication, A system that includes this.

Description

Technical Field

[0002]

[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

Means for Solving the Problems

[0005] Note: There seem to be some tags with incorrect or incomplete numbers in the original text which might cause issues in a proper patent context. Also, the "Summary of the Invention" part has tags that don't match the sequential numbering pattern, which might need to be corrected in a real patent translation scenario. I've translated as accurately as possible with the given text.To solve the aforementioned problem, this invention provides a system that offers appropriate support based on the user's needs. Specifically, the system comprises means for inputting user needs information, means for analyzing the needs information and proposing appropriate support candidates, means for recording the content of agreements with support candidates, and a collection means equipped with a feedback collection function to improve the quality of service. In this way, it reduces the burden of childcare for working women while facilitating social contribution by the elderly.

[0006] "User" refers to an individual or legal entity that inputs its needs through the system and receives support.

[0007] "Needs information" refers to data that indicates the specific requests and desires for support that users seek.

[0008] An "input method" is an interface that allows users to provide needs information to the system.

[0009] "Analysis means" refers to an algorithm or function that processes received needs information and selects appropriate support candidates.

[0010] A "support candidate" is an individual or organization selected through analytical methods that can provide support according to the user's needs.

[0011] "Communication method" refers to a communication channel or protocol used by users and potential support providers to exchange information and reach mutual agreements.

[0012] A "recording mechanism" is a mechanism for saving the agreement reached between the user and the support candidate.

[0013] "Collection methods" refer to functions for gathering feedback information from users after the service has been provided. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE 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, etc.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[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 method for realizing a system that provides appropriate support according to the various needs of users is described below. This system is designed to be easily accessible to users and has an intuitive user interface. The central elements of the system are input means, analysis means, communication means, recording means, and collection means.

[0036] First, the terminal provides an interface that allows users to input the necessary information when requesting assistance. Users input specific needs information through the terminal, and this information is sent to the server. Upon receiving the needs information, the server processes this information using analysis tools and identifies the most suitable support candidates for the user's request.

[0037] Based on the analysis results, the server selects potential support candidates and presents that information to the user's terminal. The user can then choose from the presented support candidates and communicate in detail with the selected candidate using communication methods. This includes chat and video call functions, allowing for the arrangement of specific support details and dates / times.

[0038] Agreements reached between users and support candidates through communication are stored in a database using recording mechanisms and can be used for future reference and management. After the service is provided, collection methods are used to obtain feedback from users. Feedback is an important source of information for improving the system and enhancing the quality of the service. This allows the system to be continuously improved and to become more responsive to user needs.

[0039] For example, if a user seeking childcare support requests "weekend childcare" using their device, the system analyzes the information and displays a list of nearby seniors who can provide the relevant support. The user then selects a senior who matches their needs and completes the necessary online consultation. After the service is completed, the user can provide feedback within the system, reporting their satisfaction with the experience and areas for improvement. This entire process enables efficient and effective childcare support in the local community.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal displays a screen for the user to input needs information. Once the user enters the necessary information, such as the type of support needed and the desired time, and presses the submit button, the terminal sends that information to the server.

[0043] Step 2:

[0044] The server receives needs information transmitted from the terminal and passes it to the analysis unit. Based on the received information, the analysis unit searches the database for suitable support candidates and identifies the most appropriate candidate.

[0045] Step 3:

[0046] The server generates a list of potential support candidates identified by the analysis tool and sends this information to the terminal. The terminal displays the candidate information in a list format for the user to easily review.

[0047] Step 4:

[0048] The user selects a suitable candidate from the list of support candidates displayed on the device and contacts that candidate using a communication method. The device provides an interface that enables chat and video calls, and helps in coordinating specific support details and schedules.

[0049] Step 5:

[0050] After an agreement is reached, the user sends the details based on the agreement to the server via their device. The server uses recording means to store the details of the agreement in a database for future reference.

[0051] Step 6:

[0052] After the support service is completed, the device provides the user with a feedback input screen. The user enters their satisfaction level and areas for improvement regarding the support provided, and the device sends this feedback information to the server.

[0053] Step 7:

[0054] The server analyzes feedback information collected through various means and uses the results to improve the system and services. This continuous feedback loop enables the implementation of measures to improve system quality and user satisfaction.

[0055] (Example 1)

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

[0057] In modern society, providing timely and appropriate support that meets the diverse needs of users is a crucial challenge. In particular, there is a need for a system that can quickly and accurately match users with the support they require and facilitate smooth communication. Furthermore, effectively collecting and analyzing user feedback is essential for continuously improving the quality of support.

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

[0059] In this invention, the server includes means for inputting user demand information, means for analyzing the demand information using a generation AI model to identify appropriate support candidates, and means for presenting the selected support candidates to the user. This makes it possible to respond quickly and appropriately to user needs and improve the accuracy and efficiency of support matching.

[0060] A "user" refers to an individual or group that uses the system to request services or support.

[0061] "Demand information" refers to specific information about the content and conditions of support entered by the user.

[0062] A "generative AI model" is an artificial intelligence model used to analyze user input information and understand its intent.

[0063] A "support candidate" is an individual or organization that is selected based on user needs information and has the potential to provide appropriate support.

[0064] "Communication methods" refer to technologies and tools used for detailed information exchange and communication between users and potential support providers.

[0065] "Agreement" refers to the service details and conditions agreed upon between the user and the support candidate.

[0066] "Evaluation information" refers to feedback and opinions provided by users after the service has been implemented.

[0067] "Aptitude information" refers to information about the characteristics and abilities of a specific group or individual.

[0068] "Support work" refers to specific support activities and tasks provided according to the user's needs.

[0069] This invention is a support system for responding quickly and accurately to user needs, and its hardware includes terminal devices, server devices, and a communication network. The software includes a platform for managing the entire system, a generative AI model, and a natural language processing library. This section describes the specific use of hardware and software.

[0070] The terminal provides a user interface for users to input needs information. This interface is designed using HTML / CSS / JavaScript (registered trademark) and includes a form. Users can input specific prompts in text format, such as "weekend childcare supervision."

[0071] The server receives needs information sent from the terminal and analyzes it using a generative AI model. This analysis uses Python and natural language processing libraries (e.g., SpaCy or NLTK) to understand the user's intent. The analysis results identify the most suitable support candidates and are used for subsequent processing.

[0072] After potential support candidates are identified, the server sends that information back to the terminal and presents the results to the user. At this time, the terminal displays a list of support candidates in dynamically generated HTML format, allowing the user to make a selection.

[0073] Users can select the most suitable supporter from the presented candidates and communicate in detail through various means. These means include real-time chat and video calls using WebRTC.

[0074] After the service is completed, users can provide feedback, and this feedback information will be collected and used to improve the system and enhance the quality of support services.

[0075] This enables the efficient and effective provision of support tailored to the needs of the local community. Users can smoothly receive the support they desire, and the system is continuously optimized.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The terminal provides the user with a needs input form. The user enters specific support details and conditions as prompts. At this point, the input is text data, and the terminal prepares to convert it to JSON format and send it to the server.

[0079] Step 2:

[0080] The server receives needs information in JSON format sent from the terminal. To analyze this input data, it launches a generative AI model. Specifically, it uses a Python script in combination with a natural language processing library (e.g., SpaCy or NLTK) to analyze the intent of the prompt and perform necessary data processing. As a result, the conditions for the best support candidate that meet the user's request are identified. This analysis result is then used for database searches.

[0081] Step 3:

[0082] The server searches the database based on the analyzed data and selects suitable support candidates. At this point, the input is the analyzed requirements data, and the output is a list of suitable support candidates. The database contains supporter profile information and the services they can provide, and the necessary records are extracted using SQL queries.

[0083] Step 4:

[0084] The server sends a list of selected support candidates to the terminal. The terminal receives this information and generates dynamic HTML to present to the user. Specifically, JavaScript is used to display the candidates in a list format on the web page, making it easy for the user to understand intuitively.

[0085] Step 5:

[0086] The user selects their preferred support candidate from the presented list. The input is the ID of the candidate selected by the user, and the terminal sends this selection data back to the server. The server receives this data and configures the communication method to initiate detailed communication between the user and the selected support candidate.

[0087] Step 6:

[0088] The server records the communication history and agreements between the user and the support candidate. This information is stored in a database and used for evaluation and feedback collection after the service is completed. The input is the communication content, and the output is the stored record data.

[0089] Step 7:

[0090] Users provide evaluation feedback after completing a service. The device sends this feedback to the server. The server collects this data and uses it for system evaluation and analysis. The analysis results can be used to identify areas for system improvement and as training data for generating AI models. The input is the feedback data, and the output is the analyzed improvement information.

[0091] (Application Example 1)

[0092] 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."

[0093] In modern society, effectively maintaining community safety is a crucial issue, but systems that adequately address the specific safety needs of individual users and utilize their feedback are insufficient. Therefore, there is a need for technology that allows users to individually input their safety requirements and receive appropriate support quickly in response.

[0094] 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.

[0095] In this invention, the server includes terminal means for inputting user requests, analysis means for analyzing the requests and proposing appropriate safety support candidates, and communication means for exchanging information with the safety support candidates. This enables the effective organization and rapid provision of safety support required by users, as well as the collection of feedback that helps improve the service.

[0096] A "terminal device" is a device used by users to input security requirements and send information to a server.

[0097] The "analysis means" is a function that analyzes the input requirements and identifies and proposes suitable safety support candidates.

[0098] "Communication means" refers to a function for exchanging information between users and potential safety support personnel, enabling real-time communication.

[0099] A "recording mechanism" is a function that stores agreements and feedback obtained based on information exchange in a database for future reference and service improvement.

[0100] "Collection methods" refer to functions that collect feedback from users after the service has been provided, and use this information to improve the system and enhance the quality of support.

[0101] A "diagnostic tool" is a function that analyzes local suitability information and proposes appropriate safety tasks based on that information.

[0102] "Safety support candidates" are candidates for staff or volunteers selected in response to user requests to ensure the safety of the community.

[0103] To implement this invention, a system is constructed in which a user inputs requests using a smartphone terminal, and safety support is provided based on those requests. The server receives these requests and uses analysis means to identify appropriate safety support candidates. This incorporates technology that uses a cloud backend such as AWS® Lambda to perform data analysis in real time. The analysis results are stored in a real-time database such as Firebase and presented to the user via communication means. Here, it is possible to conduct video calls or chats between the user and safety support candidates using services such as Twilio. The agreed-upon safety support content is stored in the database via recording means and made available for future reference.

[0104] Specifically, users input requests for nighttime safety patrols via a terminal. The server receives this information, uses analysis tools to identify local safety support candidates, and generates a list. Users select from this list and discuss the details with the safety support candidates via video call using Twilio to determine the specific safety support provided. Throughout this process, sufficient feedback is collected, and the system is improved based on the information gathered.

[0105] Examples of prompts to input into a generative AI model:

[0106] "Please explain how to immediately contact the appropriate security staff using the app if you notice any suspicious behavior in a shopping center during the daytime."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] Users enter their security requirements using their smartphones. A form is displayed where they can enter specific details such as location, time, and the type of support needed. The entered data is immediately transmitted to the server.

[0110] Step 2:

[0111] The server processes the request data received from the user using an analysis tool. The analysis tool uses a generative AI model to analyze the request content in order to list the most suitable safety support candidates for the user's request. The list of support candidates generated by this process is output, and the process proceeds to the next step.

[0112] Step 3:

[0113] The server sends a list of safety support candidates identified based on the analysis results to the user's terminal. The user selects an appropriate candidate from the presented list, and that information is sent back to the server. The system is designed to take into account the candidate's profile and past evaluations during this process.

[0114] Step 4:

[0115] The user selects a candidate, and the server then communicates using a chat or video call service. Twilio services are used for this purpose, enabling real-time communication. In this step, details such as specific service content and visit dates / times are discussed, and an agreement is reached.

[0116] Step 5:

[0117] The agreed-upon terms, determined via communication, are transmitted to a server and recorded in a database by recording devices. This recorded data is used for follow-up after service implementation and as reference information for similar cases. This process organizes and manages the service history.

[0118] Step 6:

[0119] After the service is provided, a server operates to collect feedback from users. A form for obtaining feedback information is displayed on the user's device through the collection mechanism, and the feedback collected there is used to improve the service in the future.

[0120] 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.

[0121] This invention embodies an advanced system that recognizes a user's emotional state and provides support accordingly. By incorporating an emotion engine, this system can analyze the user's emotional information and provide support services that are more suitable for the user. When a user inputs their needs, the terminal collects emotional data from the user's facial expressions, tone of voice, and entered text.

[0122] The emotion engine analyzes this emotional data in real time to recognize emotional states such as stress, joy, and anxiety. This recognized emotional information is sent to a server, where it is processed in combination with needs information by an analysis tool. The server then considers the emotional information to select the most appropriate support candidate for the user's state.

[0123] Once a support candidate is selected, the server suggests the selected candidate to the user. At this time, it can also adjust the communication approach based on emotions recognized by the emotion engine. For example, if the user is feeling nervous, the server might provide data suggesting that the candidate create a relaxed atmosphere.

[0124] Furthermore, after an agreement with a support candidate is recorded, the emotion engine is used again to collect user feedback information and use it to improve satisfaction based on emotional changes. This allows the system to individually optimize the user experience and more accurately meet user needs.

[0125] For example, when a user requests childcare support at home, the system detects signs of fatigue and stress from the user's facial expressions and proposes support tailored to alleviate those feelings. By utilizing emotional information, the system enables support that builds mutual trust through appropriate communication. As users become satisfied with the service and their emotions shift to a positive state, the quality and accuracy of feedback also improve, accelerating the overall improvement cycle of the system.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The device provides a screen for users to enter the information necessary when requesting assistance. Users enter information about their needs, such as the type of assistance they require and their preferred date and time, and grant permission to obtain emotional information. At this point, the device uses its camera and microphone to transmit the user's facial expressions and tone of voice to the emotion engine.

[0129] Step 2:

[0130] The emotion engine analyzes the user's facial expressions and voice data transmitted from the device to identify their emotional state. For example, it can determine whether the user is anxious or relaxed and send that information to the server.

[0131] Step 3:

[0132] The server combines the received needs information and emotional information with an analysis tool. Based on this information, the analysis tool selects appropriate support candidates. This selection process considers not only needs but also emotional information to match candidates' skill sets and personality aptitudes.

[0133] Step 4:

[0134] The server transmits information about selected support candidates to the terminal. The terminal displays the candidate's profile and proposal to the user. The user can then select the most suitable candidate and contact them using their preferred communication method.

[0135] Step 5:

[0136] Users coordinate specific support details and schedules with candidates via chat or video call. During this process, the device continues to transmit emotional information to the emotion engine, notifying the server of emotional changes in real time.

[0137] Step 6:

[0138] Once the user and the support candidate reach an agreement, the server uses recording mechanisms to save the agreement details to a database. Additionally, the emotion engine monitors and records the user's emotions at the time of agreement, using this information for further adaptation and service improvement.

[0139] Step 7:

[0140] After the support service is completed, the device provides the user with a feedback screen. The user enters their overall impressions and changes in feelings as feedback. The device sends this feedback to a server, where it is analyzed by the collection mechanism.

[0141] Step 8:

[0142] The server analyzes emotion-based feedback information collected by the emotion engine, using it to improve future services and respond quickly to emotions. This allows the system to provide high-quality support that reflects the user's emotions.

[0143] (Example 2)

[0144] 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".

[0145] There is a challenge in the lack of systems that can appropriately recognize the emotional state of users and provide optimal support based on that understanding. Conventional systems often provide uniform services without considering the user's emotions, and thus fail to provide effective support that meets the user's needs. Therefore, there is a need for a method that analyzes the user's emotions in real time and provides individualized support based on that information.

[0146] 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.

[0147] In this invention, the server includes a collection means for collecting data to recognize the user's emotional state, an analysis means for analyzing the collected emotional data and recognizing the emotional state, and a selection means for selecting appropriate support candidates based on the needs information and the recognized emotional information. This makes it possible to provide appropriate support that takes the user's emotions into consideration.

[0148] "Data collection means" refers to a series of methods and devices for acquiring data necessary to recognize the emotional state of a user from various sources such as voice, facial expressions, and text input.

[0149] "Analysis methods" refer to a set of technologies and algorithms used to analyze collected emotional data and recognize the emotional state of the user.

[0150] "Selection method" refers to the process and mechanism for selecting the most suitable support candidate based on user needs information and analyzed emotional information.

[0151] "Adjustment means" refers to methods and devices for adjusting the method and content of proposed support options based on emotional information in order to present them to the user in an appropriate manner.

[0152] "Communication methods" refer to technologies and devices used to exchange information and communicate between users and potential support providers.

[0153] "Recording means" refers to the process and system for saving agreements made between users and potential support providers, making them available for later reference.

[0154] "Analysis methods (for satisfaction evaluation)" refer to technologies and techniques used to evaluate user satisfaction based on collected feedback information and to utilize that information for service improvement.

[0155] "Diagnostic tools" refer to methods and techniques for evaluating aptitude information based on specific attributes of users and proposing corresponding support tasks.

[0156] This system is designed to understand the user's emotional state and provide optimal support tailored to their needs. The device first receives input from the user and uses a camera and microphone to capture the user's facial expressions and voice tone in real time. In addition, text entered by the user is also subject to emotion analysis. All of this data is sent to the emotion engine built into the device, where the emotional state is analyzed.

[0157] On the server, specialized algorithms run to analyze the collected emotional data. Specifically, image processing, voice analysis, and natural language processing technologies are used to classify the user's emotions into specific states such as stress, joy, and anxiety. The emotional information obtained in this way is sent to the server and processed in combination with needs information.

[0158] The server then uses a generative AI model to select the most appropriate support option for the user's situation. The selected support option is then presented to the user, taking emotional information into account. For example, if the server detects that the user is feeling anxious, it will approach the user with content that promotes relaxation. In this way, appropriate communication tailored to the user's emotions is achieved.

[0159] For example, if a user inputs "I'm feeling tired and need childcare support," the system will detect signs of fatigue from their facial expressions and voice, and based on that, suggest specific support to reduce stress. An example of a prompt to the generating AI model could be "What are some recommended relaxation methods for a tired new parent?" This allows the system to enhance the user experience and accurately meet their needs.

[0160] In this way, the embodiment for carrying out the invention aims to effectively analyze the user's emotions and provide personalized support based on those emotions.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] When a user inputs their needs into the device, the device uses its camera and microphone to capture the user's facial expressions and tone of voice. It also simultaneously collects the text the user enters. Input data includes audio files, image data, and text data. This data is sent to the emotion engine, which provides the foundational data for analyzing the user's emotional state.

[0164] Step 2:

[0165] The emotion engine built into the device analyzes collected facial expressions, voice tone, and text data. Specifically, it uses image processing algorithms to analyze facial expressions, voice analysis technology to classify voice tone, and natural language processing technology to analyze text content. As output, it generates labels indicating the user's emotional state (e.g., stress, joy, anxiety).

[0166] Step 3:

[0167] The analyzed emotional information is sent to the server. The server combines the user's needs information and emotional information and uses a generative AI model to select appropriate support candidates. The server's input includes emotional labels and the user's specific needs, and the output is a list of support candidates that best match the user's current emotional state.

[0168] Step 4:

[0169] The server proposes selected support options to the user's terminal. During this process, the server adjusts the suggestion method based on emotional information. Specifically, it prepares messages to encourage relaxation for users who are feeling anxious. Examples of output sent from the server include suggestions for relaxation methods and specific actions.

[0170] Step 5:

[0171] If the user agrees to the proposed support, that agreement is recorded on the device. The device stores this information and retains it as evidence of the agreement between the user and the support candidate. The agreed-upon support is recorded as input information, and it can be referenced later as needed.

[0172] Step 6:

[0173] After support is provided, the device captures the user's emotions again and sends the feedback information to the server. The server analyzes the feedback data and evaluates the user's satisfaction. This allows the system to select more accurate support candidates. The output is an improved service delivery policy.

[0174] (Application Example 2)

[0175] 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 device 14 will be referred to as the "terminal."

[0176] Online and virtual stores face the challenge of a decline in the quality of the purchasing experience due to the provision of uniform services that disregard customer emotions. It is necessary to improve purchase satisfaction by understanding customer interests and emotional states in real time and providing personalized information.

[0177] 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.

[0178] In this invention, the server includes emotion recognition means for analyzing the user's emotional state, information presentation means for presenting appropriate product information based on the analyzed emotional state, and eye-tracking means for tracking the user's gaze and evaluating their level of interest. This makes it possible to provide optimal product information tailored to the individual emotional state of each customer.

[0179] An "emotion recognition tool" is a device or system that analyzes a user's facial expressions and tone of voice to grasp their emotional state in real time.

[0180] An "information presentation means" is a device or system that displays appropriate product information and related information to the user on an interface based on the analyzed emotional state.

[0181] An "eye-tracking device" is a device or system for detecting a user's gaze and evaluating which part they are interested in.

[0182] "Related information generation means" refers to a device or system that generates related information to be provided to the user based on an evaluation of the level of interest obtained by eye-tracking means.

[0183] This invention relates to a system that analyzes a customer's emotional state in real time within a virtual store and provides personalized product information accordingly. This system includes emotion recognition means, information presentation means, and eye-tracking means.

[0184] First, devices such as smart glasses are used as "terminals" to collect user emotion data. These "terminals" have built-in cameras and microphones, which are used by emotion recognition tools to analyze facial expressions and voice tone. For the analysis, facial recognition libraries such as OpenCV and a voice tone analysis model using TENSORFLOW® are used.

[0185] Next, based on the analyzed emotional state, the "server" displays appropriate product information on the user's device through the "information display means." For example, if the user expresses surprise, the "server" can intuitively display information about new products or featured items.

[0186] Furthermore, the "terminal" tracks the user's eye movements through eye-tracking means to identify what they are interested in. Eye-tracking uses infrared sensors, such as those installed in smart glasses. For products the user shows interest in, a related information generation means generates related information and discount information, which is then communicated to the user by an "information presentation means."

[0187] As a concrete example, consider a user browsing skirts in a virtual store. If the user smiles upon seeing the skirt, the "terminal" analyzes the emotion and the "server" displays limited-time discount information for that skirt through the "information presentation means." Conversely, if the user is indifferent to the product, other related products can be presented to pique their interest. By using a generative AI model, emotional information can be collected through prompt messages like the following, enabling suggestions tailored to the user's needs.

[0188] Example of a prompt:

[0189] "Analyze customer emotional data and provide information about products they are interested in. If the emotion is positive, provide more information; if the emotion is negative, suggest related products."

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

[0191] Step 1:

[0192] The "user" wears smart glasses and walks around the virtual store. The glasses' camera and microphone activate, and the "terminal" collects data on the user's facial expressions and voice tone. This inputs raw emotional data related to the user's feelings.

[0193] Step 2:

[0194] The "device" analyzes collected facial expression data using the OpenCV library and voice tone data using a TensorFlow model. Through data processing, the "device" determines the user's emotional state (e.g., joy, indifference) and generates the result as output. At this point, the analysis results regarding the emotional state are obtained.

[0195] Step 3:

[0196] The "server" receives the results of the emotional state analysis sent from the "terminal" and inputs the prompt message into the AI ​​model based on this. Here, a prompt message is output to select product information appropriate to the emotional state. The "server" then prepares to send the selected product information to the "terminal".

[0197] Step 4:

[0198] The "terminal" tracks the user's gaze using "eye-tracking means" based on selected product information. Infrared sensors within the smart glasses collect gaze data, identifying the product the user is focusing on. As a result, information about products of interest and related data is obtained.

[0199] Step 5:

[0200] The "server" uses eye-tracking data to transmit detailed information and related information about products the user has shown interest in to the "terminal" via an information display device. At this point, discount information and detailed descriptions of the products the user is focusing on will be displayed on the "terminal." This process completes the presentation of product information to the user.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] As an embodiment of the present invention, a method for realizing a system that provides appropriate support according to the various needs of users is described below. This system is designed to be easily accessible to users and has an intuitive user interface. The central elements of the system are input means, analysis means, communication means, recording means, and collection means.

[0218] First, the terminal provides an interface that allows users to input the necessary information when requesting assistance. Users input specific needs information through the terminal, and this information is sent to the server. Upon receiving the needs information, the server processes this information using analysis tools and identifies the most suitable support candidates for the user's request.

[0219] Based on the analysis results, the server selects potential support candidates and presents that information to the user's terminal. The user can then choose from the presented support candidates and communicate in detail with the selected candidate using communication methods. This includes chat and video call functions, allowing for the arrangement of specific support details and dates / times.

[0220] Agreements reached between users and support candidates through communication are stored in a database using recording mechanisms and can be used for future reference and management. After the service is provided, collection methods are used to obtain feedback from users. Feedback is an important source of information for improving the system and enhancing the quality of the service. This allows the system to be continuously improved and to become more responsive to user needs.

[0221] For example, if a user seeking childcare support requests "weekend childcare" using their device, the system analyzes the information and displays a list of nearby seniors who can provide the relevant support. The user then selects a senior who matches their needs and completes the necessary online consultation. After the service is completed, the user can provide feedback within the system, reporting their satisfaction with the experience and areas for improvement. This entire process enables efficient and effective childcare support in the local community.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The terminal displays a screen for the user to input needs information. Once the user enters the necessary information, such as the type of support needed and the desired time, and presses the submit button, the terminal sends that information to the server.

[0225] Step 2:

[0226] The server receives needs information transmitted from the terminal and passes it to the analysis unit. Based on the received information, the analysis unit searches the database for suitable support candidates and identifies the most appropriate candidate.

[0227] Step 3:

[0228] The server generates a list of potential support candidates identified by the analysis tool and sends this information to the terminal. The terminal displays the candidate information in a list format for the user to easily review.

[0229] Step 4:

[0230] The user selects a suitable candidate from the list of support candidates displayed on the device and contacts that candidate using a communication method. The device provides an interface that enables chat and video calls, and helps in coordinating specific support details and schedules.

[0231] Step 5:

[0232] After an agreement is reached, the user sends the details based on the agreement to the server via their device. The server uses recording means to store the details of the agreement in a database for future reference.

[0233] Step 6:

[0234] After the support service is completed, the device provides the user with a feedback input screen. The user enters their satisfaction level and areas for improvement regarding the support provided, and the device sends this feedback information to the server.

[0235] Step 7:

[0236] The server analyzes feedback information collected through various means and uses the results to improve the system and services. This continuous feedback loop enables the implementation of measures to improve system quality and user satisfaction.

[0237] (Example 1)

[0238] 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."

[0239] In modern society, providing timely and appropriate support that meets the diverse needs of users is a crucial challenge. In particular, there is a need for a system that can quickly and accurately match users with the support they require and facilitate smooth communication. Furthermore, effectively collecting and analyzing user feedback is essential for continuously improving the quality of support.

[0240] 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.

[0241] In this invention, the server includes means for inputting user demand information, means for analyzing the demand information using a generation AI model to identify appropriate support candidates, and means for presenting the selected support candidates to the user. This makes it possible to respond quickly and appropriately to user needs and improve the accuracy and efficiency of support matching.

[0242] A "user" refers to an individual or group that uses the system to request services or support.

[0243] "Demand information" refers to specific information about the content and conditions of support entered by the user.

[0244] A "generative AI model" is an artificial intelligence model used to analyze user input information and understand its intent.

[0245] A "support candidate" is an individual or organization that is selected based on user needs information and has the potential to provide appropriate support.

[0246] "Communication methods" refer to technologies and tools used for detailed information exchange and communication between users and potential support providers.

[0247] "Agreement" refers to the service details and conditions agreed upon between the user and the support candidate.

[0248] "Evaluation information" refers to feedback and opinions provided by users after the service has been implemented.

[0249] "Aptitude information" refers to information about the characteristics and abilities of a specific group or individual.

[0250] "Support work" refers to specific support activities and tasks provided according to the user's needs.

[0251] This invention is a support system for responding quickly and accurately to user needs, and its hardware includes terminal devices, server devices, and a communication network. The software includes a platform for managing the entire system, a generative AI model, and a natural language processing library. This section describes the specific use of hardware and software.

[0252] The device provides a user interface for users to input needs information. This interface is designed using HTML / CSS / JavaScript and includes a form. Users can input specific prompts in text format, such as "weekend childcare supervision."

[0253] The server receives needs information sent from the terminal and analyzes it using a generative AI model. This analysis uses Python and natural language processing libraries (e.g., SpaCy or NLTK) to understand the user's intent. The analysis results identify the most suitable support candidates and are used for subsequent processing.

[0254] After potential support candidates are identified, the server sends that information back to the terminal and presents the results to the user. At this time, the terminal displays a list of support candidates in dynamically generated HTML format, allowing the user to make a selection.

[0255] Users can select the most suitable supporter from the presented candidates and communicate in detail through various means. These means include real-time chat and video calls using WebRTC.

[0256] After the service is completed, users can provide feedback, and this feedback information will be collected and used to improve the system and enhance the quality of support services.

[0257] This enables the efficient and effective provision of support tailored to the needs of the local community. Users can smoothly receive the support they desire, and the system is continuously optimized.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The terminal provides the user with a needs input form. The user enters specific support details and conditions as prompts. At this point, the input is text data, and the terminal prepares to convert it to JSON format and send it to the server.

[0261] Step 2:

[0262] The server receives needs information in JSON format sent from the terminal. To analyze this input data, it launches a generative AI model. Specifically, it uses a Python script in combination with a natural language processing library (e.g., SpaCy or NLTK) to analyze the intent of the prompt and perform necessary data processing. As a result, the conditions for the best support candidate that meet the user's request are identified. This analysis result is then used for database searches.

[0263] Step 3:

[0264] The server searches the database based on the analyzed data and selects suitable support candidates. At this point, the input is the analyzed requirements data, and the output is a list of suitable support candidates. The database contains supporter profile information and the services they can provide, and the necessary records are extracted using SQL queries.

[0265] Step 4:

[0266] The server sends a list of selected support candidates to the terminal. The terminal receives this information and generates dynamic HTML to present to the user. Specifically, JavaScript is used to display the candidates in a list format on the web page, making it easy for the user to understand intuitively.

[0267] Step 5:

[0268] The user selects their preferred support candidate from the presented list. The input is the ID of the candidate selected by the user, and the terminal sends this selection data back to the server. The server receives this data and configures the communication method to initiate detailed communication between the user and the selected support candidate.

[0269] Step 6:

[0270] The server records the communication history and agreements between the user and the support candidate. This information is stored in a database and used for evaluation and feedback collection after the service is completed. The input is the communication content, and the output is the stored record data.

[0271] Step 7:

[0272] Users provide evaluation feedback after completing a service. The device sends this feedback to the server. The server collects this data and uses it for system evaluation and analysis. The analysis results can be used to identify areas for system improvement and as training data for generating AI models. The input is the feedback data, and the output is the analyzed improvement information.

[0273] (Application Example 1)

[0274] 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."

[0275] In modern society, effectively maintaining community safety is a crucial issue, but systems that adequately address the specific safety needs of individual users and utilize their feedback are insufficient. Therefore, there is a need for technology that allows users to individually input their safety requirements and receive appropriate support quickly in response.

[0276] 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.

[0277] In this invention, the server includes terminal means for inputting user requests, analysis means for analyzing the requests and proposing appropriate safety support candidates, and communication means for exchanging information with the safety support candidates. This enables the effective organization and rapid provision of safety support required by users, as well as the collection of feedback that helps improve the service.

[0278] The "terminal means" is a device for a user to input requirements related to safety and transmit information to the server.

[0279] The "analysis means" is a function for analyzing the input requirements and identifying and proposing suitable safety support candidates.

[0280] The "communication means" is a function for exchanging information between the user and the safety support candidates, enabling real-time communication.

[0281] The "recording means" is a function for storing the content of the agreement and feedback obtained based on information exchange in a database for future reference and service improvement.

[0282] The "collection means" is a function for collecting opinion information from users after service provision to contribute to system improvement and quality enhancement of support.

[0283] The "diagnosis means" is a function for analyzing local suitability information and proposing appropriate safety tasks based on that information.

[0284] The "safety support candidate" is a candidate for staff or volunteers to ensure local safety selected in response to the user's request.

[0285] To implement this invention, a system is constructed that uses a smartphone terminal for users to input requirements and provides safety support based on them. The server receives these requirements and uses analysis means to identify appropriate safety support candidates. This incorporates technologies that use cloud backends such as AWS Lambda to perform data analysis in real time. The analysis results are stored in a real-time database such as Firebase and presented to the user through communication means. Here, it is possible to conduct video calls and chats between the user and safety support candidates using services such as Twilio. The agreed-upon safety support content is stored in the database through recording means for future reference.

[0286] Specifically, the user inputs a request for night-time safety patrol from the terminal. The server receives this information, identifies local safety support candidates through analysis means, and generates a list. The user makes a selection from this list and determines the specific safety support content by discussing details with the safety support candidate via a video call using Twilio. In this process, sufficient opinion information is collected, and the system is improved based on the information collected by the collection means.

[0287] Examples of prompt texts to input into the generative AI model:

[0288] "When suspicious behavior is discovered at a shopping center during the day, please explain how to immediately contact the appropriate security staff using the app."

[0289] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0290] Step 1:

[0291] The user uses a smartphone terminal to input requirements regarding their own safety. At this time, a form for inputting specific locations, times, and required support content is displayed. The input data is immediately sent to the server.

[0292] Step 2:

[0293] The server processes the request data received from the user using an analysis tool. The analysis tool uses a generative AI model to analyze the request content in order to list the most suitable safety support candidates for the user's request. The list of support candidates generated by this process is output, and the process proceeds to the next step.

[0294] Step 3:

[0295] The server sends a list of safety support candidates identified based on the analysis results to the user's terminal. The user selects an appropriate candidate from the presented list, and that information is sent back to the server. The system is designed to take into account the candidate's profile and past evaluations during this process.

[0296] Step 4:

[0297] The user selects a candidate, and the server then communicates using a chat or video call service. Twilio services are used for this purpose, enabling real-time communication. In this step, details such as specific service content and visit dates / times are discussed, and an agreement is reached.

[0298] Step 5:

[0299] The agreed-upon terms, determined via communication, are transmitted to a server and recorded in a database by recording means. This recorded data is used for follow-up after service implementation and as reference information for similar cases. This process organizes and manages the service history.

[0300] Step 6:

[0301] After the service is provided, a server operates to collect feedback from users. A form for obtaining feedback information is displayed on the user's device through the collection mechanism, and the feedback collected there is used to improve the service in the future.

[0302] 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.

[0303] This invention embodies an advanced system that recognizes a user's emotional state and provides support accordingly. By incorporating an emotion engine, this system can analyze the user's emotional information and provide support services that are more suitable for the user. When a user inputs their needs, the terminal collects emotional data from the user's facial expressions, tone of voice, and entered text.

[0304] The emotion engine analyzes this emotional data in real time to recognize emotional states such as stress, joy, and anxiety. This recognized emotional information is sent to a server, where it is processed in combination with needs information by an analysis tool. The server then considers the emotional information to select the most appropriate support candidate for the user's state.

[0305] Once a support candidate is selected, the server suggests the selected candidate to the user. At this time, it can also adjust the communication approach based on emotions recognized by the emotion engine. For example, if the user is feeling nervous, the server might provide data suggesting that the candidate create a relaxed atmosphere.

[0306] Furthermore, after reaching an agreement with the support candidate, the emotion engine is utilized again to collect the user's feedback information and use it to improve satisfaction considering the emotional changes. This enables the system to optimize the user experience individually and more accurately meet the user's needs.

[0307] For example, when a user requests childcare support at home, the system senses signs of fatigue and stress from the user's expression and proposes support content that takes these into account. By leveraging emotional information, it is possible to realize support that builds a trust relationship between both parties through appropriate communication. As the user becomes satisfied with the service and their emotion shifts positively, the quality and accuracy of the feedback also improve, promoting the overall improvement cycle of the system.

[0308] The processing flow will be described below.

[0309] Step 1:

[0310] The terminal provides a screen for the user to input the information necessary when requesting support. The user inputs need information such as the content of support and the desired date and time, and gives permission to acquire emotional information. At this point, the terminal uses the camera and microphone to transmit the user's facial expression and voice tone to the emotion engine.

[0311] Step 2:

[0312] The emotion engine analyzes the user's facial expression and voice data transmitted from the terminal and identifies the emotional state. For example, it determines whether the user is anxious or relaxed and transmits that information to the server.

[0313] Step 3:

[0314] The server combines the received needs information and emotional information with an analysis tool. Based on this information, the analysis tool selects appropriate support candidates. This selection process considers not only needs but also emotional information to match candidates' skill sets and personality aptitudes.

[0315] Step 4:

[0316] The server transmits information about selected support candidates to the terminal. The terminal displays the candidate's profile and proposal to the user. The user can then select the most suitable candidate and contact them using their preferred communication method.

[0317] Step 5:

[0318] Users coordinate specific support details and schedules with candidates via chat or video call. During this process, the device continues to transmit emotional information to the emotion engine, notifying the server of emotional changes in real time.

[0319] Step 6:

[0320] Once the user and the support candidate reach an agreement, the server uses recording mechanisms to save the agreement details to a database. Additionally, the emotion engine monitors and records the user's emotions at the time of agreement, using this information for further adaptation and service improvement.

[0321] Step 7:

[0322] After the support service is completed, the device provides the user with a feedback screen. The user enters their overall impressions and changes in feelings as feedback. The device sends this feedback to a server, where it is analyzed by the collection mechanism.

[0323] Step 8:

[0324] The server analyzes emotion-based feedback information collected by the emotion engine, using it to improve future services and respond quickly to emotions. This allows the system to provide high-quality support that reflects the user's emotions.

[0325] (Example 2)

[0326] 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".

[0327] There is a challenge in the lack of systems that can appropriately recognize the emotional state of users and provide optimal support based on that understanding. Conventional systems often provide uniform services without considering the user's emotions, and thus fail to provide effective support that meets the user's needs. Therefore, there is a need for a method that analyzes the user's emotions in real time and provides individualized support based on that information.

[0328] 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.

[0329] In this invention, the server includes a collection means for collecting data to recognize the user's emotional state, an analysis means for analyzing the collected emotional data and recognizing the emotional state, and a selection means for selecting appropriate support candidates based on the needs information and the recognized emotional information. This makes it possible to provide appropriate support that takes the user's emotions into consideration.

[0330] "Data collection means" refers to a series of methods and devices for acquiring data necessary to recognize the emotional state of a user from various sources such as voice, facial expressions, and text input.

[0331] "Analysis methods" refer to a set of technologies and algorithms used to analyze collected emotional data and recognize the emotional state of the user.

[0332] "Selection methods" refer to the process and mechanisms for selecting the most suitable support candidates based on user needs information and analyzed emotional information.

[0333] "Adjustment means" refers to methods and devices for adjusting the method and content of proposed support options based on emotional information in order to present them to the user in an appropriate manner.

[0334] "Communication methods" refer to technologies and devices used to exchange information and communicate between users and potential support providers.

[0335] "Recording means" refers to the process and system for saving agreements made between users and potential support providers, making them available for later reference.

[0336] "Analysis methods (for satisfaction evaluation)" refer to technologies and techniques used to evaluate user satisfaction based on collected feedback information and to utilize that information for service improvement.

[0337] "Diagnostic tools" refer to methods and techniques for evaluating aptitude information based on specific attributes of users and proposing corresponding support tasks.

[0338] This system is designed to understand the user's emotional state and provide optimal support tailored to their needs. The device first receives input from the user and uses a camera and microphone to capture the user's facial expressions and voice tone in real time. In addition, text entered by the user is also subject to emotion analysis. All of this data is sent to the emotion engine built into the device, where the emotional state is analyzed.

[0339] On the server, specialized algorithms run to analyze the collected emotional data. Specifically, image processing, voice analysis, and natural language processing technologies are used to classify the user's emotions into specific states such as stress, joy, and anxiety. The emotional information obtained in this way is sent to the server and processed in combination with needs information.

[0340] The server then uses a generative AI model to select the most appropriate support option for the user's situation. The selected support option is then presented to the user, taking emotional information into account. For example, if the server detects that the user is feeling anxious, it will approach the user with content that promotes relaxation. In this way, appropriate communication tailored to the user's emotions is achieved.

[0341] For example, if a user inputs "I'm feeling tired and need childcare support," the system will detect signs of fatigue from their facial expressions and voice, and based on that, suggest specific support to reduce stress. An example of a prompt to the generating AI model could be "What are some recommended relaxation methods for a tired new parent?" This allows the system to enhance the user experience and accurately meet their needs.

[0342] In this way, the embodiment for carrying out the invention aims to effectively analyze the user's emotions and provide personalized support based on those emotions.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] When a user inputs their needs into the device, the device uses its camera and microphone to capture the user's facial expressions and tone of voice. It also simultaneously collects the text the user enters. Input data includes audio files, image data, and text data. This data is sent to the emotion engine, which provides the foundational data for analyzing the user's emotional state.

[0346] Step 2:

[0347] The emotion engine built into the device analyzes collected facial expressions, voice tone, and text data. Specifically, it uses image processing algorithms to analyze facial expressions, voice analysis technology to classify voice tone, and natural language processing technology to analyze text content. As output, it generates labels indicating the user's emotional state (e.g., stress, joy, anxiety).

[0348] Step 3:

[0349] The analyzed emotional information is sent to the server. The server combines the user's needs information and emotional information and uses a generative AI model to select appropriate support candidates. The server's input includes emotional labels and the user's specific needs, and the output is a list of support candidates that best match the user's current emotional state.

[0350] Step 4:

[0351] The server proposes selected support options to the user's terminal. During this process, the server adjusts the suggestion method based on emotional information. Specifically, it prepares messages to encourage relaxation for users who are feeling anxious. Examples of output sent from the server include suggestions for relaxation methods and specific actions.

[0352] Step 5:

[0353] If the user agrees to the proposed support, that agreement is recorded on the device. The device stores this information and retains it as evidence of the agreement between the user and the support candidate. The agreed-upon support is recorded as input information, and it is recorded as output information that can be referenced later as needed.

[0354] Step 6:

[0355] After support is provided, the device captures the user's emotions again and sends the feedback information to the server. The server analyzes the feedback data and evaluates the user's satisfaction. This allows the system to select more accurate support candidates. The output is an improved service delivery policy.

[0356] (Application Example 2)

[0357] 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."

[0358] Online and virtual stores face the challenge of a decline in the quality of the purchasing experience due to the provision of uniform services that disregard customer emotions. It is necessary to improve purchase satisfaction by understanding customer interests and emotional states in real time and providing personalized information.

[0359] 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.

[0360] In this invention, the server includes emotion recognition means for analyzing the user's emotional state, information presentation means for presenting appropriate product information based on the analyzed emotional state, and eye-tracking means for tracking the user's gaze and evaluating their level of interest. This makes it possible to provide optimal product information tailored to the individual emotional state of each customer.

[0361] An "emotion recognition tool" is a device or system that analyzes a user's facial expressions and tone of voice to grasp their emotional state in real time.

[0362] "Information presentation means" refers to a device or system for displaying appropriate product information and related information to the user on an interface, based on the analyzed emotional state.

[0363] An "eye-tracking device" is a device or system for detecting a user's gaze and evaluating which part they are interested in.

[0364] "Related information generation means" refers to a device or system that generates related information to be provided to the user based on an evaluation of the level of interest obtained by eye-tracking means.

[0365] This invention relates to a system that analyzes a customer's emotional state in real time within a virtual store and provides personalized product information accordingly. This system includes emotion recognition means, information presentation means, and eye-tracking means.

[0366] First, devices such as smart glasses are used as "terminals" to collect user emotion data. These "terminals" have built-in cameras and microphones, which are used by emotion recognition tools to analyze facial expressions and voice tone. For the analysis, facial recognition libraries such as OpenCV and voice tone analysis models using TensorFlow are used.

[0367] Next, based on the analyzed emotional state, the "server" displays appropriate product information on the user's device through the "information display means." For example, if the user expresses surprise, the "server" can intuitively display information on new products or featured items.

[0368] Furthermore, the "terminal" tracks the user's eye movements through eye-tracking means to identify what they are interested in. Eye-tracking uses infrared sensors, such as those installed in smart glasses. For products the user shows interest in, a related information generation means generates related information and discount information, which is then communicated to the user by an "information presentation means."

[0369] As a concrete example, consider a user browsing skirts in a virtual store. If the user smiles upon seeing the skirt, the "terminal" analyzes the emotion and the "server" displays limited-time discount information for that skirt through the "information presentation means." Conversely, if the user is indifferent to the product, other related products can be presented to pique their interest. By using a generative AI model, emotional information can be collected through prompt messages like the following, enabling suggestions tailored to the user's needs.

[0370] Example of a prompt:

[0371] "Analyze customer emotional data and provide information about products they are interested in. If the emotion is positive, provide more information; if the emotion is negative, suggest related products."

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The "user" wears smart glasses and walks around the virtual store. The glasses' camera and microphone activate, and the "terminal" collects data on the user's facial expressions and voice tone. This inputs raw emotional data related to the user's feelings.

[0375] Step 2:

[0376] The "device" analyzes collected facial expression data using the OpenCV library and voice tone data using a TensorFlow model. Through data processing, the "device" determines the user's emotional state (e.g., joy, indifference) and generates the result as output. At this point, the analysis results regarding the emotional state are obtained.

[0377] Step 3:

[0378] The "server" receives the results of the emotional state analysis sent from the "terminal" and inputs the prompt message into the AI ​​model based on this. Here, a prompt message is output to select product information appropriate to the emotional state. The "server" then prepares to send the selected product information to the "terminal".

[0379] Step 4:

[0380] The "terminal" tracks the user's gaze using "eye-tracking means" based on selected product information. Infrared sensors within the smart glasses collect gaze data, identifying the product the user is focusing on. As a result, information about products of interest and related data is obtained.

[0381] Step 5:

[0382] The "server" uses eye-tracking data to transmit detailed information and related information about products the user has shown interest in to the "terminal" via an information display device. At this point, discount information and detailed descriptions of the products the user is focusing on will be displayed on the "terminal." This process completes the presentation of product information to the user.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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".

[0399] As an embodiment of the present invention, a method for realizing a system that provides appropriate support according to the various needs of users is described below. This system is designed to be easily accessible to users and has an intuitive user interface. The central elements of the system are input means, analysis means, communication means, recording means, and collection means.

[0400] First, the terminal provides an interface that allows users to input the necessary information when requesting assistance. Users input specific needs information through the terminal, and this information is sent to the server. Upon receiving the needs information, the server processes this information using analysis tools and identifies the most suitable support candidates for the user's request.

[0401] Based on the analysis results, the server selects potential support candidates and presents that information to the user's terminal. The user can then choose from the presented support candidates and communicate in detail with the selected candidate using communication methods. This includes chat and video call functions, allowing for the arrangement of specific support details and dates / times.

[0402] Agreements reached between users and support candidates through communication are stored in a database using recording mechanisms and can be used for future reference and management. After the service is provided, collection methods are used to obtain feedback from users. Feedback is an important source of information for improving the system and enhancing the quality of the service. This allows the system to be continuously improved and to become more responsive to user needs.

[0403] For example, if a user seeking childcare support requests "weekend childcare" using their device, the system analyzes the information and displays a list of nearby seniors who can provide the relevant support. The user then selects a senior who matches their needs and completes the necessary online consultation. After the service is completed, the user can provide feedback within the system, reporting their satisfaction with the experience and areas for improvement. This entire process enables efficient and effective childcare support in the local community.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The terminal displays a screen for the user to input needs information. Once the user enters the necessary information, such as the type of support needed and the desired time, and presses the submit button, the terminal sends that information to the server.

[0407] Step 2:

[0408] The server receives needs information transmitted from the terminal and passes it to the analysis unit. Based on the received information, the analysis unit searches the database for suitable support candidates and identifies the most appropriate candidate.

[0409] Step 3:

[0410] The server generates a list of potential support candidates identified by the analysis tool and sends this information to the terminal. The terminal displays the candidate information in a list format for the user to easily review.

[0411] Step 4:

[0412] The user selects a suitable candidate from the list of support candidates displayed on the device and contacts that candidate using a communication method. The device provides an interface that enables chat and video calls, and helps in coordinating specific support details and schedules.

[0413] Step 5:

[0414] After an agreement is reached, the user sends the details based on the agreement to the server via their device. The server uses recording means to store the details of the agreement in a database for future reference.

[0415] Step 6:

[0416] After the support service is completed, the device provides the user with a feedback input screen. The user enters their satisfaction level and areas for improvement regarding the support provided, and the device sends this feedback information to the server.

[0417] Step 7:

[0418] The server analyzes feedback information collected through various means and uses the results to improve the system and services. This continuous feedback loop enables the implementation of measures to improve system quality and user satisfaction.

[0419] (Example 1)

[0420] 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."

[0421] In modern society, providing timely and appropriate support that meets the diverse needs of users is a crucial challenge. In particular, there is a need for a system that can quickly and accurately match users with the support they require and facilitate smooth communication. Furthermore, effectively collecting and analyzing user feedback is essential for continuously improving the quality of support.

[0422] 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.

[0423] In this invention, the server includes means for inputting user demand information, means for analyzing the demand information using a generation AI model to identify appropriate support candidates, and means for presenting the selected support candidates to the user. This makes it possible to respond quickly and appropriately to user needs and improve the accuracy and efficiency of support matching.

[0424] A "user" refers to an individual or group that uses the system to request services or support.

[0425] "Demand information" refers to specific information about the content and conditions of support entered by the user.

[0426] A "generative AI model" is an artificial intelligence model used to analyze user input information and understand its intent.

[0427] A "support candidate" is an individual or organization that is selected based on user needs information and has the potential to provide appropriate support.

[0428] "Communication methods" refer to technologies and tools used for detailed information exchange and communication between users and potential support providers.

[0429] "Agreement" refers to the service details and conditions agreed upon between the user and the support candidate.

[0430] "Evaluation information" refers to feedback and opinions provided by users after the service has been implemented.

[0431] "Aptitude information" refers to information about the characteristics and abilities of a specific group or individual.

[0432] "Support work" refers to specific support activities and tasks provided according to the user's needs.

[0433] This invention is a support system for responding quickly and accurately to user needs, and its hardware includes terminal devices, server devices, and a communication network. The software includes a platform for managing the entire system, a generative AI model, and a natural language processing library. This section describes the specific use of hardware and software.

[0434] The device provides a user interface for users to input needs information. This interface is designed using HTML / CSS / JavaScript and includes a form. Users can input specific prompts in text format, such as "weekend childcare supervision."

[0435] The server receives needs information sent from the terminal and analyzes it using a generative AI model. This analysis uses Python and natural language processing libraries (e.g., SpaCy or NLTK) to understand the user's intent. The analysis results identify the most suitable support candidates and are used for subsequent processing.

[0436] After potential support candidates are identified, the server sends that information back to the terminal and presents the results to the user. At this time, the terminal displays a list of support candidates in dynamically generated HTML format, allowing the user to make a selection.

[0437] Users can select the most suitable supporter from the presented candidates and communicate in detail through various means. These means include real-time chat and video calls using WebRTC.

[0438] After the service is completed, users can provide feedback, and this feedback information will be collected and used to improve the system and enhance the quality of support services.

[0439] This enables the efficient and effective provision of support tailored to the needs of the local community. Users can smoothly receive the support they desire, and the system is continuously optimized.

[0440] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0441] Step 1:

[0442] The terminal provides the user with a needs input form. The user enters specific support details and conditions as prompts. At this point, the input is text data, and the terminal prepares to convert it to JSON format and send it to the server.

[0443] Step 2:

[0444] The server receives needs information in JSON format sent from the terminal. To analyze this input data, it launches a generative AI model. Specifically, it uses a Python script in combination with a natural language processing library (e.g., SpaCy or NLTK) to analyze the intent of the prompt and perform necessary data processing. As a result, the conditions for the best support candidate that meet the user's request are identified. This analysis result is then used for database searches.

[0445] Step 3:

[0446] The server searches the database based on the analyzed data and selects suitable support candidates. At this point, the input is the analyzed requirements data, and the output is a list of suitable support candidates. The database contains supporter profile information and the services they can provide, and the necessary records are extracted using SQL queries.

[0447] Step 4:

[0448] The server sends a list of selected support candidates to the terminal. The terminal receives this information and generates dynamic HTML to present to the user. Specifically, JavaScript is used to display the candidates in a list format on the web page, making it easy for the user to understand intuitively.

[0449] Step 5:

[0450] The user selects their preferred support candidate from the presented list. The input is the ID of the candidate selected by the user, and the terminal sends this selection data back to the server. The server receives this data and configures the communication method to initiate detailed communication between the user and the selected support candidate.

[0451] Step 6:

[0452] The server records the communication history and agreements between the user and the support candidate. This information is stored in a database and used for evaluation and feedback collection after the service is completed. The input is the communication content, and the output is the stored record data.

[0453] Step 7:

[0454] Users provide evaluation feedback after completing a service. The device sends this feedback to the server. The server collects this data and uses it for system evaluation and analysis. The analysis results can be used to identify areas for system improvement and as training data for generating AI models. The input is the feedback data, and the output is the analyzed improvement information.

[0455] (Application Example 1)

[0456] 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."

[0457] In modern society, effectively maintaining community safety is a crucial issue, but systems that adequately address the specific safety needs of individual users and utilize their feedback are insufficient. Therefore, there is a need for technology that allows users to individually input their safety requirements and receive appropriate support quickly in response.

[0458] 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.

[0459] In this invention, the server includes terminal means for inputting user requests, analysis means for analyzing the requests and proposing appropriate safety support candidates, and communication means for exchanging information with the safety support candidates. This enables the effective organization and rapid provision of safety support required by users, as well as the collection of feedback that helps improve the service.

[0460] A "terminal device" is a device used by users to input security requirements and transmit information to a server.

[0461] The "analysis means" is a function that analyzes the input requirements and identifies and proposes suitable safety support candidates.

[0462] "Communication means" refers to a function for exchanging information between users and potential safety support personnel, enabling real-time communication.

[0463] A "recording mechanism" is a function that stores agreements and feedback obtained based on information exchange in a database for future reference and service improvement.

[0464] "Collection methods" refer to functions that collect feedback from users after the service has been provided, and use this information to improve the system and enhance the quality of support.

[0465] A "diagnostic tool" is a function that analyzes local suitability information and proposes appropriate safety tasks based on that information.

[0466] "Safety support candidates" are candidates for staff or volunteers selected in response to user requests to ensure the safety of the community.

[0467] To implement this invention, a system is constructed in which a user inputs requests using a smartphone terminal, and safety support is provided based on those requests. The server receives these requests and uses analysis means to identify appropriate safety support candidates. This incorporates technology that performs data analysis in real time using a cloud backend such as AWS Lambda. The analysis results are stored in a real-time database such as Firebase and presented to the user via communication means. Here, it is possible to conduct video calls or chats between the user and safety support candidates using services such as Twilio. The agreed-upon safety support content is stored in the database via recording means and made available for future reference.

[0468] Specifically, users input requests for nighttime safety patrols via a terminal. The server receives this information, uses analysis tools to identify local safety support candidates, and generates a list. Users select from this list and discuss the details with the safety support candidates via video call using Twilio to determine the specific safety support provided. Throughout this process, sufficient feedback is collected, and the system is improved based on the information gathered.

[0469] Examples of prompts to input into a generative AI model:

[0470] "Please explain how to immediately contact the appropriate security staff using the app if you notice any suspicious behavior in a shopping center during the daytime."

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] Users enter their security requirements using their smartphones. A form is displayed where they can enter specific details such as location, time, and the type of support needed. The entered data is immediately transmitted to the server.

[0474] Step 2:

[0475] The server processes the request data received from the user using an analysis tool. The analysis tool uses a generative AI model to analyze the request content in order to list the most suitable safety support candidates for the user's request. The list of support candidates generated by this process is output, and the process proceeds to the next step.

[0476] Step 3:

[0477] The server sends a list of safety support candidates identified based on the analysis results to the user's terminal. The user selects an appropriate candidate from the presented list, and that information is sent back to the server. The system is designed to take into account the candidate's profile and past evaluations during this process.

[0478] Step 4:

[0479] The user selects a candidate, and the server then communicates using a chat or video call service. Twilio services are used for this purpose, enabling real-time communication. In this step, details such as specific service content and visit dates / times are discussed, and an agreement is reached.

[0480] Step 5:

[0481] The agreed-upon terms, determined via communication, are transmitted to a server and recorded in a database by recording devices. This recorded data is used for follow-up after service implementation and as reference information for similar cases. This process organizes and manages the service history.

[0482] Step 6:

[0483] After the service is provided, a server operates to collect feedback from users. A form for obtaining feedback information is displayed on the user's device through the collection mechanism, and the feedback collected there is used to improve the service in the future.

[0484] 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.

[0485] This invention embodies an advanced system that recognizes a user's emotional state and provides support accordingly. By incorporating an emotion engine, this system can analyze the user's emotional information and provide support services that are more suitable for the user. When a user inputs their needs, the terminal collects emotional data from the user's facial expressions, tone of voice, and entered text.

[0486] The emotion engine analyzes this emotional data in real time to recognize emotional states such as stress, joy, and anxiety. This recognized emotional information is sent to a server, where it is processed in combination with needs information by an analysis tool. The server then considers the emotional information to select the most appropriate support candidate for the user's state.

[0487] Once a support candidate is selected, the server suggests the selected candidate to the user. At this time, it can also adjust the communication approach based on emotions recognized by the emotion engine. For example, if the user is feeling nervous, the server might provide data suggesting that the candidate create a relaxed atmosphere.

[0488] Furthermore, after an agreement with a support candidate is recorded, the emotion engine is used again to collect user feedback information and use it to improve satisfaction based on emotional changes. This allows the system to individually optimize the user experience and more accurately meet user needs.

[0489] For example, when a user requests childcare support at home, the system detects signs of fatigue and stress from the user's facial expressions and proposes support tailored to alleviate those feelings. By utilizing emotional information, the system enables support that builds mutual trust through appropriate communication. As users become satisfied with the service and their emotions shift to a positive state, the quality and accuracy of feedback also improve, accelerating the overall improvement cycle of the system.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] The device provides a screen for users to enter the information necessary when requesting assistance. Users enter information about their needs, such as the type of assistance they require and their preferred date and time, and grant permission to obtain emotional information. At this point, the device uses its camera and microphone to transmit the user's facial expressions and tone of voice to the emotion engine.

[0493] Step 2:

[0494] The emotion engine analyzes the user's facial expressions and voice data transmitted from the device to identify their emotional state. For example, it can determine whether the user is anxious or relaxed and send that information to the server.

[0495] Step 3:

[0496] The server combines the received needs information and emotional information with an analysis tool. Based on this information, the analysis tool selects appropriate support candidates. This selection process considers not only needs but also emotional information to match candidates' skill sets and personality aptitudes.

[0497] Step 4:

[0498] The server transmits information about selected support candidates to the terminal. The terminal displays the candidate's profile and proposal to the user. The user can then select the most suitable candidate and contact them using their preferred communication method.

[0499] Step 5:

[0500] Users coordinate specific support details and schedules with candidates via chat or video call. During this process, the device continues to transmit emotional information to the emotion engine, notifying the server of emotional changes in real time.

[0501] Step 6:

[0502] Once the user and the support candidate reach an agreement, the server uses recording mechanisms to save the agreement details to a database. Additionally, the emotion engine monitors and records the user's emotions at the time of agreement, using this information for further adaptation and service improvement.

[0503] Step 7:

[0504] After the support service is completed, the device provides the user with a feedback screen. The user enters their overall impressions and changes in feelings as feedback. The device sends this feedback to a server, where it is analyzed by the collection mechanism.

[0505] Step 8:

[0506] The server analyzes emotion-based feedback information collected by the emotion engine, using it to improve future services and respond quickly to emotions. This allows the system to provide high-quality support that reflects the user's emotions.

[0507] (Example 2)

[0508] 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."

[0509] There is a challenge in the lack of systems that can appropriately recognize the emotional state of users and provide optimal support based on that understanding. Conventional systems often provide uniform services without considering the user's emotions, and thus fail to provide effective support that meets the user's needs. Therefore, there is a need for a method that analyzes the user's emotions in real time and provides individualized support based on that information.

[0510] 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.

[0511] In this invention, the server includes a collection means for collecting data to recognize the user's emotional state, an analysis means for analyzing the collected emotional data and recognizing the emotional state, and a selection means for selecting appropriate support candidates based on the needs information and the recognized emotional information. This makes it possible to provide appropriate support that takes the user's emotions into consideration.

[0512] "Data collection means" refers to a series of methods and devices for acquiring data necessary to recognize the emotional state of a user from various sources such as voice, facial expressions, and text input.

[0513] "Analysis methods" refer to a set of technologies and algorithms used to analyze collected emotional data and recognize the emotional state of the user.

[0514] "Selection methods" refer to the process and mechanisms for selecting the most suitable support candidates based on user needs information and analyzed emotional information.

[0515] "Adjustment means" refers to methods and devices for adjusting the method and content of proposed support options based on emotional information in order to present them to the user in an appropriate manner.

[0516] "Communication methods" refer to technologies and devices used to exchange information and communicate between users and potential support providers.

[0517] "Recording means" refers to the process and system for saving agreements made between users and potential support providers, making them available for later reference.

[0518] "Analysis methods (for satisfaction evaluation)" refer to technologies and techniques used to evaluate user satisfaction based on collected feedback information and to utilize that information for service improvement.

[0519] "Diagnostic tools" refer to methods and techniques for evaluating aptitude information based on specific attributes of users and proposing corresponding support tasks.

[0520] This system is designed to understand the user's emotional state and provide optimal support tailored to their needs. The device first receives input from the user and uses a camera and microphone to capture the user's facial expressions and voice tone in real time. In addition, text entered by the user is also subject to emotion analysis. All of this data is sent to the emotion engine built into the device, where the emotional state is analyzed.

[0521] On the server, specialized algorithms run to analyze the collected emotional data. Specifically, image processing, voice analysis, and natural language processing technologies are used to classify the user's emotions into specific states such as stress, joy, and anxiety. The emotional information obtained in this way is sent to the server and processed in combination with needs information.

[0522] The server then uses a generative AI model to select the most appropriate support option for the user's situation. The selected support option is then presented to the user, taking emotional information into account. For example, if the server detects that the user is feeling anxious, it will approach the user with content that promotes relaxation. In this way, appropriate communication tailored to the user's emotions is achieved.

[0523] For example, if a user inputs "I'm feeling tired and need childcare support," the system will detect signs of fatigue from their facial expressions and voice, and based on that, suggest specific support to reduce stress. An example of a prompt to the generating AI model could be "What are some recommended relaxation methods for a tired new parent?" This allows the system to enhance the user experience and accurately meet their needs.

[0524] In this way, the embodiment for carrying out the invention aims to effectively analyze the user's emotions and provide personalized support based on those emotions.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] When a user inputs their needs into the device, the device uses its camera and microphone to capture the user's facial expressions and tone of voice. It also simultaneously collects the text the user enters. Input data includes audio files, image data, and text data. This data is sent to the emotion engine, which provides the foundational data for analyzing the user's emotional state.

[0528] Step 2:

[0529] The emotion engine built into the device analyzes collected facial expressions, voice tone, and text data. Specifically, it uses image processing algorithms to analyze facial expressions, voice analysis technology to classify voice tone, and natural language processing technology to analyze text content. As output, it generates labels indicating the user's emotional state (e.g., stress, joy, anxiety).

[0530] Step 3:

[0531] The analyzed emotional information is sent to the server. The server combines the user's needs information and emotional information and uses a generative AI model to select appropriate support candidates. The server's input includes emotional labels and the user's specific needs, and the output is a list of support candidates that best match the user's current emotional state.

[0532] Step 4:

[0533] The server proposes selected support options to the user's terminal. During this process, the server adjusts the suggestion method based on emotional information. Specifically, it prepares messages to encourage relaxation for users who are feeling anxious. Examples of output sent from the server include suggestions for relaxation methods and specific actions.

[0534] Step 5:

[0535] If the user agrees to the proposed support, that agreement is recorded on the device. The device stores this information and retains it as evidence of the agreement between the user and the support candidate. The agreed-upon support is recorded as input information, and it is recorded as output information that can be referenced later as needed.

[0536] Step 6:

[0537] After support is provided, the device captures the user's emotions again and sends the feedback information to the server. The server analyzes the feedback data and evaluates the user's satisfaction. This allows the system to select more accurate support candidates. The output is an improved service delivery policy.

[0538] (Application Example 2)

[0539] 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."

[0540] Online and virtual stores face the challenge of a decline in the quality of the purchasing experience due to the provision of uniform services that disregard customer emotions. It is necessary to improve purchase satisfaction by understanding customer interests and emotional states in real time and providing personalized information.

[0541] 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.

[0542] In this invention, the server includes emotion recognition means for analyzing the user's emotional state, information presentation means for presenting appropriate product information based on the analyzed emotional state, and eye-tracking means for tracking the user's gaze and evaluating their level of interest. This makes it possible to provide optimal product information tailored to the individual emotional state of each customer.

[0543] An "emotion recognition tool" is a device or system that analyzes a user's facial expressions and tone of voice to grasp their emotional state in real time.

[0544] An "information presentation means" is a device or system that displays appropriate product information and related information to the user on an interface based on the analyzed emotional state.

[0545] An "eye-tracking device" is a device or system for detecting a user's gaze and evaluating which part they are interested in.

[0546] "Related information generation means" refers to a device or system that generates related information to be provided to the user based on an evaluation of the level of interest obtained by eye-tracking means.

[0547] This invention relates to a system that analyzes a customer's emotional state in real time within a virtual store and provides personalized product information accordingly. This system includes emotion recognition means, information presentation means, and eye-tracking means.

[0548] First, devices such as smart glasses are used as "terminals" to collect user emotion data. These "terminals" have built-in cameras and microphones, which are used by emotion recognition tools to analyze facial expressions and voice tone. For the analysis, facial recognition libraries such as OpenCV and voice tone analysis models using TensorFlow are used.

[0549] Next, based on the analyzed emotional state, the "server" displays appropriate product information on the user's device through the "information display means." For example, if the user expresses surprise, the "server" can intuitively display information about new products or featured items.

[0550] Furthermore, the "terminal" tracks the user's eye movements through eye-tracking means to identify what they are interested in. Eye-tracking uses infrared sensors, such as those installed in smart glasses. For products the user shows interest in, a related information generation means generates related information and discount information, which is then communicated to the user by an "information presentation means."

[0551] As a concrete example, consider a user browsing skirts in a virtual store. If the user smiles upon seeing the skirt, the "terminal" analyzes the emotion and the "server" displays limited-time discount information for that skirt through the "information presentation means." Conversely, if the user is indifferent to the product, other related products can be presented to pique their interest. By using a generative AI model, emotional information can be collected through prompt messages like the following, enabling suggestions tailored to the user's needs.

[0552] Example of a prompt:

[0553] "Analyze customer emotional data and provide information about products they are interested in. If the emotion is positive, provide more information; if the emotion is negative, suggest related products."

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The "user" wears smart glasses and walks around the virtual store. The glasses' camera and microphone activate, and the "terminal" collects data on the user's facial expressions and voice tone. This inputs raw emotional data related to the user's feelings.

[0557] Step 2:

[0558] The "device" analyzes collected facial expression data using the OpenCV library and voice tone data using a TensorFlow model. Through data processing, the "device" determines the user's emotional state (e.g., joy, indifference) and generates the result as output. At this point, the analysis results regarding the emotional state are obtained.

[0559] Step 3:

[0560] The "server" receives the results of the emotional state analysis sent from the "terminal" and inputs the prompt message into the AI ​​model based on this. Here, a prompt message is output to select product information appropriate to the emotional state. The "server" then prepares to send the selected product information to the "terminal".

[0561] Step 4:

[0562] The "terminal" tracks the user's gaze using "eye-tracking means" based on selected product information. Infrared sensors within the smart glasses collect gaze data, identifying the product the user is focusing on. As a result, information about products of interest and related data is obtained.

[0563] Step 5:

[0564] The "server" uses eye-tracking data to transmit detailed information and related information about products the user has shown interest in to the "terminal" via an information display device. At this point, discount information and detailed descriptions of the products the user is focusing on will be displayed on the "terminal." This process completes the presentation of product information to the user.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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".

[0582] As an embodiment of the present invention, a method for realizing a system that provides appropriate support according to the various needs of users is described below. This system is designed to be easily accessible to users and has an intuitive user interface. The central elements of the system are input means, analysis means, communication means, recording means, and collection means.

[0583] First, the terminal provides an interface that allows users to input the necessary information when requesting assistance. Users input specific needs information through the terminal, and this information is sent to the server. Upon receiving the needs information, the server processes this information using analysis tools and identifies the most suitable support candidates for the user's request.

[0584] Based on the analysis results, the server selects potential support candidates and presents that information to the user's terminal. The user can then choose from the presented support candidates and communicate in detail with the selected candidate using communication methods. This includes chat and video call functions, allowing for the arrangement of specific support details and dates / times.

[0585] Agreements reached between users and support candidates through communication are stored in a database using recording mechanisms and can be used for future reference and management. After the service is provided, collection methods are used to obtain feedback from users. Feedback is an important source of information for improving the system and enhancing the quality of the service. This allows the system to be continuously improved and to become more responsive to user needs.

[0586] For example, if a user seeking childcare support requests "weekend childcare" using their device, the system analyzes the information and displays a list of nearby seniors who can provide the relevant support. The user then selects a senior who matches their needs and completes the necessary online consultation. After the service is completed, the user can provide feedback within the system, reporting their satisfaction with the experience and areas for improvement. This entire process enables efficient and effective childcare support in the local community.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The terminal displays a screen for the user to input needs information. Once the user enters the necessary information, such as the type of support needed and the desired time, and presses the submit button, the terminal sends that information to the server.

[0590] Step 2:

[0591] The server receives needs information transmitted from the terminal and passes it to the analysis unit. Based on the received information, the analysis unit searches the database for suitable support candidates and identifies the most appropriate candidate.

[0592] Step 3:

[0593] The server generates a list of potential support candidates identified by the analysis tool and sends this information to the terminal. The terminal displays the candidate information in a list format for the user to easily review.

[0594] Step 4:

[0595] The user selects a suitable candidate from the list of support candidates displayed on the device and contacts that candidate using a communication method. The device provides an interface that enables chat and video calls, and helps in coordinating specific support details and schedules.

[0596] Step 5:

[0597] After an agreement is reached, the user sends the details based on the agreement to the server via their device. The server uses recording means to store the details of the agreement in a database for future reference.

[0598] Step 6:

[0599] After the support service is completed, the device provides the user with a feedback input screen. The user enters their satisfaction level and areas for improvement regarding the support provided, and the device sends this feedback information to the server.

[0600] Step 7:

[0601] The server analyzes feedback information collected through various means and uses the results to improve the system and services. This continuous feedback loop enables the implementation of measures to improve system quality and user satisfaction.

[0602] (Example 1)

[0603] 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".

[0604] In modern society, providing timely and appropriate support that meets the diverse needs of users is a crucial challenge. In particular, there is a need for a system that can quickly and accurately match users with the support they require and facilitate smooth communication. Furthermore, effectively collecting and analyzing user feedback is essential for continuously improving the quality of support.

[0605] 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.

[0606] In this invention, the server includes means for inputting user demand information, means for analyzing the demand information using a generation AI model to identify appropriate support candidates, and means for presenting the selected support candidates to the user. This makes it possible to respond quickly and appropriately to user needs and improve the accuracy and efficiency of support matching.

[0607] A "user" refers to an individual or group that uses the system to request services or support.

[0608] "Demand information" refers to specific information about the content and conditions of support entered by the user.

[0609] A "generative AI model" is an artificial intelligence model used to analyze user input information and understand its intent.

[0610] A "support candidate" is an individual or organization that is selected based on user needs information and has the potential to provide appropriate support.

[0611] "Communication methods" refer to technologies and tools used for detailed information exchange and communication between users and potential support providers.

[0612] "Agreement" refers to the service details and conditions agreed upon between the user and the support candidate.

[0613] "Evaluation information" refers to feedback and opinions provided by users after the service has been implemented.

[0614] "Aptitude information" refers to information about the characteristics and abilities of a specific group or individual.

[0615] "Support work" refers to specific support activities and tasks provided according to the user's needs.

[0616] This invention is a support system for responding quickly and accurately to user needs, and its hardware includes terminal devices, server devices, and a communication network. The software includes a platform for managing the entire system, a generative AI model, and a natural language processing library. This section describes the specific use of hardware and software.

[0617] The device provides a user interface for users to input needs information. This interface is designed using HTML / CSS / JavaScript and includes a form. Users can input specific prompts in text format, such as "weekend childcare supervision."

[0618] The server receives needs information sent from the terminal and analyzes it using a generative AI model. This analysis uses Python and natural language processing libraries (e.g., SpaCy or NLTK) to understand the user's intent. The analysis results identify the most suitable support candidates and are used for subsequent processing.

[0619] After potential support candidates are identified, the server sends that information back to the terminal and presents the results to the user. At this time, the terminal displays a list of support candidates in dynamically generated HTML format, allowing the user to make a selection.

[0620] Users can select the most suitable supporter from the presented candidates and communicate in detail through various means. These means include real-time chat and video calls using WebRTC.

[0621] After the service is completed, users can provide feedback, and this feedback information will be collected and used to improve the system and enhance the quality of support services.

[0622] This enables the efficient and effective provision of support tailored to the needs of the local community. Users can smoothly receive the support they desire, and the system is continuously optimized.

[0623] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0624] Step 1:

[0625] The terminal provides the user with a needs input form. The user enters specific support details and conditions as prompts. At this point, the input is text data, and the terminal prepares to convert it to JSON format and send it to the server.

[0626] Step 2:

[0627] The server receives needs information in JSON format sent from the terminal. To analyze this input data, it launches a generative AI model. Specifically, it uses a Python script in combination with a natural language processing library (e.g., SpaCy or NLTK) to analyze the intent of the prompt and perform necessary data processing. As a result, the conditions for the best support candidate that meet the user's request are identified. This analysis result is then used for database searches.

[0628] Step 3:

[0629] The server searches the database based on the analyzed data and selects suitable support candidates. At this point, the input is the analyzed requirements data, and the output is a list of suitable support candidates. The database contains supporter profile information and the services they can provide, and the necessary records are extracted using SQL queries.

[0630] Step 4:

[0631] The server sends a list of selected support candidates to the terminal. The terminal receives this information and generates dynamic HTML to present to the user. Specifically, JavaScript is used to display the candidates in a list format on the web page, making it easy for the user to understand intuitively.

[0632] Step 5:

[0633] The user selects their preferred support candidate from the presented list. The input is the ID of the candidate selected by the user, and the terminal sends this selection data back to the server. The server receives this data and configures the communication method to initiate detailed communication between the user and the selected support candidate.

[0634] Step 6:

[0635] The server records the communication history and agreements between the user and the support candidate. This information is stored in a database and used for evaluation and feedback collection after the service is completed. The input is the communication content, and the output is the stored record data.

[0636] Step 7:

[0637] Users provide evaluation feedback after completing a service. The device sends this feedback to the server. The server collects this data and uses it for system evaluation and analysis. The analysis results can be used to identify areas for system improvement and as training data for generating AI models. The input is the feedback data, and the output is the analyzed improvement information.

[0638] (Application Example 1)

[0639] 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".

[0640] In modern society, effectively maintaining community safety is a crucial issue, but systems that adequately address the specific safety needs of individual users and utilize their feedback are insufficient. Therefore, there is a need for technology that allows users to individually input their safety requirements and receive appropriate support quickly in response.

[0641] 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.

[0642] In this invention, the server includes terminal means for inputting user requests, analysis means for analyzing the requests and proposing appropriate safety support candidates, and communication means for exchanging information with the safety support candidates. This enables the effective organization and rapid provision of safety support required by users, as well as the collection of feedback that helps improve the service.

[0643] A "terminal device" is a device used by users to input security requirements and transmit information to a server.

[0644] The "analysis means" is a function that analyzes the input requirements and identifies and proposes suitable safety support candidates.

[0645] "Communication means" refers to a function for exchanging information between users and potential safety support personnel, enabling real-time communication.

[0646] A "recording mechanism" is a function that stores agreements and feedback obtained based on information exchange in a database for future reference and service improvement.

[0647] "Collection methods" refer to functions that collect feedback from users after the service has been provided, and use this information to improve the system and enhance the quality of support.

[0648] A "diagnostic tool" is a function that analyzes local suitability information and proposes appropriate safety tasks based on that information.

[0649] "Safety support candidates" are candidates for staff or volunteers selected in response to user requests to ensure the safety of the community.

[0650] To implement this invention, a system is constructed in which a user inputs requests using a smartphone terminal, and safety support is provided based on those requests. The server receives these requests and uses analysis means to identify appropriate safety support candidates. This incorporates technology that performs data analysis in real time using a cloud backend such as AWS Lambda. The analysis results are stored in a real-time database such as Firebase and presented to the user via communication means. Here, it is possible to conduct video calls or chats between the user and safety support candidates using services such as Twilio. The agreed-upon safety support content is stored in the database via recording means and made available for future reference.

[0651] Specifically, users input requests for nighttime safety patrols via a terminal. The server receives this information, uses analysis tools to identify local safety support candidates, and generates a list. Users select from this list and discuss the details with the safety support candidates via video call using Twilio to determine the specific safety support provided. Throughout this process, sufficient feedback is collected, and the system is improved based on the information gathered.

[0652] Examples of prompts to input into a generative AI model:

[0653] "Please explain how to immediately contact the appropriate security staff using the app if you notice any suspicious behavior in a shopping center during the daytime."

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1:

[0656] Users enter their security requirements using their smartphones. A form is displayed where they can enter specific details such as location, time, and the type of support needed. The entered data is immediately transmitted to the server.

[0657] Step 2:

[0658] The server processes the request data received from the user using an analysis tool. The analysis tool uses a generative AI model to analyze the request content in order to list the most suitable safety support candidates for the user's request. The list of support candidates generated by this process is output, and the process proceeds to the next step.

[0659] Step 3:

[0660] The server sends a list of safety support candidates identified based on the analysis results to the user's terminal. The user selects an appropriate candidate from the presented list, and that information is sent back to the server. The system is designed to take into account the candidate's profile and past evaluations during this process.

[0661] Step 4:

[0662] The user selects a candidate, and the server then communicates using a chat or video call service. Twilio services are used for this purpose, enabling real-time communication. In this step, details such as specific service content and visit dates / times are discussed, and an agreement is reached.

[0663] Step 5:

[0664] The agreed-upon terms, determined via communication, are transmitted to a server and recorded in a database by recording devices. This recorded data is used for follow-up after service implementation and as reference information for similar cases. This process organizes and manages the service history.

[0665] Step 6:

[0666] After the service is provided, a server operates to collect feedback from users. A form for obtaining feedback information is displayed on the user's device through the collection mechanism, and the feedback collected there is used to improve the service in the future.

[0667] 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.

[0668] This invention embodies an advanced system that recognizes a user's emotional state and provides support accordingly. By incorporating an emotion engine, this system can analyze the user's emotional information and provide support services that are more suitable for the user. When a user inputs their needs, the terminal collects emotional data from the user's facial expressions, tone of voice, and entered text.

[0669] The emotion engine analyzes this emotional data in real time to recognize emotional states such as stress, joy, and anxiety. This recognized emotional information is sent to a server, where it is processed in combination with needs information by an analysis tool. The server then considers the emotional information to select the most appropriate support candidate for the user's state.

[0670] Once a support candidate is selected, the server suggests the selected candidate to the user. At this time, it can also adjust the communication approach based on emotions recognized by the emotion engine. For example, if the user is feeling nervous, the server might provide data suggesting that the candidate create a relaxed atmosphere.

[0671] Furthermore, after an agreement with a support candidate is recorded, the emotion engine is used again to collect user feedback information and use it to improve satisfaction based on emotional changes. This allows the system to individually optimize the user experience and more accurately meet user needs.

[0672] For example, when a user requests childcare support at home, the system detects signs of fatigue and stress from the user's facial expressions and proposes support tailored to alleviate those feelings. By utilizing emotional information, the system enables support that builds mutual trust through appropriate communication. As users become satisfied with the service and their emotions shift to a positive state, the quality and accuracy of feedback also improve, accelerating the overall improvement cycle of the system.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] The device provides a screen for users to enter the information necessary when requesting assistance. Users enter information about their needs, such as the type of assistance they require and their preferred date and time, and grant permission to obtain emotional information. At this point, the device uses its camera and microphone to transmit the user's facial expressions and tone of voice to the emotion engine.

[0676] Step 2:

[0677] The emotion engine analyzes the user's facial expressions and voice data transmitted from the device to identify their emotional state. For example, it can determine whether the user is anxious or relaxed and send that information to the server.

[0678] Step 3:

[0679] The server combines the received needs information and emotional information with an analysis tool. Based on this information, the analysis tool selects appropriate support candidates. This selection process considers not only needs but also emotional information to match candidates' skill sets and personality aptitudes.

[0680] Step 4:

[0681] The server transmits information about selected support candidates to the terminal. The terminal displays the candidate's profile and proposal to the user. The user can then select the most suitable candidate and contact them using their preferred communication method.

[0682] Step 5:

[0683] Users coordinate specific support details and schedules with candidates via chat or video call. During this process, the device continues to transmit emotional information to the emotion engine, notifying the server of emotional changes in real time.

[0684] Step 6:

[0685] Once the user and the support candidate reach an agreement, the server uses recording mechanisms to save the agreement details to a database. Additionally, the emotion engine monitors and records the user's emotions at the time of agreement, using this information for further adaptation and service improvement.

[0686] Step 7:

[0687] After the support service is completed, the device provides the user with a feedback screen. The user enters their overall impressions and changes in feelings as feedback. The device sends this feedback to a server, where it is analyzed by the collection mechanism.

[0688] Step 8:

[0689] The server analyzes emotion-based feedback information collected by the emotion engine, using it to improve future services and respond quickly to emotions. This allows the system to provide high-quality support that reflects the user's emotions.

[0690] (Example 2)

[0691] 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".

[0692] There is a challenge in the lack of systems that can appropriately recognize the emotional state of users and provide optimal support based on that understanding. Conventional systems often provide uniform services without considering the user's emotions, and thus fail to provide effective support that meets the user's needs. Therefore, there is a need for a method that analyzes the user's emotions in real time and provides individualized support based on that information.

[0693] 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.

[0694] In this invention, the server includes a collection means for collecting data to recognize the user's emotional state, an analysis means for analyzing the collected emotional data and recognizing the emotional state, and a selection means for selecting appropriate support candidates based on the needs information and the recognized emotional information. This makes it possible to provide appropriate support that takes the user's emotions into consideration.

[0695] "Data collection means" refers to a series of methods and devices for acquiring data necessary to recognize the emotional state of a user from various sources such as voice, facial expressions, and text input.

[0696] "Analysis methods" refer to a set of technologies and algorithms used to analyze collected emotional data and recognize the emotional state of the user.

[0697] "Selection methods" refer to the process and mechanisms for selecting the most suitable support candidates based on user needs information and analyzed emotional information.

[0698] "Adjustment means" refers to methods and devices for adjusting the method and content of proposed support options based on emotional information in order to present them to the user in an appropriate manner.

[0699] "Communication methods" refer to technologies and devices used to exchange information and communicate between users and potential support providers.

[0700] "Recording means" refers to the process and system for saving agreements made between users and potential support providers, making them available for later reference.

[0701] "Analysis methods (for satisfaction evaluation)" refer to technologies and techniques used to evaluate user satisfaction based on collected feedback information and to utilize that information for service improvement.

[0702] "Diagnostic tools" refer to methods and techniques for evaluating aptitude information based on specific attributes of users and proposing corresponding support tasks.

[0703] This system is designed to understand the user's emotional state and provide optimal support tailored to their needs. The device first receives input from the user and uses a camera and microphone to capture the user's facial expressions and voice tone in real time. In addition, text entered by the user is also subject to emotion analysis. All of this data is sent to the emotion engine built into the device, where the emotional state is analyzed.

[0704] On the server, specialized algorithms run to analyze the collected emotional data. Specifically, image processing, voice analysis, and natural language processing technologies are used to classify the user's emotions into specific states such as stress, joy, and anxiety. The emotional information obtained in this way is sent to the server and processed in combination with needs information.

[0705] The server then uses a generative AI model to select the most appropriate support option for the user's situation. The selected support option is then presented to the user, taking emotional information into account. For example, if the server detects that the user is feeling anxious, it will approach the user with content that promotes relaxation. In this way, appropriate communication tailored to the user's emotions is achieved.

[0706] For example, if a user inputs "I'm feeling tired and need childcare support," the system will detect signs of fatigue from their facial expressions and voice, and based on that, suggest specific support to reduce stress. An example of a prompt to the generating AI model could be "What are some recommended relaxation methods for a tired new parent?" This allows the system to enhance the user experience and accurately meet their needs.

[0707] In this way, the embodiment for carrying out the invention aims to effectively analyze the user's emotions and provide personalized support based on those emotions.

[0708] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0709] Step 1:

[0710] When a user inputs their needs into the device, the device uses its camera and microphone to capture the user's facial expressions and tone of voice. It also simultaneously collects the text the user enters. Input data includes audio files, image data, and text data. This data is sent to the emotion engine, which provides the foundational data for analyzing the user's emotional state.

[0711] Step 2:

[0712] The emotion engine built into the device analyzes collected facial expressions, voice tone, and text data. Specifically, it uses image processing algorithms to analyze facial expressions, voice analysis technology to classify voice tone, and natural language processing technology to analyze text content. As output, it generates labels indicating the user's emotional state (e.g., stress, joy, anxiety).

[0713] Step 3:

[0714] The analyzed emotional information is sent to the server. The server combines the user's needs information and emotional information and uses a generative AI model to select appropriate support candidates. The server's input includes emotional labels and the user's specific needs, and the output is a list of support candidates that best match the user's current emotional state.

[0715] Step 4:

[0716] The server proposes selected support options to the user's terminal. During this process, the server adjusts the suggestion method based on emotional information. Specifically, it prepares messages to encourage relaxation for users who are feeling anxious. Examples of output sent from the server include suggestions for relaxation methods and specific actions.

[0717] Step 5:

[0718] If the user agrees to the proposed support, that agreement is recorded on the device. The device stores this information and retains it as evidence of the agreement between the user and the support candidate. The agreed-upon support is recorded as input information, and it is recorded as output information that can be referenced later as needed.

[0719] Step 6:

[0720] After support is provided, the device captures the user's emotions again and sends the feedback information to the server. The server analyzes the feedback data and evaluates the user's satisfaction. This allows the system to select more accurate support candidates. The output is an improved service delivery policy.

[0721] (Application Example 2)

[0722] 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".

[0723] Online and virtual stores face the challenge of a decline in the quality of the purchasing experience due to the provision of uniform services that disregard customer emotions. It is necessary to improve purchase satisfaction by understanding customer interests and emotional states in real time and providing personalized information.

[0724] 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.

[0725] In this invention, the server includes emotion recognition means for analyzing the user's emotional state, information presentation means for presenting appropriate product information based on the analyzed emotional state, and eye-tracking means for tracking the user's gaze and evaluating their level of interest. This makes it possible to provide optimal product information tailored to the individual emotional state of each customer.

[0726] An "emotion recognition tool" is a device or system that analyzes a user's facial expressions and tone of voice to grasp their emotional state in real time.

[0727] An "information presentation means" is a device or system that displays appropriate product information and related information to the user on an interface based on the analyzed emotional state.

[0728] An "eye-tracking device" is a device or system for detecting a user's gaze and evaluating which part they are interested in.

[0729] "Related information generation means" refers to a device or system that generates related information to be provided to the user based on an evaluation of the level of interest obtained by eye-tracking means.

[0730] This invention relates to a system that analyzes a customer's emotional state in real time within a virtual store and provides personalized product information accordingly. This system includes emotion recognition means, information presentation means, and eye-tracking means.

[0731] First, devices such as smart glasses are used as "terminals" to collect user emotion data. These "terminals" have built-in cameras and microphones, which are used by emotion recognition tools to analyze facial expressions and voice tone. For the analysis, facial recognition libraries such as OpenCV and voice tone analysis models using TensorFlow are used.

[0732] Next, based on the analyzed emotional state, the "server" displays appropriate product information on the user's device through the "information display means." For example, if the user expresses surprise, the "server" can intuitively display information about new products or featured items.

[0733] Furthermore, the "terminal" tracks the user's eye movements through eye-tracking means to identify what they are interested in. Eye-tracking uses infrared sensors, such as those installed in smart glasses. For products the user shows interest in, a related information generation means generates related information and discount information, which is then communicated to the user by an "information presentation means."

[0734] As a concrete example, consider a user browsing skirts in a virtual store. If the user smiles upon seeing the skirt, the "terminal" analyzes the emotion and the "server" displays limited-time discount information for that skirt through the "information presentation means." Conversely, if the user is indifferent to the product, other related products can be presented to pique their interest. By using a generative AI model, emotional information can be collected through prompt messages like the following, enabling suggestions tailored to the user's needs.

[0735] Example of a prompt:

[0736] "Analyze customer emotional data and provide information about products they are interested in. If the emotion is positive, provide more information; if the emotion is negative, suggest related products."

[0737] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0738] Step 1:

[0739] The "user" wears smart glasses and walks around the virtual store. The glasses' camera and microphone activate, and the "terminal" collects data on the user's facial expressions and voice tone. This inputs raw emotional data related to the user's feelings.

[0740] Step 2:

[0741] The "device" analyzes collected facial expression data using the OpenCV library and voice tone data using a TensorFlow model. Through data processing, the "device" determines the user's emotional state (e.g., joy, indifference) and generates the result as output. At this point, the analysis results regarding the emotional state are obtained.

[0742] Step 3:

[0743] The "server" receives the results of the emotional state analysis sent from the "terminal" and inputs the prompt message into the AI ​​model based on this. Here, a prompt message is output to select product information appropriate to the emotional state. The "server" then prepares to send the selected product information to the "terminal".

[0744] Step 4:

[0745] The "terminal" tracks the user's gaze using "eye-tracking means" based on selected product information. Infrared sensors within the smart glasses collect gaze data, identifying the product the user is focusing on. As a result, information about products of interest and related data is obtained.

[0746] Step 5:

[0747] The "server" uses eye-tracking data to transmit detailed information and related information about products the user has shown interest in to the "terminal" via an information display device. At this point, discount information and detailed descriptions of the products the user is focusing on will be displayed on the "terminal." This process completes the presentation of product information to the user.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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."

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] The following is further disclosed regarding the embodiments described above.

[0770] (Claim 1)

[0771] An input method for entering user needs information,

[0772] An analytical means that analyzes the aforementioned needs information and proposes appropriate support candidates,

[0773] A means of communication for communicating with the aforementioned support candidate,

[0774] A recording means for recording the agreement of both parties based on the aforementioned communication,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, comprising means for collecting user feedback information and using it to improve the service.

[0778] (Claim 3)

[0779] The system according to claim 1, further comprising a diagnostic means for diagnosing the aptitude information of senior citizens and proposing appropriate support tasks.

[0780] "Example 1"

[0781] (Claim 1)

[0782] A means of inputting user demand information,

[0783] A means for receiving the aforementioned demand information, analyzing its contents using a generation AI model, and identifying appropriate support candidates,

[0784] A means for selecting the aforementioned support candidates and displaying the results on the user's display device,

[0785] A communication method that enables detailed interaction with the support candidate selected by the user,

[0786] A means of saving the agreement between the user and the support candidate based on the aforementioned exchange,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, comprising means for collecting user evaluation information and analyzing said information to improve the quality of the system.

[0790] (Claim 3)

[0791] The system according to claim 1, further comprising means for determining suitability information for a specific group and identifying appropriate support tasks.

[0792] "Application Example 1"

[0793] (Claim 1)

[0794] A terminal means for inputting user requests,

[0795] An analysis means for analyzing the aforementioned requirements and proposing appropriate safety support candidates,

[0796] A communication means for exchanging information with the aforementioned safety support candidate,

[0797] A recording means for recording the agreed-upon content based on the aforementioned information exchange,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, comprising means for collecting user feedback and contributing to service improvement.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising a diagnostic means for diagnosing regional suitability information and proposing appropriate safety tasks.

[0803] "Example 2 of combining an emotion engine"

[0804] (Claim 1)

[0805] A means of collecting data to recognize the emotional state of users,

[0806] An analysis means for analyzing the collected emotional data and recognizing the emotional state,

[0807] A selection method for selecting appropriate support candidates based on the aforementioned needs information and recognized emotional information,

[0808] An adjustment means that proposes the aforementioned support candidates to the user and adjusts the proposal means based on emotional information,

[0809] A means of communication for communicating with the aforementioned support candidate,

[0810] A recording means for recording the agreement of both parties based on the aforementioned communication,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, comprising analytical means for collecting user feedback information, evaluating satisfaction levels based on the feedback information, and using the results to improve the service.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising a diagnostic means for diagnosing aptitude information based on specific user attributes and proposing appropriate support tasks.

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] An emotion recognition method for analyzing the emotional state of the user,

[0819] An information presentation means that presents appropriate product information based on the analyzed emotional state,

[0820] An eye-tracking device that tracks the user's gaze and evaluates their level of interest,

[0821] Related information generation means that generates related information based on the level of interest evaluated by the eye-tracking means,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, comprising means for collecting user emotional data and using it to improve the purchasing experience.

[0825] (Claim 3)

[0826] The system according to claim 1, further comprising analytical means for analyzing feedback information to optimize the user experience and promote improvement. [Explanation of Symbols]

[0827] 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. An input method for entering user needs information, An analytical means that analyzes the aforementioned needs information and proposes appropriate support candidates, A means of communication for communicating with the aforementioned support candidate, A recording means for recording the agreement of both parties based on the aforementioned communication, A system that includes this.

2. The system according to claim 1, further comprising means for collecting user feedback information and using it to improve the service.

3. The system according to claim 1, further comprising a diagnostic means for diagnosing the aptitude information of senior citizens and proposing appropriate support tasks.

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