system

Abstract

We provide the system. [Solution] A means for receiving requests from users regarding praise (approval), Means for sending the aforementioned request to the server, Means for collecting and managing user data, A means for training a generative AI model to generate compliments or compliment songs based on the user's data, A means of providing the user with generated compliments or compliment songs, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, there is a problem of insufficient opportunities for praise (approval). Especially in a business environment, employees rarely recognize each other, and their achievements and efforts are not fully evaluated, resulting in a decline in motivation and productivity and a progression of mental fatigue. This tendency is also seen in family and private areas, and there is a lack of appropriate opportunities for praise (approval) not only for adults but also for children. It is required to improve such a situation and create a new approval culture that satisfies everyone.

Means for Solving the Problems

[0005] To solve the aforementioned problems, the present invention provides a system that includes means for receiving requests for praise (approval) from users and means for sending requests to a server. The server has means for collecting and managing user data and means for training a generative AI model for generating praise words or praise songs based on user data. It also includes means for providing the generated praise words or praise songs to the user. Furthermore, by providing means for receiving feedback provided by the user and storing it as training data to improve the generative AI model, the quality of praise (approval) is continuously improved. In addition, by realizing a system that provides praise (approval) in stages for business, private, and all people, a new culture of approval can be formed across a wide range of people.

[0006] A "user" refers to an individual or legal entity that uses this system to seek praise (recognition).

[0007] A "device" refers to a device used by a user to enter a request and receive praise (approval). Examples include smartphones, tablets, and personal computers.

[0008] A "server" refers to a centralized computer system that collects and manages user data and generates compliments and compliment songs using a generative AI model.

[0009] "Praise (acknowledgment)" refers to the act of appropriately evaluating a user's actions and achievements and providing acknowledgment in the form of praise, music, or other forms.

[0010] A "request" refers to information that a user enters via their device to request specific praise (approval).

[0011] A "generative AI model" refers to an artificial intelligence model that generates appropriate compliments and compliment songs based on collected user data.

[0012] "Praise" refers to a string of characters created by a generative AI model to evaluate a user's actions and results.

[0013] A "praise song" refers to a musical work created by a generative AI model to evaluate a user's actions and achievements.

[0014] "Feedback" refers to the opinions and impressions that users provide in response to praise (acknowledgment) they receive.

[0015] "Training data" refers to data stored by the server to improve the performance of the generated AI model based on user feedback.

[0016] The "business domain" refers to situations involving praise (recognition) within the workplace or corporate environment.

[0017] "Private space" refers to situations in the home or personal living environment where praise (acknowledgment) occurs. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] 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

[0019] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Further, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] To implement this invention, three elements are necessary: ​​a terminal that provides a user interface, a server that manages data and maintains a generation AI model, and a user who makes a request for praise (approval). The system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server. The server collects and analyzes the user's data and uses the generation AI model to generate appropriate words of praise or praise songs. The generated words of praise or praise songs are then provided to the user through the terminal.

[0040] Server Processing

[0041] 1. Collection and management of user data

[0042] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a database. For example, it retrieves data via an API to capture presentations that a user has previously given high ratings to, or specific behavioral history.

[0043] 2. Training the Generative AI Model

[0044] The server trains a generative AI model based on the collected data. Machine learning techniques are used for training, learning user behavior patterns and preferences. This allows for the provision of more personalized praise (affirmation).

[0045] 3. Generating compliments and compliment songs

[0046] The server uses a trained generative AI model to generate personalized compliments and songs for the user. For example, it can create specific messages such as, "Your presentation today was fantastic! Your passion really came through," or original songs that include the user's name.

[0047] Terminal processing

[0048] 1. Providing a user interface

[0049] The terminal provides an interface for the user to enter requests. Possible input methods include text boxes, voice recognition, and selection menus.

[0050] 2. Communication with the server

[0051] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[0052] 3. Providing praise (acknowledgment)

[0053] The terminal provides users with compliments and songs received from the server using text display, audio output, and video playback functions.

[0054] User actions

[0055] 1. Information Entry

[0056] Users input the actions or achievements they want to be praised for through their device. For example, they can even voice-in requests such as, "I'd like to be praised for the proposal I made at today's meeting."

[0057] 2. Providing feedback

[0058] After receiving praise (acknowledgment), the user enters feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[0059] 3. Use both inside and outside the company

[0060] Users who receive praise (acknowledgment) gain a positive experience, which encourages them to praise others. This fosters a new culture of recognition both within and outside the organization.

[0061] Specific example

[0062] The following are specific examples of how this system can be used in both the business and personal spheres.

[0063] Examples of use in the business field

[0064] User A enters a request into the terminal asking for praise for their presentation. The terminal sends this request and User A's ID to the server. The server refers to User A's past presentation evaluation data and uses a generative AI model to generate the most appropriate praise. The generated result is sent to the terminal in text format, and the terminal displays it to User A.

[0065] Examples of use in private areas

[0066] User B enters a request into the device to be praised for making a new dish. The device sends this request to the server. The server uses a generative AI model based on past feedback data about User B's cooking to generate appropriate praise. The generated result is sent to the device in the form of a voice message, which the device then provides to User B via voice.

[0067] In this way, the present invention realizes a system that provides praise (recognition) tailored to the individual circumstances of each user, and can create a new culture of recognition throughout society.

[0068] The following describes the processing flow.

[0069] Program processing flow

[0070] Step 1:

[0071] The user enters a request on the device.

[0072] Users input the behaviors or achievements they want to be praised for into their device as text or voice.

[0073] Example: User A types "I want you to praise my presentation today."

[0074] Step 2:

[0075] The terminal receives the input data.

[0076] The device receives user input and temporarily stores the necessary data (text or voice).

[0077] Example: The device saves data that says, "I want you to praise my presentation today."

[0078] Step 3:

[0079] The device sends the request to the server.

[0080] The terminal sends the user's request and related data, such as the user ID and past activity history, to the server.

[0081] Example: The device sends a request to the server saying "I want to be praised for today's presentation" along with the ID of user A.

[0082] Step 4:

[0083] The server receives the request.

[0084] The server receives requests sent from the terminal and parses the request content.

[0085] Example: The server receives a request to "praise today's presentation" and begins analysis.

[0086] Step 5:

[0087] The server retrieves user data.

[0088] The server retrieves the user's basic information and past activity history from the database.

[0089] Example: The server retrieves evaluations and feedback on user A's past presentations.

[0090] Step 6:

[0091] The server applies the generated AI model.

[0092] The server inputs the acquired user data into an AI model to generate appropriate compliments and praise songs.

[0093] Example: The server uses a generated AI model to produce a compliment such as, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0094] Step 7:

[0095] The server compiles the generated results.

[0096] The server compiles the generated results, such as compliments and compliment songs, and formats them into an appropriate format.

[0097] Example: Format the compliments generated by the server into text format.

[0098] Step 8:

[0099] The server sends the generated results to the terminal.

[0100] The server transfers the generated results to the terminal.

[0101] Example: The server sends a compliment to the terminal saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0102] Step 9:

[0103] The terminal receives the generated result.

[0104] The terminal receives the generated results sent from the server and converts them into the corresponding output format (text, audio, video, etc.).

[0105] Example: The device receives the text message, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0106] Step 10:

[0107] The device provides praise (acknowledgment) to the user.

[0108] The device provides the user with generated compliments and compliment songs using text display, audio playback, and video playback functions.

[0109] Example: The device plays an audio message saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0110] Step 11:

[0111] Users provide feedback

[0112] Users who receive praise (approval) enter feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[0113] Example: User A enters the feedback, "I was so happy to receive that compliment!"

[0114] Step 12:

[0115] The device sends feedback to the server.

[0116] The device sends user feedback to the server.

[0117] Example: The device sends feedback to the server saying, "I was so happy to receive that compliment!"

[0118] Step 13:

[0119] The server receives the feedback and saves it as training data.

[0120] The server receives feedback and stores it as training data. It also uses this data to improve the generative AI model.

[0121] Example: The server receives user A's feedback and adds it to a database to be used for future praise (approval).

[0122] This allows for efficient praise (recognition) through collaboration between users, devices, and servers, gradually fostering a new culture of recognition.

[0123] (Example 1)

[0124] 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."

[0125] The lack of systems that provide appropriate praise and encouragement for individual user actions and achievements hinders the enhancement of user motivation and the fostering of a positive culture. In this situation, it is difficult to create a culture of mutual recognition within organizations and among individuals. Furthermore, existing systems fail to effectively utilize user feedback, making it difficult to continuously improve the quality of praise (recognition). Moreover, the provision of praise (recognition) is limited, lacking broad application in business and personal spheres.

[0126] 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.

[0127] In this invention, the server includes means for collecting and managing user data, means for preprocessing and feature extracting data for use in training, and means for training a generative AI model for generating compliments or compliment songs based on the user data. This makes it possible to provide appropriate compliments or compliment songs based on the user's behavior and achievements. Furthermore, by including means for receiving user feedback and storing it as training data to improve the generative AI model, the quality of compliments (recognition) can be continuously improved. In addition, by including means for providing compliments (recognition) in stages for business, private, and all people, a wide range of applications becomes possible.

[0128] A "user" refers to an individual or legal entity that requests praise (approval).

[0129] A "request" refers to the information a user enters into the system to seek praise (approval).

[0130] A "server" refers to a computer system that collects and manages user data, trains and runs a generation AI model to generate compliments and praise songs.

[0131] "Data" refers to a collection of information such as a user's basic information, behavioral history, and feedback.

[0132] A "generative AI model" refers to an algorithm that uses machine learning techniques to generate compliments and compliment songs based on user data.

[0133] "Training" refers to the learning process in which the server uses collected user data to enable the generating AI model to create appropriate compliments and praise songs.

[0134] "Praise" refers to sentences that acknowledge and encourage a user's actions and achievements.

[0135] "Praise songs" refer to musical content that celebrates and encourages users' actions and achievements.

[0136] "Feedback" refers to information that users provide to the system, such as their evaluations and impressions of praise (approval).

[0137] The "business domain" refers to the environment related to the workplace and business operations.

[0138] "Private space" refers to the environment associated with home and personal activities.

[0139] "Gradual delivery" refers to the process of providing praise (acknowledgment) in a flexible manner, adapting it to various situations and recipients.

[0140] To implement this invention, three elements are necessary: ​​a terminal that provides a user interface, a server that manages data and holds a generating AI model, and a user who makes a request for praise (approval). This system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server.

[0141] Server Processing

[0142] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a MySQL database. This data is used for later analysis and training of generative AI models. Specifically, it uses a RESTful API to retrieve user presentation evaluation data.

[0143] Next, the server preprocesses and extracts features from the collected data. This process uses Python and TensorFlow. Based on the preprocessed data, the server trains a generative AI model. This training improves the model's ability to generate compliments and praise songs that are tailored to the user's actions and achievements.

[0144] A trained generative AI model generates compliments and praise songs in real time in response to user requests. The generated content is converted into text or audio files and provided to the user via a user interface. This process can utilize GPT-3 models or open-source speech synthesis libraries.

[0145] Terminal processing

[0146] The device provides an interface for the user to input requests using HTML / CSS and JavaScript. Examples include web pages with text boxes or voice input functions. When the user inputs a request, the device converts this information into JSON format and sends it to the server using the HTTPS protocol. Specifically, it sends the request data as a POST request to the server's API endpoint.

[0147] The device also plays a role in providing users with compliments and praise songs received from the server. Text is displayed on the web page, and audio is played via the Audio tag.

[0148] User actions

[0149] The user enters what they want to be praised for through the device's interface. For example, they might type "I want to be praised for completing today's project" into the text box. For voice input, they can click the microphone icon to use a dedicated speech recognition API. Once the user sends the request, the device sends it to the server, which then generates appropriate praise or praise songs.

[0150] Users who receive the generated compliments or compliment songs can input feedback on their device. For example, they can enter comments such as "I liked this compliment" or "I would like more specific details" into the text box and click the "Send Feedback" button to send the feedback to the server.

[0151] This system can provide praise (recognition) in the business, personal, and all-encompassing spheres. Specific examples are given below.

[0152] Examples of use in the business field

[0153] User A enters a request into the terminal asking for praise for their presentation. The terminal sends this request and User A's ID to the server. The server refers to User A's past presentation evaluation data and uses a generative AI model to generate the most appropriate praise. The generated result is sent to the terminal in text format, and the terminal displays it to User A.

[0154] Example of a prompt

[0155] "Please praise my presentation today. User ID: 00123"

[0156] Examples of use in private areas

[0157] User B enters a request into the device to be praised for making a new dish. The device sends this request to the server. The server uses a generative AI model based on past feedback data about User B's cooking to generate appropriate praise. The generated result is sent to the device in the form of a voice message, which the device then provides to User B via voice.

[0158] Example of a prompt

[0159] "Please praise me for creating a new dish. User ID: 00456"

[0160] In this way, the present invention can provide praise (recognition) tailored to the individual circumstances of each user, thereby fostering a new culture of recognition throughout society.

[0161] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0162] Step 1: Enter your request

[0163] The user enters the content they want to be praised for into the device's interface. For example, they might type "I'd like to be praised for today's presentation" into the text box. For voice input, they click the microphone icon and use a dedicated speech recognition API.

[0164] Input: A text or voice request entered by the user.

[0165] Output: Request data (text format or audio data)

[0166] Step 2: Sending Request Data

[0167] The terminal converts the user's input request into JSON format and sends it to the server using the HTTPS protocol.

[0168] Input: Request data (text format or audio data)

[0169] Output: Request data in JSON format to be sent to the server

[0170] Step 3: Collecting User Data

[0171] The server collects data such as basic user information, behavioral history, and feedback based on the received request data. Specifically, it uses a RESTful API to obtain user presentation evaluation data.

[0172] Input: User ID included in the request data

[0173] Output: Data set of user basic information, behavioral history, and feedback.

[0174] Step 4: Data Management

[0175] The server saves the collected data to a MySQL database. For example, it executes an SQL query like "INSERT INTO user_data (user_id, presentation_score) VALUES (1, 95);".

[0176] Input: Collected user data

[0177] Output: User data stored in the MySQL database

[0178] Step 5: Data preprocessing and feature extraction

[0179] The server uses Python and TensorFlow to retrieve data from the database and perform preprocessing and feature extraction. Preprocessing includes denoising and data normalization.

[0180] Input: User data stored in a MySQL database

[0181] Output: Preprocessed and feature-extracted data

[0182] Step 6: Training the AI ​​Model

[0183] The server trains a generative AI model based on preprocessed data, using feedforward and backpropagation algorithms.

[0184] Input: Preprocessed and feature-extracted data

[0185] Output: Trained generative AI model

[0186] Step 7: Generating compliments and compliment songs

[0187] The server uses a trained generative AI model to generate personalized compliments and praise songs for each user. For example, it can create a compliment such as "Your presentation today was excellent!" along with an original song that includes the user's name.

[0188] Input: User data and AI model

[0189] Output: Data of the generated compliments or compliment songs

[0190] Step 8: Providing praise (acknowledgment) results

[0191] The server sends the generated compliments and compliment songs to the device. The device receives them and provides them to the user using text display, audio output, or video playback functions.

[0192] Input: Data for generated compliments or compliment songs

[0193] Output: Compliments in text format displayed to the user, or audio / video played.

[0194] Step 9: Entering Feedback

[0195] The user enters feedback on the praise (acknowledgment) provided into the device. For example, they might enter a rating such as, "I liked this compliment."

[0196] Input: Feedback text or voice

[0197] Output: Feedback data

[0198] Step 10: Send and save feedback

[0199] The device sends user feedback to the server, which receives it and stores it as training data to improve the generated AI model. The server stores the feedback data in a MySQL database using INSERT statements.

[0200] Input: User feedback data

[0201] Output: Feedback data stored in the MySQL database

[0202] (Application Example 1)

[0203] 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."

[0204] It is necessary to alleviate the anxiety and uncertainty users feel while shopping in physical stores and to encourage purchasing behavior. In particular, it is crucial that users feel confident in their product selection and purchase decisions. Furthermore, personalized praise is an effective way to boost motivation and improve the user experience in physical stores, thereby increasing customer satisfaction. However, current technology lacks the appropriate systems to achieve this.

[0205] 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.

[0206] In this invention, the server includes means for receiving requests for praise (approval) from users, means for transmitting such requests to the server, means for collecting and managing user data, means for training a generative AI model for generating praise words or praise songs based on the user data, means for providing the generated praise words or praise songs to users, means for providing the generated praise words or praise songs in physical stores, and means for generating words of praise for the user's actions when they purchase or use a product. This makes it possible to improve the user experience in physical stores and increase the user's confidence in selecting and purchasing products.

[0207] 1. "Praise (acknowledgment)" refers to the positive evaluation or recognition that a user receives for a specific action or achievement.

[0208] 2. "Means for receiving requests" refers to interfaces or systems for receiving requests from users for praise (approval).

[0209] 3. "Means for sending requests to a server" refers to a system that has a communication function for sending received user requests to an external server.

[0210] 4. "Means for collecting and managing data" refers to systems and functions for collecting, appropriately storing, and managing user behavior history, feedback information, etc.

[0211] 5. A "generative AI model" refers to a machine learning model that generates compliments and compliment songs tailored to the user based on collected data.

[0212] 6. "Training methods" refer to the process and methods of optimizing and learning a generative AI model using collected data.

[0213] 7. "Means for providing generated compliments or compliment songs" refers to interfaces or output devices for providing generated compliments or compliment songs to users.

[0214] 8. "Means of providing in physical stores" refers to systems and methods for providing generated compliments and compliment songs to users in a physical store environment.

[0215] 9. "Means for generating compliments for user actions when purchasing or using a product" refers to methods and systems for generating appropriate compliments for user actions when selecting, purchasing, or using a product in a physical store.

[0216] To implement this invention, the following elements are necessary to constitute a system that provides praise (approval): a server, a terminal, and a user that communicate with each other and utilize a generative AI model to generate and provide personalized praise messages and praise songs.

[0217] Server Processing

[0218] 1. Collection and management of user data

[0219] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a database. This includes retrieving various user data via APIs. For example, this could include shopping behavior history and past purchase history.

[0220] 2. Training the Generative AI Model

[0221] The server uses machine learning techniques based on the collected data to train a generative AI model. This training allows the model to learn user behavior patterns and preferences, enabling it to provide more personalized praise (affirmation).

[0222] 3. Generating compliments and compliment songs

[0223] The server uses a trained generative AI model to generate compliments and praise songs tailored to the user's behavior and product choices. For example, it can generate specific messages such as, "That's a great choice! Your taste is excellent."

[0224] Terminal processing

[0225] 1. Providing a user interface

[0226] The terminal provides an interface for users to enter requests, including text boxes, voice recognition, and selection menus.

[0227] 2. Communication with the server

[0228] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[0229] 3. Providing praise (acknowledgment)

[0230] The device provides users with compliments and songs received from the server using text display, audio output, and video playback functions. In particular, in physical stores, compliments can be displayed using smart glasses or smartphones.

[0231] User actions

[0232] 1. Information Entry

[0233] Users input the actions or achievements they want to be praised for through their device. For example, they might request praise (approval) for a newly purchased product.

[0234] 2. Providing feedback

[0235] After receiving praise (acknowledgment), users input feedback on the content into their device. This includes whether they were satisfied with the content and suggestions for improvement.

[0236] 3. Use both inside and outside the company

[0237] Users who receive praise (acknowledgment) are more likely to praise others based on that positive experience. This fosters a new culture of recognition both within and outside the organization.

[0238] Specific examples of implementation in physical stores

[0239] Hardware:

[0240] Smart glasses (for example, Google Glass)

[0241] Smartphone (iOS or Android device)

[0242] software:

[0243] Server-side: Python, Flask (as an API server)

[0244] Client-side: Python (for API calls), UI for video display

[0245] The server receives compliment (approval) requests from users, collects and manages user data, and trains a generative AI model. It then provides the user with the generated compliments. For example, when a user picks up an item in a physical store, displaying a compliment such as "You have excellent taste in this item you've chosen!" on smart glasses can improve the user experience.

[0246] Prompt example:

[0247] User ID: user123

[0248] Action: recommended_product_acquired

[0249] Prompt message:

[0250] User123 has decided to purchase the newly recommended item. Let's praise them for choosing this item. This item is based on the latest fashion trends and is a very tasteful choice.

[0251] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0252] Step 1:

[0253] The user enters a praise (approval) request using their device. The user enters the action they want to be praised for, either through text or voice, regarding the product selection or purchase they made while shopping. This becomes the input data.

[0254] Step 2:

[0255] The terminal sends the input request to the server. A secure communication protocol such as HTTPS is used for transmission. The output is the request data sent to the server.

[0256] Step 3:

[0257] Based on the received request data, the server collects the user's past behavior history and feedback information from the database. This becomes the new input data.

[0258] Step 4:

[0259] The server trains a generative AI model using collected user data. It performs data preprocessing, feature extraction, and model retraining to learn user behavior patterns and preferences. The output is the trained generative AI model.

[0260] Step 5:

[0261] The server uses a trained generative AI model to generate praise for the requested action. Here, the prompt text serves as input data, and the corresponding praise is output. Specifically, the prompt text is input to the generative AI model, which then generates appropriate praise.

[0262] Step 6:

[0263] The server sends the generated compliment to the terminal. This becomes the new output data. The terminal sends the received compliment from the server to an output device that provides it to the user.

[0264] Step 7:

[0265] The device displays received compliments using smart glasses or a smartphone. Being complimented increases the user's willingness to purchase, leading to a positive shopping experience. The output is the displayed compliment.

[0266] Step 8:

[0267] Users input feedback on the compliments they receive into their device. This input data includes their satisfaction level with the compliments and areas for improvement.

[0268] Step 9:

[0269] The terminal sends feedback data to the server. The server stores the received feedback in a database and uses it to further improve the generative AI model. The output is an improvement in the performance of the generative AI model.

[0270] This enables a personalized praise (acknowledgment) system that improves the user experience in physical stores by coordinating between users, devices, and servers.

[0271] 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.

[0272] To implement this invention, four elements are required: a terminal that provides a user interface, a server that manages data and houses a generation AI model, an emotion engine that recognizes the user's emotions, and a user who makes a request for praise (approval). This system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server. The server collects and analyzes the user's data and the results of the emotion engine's analysis, and uses the generation AI model to generate appropriate words of praise or praise songs. The generated words of praise or praise songs are then provided to the user through the terminal.

[0273] Server Processing

[0274] 1. Collection and management of user data

[0275] The server collects and stores data such as basic user information, behavioral history, feedback, and analysis results from the emotion engine in a database. For example, it retrieves data via API to capture presentations that the user has previously given high ratings to, specific behavioral history, and emotional states.

[0276] 2. Training the Generative AI Model

[0277] The server trains a generative AI model based on the collected data. Machine learning techniques are used for training, learning user behavioral tendencies, preferences, and emotional states. This allows for the provision of more personalized praise (affirmation).

[0278] 3. Generating compliments and compliment songs

[0279] The server uses the trained generative AI model to generate compliments and compliment songs suitable for the user. For example, it creates specific messages such as "Today's presentation was really wonderful! Your enthusiasm came through." or original songs that include the user's name.

[0280] Terminal Processing

[0281] 1. Provision of User Interface

[0282] The terminal provides an interface for the user to input requests. As input means, a text box, voice recognition, a selection menu, etc. can be considered.

[0283] 2. Communication with Server

[0284] The terminal sends the user's requests and feedback to the server. A secure protocol such as HTTPS is used for communication to maintain confidentiality.

[0285] 3. Cooperation with Emotion Engine

[0286] The terminal cooperates with the emotion engine to acquire and analyze the user's voice and facial expression data. The analysis results are sent to the server as data regarding the user's emotional state.

[0287] 4. Provision of Compliments (Approval)

[0288] The terminal provides the compliments and compliment songs received from the server to the user using text display, voice output, and video playback functions.

[0289] User Operations

[0290] 1. Information Input

[0291] Users input the actions or achievements they want to be praised for through their device. For example, they can voice-in requests such as, "I'd like to be praised for the proposal I made at today's meeting."

[0292] 2. Providing feedback

[0293] After receiving praise (acknowledgment), the user enters feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[0294] 3. Use both inside and outside the company

[0295] Users who receive praise (acknowledgment) gain a positive experience, which encourages them to praise others. This fosters a new culture of recognition both within and outside the organization.

[0296] The role of the emotional engine

[0297] 1. Analysis of user sentiment

[0298] The emotion engine analyzes the user's emotional state from their voice, facial expressions, and text, and sends the results to the server. This allows the generative AI model to generate appropriate compliments and praise songs that take the user's emotional state into account.

[0299] 2. Sending emotional data

[0300] The analysis results from the emotion engine are sent to the server as data indicating the user's emotional state. This data is also used as training data for the generative AI model.

[0301] Specific example

[0302] The following are specific examples of how this system can be used in both the business and personal spheres.

[0303] Examples of use in the business field

[0304] User A inputs a request to the terminal, asking to be complimented on his presentation. At the same time, the emotion engine analyzes User A's voice and expression, and transmits his emotional state to the server. Based on the request content and the emotion data, the server generates the most appropriate compliment and transmits the generated compliment to the terminal. The terminal displays it to User A and also plays it as audio.

[0305] Usage example in the private area

[0306] User B inputs a request to the terminal, asking to be complimented on having made a new dish. At the same time, the emotion engine analyzes User B's expression, and transmits his emotional state to the server. Based on the request content and the emotion data, the server generates the most appropriate compliment and transmits the generated compliment to the terminal. The terminal displays it to User B and plays it as audio.

[0307] In this way, the present invention realizes a system that can provide compliments (approvals) considering the user's emotional state and form a new, more personalized approval culture.

[0308] The following describes the processing flow.

[0309] Processing flow of the program

[0310] [[ID=j26]]Step 1:

[0311] The user inputs a request to the terminal

[0312] The user inputs the action or result for which he wants to be complimented to the terminal in text or by voice.

[0313] Example: User A inputs "I want to be complimented on today's presentation".

[0314] Step 2:

[0315] The terminal receives the input data

[0316] The device receives user input and temporarily stores the necessary data (text or voice).

[0317] Example: The device saves data that says, "I want you to praise my presentation today."

[0318] Step 3:

[0319] The device collects emotional data.

[0320] The device activates an emotion engine and collects user voice and facial expression data.

[0321] Example: The device records user A's voice tone and facial expressions using its camera and microphone.

[0322] Step 4:

[0323] The emotion engine analyzes emotional data.

[0324] The emotion engine analyzes the collected data to identify the user's emotional state. For example, it categorizes it into positive, negative, or neutral.

[0325] Example: The emotion engine identifies a "positive" emotional state from user A's facial expressions and voice.

[0326] Step 5:

[0327] The device sends the request and sentiment data to the server.

[0328] The terminal sends the user's request and the results of the emotion engine's analysis to the server.

[0329] Example: The device sends a request to the server saying "I want to be praised for today's presentation" along with "positive" sentiment data.

[0330] Step 6:

[0331] The server receives the request and sentiment data.

[0332] The server receives requests and emotion data sent from the terminal and analyzes the request content and emotion state.

[0333] Example: The server receives a request to "praise today's presentation" and "positive" sentiment data.

[0334] Step 7:

[0335] The server retrieves user data.

[0336] The server retrieves the user's basic information, past behavioral history, and sentiment data from the database.

[0337] Example: The server retrieves user A's past presentation evaluations, behavioral history, and sentiment data.

[0338] Step 8:

[0339] The server applies the generated AI model.

[0340] The server inputs the acquired user data into an AI model to generate appropriate compliments and praise songs. Emotional data is also taken into consideration during the generation process.

[0341] Example: The server uses a generated AI model to produce a compliment such as, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0342] Step 9:

[0343] The server compiles the generated results.

[0344] The server compiles the generated results, such as compliments and compliment songs, and formats them into an appropriate format.

[0345] Example: Format the compliments generated by the server into text format.

[0346] Step 10:

[0347] The server sends the generated results to the terminal.

[0348] The server transfers the generated results to the terminal.

[0349] Example: The server sends a compliment to the terminal saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0350] Step 11:

[0351] The terminal receives the generated result.

[0352] The terminal receives the generated results sent from the server and converts them into the corresponding output format (text, audio, video, etc.).

[0353] Example: The device receives the text message, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0354] Step 12:

[0355] The device provides praise (acknowledgment) to the user.

[0356] The device provides the user with generated compliments and compliment songs using text display, audio playback, and video playback functions.

[0357] Example: The device plays an audio message saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0358] Step 13:

[0359] Users provide feedback

[0360] Users who receive praise (approval) enter feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[0361] Example: User A enters the feedback, "I was so happy to receive that compliment!"

[0362] Step 14:

[0363] The device sends feedback to the server.

[0364] The device sends user feedback to the server.

[0365] Example: The device sends feedback to the server saying, "I was so happy to receive that compliment!"

[0366] Step 15:

[0367] The server receives the feedback and saves it as training data.

[0368] The server receives feedback and stores it as training data. It also uses this data to improve the generative AI model.

[0369] Example: The server receives user A's feedback and adds it to a database to be used for future praise (approval).

[0370] This allows for efficient praise (acknowledgment) through the collaboration of users, devices, servers, and the emotion engine, gradually fostering a new culture of recognition.

[0371] (Example 2)

[0372] 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".

[0373] Traditional praise (acknowledgment) systems often suffer from low user satisfaction because they simply generate compliments and praise songs mechanically, without adequately considering user behavior or emotions. Furthermore, they lack mechanisms for effectively utilizing user feedback to improve the system. Therefore, there is a need for more personalized praise (acknowledgment) that resonates with user emotions.

[0374] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting and managing user data, means for analyzing the user's emotional data using an emotion engine, means for training a generative AI model for generating compliments or compliment songs based on the user data and emotional data, means for providing the generated compliments or compliment songs to the user, means for receiving user feedback on the provided compliments or compliment songs, and means for improving the generative AI model based on the feedback. This makes it possible to provide personalized compliments (approvals) that take into account the user's emotional state, and also enables continuous system improvement based on feedback.

[0375] "User data" is a general term for data related to individual users, including basic user information, behavioral history, and feedback.

[0376] An "emotion engine" is a general term for software or hardware used to analyze a user's emotional state from their voice, facial expressions, text, etc.

[0377] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to learn user behavior and emotional states, and then generates appropriate compliments and compliment songs.

[0378] A "praise" is a sentence or message used to acknowledge a user's actions or achievements.

[0379] "Praise songs" are audio or musical content created to praise users.

[0380] "Generating" means creating new data or information based on input data.

[0381] "Training" refers to the process of using collected data to train a generative AI model and improve its performance.

[0382] "Feedback" refers to the opinions and evaluations that users provide regarding compliments or songs that praise others.

[0383] "To analyze" means to examine data and find meaning or trends.

[0384] "Personalized" means that it is tailored to the specific needs and preferences of each individual user.

[0385] A "system" is a collection of devices or programs in which multiple components work together to achieve a specific function.

[0386] To implement this invention, four elements are necessary: ​​a terminal that provides a user interface, a server that manages and generates data and possesses an AI model, an emotion engine that recognizes the user's emotions, and a user who makes a request for praise (approval).

[0387] First, the user uses their device to enter a request for praise (approval). Input methods include text boxes and speech recognition. For example, the user might enter a request such as, "I want you to praise my presentation." The device then sends the entered request to the server. A secure protocol such as HTTPS is used for this communication.

[0388] Next, the server works with the emotion engine to acquire and analyze the user's voice and facial expression data. The emotion engine sends the analysis results to the server, and based on these results, it determines the user's emotional state. The server also collects the user's behavior history and past feedback and stores it in a database.

[0389] The server uses this data to train a generative AI model. Machine learning techniques are used for training, learning user behavior patterns and emotional states. This allows the generative AI model to generate the most appropriate compliments and praise songs for each user.

[0390] For example, it can generate specific compliments such as, "Your presentation was fantastic! Your passion really came through," or an original compliment song that includes the user's name. The generated compliments and songs are sent from the server to the terminal, which then displays or plays them for the user. Possible display methods include text display, audio output, and video playback.

[0391] Users who receive praise (approval) enter feedback on the content into their device. This feedback is sent to the server and used to improve the generative AI model. The server uses this feedback to retrain the generative AI model and improve the quality of future praise messages and praise songs.

[0392] As a concrete example, in the business world, a user might request "praise for a proposal made in a meeting," and an emotion engine would analyze their emotions at that time. The server would generate a compliment based on the analysis results and the user's behavioral history, and send it to the device. The device would then display the compliment, and the user would provide feedback in response.

[0393] In the private area, the user can request to be praised for creating a new dish, and the emotion engine analyzes their facial expressions. Similarly, the server generates compliments and praise songs based on the analysis results and the user's behavioral history, and provides them to the user through the device.

[0394] Example of a prompt

[0395] Please tell me what you would like to be praised for. For example, "I made a great suggestion at today's meeting, so I'd like to be praised for it." Also, please tell me how you felt at the time.

[0396] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0397] Step 1:

[0398] Users use their devices to enter requests for praise (approval). Input methods include text boxes and voice recognition. For example, a user might say, "I'd like to be praised for my proposal at the meeting." The input from the device is saved as text data in its internal memory.

[0399] Step 2:

[0400] The terminal sends the entered request to the server via a secure protocol such as HTTPS. The terminal's input data is transmitted through network components, and the server receives the request. The input data is sent in text format, and the server receives it.

[0401] Step 3:

[0402] The server uses an emotion engine to analyze the user's voice and facial expression data. It takes voice and image data as input, and the emotion engine analyzes this data to output the emotional state. For example, emotion tags such as "joy," "surprise," and "sadness" are output.

[0403] Step 4:

[0404] The server collects user behavior history and past feedback and stores it in a database. By taking past behavioral data and feedback data as input and storing them in the database, it builds a user model. For example, evaluations of past presentations and past emotional states are recorded.

[0405] Step 5:

[0406] The server trains a generative AI model based on collected user data and sentiment data. Behavioral history, emotional states, and feedback are used as training data, which the generative AI model learns from. Through training, the generative AI model understands user behavior patterns and emotional tendencies.

[0407] Step 6:

[0408] Using a generative AI model, the server generates appropriate compliments or compliment songs. Training data is used as input, and compliments or compliment songs are generated as output. For example, a compliment such as "Your presentation was fantastic! Your passion came through!" might be generated.

[0409] Step 7:

[0410] The server sends the generated compliments and compliment songs to the device. The data output by the generating AI model is sent in text or audio format and received by the device. The output data is securely transferred using the HTTPS protocol.

[0411] Step 8:

[0412] The device displays or plays received compliments and compliment songs to the user through its user interface. It receives data sent from the server as input and outputs screen displays and audio playback. Text may be displayed on the screen, or audio may play from the speaker.

[0413] Step 9:

[0414] Users input feedback on the praise (acknowledgment) they receive into their device. They use text boxes or rating buttons to enter their feedback. For example, they might write, "This praise was very encouraging."

[0415] Step 10:

[0416] The device sends feedback to the server via the HTTPS protocol. Input data is sent in text format, and the server receives the feedback data. This feedback is then stored in a database and used as training data for the next session.

[0417] Step 11:

[0418] The server improves the generative AI model based on the feedback. It analyzes the feedback data and uses it as training data for the generative AI model. This improves the quality of the praise and compliment songs generated in the future.

[0419] The above describes the specific processing steps of the program in this system.

[0420] (Application Example 2)

[0421] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0422] Traditional praise (acknowledgment) systems primarily generate compliments and praise songs based on user input, without considering the user's real-time emotional state. Therefore, providing appropriate praise (acknowledgment) tailored to the user's emotional state was difficult. Furthermore, there were no systems in place to offer real-time, emotionally appropriate compliments during face-to-face interactions with customers in physical stores.

[0423] 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.

[0424] In this invention, the server includes means for receiving requests for praise (approval) from users, means for transmitting such requests to the server, means for collecting and managing user data, means for training a generative AI model for generating praise words or praise songs based on the user data, means for providing the generated praise words or praise songs to the user, means for acquiring customer facial expression data and voice data and analyzing them using an emotion engine, and means for using the generative AI model based on the emotion analysis results to display appropriate praise words on the user terminal in real time. This enables the provision of personalized praise (approval) that takes into account the user's real-time emotional state, and the presentation of emotion-appropriate praise words in real time when interacting with customers in physical stores.

[0425] "A means of receiving requests for praise (approval) from users" refers to an interface that receives information via the device about what the user wants to be praised for.

[0426] "Means for sending the aforementioned request to the server" refers to a function that sends a request for praise (approval) received from the user terminal to the server via the network.

[0427] "Means for collecting and managing user data" refers to a system that collects and stores user basic information, behavioral history, feedback, etc., in a database for management.

[0428] "Means for training a generative AI model to generate compliments or compliment songs based on user data" refers to a method of training a generative AI model to generate compliments or compliment songs using a machine learning algorithm with collected user data.

[0429] "Means of providing users with generated compliments or compliment songs" refers to a function that presents users with compliments or compliment songs generated by a trained generative AI model through their device.

[0430] "Means for acquiring customer facial expression data and voice data and analyzing it using an emotion engine" refers to a system that captures customer facial expressions and voices using a camera and microphone, and analyzes that data using an emotion engine.

[0431] "A method for displaying appropriate compliments on the user's device in real time using a generative AI model based on emotion analysis results" refers to a method in which a generative AI model generates compliments tailored to the user in real time based on data analyzed by an emotion engine, and displays them immediately on the user's device.

[0432] To implement this invention, it is necessary to construct a system that combines a user interface using a smart device, a server, an emotion engine, and a generative AI model. The aim of this system is to provide users wearing smart devices in physical stores with real-time, emotion-responsive compliments.

[0433] Server Processing

[0434] The server uses the following hardware and software:

[0435] Hardware: The server can use an EC2 instance from Amazon Web Services (AWS).

[0436] Software: The necessary software to be installed on the server includes Flask (a Python micro web framework) and the Requests library (for sending HTTP requests).

[0437] The server performs the following operations.

[0438] 1. Collection and management of user data

[0439] The server collects user facial expression and voice data transmitted from smart devices and stores it in a database. This allows for the accumulation of the user's emotional state and behavioral history.

[0440] 2. Emotion analysis

[0441] An emotion engine (for example, Microsoft Azure's Emotion API) is used to analyze acquired facial expression and voice data to identify the user's emotional state.

[0442] 3. Generating compliments

[0443] Based on the analyzed sentiment data, a generative AI model (e.g., OpenAI's GPT-4) generates appropriate compliments. The following prompts are used in this generation process.

[0444] Example of a prompt:

[0445] Based on the customer's facial expression analysis results and emotional state, please create compliments and suggestions appropriate for the following situations.

[0446] Customer's emotional state: {emotional state}

[0447] Customer behavior: {Actions}

[0448] Example: A customer looks a little anxious when choosing new clothes. Compliments and suggestions to reassure them.

[0449] 4. Offering compliments

[0450] The generated compliments are displayed in real time on the smart device's screen. This allows store employees to instantly convey appropriate compliments to customers.

[0451] Terminal processing

[0452] Smart devices have the following functions:

[0453] Providing a user interface

[0454] Smart devices (such as smart glasses and head-mounted displays) provide an interface that allows users to input what they want to be praised for using voice input or touch controls.

[0455] Communication with the server

[0456] Smart devices transmit user input, facial expression data, and voice data to a server. This is done using secure communication protocols such as HTTPS.

[0457] Integration with the emotion engine

[0458] The system uses the camera and microphone built into the smart device to capture the user's facial expressions and voice, and then analyzes them for transmission to a server.

[0459] Compliment display

[0460] Compliments sent from the server are displayed in real time on the smart device's screen. This allows store staff to use that information to give customers appropriate compliments and suggestions.

[0461] User actions

[0462] Information entry

[0463] Users input the behaviors and achievements they want to be praised for in real time via their smart devices. This input is done through voice recognition or touch operation.

[0464] Provide feedback

[0465] After receiving praise (acknowledgment), users provide feedback on the content via their smart devices. This feedback is stored on a server and used to improve the generated AI model.

[0466] Specific example

[0467] In a physical store, if a customer choosing new clothes appears slightly anxious, a salesperson wearing smart glasses can capture that anxious expression. An emotion engine analyzes the expression as "anxiety," and based on this analysis, a generative AI model generates a compliment in real time, such as, "Those clothes look great on you. You've made a really great choice." The salesperson can then view this on the smart glasses' display and convey the compliment to the customer. In this way, the customer's shopping experience can be improved.

[0468] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0469] Step 1:

[0470] Users input the things or actions they want to be praised for through a smart device (such as smart glasses or a head-mounted display). This input is done using voice recognition or touch controls. The input information is temporarily stored as data within the device.

[0471] Input: Information (text or audio) about the user's desired praise or actions.

[0472] Output: Temporarily held input data

[0473] Step 2:

[0474] The terminal sends user input data to the server via a secure communication protocol (e.g., HTTPS). Simultaneously, the user's facial expressions and voice data are also captured and sent to the server.

[0475] Input: Temporarily stored user input data, facial expression data, voice data

[0476] Output: User input data, facial expression data, and voice data sent to the server.

[0477] Step 3:

[0478] The server collects basic user information, behavioral history, and feedback based on the received data and stores it in a database. This allows for the accumulation of user emotional states and behavioral history.

[0479] Input: User input data, facial expression data, and voice data sent from the terminal.

[0480] Output: User information, activity history, and feedback stored in the database.

[0481] Step 4:

[0482] The server uses an emotion engine (for example, Microsoft Azure's Emotion API) to analyze the acquired facial expression and voice data and identify the user's emotional state.

[0483] Input: Saved user facial expression data, voice data

[0484] Output: Analyzed user emotional state data

[0485] Step 5:

[0486] Based on the analyzed sentiment data, the server uses a generative AI model (e.g., OpenAI's GPT-4) to generate appropriate compliments. This generation process is based on prompt statements.

[0487] Example of a prompt:

[0488] Based on the customer's facial expression analysis results and emotional state, please create compliments and suggestions appropriate for the following situations.

[0489] Customer's emotional state: {emotional state}

[0490] Customer behavior: {Actions}

[0491] Example: A customer looks a little anxious when choosing new clothes. Compliments and suggestions to reassure them.

[0492] Input: Analyzed user emotion state data, prompt text

[0493] Output: Generated compliments

[0494] Step 6:

[0495] The server sends the generated compliments to the smart device's display in real time.

[0496] Input: Generated compliments

[0497] Output: Compliments sent to smart devices

[0498] Step 7:

[0499] The terminal displays compliments sent from the server in real time, providing them to the user. This allows users to instantly deliver appropriate compliments when interacting with customers face-to-face in physical stores.

[0500] Input: Compliments sent from the server

[0501] Output: Compliments displayed on the smart device's screen.

[0502] Step 8:

[0503] After receiving praise (acknowledgment), users provide feedback on the content via their device. This feedback is sent to the server and used to improve the generated AI model.

[0504] Input: User feedback data

[0505] Output: Feedback sent to the server, improvements to the generated AI model.

[0506] 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.

[0507] 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.

[0508] 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.

[0509] [Second Embodiment]

[0510] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0511] 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.

[0512] 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).

[0513] 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.

[0514] 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.

[0515] 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).

[0516] 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.

[0517] 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.

[0518] 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.

[0519] 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.

[0520] 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.

[0521] 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".

[0522] To implement this invention, three elements are necessary: ​​a terminal that provides a user interface, a server that manages data and maintains a generation AI model, and a user who makes a request for praise (approval). The system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server. The server collects and analyzes the user's data and uses the generation AI model to generate appropriate words of praise or praise songs. The generated words of praise or praise songs are then provided to the user through the terminal.

[0523] Server Processing

[0524] 1. Collection and management of user data

[0525] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a database. For example, it retrieves data via an API to capture presentations that a user has previously given high ratings to, or specific behavioral history.

[0526] 2. Training the Generative AI Model

[0527] The server trains a generative AI model based on the collected data. Machine learning techniques are used for training, learning user behavior patterns and preferences. This allows for the provision of more personalized praise (affirmation).

[0528] 3. Generating compliments and compliment songs

[0529] The server uses a trained generative AI model to generate personalized compliments and songs for the user. For example, it can create specific messages such as, "Your presentation today was fantastic! Your passion really came through," or original songs that include the user's name.

[0530] Terminal processing

[0531] 1. Providing a user interface

[0532] The terminal provides an interface for the user to enter requests. Possible input methods include text boxes, voice recognition, and selection menus.

[0533] 2. Communication with the server

[0534] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[0535] 3. Providing praise (acknowledgment)

[0536] The terminal provides users with compliments and songs received from the server using text display, audio output, and video playback functions.

[0537] User actions

[0538] 1. Information Entry

[0539] Users input the actions or achievements they want to be praised for through their device. For example, they can even voice-in requests such as, "I'd like to be praised for the proposal I made at today's meeting."

[0540] 2. Providing feedback

[0541] After receiving praise (acknowledgment), the user enters feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[0542] 3. Use both inside and outside the company

[0543] Users who receive praise (acknowledgment) gain a positive experience, which encourages them to praise others. This fosters a new culture of recognition both within and outside the organization.

[0544] Specific example

[0545] The following are specific examples of how this system can be used in both the business and personal spheres.

[0546] Examples of use in the business field

[0547] User A enters a request into the terminal asking for praise for their presentation. The terminal sends this request and User A's ID to the server. The server refers to User A's past presentation evaluation data and uses a generative AI model to generate the most appropriate praise. The generated result is sent to the terminal in text format, and the terminal displays it to User A.

[0548] Examples of use in private areas

[0549] User B enters a request into the device to be praised for making a new dish. The device sends this request to the server. The server uses a generative AI model based on past feedback data about User B's cooking to generate appropriate praise. The generated result is sent to the device in the form of a voice message, which the device then provides to User B via voice.

[0550] In this way, the present invention realizes a system that provides praise (recognition) tailored to the individual circumstances of each user, and can create a new culture of recognition throughout society.

[0551] The following describes the processing flow.

[0552] Program processing flow

[0553] Step 1:

[0554] The user enters a request on the device.

[0555] Users input the behaviors or achievements they want to be praised for into their device as text or voice.

[0556] Example: User A types "I want you to praise my presentation today."

[0557] Step 2:

[0558] The terminal receives the input data.

[0559] The device receives user input and temporarily stores the necessary data (text or voice).

[0560] Example: The device saves data that says, "I want you to praise my presentation today."

[0561] Step 3:

[0562] The device sends the request to the server.

[0563] The terminal sends the user's request and related data, such as the user ID and past activity history, to the server.

[0564] Example: The device sends a request to the server saying "I want to be praised for today's presentation" along with the ID of user A.

[0565] Step 4:

[0566] The server receives the request.

[0567] The server receives requests sent from the terminal and parses the request content.

[0568] Example: The server receives a request to "praise today's presentation" and begins analysis.

[0569] Step 5:

[0570] The server retrieves user data.

[0571] The server retrieves the user's basic information and past activity history from the database.

[0572] Example: The server retrieves evaluations and feedback on user A's past presentations.

[0573] Step 6:

[0574] The server applies the generated AI model.

[0575] The server inputs the acquired user data into an AI model to generate appropriate compliments and praise songs.

[0576] Example: The server uses a generated AI model to produce a compliment such as, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0577] Step 7:

[0578] The server compiles the generated results.

[0579] The server compiles the generated results, such as compliments and compliment songs, and formats them into an appropriate format.

[0580] Example: Format the compliments generated by the server into text format.

[0581] Step 8:

[0582] The server sends the generated results to the terminal.

[0583] The server transfers the generated results to the terminal.

[0584] Example: The server sends a compliment to the terminal saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0585] Step 9:

[0586] The terminal receives the generated result.

[0587] The terminal receives the generated results sent from the server and converts them into the corresponding output format (text, audio, video, etc.).

[0588] Example: The device receives the text message, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0589] Step 10:

[0590] The device provides praise (acknowledgment) to the user.

[0591] The device provides the user with generated compliments and compliment songs using text display, audio playback, and video playback functions.

[0592] Example: The device plays an audio message saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0593] Step 11:

[0594] Users provide feedback

[0595] Users who receive praise (approval) enter feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[0596] Example: User A enters the feedback, "I was so happy to receive that compliment!"

[0597] Step 12:

[0598] The device sends feedback to the server.

[0599] The device sends user feedback to the server.

[0600] Example: The device sends feedback to the server saying, "I was so happy to receive that compliment!"

[0601] Step 13:

[0602] The server receives the feedback and saves it as training data.

[0603] The server receives feedback and stores it as training data. It also uses this data to improve the generative AI model.

[0604] Example: The server receives user A's feedback and adds it to a database to be used for future praise (approval).

[0605] This allows for efficient praise (recognition) through collaboration between users, devices, and servers, gradually fostering a new culture of recognition.

[0606] (Example 1)

[0607] 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".

[0608] The lack of systems that provide appropriate praise and encouragement for individual user actions and achievements hinders the enhancement of user motivation and the fostering of a positive culture. In this situation, it is difficult to create a culture of mutual recognition within organizations and among individuals. Furthermore, existing systems fail to effectively utilize user feedback, making it difficult to continuously improve the quality of praise (recognition). Moreover, the provision of praise (recognition) is limited, lacking broad application in business and personal spheres.

[0609] 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.

[0610] In this invention, the server includes means for collecting and managing user data, means for preprocessing and feature extracting data for use in training, and means for training a generative AI model for generating compliments or compliment songs based on the user data. This makes it possible to provide appropriate compliments or compliment songs based on the user's behavior and achievements. Furthermore, by including means for receiving user feedback and storing it as training data to improve the generative AI model, the quality of compliments (recognition) can be continuously improved. In addition, by including means for providing compliments (recognition) in stages for business, private, and all people, a wide range of applications becomes possible.

[0611] A "user" refers to an individual or legal entity that requests praise (approval).

[0612] A "request" refers to the information a user enters into the system to seek praise (approval).

[0613] A "server" refers to a computer system that collects and manages user data, trains and runs a generation AI model to generate compliments and praise songs.

[0614] "Data" refers to a collection of information such as a user's basic information, behavioral history, and feedback.

[0615] A "generative AI model" refers to an algorithm that uses machine learning techniques to generate compliments and compliment songs based on user data.

[0616] "Training" refers to the learning process in which the server uses collected user data to enable the generating AI model to create appropriate compliments and praise songs.

[0617] "Praise" refers to sentences that acknowledge and encourage a user's actions and achievements.

[0618] "Praise songs" refer to musical content that celebrates and encourages users' actions and achievements.

[0619] "Feedback" refers to information that users provide to the system, such as their evaluations and impressions of praise (approval).

[0620] The "business domain" refers to the environment related to the workplace and business operations.

[0621] "Private space" refers to the environment associated with home and personal activities.

[0622] "Gradual delivery" refers to the process of providing praise (acknowledgment) in a flexible manner, adapting it to various situations and recipients.

[0623] To implement this invention, three elements are necessary: ​​a terminal that provides a user interface, a server that manages data and holds a generating AI model, and a user who makes a request for praise (approval). This system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server.

[0624] Server Processing

[0625] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a MySQL database. This data is used for later analysis and training of generative AI models. Specifically, it uses a RESTful API to retrieve user presentation evaluation data.

[0626] Next, the server preprocesses and extracts features from the collected data. This process uses Python and TensorFlow. Based on the preprocessed data, the server trains a generative AI model. This training improves the model's ability to generate compliments and praise songs that are tailored to the user's actions and achievements.

[0627] A trained generative AI model generates compliments and praise songs in real time in response to user requests. The generated content is converted into text or audio files and provided to the user via a user interface. This process can utilize GPT-3 models or open-source speech synthesis libraries.

[0628] Terminal processing

[0629] The device provides an interface for the user to input requests using HTML / CSS and JavaScript. Examples include web pages with text boxes or voice input functions. When the user inputs a request, the device converts this information into JSON format and sends it to the server using the HTTPS protocol. Specifically, it sends the request data as a POST request to the server's API endpoint.

[0630] The device also plays a role in providing users with compliments and praise songs received from the server. Text is displayed on the web page, and audio is played via the Audio tag.

[0631] User actions

[0632] The user enters what they want to be praised for through the device's interface. For example, they might type "I want to be praised for completing today's project" into the text box. For voice input, they can click the microphone icon to use a dedicated speech recognition API. Once the user sends the request, the device sends it to the server, which then generates appropriate praise or praise songs.

[0633] Users who receive the generated compliments or compliment songs can input feedback on their device. For example, they can enter comments such as "I liked this compliment" or "I would like more specific details" into the text box and click the "Send Feedback" button to send the feedback to the server.

[0634] This system can provide praise (recognition) in the business, personal, and all-encompassing spheres. Specific examples are given below.

[0635] Examples of use in the business field

[0636] User A enters a request into the terminal asking for praise for their presentation. The terminal sends this request and User A's ID to the server. The server refers to User A's past presentation evaluation data and uses a generative AI model to generate the most appropriate praise. The generated result is sent to the terminal in text format, and the terminal displays it to User A.

[0637] Example of a prompt

[0638] "Please praise my presentation today. User ID: 00123"

[0639] Examples of use in private areas

[0640] User B enters a request into the device to be praised for making a new dish. The device sends this request to the server. The server uses a generative AI model based on past feedback data about User B's cooking to generate appropriate praise. The generated result is sent to the device in the form of a voice message, which the device then provides to User B via voice.

[0641] Example of a prompt

[0642] "Please praise me for creating a new dish. User ID: 00456"

[0643] In this way, the present invention can provide praise (recognition) tailored to the individual circumstances of each user, thereby fostering a new culture of recognition throughout society.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1: Enter your request

[0646] The user enters the content they want to be praised for into the device's interface. For example, they might type "I'd like to be praised for today's presentation" into the text box. For voice input, they click the microphone icon and use a dedicated speech recognition API.

[0647] Input: A text or voice request entered by the user.

[0648] Output: Request data (text format or audio data)

[0649] Step 2: Sending Request Data

[0650] The terminal converts the user's input request into JSON format and sends it to the server using the HTTPS protocol.

[0651] Input: Request data (text format or audio data)

[0652] Output: Request data in JSON format to be sent to the server

[0653] Step 3: Collecting User Data

[0654] The server collects data such as basic user information, behavioral history, and feedback based on the received request data. Specifically, it uses a RESTful API to obtain user presentation evaluation data.

[0655] Input: User ID included in the request data

[0656] Output: Data set of user basic information, behavioral history, and feedback.

[0657] Step 4: Data Management

[0658] The server saves the collected data to a MySQL database. For example, it executes an SQL query like "INSERT INTO user_data (user_id, presentation_score) VALUES (1, 95);".

[0659] Input: Collected user data

[0660] Output: User data stored in the MySQL database

[0661] Step 5: Data preprocessing and feature extraction

[0662] The server uses Python and TensorFlow to retrieve data from the database and perform preprocessing and feature extraction. Preprocessing includes denoising and data normalization.

[0663] Input: User data stored in a MySQL database

[0664] Output: Preprocessed and feature-extracted data

[0665] Step 6: Training the AI ​​Model

[0666] The server trains a generative AI model based on preprocessed data, using feedforward and backpropagation algorithms.

[0667] Input: Preprocessed and feature-extracted data

[0668] Output: Trained generative AI model

[0669] Step 7: Generating compliments and compliment songs

[0670] The server uses a trained generative AI model to generate personalized compliments and praise songs for each user. For example, it can create a compliment such as "Your presentation today was excellent!" along with an original song that includes the user's name.

[0671] Input: User data and AI model

[0672] Output: Data of the generated compliments or compliment songs

[0673] Step 8: Providing praise (acknowledgment) results

[0674] The server sends the generated compliments and compliment songs to the device. The device receives them and provides them to the user using text display, audio output, or video playback functions.

[0675] Input: Data for generated compliments or compliment songs

[0676] Output: Compliments in text format displayed to the user, or audio / video played.

[0677] Step 9: Entering Feedback

[0678] The user enters feedback on the praise (acknowledgment) provided into the device. For example, they might enter a rating such as, "I liked this compliment."

[0679] Input: Feedback text or voice

[0680] Output: Feedback data

[0681] Step 10: Send and save feedback

[0682] The device sends user feedback to the server, which receives it and stores it as training data to improve the generated AI model. The server stores the feedback data in a MySQL database using INSERT statements.

[0683] Input: User feedback data

[0684] Output: Feedback data stored in the MySQL database

[0685] (Application Example 1)

[0686] 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."

[0687] It is necessary to alleviate the anxiety and uncertainty users feel while shopping in physical stores and to encourage purchasing behavior. In particular, it is crucial that users feel confident in their product selection and purchase decisions. Furthermore, personalized praise is an effective way to boost motivation and improve the user experience in physical stores, thereby increasing customer satisfaction. However, current technology lacks the appropriate systems to achieve this.

[0688] 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.

[0689] In this invention, the server includes means for receiving requests for praise (approval) from users, means for transmitting such requests to the server, means for collecting and managing user data, means for training a generative AI model for generating praise words or praise songs based on the user data, means for providing the generated praise words or praise songs to users, means for providing the generated praise words or praise songs in physical stores, and means for generating words of praise for the user's actions when they purchase or use a product. This makes it possible to improve the user experience in physical stores and increase the user's confidence in selecting and purchasing products.

[0690] 1. "Praise (acknowledgment)" refers to the positive evaluation or recognition that a user receives for a specific action or achievement.

[0691] 2. "Means for receiving requests" refers to interfaces or systems for receiving requests from users for praise (approval).

[0692] 3. "Means for sending requests to a server" refers to a system that has a communication function for sending received user requests to an external server.

[0693] 4. "Means for collecting and managing data" refers to systems and functions for collecting, appropriately storing, and managing user behavior history, feedback information, etc.

[0694] 5. A "generative AI model" refers to a machine learning model that generates compliments and compliment songs tailored to the user based on collected data.

[0695] 6. "Training methods" refer to the process and methods of optimizing and learning a generative AI model using collected data.

[0696] 7. "Means for providing generated compliments or compliment songs" refers to interfaces or output devices for providing generated compliments or compliment songs to users.

[0697] 8. "Means of providing in physical stores" refers to systems and methods for providing generated compliments and compliment songs to users in a physical store environment.

[0698] 9. "Means for generating compliments for user actions when purchasing or using a product" refers to methods and systems for generating appropriate compliments for user actions when selecting, purchasing, or using a product in a physical store.

[0699] To implement this invention, the following elements are necessary to constitute a system that provides praise (approval): a server, a terminal, and a user that communicate with each other and utilize a generative AI model to generate and provide personalized praise messages and praise songs.

[0700] Server Processing

[0701] 1. Collection and management of user data

[0702] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a database. This includes retrieving various user data via APIs. For example, this could include shopping behavior history and past purchase history.

[0703] 2. Training the Generative AI Model

[0704] The server uses machine learning techniques based on the collected data to train a generative AI model. This training allows the model to learn user behavior patterns and preferences, enabling it to provide more personalized praise (affirmation).

[0705] 3. Generating compliments and compliment songs

[0706] The server uses a trained generative AI model to generate compliments and praise songs tailored to the user's behavior and product choices. For example, it can generate specific messages such as, "That's a great choice! Your taste is excellent."

[0707] Terminal processing

[0708] 1. Providing a user interface

[0709] The terminal provides an interface for users to enter requests, including text boxes, voice recognition, and selection menus.

[0710] 2. Communication with the server

[0711] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[0712] 3. Providing praise (acknowledgment)

[0713] The device provides users with compliments and songs received from the server using text display, audio output, and video playback functions. In particular, in physical stores, compliments can be displayed using smart glasses or smartphones.

[0714] User actions

[0715] 1. Information Entry

[0716] Users input the actions or achievements they want to be praised for through their device. For example, they might request praise (approval) for a newly purchased product.

[0717] 2. Providing feedback

[0718] After receiving praise (acknowledgment), users input feedback on the content into their device. This includes whether they were satisfied with the content and suggestions for improvement.

[0719] 3. Use both inside and outside the company

[0720] Users who receive praise (acknowledgment) are more likely to praise others based on that positive experience. This fosters a new culture of recognition both within and outside the organization.

[0721] Specific examples of implementation in physical stores

[0722] Hardware:

[0723] Smart glasses (for example, Google Glass)

[0724] Smartphone (iOS or Android device)

[0725] software:

[0726] Server-side: Python, Flask (as an API server)

[0727] Client-side: Python (for API calls), UI for video display

[0728] The server receives compliment (approval) requests from users, collects and manages user data, and trains a generative AI model. It then provides the user with the generated compliments. For example, when a user picks up an item in a physical store, displaying a compliment such as "You have excellent taste in this item you've chosen!" on smart glasses can improve the user experience.

[0729] Prompt example:

[0730] User ID: user123

[0731] Action: recommended_product_acquired

[0732] Prompt message:

[0733] User123 has decided to purchase the newly recommended item. Let's praise them for choosing this item. This item is based on the latest fashion trends and is a very tasteful choice.

[0734] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0735] Step 1:

[0736] The user enters a praise (approval) request using their device. The user enters the action they want to be praised for, either through text or voice, regarding the product selection or purchase they made while shopping. This becomes the input data.

[0737] Step 2:

[0738] The terminal sends the input request to the server. A secure communication protocol such as HTTPS is used for transmission. The output is the request data sent to the server.

[0739] Step 3:

[0740] Based on the received request data, the server collects the user's past behavior history and feedback information from the database. This becomes the new input data.

[0741] Step 4:

[0742] The server trains a generative AI model using collected user data. It performs data preprocessing, feature extraction, and model retraining to learn user behavior patterns and preferences. The output is the trained generative AI model.

[0743] Step 5:

[0744] The server uses a trained generative AI model to generate praise for the requested action. Here, the prompt text serves as input data, and the corresponding praise is output. Specifically, the prompt text is input to the generative AI model, which then generates appropriate praise.

[0745] Step 6:

[0746] The server sends the generated compliment to the terminal. This becomes the new output data. The terminal sends the received compliment from the server to an output device that provides it to the user.

[0747] Step 7:

[0748] The device displays received compliments using smart glasses or a smartphone. Being complimented increases the user's willingness to purchase, leading to a positive shopping experience. The output is the displayed compliment.

[0749] Step 8:

[0750] Users input feedback on the compliments they receive into their device. This input data includes their satisfaction level with the compliments and areas for improvement.

[0751] Step 9:

[0752] The terminal sends feedback data to the server. The server stores the received feedback in a database and uses it to further improve the generative AI model. The output is an improvement in the performance of the generative AI model.

[0753] This enables a personalized praise (acknowledgment) system that improves the user experience in physical stores by coordinating between users, devices, and servers.

[0754] 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.

[0755] To implement this invention, four elements are required: a terminal that provides a user interface, a server that manages data and houses a generation AI model, an emotion engine that recognizes the user's emotions, and a user who makes a request for praise (approval). This system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server. The server collects and analyzes the user's data and the results of the emotion engine's analysis, and uses the generation AI model to generate appropriate words of praise or praise songs. The generated words of praise or praise songs are then provided to the user through the terminal.

[0756] Server Processing

[0757] 1. Collection and management of user data

[0758] The server collects and stores data such as basic user information, behavioral history, feedback, and analysis results from the emotion engine in a database. For example, it retrieves data via API to capture presentations that the user has previously given high ratings to, specific behavioral history, and emotional states.

[0759] 2. Training the Generative AI Model

[0760] The server trains a generative AI model based on the collected data. Machine learning techniques are used for training, learning user behavioral tendencies, preferences, and emotional states. This allows for the provision of more personalized praise (affirmation).

[0761] 3. Generating compliments and compliment songs

[0762] The server uses a trained generative AI model to generate personalized compliments and songs for the user. For example, it can create specific messages such as, "Your presentation today was fantastic! Your passion really came through," or original songs that include the user's name.

[0763] Terminal processing

[0764] 1. Providing a user interface

[0765] The terminal provides an interface for the user to enter requests. Possible input methods include text boxes, voice recognition, and selection menus.

[0766] 2. Communication with the server

[0767] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[0768] 3. Integration with the emotion engine

[0769] The device works in conjunction with an emotion engine to acquire and analyze the user's voice and facial expression data. The analysis results are sent to the server as data related to the user's emotional state.

[0770] 4. Providing praise (acknowledgment)

[0771] The terminal provides users with compliments and songs received from the server using text display, audio output, and video playback functions.

[0772] User actions

[0773] 1. Information Entry

[0774] Users input the actions or achievements they want to be praised for through their device. For example, they can voice-in requests such as, "I'd like to be praised for the proposal I made at today's meeting."

[0775] 2. Providing feedback

[0776] After receiving praise (acknowledgment), the user enters feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[0777] 3. Use both inside and outside the company

[0778] Users who receive praise (acknowledgment) gain a positive experience, which encourages them to praise others. This fosters a new culture of recognition both within and outside the organization.

[0779] The role of the emotional engine

[0780] 1. Analysis of user sentiment

[0781] The emotion engine analyzes the user's emotional state from their voice, facial expressions, and text, and sends the results to the server. This allows the generative AI model to generate appropriate compliments and praise songs that take the user's emotional state into account.

[0782] 2. Sending emotional data

[0783] The analysis results from the emotion engine are sent to the server as data indicating the user's emotional state. This data is also used as training data for the generative AI model.

[0784] Specific example

[0785] The following are specific examples of how this system can be used in both the business and personal spheres.

[0786] Examples of use in the business field

[0787] User A enters a request into the device asking for praise for their presentation. Simultaneously, the emotion engine analyzes User A's voice and facial expressions and sends their emotional state to the server. The server generates the most appropriate praise based on the request and emotion data, and sends the generated praise to the device. The device displays the praise to User A and also plays it aloud.

[0788] Examples of use in private areas

[0789] User B enters a request into the terminal asking to be praised for making a new dish. Simultaneously, the emotion engine analyzes User B's facial expressions and sends their emotional state to the server. The server generates the most appropriate praise based on the request and emotion data, and sends the generated praise to the terminal. The terminal displays and plays the praise aloud for User B.

[0790] In this way, the present invention realizes a system that can provide praise (acknowledgment) that takes into account the user's emotional state, thereby forming a new, more personalized culture of acknowledgment.

[0791] The following describes the processing flow.

[0792] Program processing flow

[0793] Step 1:

[0794] The user enters a request on the device.

[0795] Users input the behaviors or achievements they want to be praised for into their device as text or voice.

[0796] Example: User A types "I want you to praise my presentation today."

[0797] Step 2:

[0798] The terminal receives the input data.

[0799] The device receives user input and temporarily stores the necessary data (text or voice).

[0800] Example: The device saves data that says, "I want you to praise my presentation today."

[0801] Step 3:

[0802] The device collects emotional data.

[0803] The device activates an emotion engine and collects user voice and facial expression data.

[0804] Example: The device records user A's voice tone and facial expressions using its camera and microphone.

[0805] Step 4:

[0806] The emotion engine analyzes emotional data.

[0807] The emotion engine analyzes the collected data to identify the user's emotional state. For example, it categorizes it into positive, negative, or neutral.

[0808] Example: The emotion engine identifies a "positive" emotional state from user A's facial expressions and voice.

[0809] Step 5:

[0810] The device sends the request and sentiment data to the server.

[0811] The terminal sends the user's request and the results of the emotion engine's analysis to the server.

[0812] Example: The device sends a request to the server saying "I want to be praised for today's presentation" along with "positive" sentiment data.

[0813] Step 6:

[0814] The server receives the request and sentiment data.

[0815] The server receives requests and emotion data sent from the terminal and analyzes the request content and emotion state.

[0816] Example: The server receives a request to "praise today's presentation" and "positive" sentiment data.

[0817] Step 7:

[0818] The server retrieves user data.

[0819] The server retrieves the user's basic information, past behavioral history, and sentiment data from the database.

[0820] Example: The server retrieves user A's past presentation evaluations, behavioral history, and sentiment data.

[0821] Step 8:

[0822] The server applies the generated AI model.

[0823] The server inputs the acquired user data into an AI model to generate appropriate compliments and praise songs. Emotional data is also taken into consideration during the generation process.

[0824] Example: The server uses a generated AI model to produce a compliment such as, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0825] Step 9:

[0826] The server compiles the generated results.

[0827] The server compiles the generated results, such as compliments and compliment songs, and formats them into an appropriate format.

[0828] Example: Format the compliments generated by the server into text format.

[0829] Step 10:

[0830] The server sends the generated results to the terminal.

[0831] The server transfers the generated results to the terminal.

[0832] Example: The server sends a compliment to the terminal saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0833] Step 11:

[0834] The terminal receives the generated result.

[0835] The terminal receives the generated results sent from the server and converts them into the corresponding output format (text, audio, video, etc.).

[0836] Example: The device receives the text message, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0837] Step 12:

[0838] The device provides praise (acknowledgment) to the user.

[0839] The device provides the user with generated compliments and compliment songs using text display, audio playback, and video playback functions.

[0840] Example: The device plays an audio message saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[0841] Step 13:

[0842] Users provide feedback

[0843] Users who receive praise (approval) enter feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[0844] Example: User A enters the feedback, "I was so happy to receive that compliment!"

[0845] Step 14:

[0846] The device sends feedback to the server.

[0847] The device sends user feedback to the server.

[0848] Example: The device sends feedback to the server saying, "I was so happy to receive that compliment!"

[0849] Step 15:

[0850] The server receives the feedback and saves it as training data.

[0851] The server receives feedback and stores it as training data. It also uses this data to improve the generative AI model.

[0852] Example: The server receives user A's feedback and adds it to a database to be used for future praise (approval).

[0853] This allows for efficient praise (acknowledgment) through the collaboration of users, devices, servers, and the emotion engine, gradually fostering a new culture of recognition.

[0854] (Example 2)

[0855] 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".

[0856] Traditional praise (acknowledgment) systems often suffer from low user satisfaction because they simply generate compliments and praise songs mechanically, without adequately considering user behavior or emotions. Furthermore, they lack mechanisms for effectively utilizing user feedback to improve the system. Therefore, there is a need for more personalized praise (acknowledgment) that resonates with user emotions.

[0857] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting and managing user data, means for analyzing the user's emotional data using an emotion engine, means for training a generative AI model for generating compliments or compliment songs based on the user data and emotional data, means for providing the generated compliments or compliment songs to the user, means for receiving user feedback on the provided compliments or compliment songs, and means for improving the generative AI model based on the feedback. This makes it possible to provide personalized compliments (approvals) that take into account the user's emotional state, and also enables continuous system improvement based on feedback.

[0858] "User data" is a general term for data related to individual users, including basic user information, behavioral history, and feedback.

[0859] An "emotion engine" is a general term for software or hardware used to analyze a user's emotional state from their voice, facial expressions, text, etc.

[0860] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to learn user behavior and emotional states, and then generates appropriate compliments and compliment songs.

[0861] A "praise" is a sentence or message used to acknowledge a user's actions or achievements.

[0862] "Praise songs" are audio or musical content created to praise users.

[0863] "Generating" means creating new data or information based on input data.

[0864] "Training" refers to the process of using collected data to train a generative AI model and improve its performance.

[0865] "Feedback" refers to the opinions and evaluations that users provide regarding compliments or songs that praise others.

[0866] "To analyze" means to examine data and find meaning or trends.

[0867] "Personalized" means that it is tailored to the specific needs and preferences of each individual user.

[0868] A "system" is a collection of devices or programs in which multiple components work together to achieve a specific function.

[0869] To implement this invention, four elements are necessary: ​​a terminal that provides a user interface, a server that manages and generates data and possesses an AI model, an emotion engine that recognizes the user's emotions, and a user who makes a request for praise (approval).

[0870] First, the user uses their device to enter a request for praise (approval). Input methods include text boxes and speech recognition. For example, the user might enter a request such as, "I want you to praise my presentation." The device then sends the entered request to the server. A secure protocol such as HTTPS is used for this communication.

[0871] Next, the server works with the emotion engine to acquire and analyze the user's voice and facial expression data. The emotion engine sends the analysis results to the server, and based on these results, it determines the user's emotional state. The server also collects the user's behavior history and past feedback and stores it in a database.

[0872] The server uses this data to train a generative AI model. Machine learning techniques are used for training, learning user behavior patterns and emotional states. This allows the generative AI model to generate the most appropriate compliments and praise songs for each user.

[0873] For example, it can generate specific compliments such as, "Your presentation was fantastic! Your passion really came through," or an original compliment song that includes the user's name. The generated compliments and songs are sent from the server to the terminal, which then displays or plays them for the user. Possible display methods include text display, audio output, and video playback.

[0874] Users who receive praise (approval) enter feedback on the content into their device. This feedback is sent to the server and used to improve the generative AI model. The server uses this feedback to retrain the generative AI model and improve the quality of future praise messages and praise songs.

[0875] As a concrete example, in the business world, a user might request "praise for a proposal made in a meeting," and an emotion engine would analyze their emotions at that time. The server would generate a compliment based on the analysis results and the user's behavioral history, and send it to the device. The device would then display the compliment, and the user would provide feedback in response.

[0876] In the private area, the user can request to be praised for creating a new dish, and the emotion engine analyzes their facial expressions. Similarly, the server generates compliments and praise songs based on the analysis results and the user's behavioral history, and provides them to the user through the device.

[0877] Example of a prompt

[0878] Please tell me what you would like to be praised for. For example, "I made a great suggestion at today's meeting, so I'd like to be praised for it." Also, please tell me how you felt at the time.

[0879] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0880] Step 1:

[0881] Users use their devices to enter requests for praise (approval). Input methods include text boxes and voice recognition. For example, a user might say, "I'd like to be praised for my proposal at the meeting." The input from the device is saved as text data in its internal memory.

[0882] Step 2:

[0883] The terminal sends the entered request to the server via a secure protocol such as HTTPS. The terminal's input data is transmitted through network components, and the server receives the request. The input data is sent in text format, and the server receives it.

[0884] Step 3:

[0885] The server uses an emotion engine to analyze the user's voice and facial expression data. It takes voice and image data as input, and the emotion engine analyzes this data to output the emotional state. For example, emotion tags such as "joy," "surprise," and "sadness" are output.

[0886] Step 4:

[0887] The server collects user behavior history and past feedback and stores it in a database. By taking past behavioral data and feedback data as input and storing them in the database, it builds a user model. For example, evaluations of past presentations and past emotional states are recorded.

[0888] Step 5:

[0889] The server trains a generative AI model based on collected user data and sentiment data. Behavioral history, emotional states, and feedback are used as training data, which the generative AI model learns from. Through training, the generative AI model understands user behavior patterns and emotional tendencies.

[0890] Step 6:

[0891] Using a generative AI model, the server generates appropriate compliments or compliment songs. Training data is used as input, and compliments or compliment songs are generated as output. For example, a compliment such as "Your presentation was fantastic! Your passion came through!" might be generated.

[0892] Step 7:

[0893] The server sends the generated compliments and compliment songs to the device. The data output by the generating AI model is sent in text or audio format and received by the device. The output data is securely transferred using the HTTPS protocol.

[0894] Step 8:

[0895] The device displays or plays received compliments and compliment songs to the user through its user interface. It receives data sent from the server as input and outputs screen displays and audio playback. Text may be displayed on the screen, or audio may play from the speaker.

[0896] Step 9:

[0897] Users input feedback on the praise (acknowledgment) they receive into their device. They use text boxes or rating buttons to enter their feedback. For example, they might write, "This praise was very encouraging."

[0898] Step 10:

[0899] The device sends feedback to the server via the HTTPS protocol. Input data is sent in text format, and the server receives the feedback data. This feedback is then stored in a database and used as training data for the next session.

[0900] Step 11:

[0901] The server improves the generative AI model based on the feedback. It analyzes the feedback data and uses it as training data for the generative AI model. This improves the quality of the praise and compliment songs generated in the future.

[0902] The above describes the specific processing steps of the program in this system.

[0903] (Application Example 2)

[0904] 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."

[0905] Traditional praise (acknowledgment) systems primarily generate compliments and praise songs based on user input, without considering the user's real-time emotional state. Therefore, providing appropriate praise (acknowledgment) tailored to the user's emotional state was difficult. Furthermore, there were no systems in place to offer real-time, emotionally appropriate compliments during face-to-face interactions with customers in physical stores.

[0906] 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.

[0907] In this invention, the server includes means for receiving requests for praise (approval) from users, means for transmitting such requests to the server, means for collecting and managing user data, means for training a generative AI model for generating praise words or praise songs based on the user data, means for providing the generated praise words or praise songs to the user, means for acquiring customer facial expression data and voice data and analyzing them using an emotion engine, and means for using the generative AI model based on the emotion analysis results to display appropriate praise words on the user terminal in real time. This enables the provision of personalized praise (approval) that takes into account the user's real-time emotional state, and the presentation of emotion-appropriate praise words in real time when interacting with customers in physical stores.

[0908] "A means of receiving requests for praise (approval) from users" refers to an interface that receives information via the device about what the user wants to be praised for.

[0909] "Means for sending the aforementioned request to the server" refers to a function that sends a request for praise (approval) received from the user terminal to the server via the network.

[0910] "Means for collecting and managing user data" refers to a system that collects and stores user basic information, behavioral history, feedback, etc., in a database for management.

[0911] "Means for training a generative AI model to generate compliments or compliment songs based on user data" refers to a method of training a generative AI model to generate compliments or compliment songs using a machine learning algorithm with collected user data.

[0912] "Means of providing users with generated compliments or compliment songs" refers to a function that presents users with compliments or compliment songs generated by a trained generative AI model through their device.

[0913] "Means for acquiring customer facial expression data and voice data and analyzing it using an emotion engine" refers to a system that captures customer facial expressions and voices using a camera and microphone, and analyzes that data using an emotion engine.

[0914] "A method for displaying appropriate compliments on the user's device in real time using a generative AI model based on emotion analysis results" refers to a method in which a generative AI model generates compliments tailored to the user in real time based on data analyzed by an emotion engine, and displays them immediately on the user's device.

[0915] To implement this invention, it is necessary to construct a system that combines a user interface using a smart device, a server, an emotion engine, and a generative AI model. The aim of this system is to provide users wearing smart devices in physical stores with real-time, emotion-responsive compliments.

[0916] Server Processing

[0917] The server uses the following hardware and software:

[0918] Hardware: The server can use an EC2 instance from Amazon Web Services (AWS).

[0919] Software: The necessary software to be installed on the server includes Flask (a Python micro web framework) and the Requests library (for sending HTTP requests).

[0920] The server performs the following operations.

[0921] 1. Collection and management of user data

[0922] The server collects user facial expression and voice data transmitted from smart devices and stores it in a database. This allows for the accumulation of the user's emotional state and behavioral history.

[0923] 2. Emotion analysis

[0924] An emotion engine (for example, Microsoft Azure's Emotion API) is used to analyze acquired facial expression and voice data to identify the user's emotional state.

[0925] 3. Generating compliments

[0926] Based on the analyzed sentiment data, a generative AI model (e.g., OpenAI's GPT-4) generates appropriate compliments. The following prompts are used in this generation process.

[0927] Example of a prompt:

[0928] Based on the customer's facial expression analysis results and emotional state, please create compliments and suggestions appropriate for the following situations.

[0929] Customer's emotional state: {emotional state}

[0930] Customer behavior: {Actions}

[0931] Example: A customer looks a little anxious when choosing new clothes. Compliments and suggestions to reassure them.

[0932] 4. Offering compliments

[0933] The generated compliments are displayed in real time on the smart device's screen. This allows store employees to instantly convey appropriate compliments to customers.

[0934] Terminal processing

[0935] Smart devices have the following functions:

[0936] Providing a user interface

[0937] Smart devices (such as smart glasses and head-mounted displays) provide an interface that allows users to input what they want to be praised for using voice input or touch controls.

[0938] Communication with the server

[0939] Smart devices transmit user input, facial expression data, and voice data to a server. This is done using secure communication protocols such as HTTPS.

[0940] Integration with the emotion engine

[0941] The system uses the camera and microphone built into the smart device to capture the user's facial expressions and voice, and then analyzes them for transmission to a server.

[0942] Compliment display

[0943] Compliments sent from the server are displayed in real time on the smart device's screen. This allows store staff to use that information to give customers appropriate compliments and suggestions.

[0944] User actions

[0945] Information entry

[0946] Users input the behaviors and achievements they want to be praised for in real time via their smart devices. This input is done through voice recognition or touch operation.

[0947] Provide feedback

[0948] After receiving praise (acknowledgment), users provide feedback on the content via their smart devices. This feedback is stored on a server and used to improve the generated AI model.

[0949] Specific example

[0950] In a physical store, if a customer choosing new clothes appears slightly anxious, a salesperson wearing smart glasses can capture that anxious expression. An emotion engine analyzes the expression as "anxiety," and based on this analysis, a generative AI model generates a compliment in real time, such as, "Those clothes look great on you. You've made a really great choice." The salesperson can then view this on the smart glasses' display and convey the compliment to the customer. In this way, the customer's shopping experience can be improved.

[0951] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0952] Step 1:

[0953] Users input the things or actions they want to be praised for through a smart device (such as smart glasses or a head-mounted display). This input is done using voice recognition or touch controls. The input information is temporarily stored as data within the device.

[0954] Input: Information (text or audio) about the user's desired praise or actions.

[0955] Output: Temporarily held input data

[0956] Step 2:

[0957] The terminal sends user input data to the server via a secure communication protocol (e.g., HTTPS). Simultaneously, the user's facial expressions and voice data are also captured and sent to the server.

[0958] Input: Temporarily stored user input data, facial expression data, voice data

[0959] Output: User input data, facial expression data, and voice data sent to the server.

[0960] Step 3:

[0961] The server collects basic user information, behavioral history, and feedback based on the received data and stores it in a database. This allows for the accumulation of user emotional states and behavioral history.

[0962] Input: User input data, facial expression data, and voice data sent from the terminal.

[0963] Output: User information, activity history, and feedback stored in the database.

[0964] Step 4:

[0965] The server uses an emotion engine (for example, Microsoft Azure's Emotion API) to analyze the acquired facial expression and voice data to determine the user's emotional state.

[0966] Input: Saved user facial expression data, voice data

[0967] Output: Analyzed user emotional state data

[0968] Step 5:

[0969] Based on the analyzed sentiment data, the server uses a generative AI model (e.g., OpenAI's GPT-4) to generate appropriate compliments. This generation process is based on prompt statements.

[0970] Example of a prompt:

[0971] Based on the customer's facial expression analysis results and emotional state, please create compliments and suggestions appropriate for the following situations.

[0972] Customer's emotional state: {emotional state}

[0973] Customer behavior: {Actions}

[0974] Example: A customer looks a little anxious when choosing new clothes. Compliments and suggestions to reassure them.

[0975] Input: Analyzed user emotion state data, prompt text

[0976] Output: Generated compliments

[0977] Step 6:

[0978] The server sends the generated compliments to the smart device's display in real time.

[0979] Input: Generated compliments

[0980] Output: Compliments sent to smart devices

[0981] Step 7:

[0982] The terminal displays compliments sent from the server in real time, providing them to the user. This allows users to instantly deliver appropriate compliments when interacting with customers face-to-face in physical stores.

[0983] Input: Compliments sent from the server

[0984] Output: Compliments displayed on the smart device's screen.

[0985] Step 8:

[0986] After receiving praise (acknowledgment), users provide feedback on the content via their device. This feedback is sent to the server and used to improve the generated AI model.

[0987] Input: User feedback data

[0988] Output: Feedback sent to the server, improvements to the generated AI model.

[0989] 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.

[0990] 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.

[0991] 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.

[0992] [Third Embodiment]

[0993] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0994] 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.

[0995] 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).

[0996] 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.

[0997] 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.

[0998] 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).

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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.

[1003] 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.

[1004] 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".

[1005] To implement this invention, three elements are necessary: ​​a terminal that provides a user interface, a server that manages data and maintains a generation AI model, and a user who makes a request for praise (approval). The system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server. The server collects and analyzes the user's data and uses the generation AI model to generate appropriate words of praise or praise songs. The generated words of praise or praise songs are then provided to the user through the terminal.

[1006] Server Processing

[1007] 1. Collection and management of user data

[1008] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a database. For example, it retrieves data via an API to capture presentations that a user has previously given high ratings to, or specific behavioral history.

[1009] 2. Training the Generative AI Model

[1010] The server trains a generative AI model based on the collected data. Machine learning techniques are used for training, learning user behavior patterns and preferences. This allows for the provision of more personalized praise (affirmation).

[1011] 3. Generating compliments and compliment songs

[1012] The server uses a trained generative AI model to generate personalized compliments and songs for the user. For example, it can create specific messages such as, "Your presentation today was fantastic! Your passion really came through," or original songs that include the user's name.

[1013] Terminal processing

[1014] 1. Providing a user interface

[1015] The terminal provides an interface for the user to enter requests. Possible input methods include text boxes, voice recognition, and selection menus.

[1016] 2. Communication with the server

[1017] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[1018] 3. Providing praise (acknowledgment)

[1019] The terminal provides users with compliments and songs received from the server using text display, audio output, and video playback functions.

[1020] User actions

[1021] 1. Information Entry

[1022] Users input the actions or achievements they want to be praised for through their device. For example, they can even voice-in requests such as, "I'd like to be praised for the proposal I made at today's meeting."

[1023] 2. Providing feedback

[1024] After receiving praise (acknowledgment), the user enters feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[1025] 3. Use both inside and outside the company

[1026] Users who receive praise (acknowledgment) gain a positive experience, which encourages them to praise others. This fosters a new culture of recognition both within and outside the organization.

[1027] Specific example

[1028] The following are specific examples of how this system can be used in both the business and personal spheres.

[1029] Examples of use in the business field

[1030] User A enters a request into the terminal asking for praise for their presentation. The terminal sends this request and User A's ID to the server. The server refers to User A's past presentation evaluation data and uses a generative AI model to generate the most appropriate praise. The generated result is sent to the terminal in text format, and the terminal displays it to User A.

[1031] Examples of use in private areas

[1032] User B enters a request into the device to be praised for making a new dish. The device sends this request to the server. The server uses a generative AI model based on past feedback data about User B's cooking to generate appropriate praise. The generated result is sent to the device in the form of a voice message, which the device then provides to User B via voice.

[1033] In this way, the present invention realizes a system that provides praise (recognition) tailored to the individual circumstances of each user, and can create a new culture of recognition throughout society.

[1034] The following describes the processing flow.

[1035] Program processing flow

[1036] Step 1:

[1037] The user enters a request on the device.

[1038] Users input the behaviors or achievements they want to be praised for into their device as text or voice.

[1039] Example: User A types "I want you to praise my presentation today."

[1040] Step 2:

[1041] The terminal receives the input data.

[1042] The device receives user input and temporarily stores the necessary data (text or voice).

[1043] Example: The device saves data that says, "I want you to praise my presentation today."

[1044] Step 3:

[1045] The device sends the request to the server.

[1046] The terminal sends the user's request and related data, such as the user ID and past activity history, to the server.

[1047] Example: The device sends a request to the server saying "I want to be praised for today's presentation" along with the ID of user A.

[1048] Step 4:

[1049] The server receives the request.

[1050] The server receives requests sent from the terminal and parses the request content.

[1051] Example: The server receives a request to "praise today's presentation" and begins analysis.

[1052] Step 5:

[1053] The server retrieves user data.

[1054] The server retrieves the user's basic information and past activity history from the database.

[1055] Example: The server retrieves evaluations and feedback on user A's past presentations.

[1056] Step 6:

[1057] The server applies the generated AI model.

[1058] The server inputs the acquired user data into an AI model to generate appropriate compliments and praise songs.

[1059] Example: The server uses a generated AI model to produce a compliment such as, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1060] Step 7:

[1061] The server compiles the generated results.

[1062] The server compiles the generated results, such as compliments and compliment songs, and formats them into an appropriate format.

[1063] Example: Format the compliments generated by the server into text format.

[1064] Step 8:

[1065] The server sends the generated results to the terminal.

[1066] The server transfers the generated results to the terminal.

[1067] Example: The server sends a compliment to the terminal saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1068] Step 9:

[1069] The terminal receives the generated result.

[1070] The terminal receives the generated results sent from the server and converts them into the corresponding output format (text, audio, video, etc.).

[1071] Example: The device receives the text message, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1072] Step 10:

[1073] The device provides praise (acknowledgment) to the user.

[1074] The device provides the user with generated compliments and compliment songs using text display, audio playback, and video playback functions.

[1075] Example: The device plays an audio message saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1076] Step 11:

[1077] Users provide feedback

[1078] Users who receive praise (approval) enter feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[1079] Example: User A enters the feedback, "I was so happy to receive that compliment!"

[1080] Step 12:

[1081] The device sends feedback to the server.

[1082] The device sends user feedback to the server.

[1083] Example: The device sends feedback to the server saying, "I was so happy to receive that compliment!"

[1084] Step 13:

[1085] The server receives the feedback and saves it as training data.

[1086] The server receives feedback and stores it as training data. It also uses this data to improve the generative AI model.

[1087] Example: The server receives user A's feedback and adds it to a database to be used for future praise (approval).

[1088] This allows for efficient praise (recognition) through collaboration between users, devices, and servers, gradually fostering a new culture of recognition.

[1089] (Example 1)

[1090] 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."

[1091] The lack of systems that provide appropriate praise and encouragement for individual user actions and achievements hinders the enhancement of user motivation and the fostering of a positive culture. In this situation, it is difficult to create a culture of mutual recognition within organizations and among individuals. Furthermore, existing systems fail to effectively utilize user feedback, making it difficult to continuously improve the quality of praise (recognition). Moreover, the provision of praise (recognition) is limited, lacking broad application in business and personal spheres.

[1092] 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.

[1093] In this invention, the server includes means for collecting and managing user data, means for preprocessing and feature extracting data for use in training, and means for training a generative AI model for generating compliments or compliment songs based on the user data. This makes it possible to provide appropriate compliments or compliment songs based on the user's behavior and achievements. Furthermore, by including means for receiving user feedback and storing it as training data to improve the generative AI model, the quality of compliments (recognition) can be continuously improved. In addition, by including means for providing compliments (recognition) in stages for business, private, and all people, a wide range of applications becomes possible.

[1094] A "user" refers to an individual or legal entity that requests praise (approval).

[1095] A "request" refers to the information a user enters into the system to seek praise (approval).

[1096] A "server" refers to a computer system that collects and manages user data, trains and runs a generation AI model to generate compliments and praise songs.

[1097] "Data" refers to a collection of information such as a user's basic information, behavioral history, and feedback.

[1098] A "generative AI model" refers to an algorithm that uses machine learning techniques to generate compliments and compliment songs based on user data.

[1099] "Training" refers to the learning process in which the server uses collected user data to enable the generating AI model to create appropriate compliments and praise songs.

[1100] "Praise" refers to sentences that acknowledge and encourage a user's actions and achievements.

[1101] "Praise songs" refer to musical content that celebrates and encourages users' actions and achievements.

[1102] "Feedback" refers to information that users provide to the system, such as their evaluations and impressions of praise (approval).

[1103] The "business domain" refers to the environment related to the workplace and business operations.

[1104] "Private space" refers to the environment associated with home and personal activities.

[1105] "Gradual delivery" refers to the process of providing praise (acknowledgment) in a flexible manner, adapting it to various situations and recipients.

[1106] To implement this invention, three elements are necessary: ​​a terminal that provides a user interface, a server that manages data and holds a generating AI model, and a user who makes a request for praise (approval). This system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server.

[1107] Server Processing

[1108] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a MySQL database. This data is used for later analysis and training of generative AI models. Specifically, it uses a RESTful API to retrieve user presentation evaluation data.

[1109] Next, the server preprocesses and extracts features from the collected data. This process uses Python and TensorFlow. Based on the preprocessed data, the server trains a generative AI model. This training improves the model's ability to generate compliments and praise songs that are tailored to the user's actions and achievements.

[1110] A trained generative AI model generates compliments and praise songs in real time in response to user requests. The generated content is converted into text or audio files and provided to the user via a user interface. This process can utilize GPT-3 models or open-source speech synthesis libraries.

[1111] Terminal processing

[1112] The device provides an interface for the user to input requests using HTML / CSS and JavaScript. Examples include web pages with text boxes or voice input functions. When the user inputs a request, the device converts this information into JSON format and sends it to the server using the HTTPS protocol. Specifically, it sends the request data as a POST request to the server's API endpoint.

[1113] The device also plays a role in providing users with compliments and praise songs received from the server. Text is displayed on the web page, and audio is played via the Audio tag.

[1114] User actions

[1115] The user enters what they want to be praised for through the device's interface. For example, they might type "I want to be praised for completing today's project" into the text box. For voice input, they can click the microphone icon to use a dedicated speech recognition API. Once the user sends the request, the device sends it to the server, which then generates appropriate praise or praise songs.

[1116] Users who receive the generated compliments or compliment songs can input feedback on their device. For example, they can enter comments such as "I liked this compliment" or "I would like more specific details" into the text box and click the "Send Feedback" button to send the feedback to the server.

[1117] This system can provide praise (recognition) in the business, personal, and all-encompassing spheres. Specific examples are given below.

[1118] Examples of use in the business field

[1119] User A enters a request into the terminal asking for praise for their presentation. The terminal sends this request and User A's ID to the server. The server refers to User A's past presentation evaluation data and uses a generative AI model to generate the most appropriate praise. The generated result is sent to the terminal in text format, and the terminal displays it to User A.

[1120] Example of a prompt

[1121] "Please praise my presentation today. User ID: 00123"

[1122] Examples of use in private areas

[1123] User B enters a request into the device to be praised for making a new dish. The device sends this request to the server. The server uses a generative AI model based on past feedback data about User B's cooking to generate appropriate praise. The generated result is sent to the device in the form of a voice message, which the device then provides to User B via voice.

[1124] Example of a prompt

[1125] "Please praise me for creating a new dish. User ID: 00456"

[1126] In this way, the present invention can provide praise (recognition) tailored to the individual circumstances of each user, thereby fostering a new culture of recognition throughout society.

[1127] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1128] Step 1: Enter your request

[1129] The user enters the content they want to be praised for into the device's interface. For example, they might type "I'd like to be praised for today's presentation" into the text box. For voice input, they click the microphone icon and use a dedicated speech recognition API.

[1130] Input: A text or voice request entered by the user.

[1131] Output: Request data (text format or audio data)

[1132] Step 2: Sending Request Data

[1133] The terminal converts the user's input request into JSON format and sends it to the server using the HTTPS protocol.

[1134] Input: Request data (text format or audio data)

[1135] Output: Request data in JSON format to be sent to the server

[1136] Step 3: Collecting User Data

[1137] The server collects data such as basic user information, behavioral history, and feedback based on the received request data. Specifically, it uses a RESTful API to obtain user presentation evaluation data.

[1138] Input: User ID included in the request data

[1139] Output: Data set of user basic information, behavioral history, and feedback.

[1140] Step 4: Data Management

[1141] The server saves the collected data to a MySQL database. For example, it executes an SQL query like "INSERT INTO user_data (user_id, presentation_score) VALUES (1, 95);".

[1142] Input: Collected user data

[1143] Output: User data stored in the MySQL database

[1144] Step 5: Data preprocessing and feature extraction

[1145] The server uses Python and TensorFlow to retrieve data from the database and perform preprocessing and feature extraction. Preprocessing includes denoising and data normalization.

[1146] Input: User data stored in a MySQL database

[1147] Output: Preprocessed and feature-extracted data

[1148] Step 6: Training the AI ​​Model

[1149] The server trains a generative AI model based on preprocessed data, using feedforward and backpropagation algorithms.

[1150] Input: Preprocessed and feature-extracted data

[1151] Output: Trained generative AI model

[1152] Step 7: Generating compliments and compliment songs

[1153] The server uses a trained generative AI model to generate personalized compliments and praise songs for each user. For example, it can create a compliment such as "Your presentation today was excellent!" along with an original song that includes the user's name.

[1154] Input: User data and AI model

[1155] Output: Data of the generated compliments or compliment songs

[1156] Step 8: Providing praise (acknowledgment) results

[1157] The server sends the generated compliments and compliment songs to the device. The device receives them and provides them to the user using text display, audio output, or video playback functions.

[1158] Input: Data for generated compliments or compliment songs

[1159] Output: Compliments in text format displayed to the user, or audio / video played.

[1160] Step 9: Entering Feedback

[1161] The user enters feedback on the praise (acknowledgment) provided into the device. For example, they might enter a rating such as, "I liked this compliment."

[1162] Input: Feedback text or voice

[1163] Output: Feedback data

[1164] Step 10: Send and save feedback

[1165] The device sends user feedback to the server, which receives it and stores it as training data to improve the generated AI model. The server stores the feedback data in a MySQL database using INSERT statements.

[1166] Input: User feedback data

[1167] Output: Feedback data stored in the MySQL database

[1168] (Application Example 1)

[1169] 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."

[1170] It is necessary to alleviate the anxiety and uncertainty users feel while shopping in physical stores and to encourage purchasing behavior. In particular, it is crucial that users feel confident in their product selection and purchase decisions. Furthermore, personalized praise is an effective way to boost motivation and improve the user experience in physical stores, thereby increasing customer satisfaction. However, current technology lacks the appropriate systems to achieve this.

[1171] 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.

[1172] In this invention, the server includes means for receiving requests for praise (approval) from users, means for transmitting such requests to the server, means for collecting and managing user data, means for training a generative AI model for generating praise words or praise songs based on the user data, means for providing the generated praise words or praise songs to users, means for providing the generated praise words or praise songs in physical stores, and means for generating words of praise for the user's actions when they purchase or use a product. This makes it possible to improve the user experience in physical stores and increase the user's confidence in selecting and purchasing products.

[1173] 1. "Praise (acknowledgment)" refers to the positive evaluation or recognition that a user receives for a specific action or achievement.

[1174] 2. "Means for receiving requests" refers to interfaces or systems for receiving requests from users for praise (approval).

[1175] 3. "Means for sending requests to a server" refers to a system that has a communication function for sending received user requests to an external server.

[1176] 4. "Means for collecting and managing data" refers to systems and functions for collecting, appropriately storing, and managing user behavior history, feedback information, etc.

[1177] 5. A "generative AI model" refers to a machine learning model that generates compliments and compliment songs tailored to the user based on collected data.

[1178] 6. "Training methods" refer to the process and methods of optimizing and learning a generative AI model using collected data.

[1179] 7. "Means for providing generated compliments or compliment songs" refers to interfaces or output devices for providing generated compliments or compliment songs to users.

[1180] 8. "Means of providing in physical stores" refers to systems and methods for providing generated compliments and compliment songs to users in a physical store environment.

[1181] 9. "Means for generating compliments for user actions when purchasing or using a product" refers to methods and systems for generating appropriate compliments for user actions when selecting, purchasing, or using a product in a physical store.

[1182] To implement this invention, the following elements are necessary to constitute a system that provides praise (approval): a server, a terminal, and a user that communicate with each other and utilize a generative AI model to generate and provide personalized praise messages and praise songs.

[1183] Server Processing

[1184] 1. Collection and management of user data

[1185] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a database. This includes retrieving various user data via APIs. For example, this could include shopping behavior history and past purchase history.

[1186] 2. Training the Generative AI Model

[1187] The server uses machine learning techniques based on the collected data to train a generative AI model. This training allows the model to learn user behavior patterns and preferences, enabling it to provide more personalized praise (affirmation).

[1188] 3. Generating compliments and compliment songs

[1189] The server uses a trained generative AI model to generate compliments and praise songs tailored to the user's behavior and product choices. For example, it can generate specific messages such as, "That's a great choice! Your taste is excellent."

[1190] Terminal processing

[1191] 1. Providing a user interface

[1192] The terminal provides an interface for users to enter requests, including text boxes, voice recognition, and selection menus.

[1193] 2. Communication with the server

[1194] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[1195] 3. Providing praise (acknowledgment)

[1196] The device provides users with compliments and songs received from the server using text display, audio output, and video playback functions. In particular, in physical stores, compliments can be displayed using smart glasses or smartphones.

[1197] User actions

[1198] 1. Information Entry

[1199] Users input the actions or achievements they want to be praised for through their device. For example, they might request praise (approval) for a newly purchased product.

[1200] 2. Providing feedback

[1201] After receiving praise (acknowledgment), users input feedback on the content into their device. This includes whether they were satisfied with the content and suggestions for improvement.

[1202] 3. Use both inside and outside the company

[1203] Users who receive praise (acknowledgment) are more likely to praise others based on that positive experience. This fosters a new culture of recognition both within and outside the organization.

[1204] Specific examples of implementation in physical stores

[1205] Hardware:

[1206] Smart glasses (for example, Google Glass)

[1207] Smartphone (iOS or Android device)

[1208] software:

[1209] Server-side: Python, Flask (as an API server)

[1210] Client-side: Python (for API calls), UI for video display

[1211] The server receives compliment (approval) requests from users, collects and manages user data, and trains a generative AI model. It then provides the user with the generated compliments. For example, when a user picks up an item in a physical store, displaying a compliment such as "You have excellent taste in this item you've chosen!" on smart glasses can improve the user experience.

[1212] Prompt example:

[1213] User ID: user123

[1214] Action: recommended_product_acquired

[1215] Prompt message:

[1216] User123 has decided to purchase the newly recommended item. Let's praise them for choosing this item. This item is based on the latest fashion trends and is a very tasteful choice.

[1217] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1218] Step 1:

[1219] The user enters a praise (approval) request using their device. The user enters the action they want to be praised for, either through text or voice, regarding the product selection or purchase they made while shopping. This becomes the input data.

[1220] Step 2:

[1221] The terminal sends the input request to the server. A secure communication protocol such as HTTPS is used for transmission. The output is the request data sent to the server.

[1222] Step 3:

[1223] Based on the received request data, the server collects the user's past behavior history and feedback information from the database. This becomes the new input data.

[1224] Step 4:

[1225] The server trains a generative AI model using collected user data. It performs data preprocessing, feature extraction, and model retraining to learn user behavior patterns and preferences. The output is the trained generative AI model.

[1226] Step 5:

[1227] The server uses a trained generative AI model to generate praise for the requested action. Here, the prompt text serves as input data, and the corresponding praise is output. Specifically, the prompt text is input to the generative AI model, which then generates appropriate praise.

[1228] Step 6:

[1229] The server sends the generated compliment to the terminal. This becomes the new output data. The terminal sends the received compliment from the server to an output device that provides it to the user.

[1230] Step 7:

[1231] The device displays received compliments using smart glasses or a smartphone. Being complimented increases the user's willingness to purchase, leading to a positive shopping experience. The output is the displayed compliment.

[1232] Step 8:

[1233] Users input feedback on the compliments they receive into their device. This input data includes their satisfaction level with the compliments and areas for improvement.

[1234] Step 9:

[1235] The terminal sends feedback data to the server. The server stores the received feedback in a database and uses it to further improve the generative AI model. The output is an improvement in the performance of the generative AI model.

[1236] This enables a personalized praise (acknowledgment) system that improves the user experience in physical stores by coordinating between users, devices, and servers.

[1237] 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.

[1238] To implement this invention, four elements are required: a terminal that provides a user interface, a server that manages data and houses a generation AI model, an emotion engine that recognizes the user's emotions, and a user who makes a request for praise (approval). This system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server. The server collects and analyzes the user's data and the results of the emotion engine's analysis, and uses the generation AI model to generate appropriate words of praise or praise songs. The generated words of praise or praise songs are then provided to the user through the terminal.

[1239] Server Processing

[1240] 1. Collection and management of user data

[1241] The server collects and stores data such as basic user information, behavioral history, feedback, and analysis results from the emotion engine in a database. For example, it retrieves data via API to capture presentations that the user has previously given high ratings to, specific behavioral history, and emotional states.

[1242] 2. Training the Generative AI Model

[1243] The server trains a generative AI model based on the collected data. Machine learning techniques are used for training, learning user behavioral tendencies, preferences, and emotional states. This allows for the provision of more personalized praise (affirmation).

[1244] 3. Generating compliments and compliment songs

[1245] The server uses a trained generative AI model to generate personalized compliments and songs for the user. For example, it can create specific messages such as, "Your presentation today was fantastic! Your passion really came through," or original songs that include the user's name.

[1246] Terminal processing

[1247] 1. Providing a user interface

[1248] The terminal provides an interface for the user to enter requests. Possible input methods include text boxes, voice recognition, and selection menus.

[1249] 2. Communication with the server

[1250] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[1251] 3. Integration with the emotion engine

[1252] The device works in conjunction with an emotion engine to acquire and analyze the user's voice and facial expression data. The analysis results are sent to the server as data related to the user's emotional state.

[1253] 4. Providing praise (acknowledgment)

[1254] The terminal provides users with compliments and songs received from the server using text display, audio output, and video playback functions.

[1255] User actions

[1256] 1. Information Entry

[1257] Users input the actions or achievements they want to be praised for through their device. For example, they can voice-in requests such as, "I'd like to be praised for the proposal I made at today's meeting."

[1258] 2. Providing feedback

[1259] After receiving praise (acknowledgment), the user enters feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[1260] 3. Use both inside and outside the company

[1261] Users who receive praise (acknowledgment) gain a positive experience, which encourages them to praise others. This fosters a new culture of recognition both within and outside the organization.

[1262] The role of the emotional engine

[1263] 1. Analysis of user sentiment

[1264] The emotion engine analyzes the user's emotional state from their voice, facial expressions, and text, and sends the results to the server. This allows the generative AI model to generate appropriate compliments and praise songs that take the user's emotional state into account.

[1265] 2. Sending emotional data

[1266] The analysis results from the emotion engine are sent to the server as data indicating the user's emotional state. This data is also used as training data for the generative AI model.

[1267] Specific example

[1268] The following are specific examples of how this system can be used in both the business and personal spheres.

[1269] Examples of use in the business field

[1270] User A enters a request into the device asking for praise for their presentation. Simultaneously, the emotion engine analyzes User A's voice and facial expressions and sends their emotional state to the server. The server generates the most appropriate praise based on the request and emotion data, and sends the generated praise to the device. The device displays the praise to User A and also plays it aloud.

[1271] Examples of use in private areas

[1272] User B enters a request into the terminal asking to be praised for making a new dish. Simultaneously, the emotion engine analyzes User B's facial expressions and sends their emotional state to the server. The server generates the most appropriate praise based on the request and emotion data, and sends the generated praise to the terminal. The terminal displays and plays the praise aloud for User B.

[1273] In this way, the present invention realizes a system that can provide praise (acknowledgment) that takes into account the user's emotional state, thereby forming a new, more personalized culture of acknowledgment.

[1274] The following describes the processing flow.

[1275] Program processing flow

[1276] Step 1:

[1277] The user enters a request on the device.

[1278] Users input the behaviors or achievements they want to be praised for into their device as text or voice.

[1279] Example: User A types "I want you to praise my presentation today."

[1280] Step 2:

[1281] The terminal receives the input data.

[1282] The device receives user input and temporarily stores the necessary data (text or voice).

[1283] Example: The device saves data that says, "I want you to praise my presentation today."

[1284] Step 3:

[1285] The device collects emotional data.

[1286] The device activates an emotion engine and collects user voice and facial expression data.

[1287] Example: The device records user A's voice tone and facial expressions using its camera and microphone.

[1288] Step 4:

[1289] The emotion engine analyzes emotional data.

[1290] The emotion engine analyzes the collected data to identify the user's emotional state. For example, it categorizes it into positive, negative, or neutral.

[1291] Example: The emotion engine identifies a "positive" emotional state from user A's facial expressions and voice.

[1292] Step 5:

[1293] The device sends the request and sentiment data to the server.

[1294] The terminal sends the user's request and the results of the emotion engine's analysis to the server.

[1295] Example: The device sends a request to the server saying "I want to be praised for today's presentation" along with "positive" sentiment data.

[1296] Step 6:

[1297] The server receives the request and sentiment data.

[1298] The server receives requests and emotion data sent from the terminal and analyzes the request content and emotion state.

[1299] Example: The server receives a request to "praise today's presentation" and "positive" sentiment data.

[1300] Step 7:

[1301] The server retrieves user data.

[1302] The server retrieves the user's basic information, past behavioral history, and sentiment data from the database.

[1303] Example: The server retrieves user A's past presentation evaluations, behavioral history, and sentiment data.

[1304] Step 8:

[1305] The server applies the generated AI model.

[1306] The server inputs the acquired user data into an AI model to generate appropriate compliments and praise songs. Emotional data is also taken into consideration during the generation process.

[1307] Example: The server uses a generated AI model to produce a compliment such as, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1308] Step 9:

[1309] The server compiles the generated results.

[1310] The server compiles the generated results, such as compliments and compliment songs, and formats them into an appropriate format.

[1311] Example: Format the compliments generated by the server into text format.

[1312] Step 10:

[1313] The server sends the generated results to the terminal.

[1314] The server transfers the generated results to the terminal.

[1315] Example: The server sends a compliment to the terminal saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1316] Step 11:

[1317] The terminal receives the generated result.

[1318] The terminal receives the generated results sent from the server and converts them into the corresponding output format (text, audio, video, etc.).

[1319] Example: The device receives the text message, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1320] Step 12:

[1321] The device provides praise (acknowledgment) to the user.

[1322] The device provides the user with generated compliments and compliment songs using text display, audio playback, and video playback functions.

[1323] Example: The device plays an audio message saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1324] Step 13:

[1325] Users provide feedback

[1326] Users who receive praise (approval) enter feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[1327] Example: User A enters the feedback, "I was so happy to receive that compliment!"

[1328] Step 14:

[1329] The device sends feedback to the server.

[1330] The device sends user feedback to the server.

[1331] Example: The device sends feedback to the server saying, "I was so happy to receive that compliment!"

[1332] Step 15:

[1333] The server receives the feedback and saves it as training data.

[1334] The server receives feedback and stores it as training data. It also uses this data to improve the generative AI model.

[1335] Example: The server receives user A's feedback and adds it to a database to be used for future praise (approval).

[1336] This allows for efficient praise (acknowledgment) through the collaboration of users, devices, servers, and the emotion engine, gradually fostering a new culture of recognition.

[1337] (Example 2)

[1338] 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."

[1339] Traditional praise (acknowledgment) systems often suffer from low user satisfaction because they simply generate compliments and praise songs mechanically, without adequately considering user behavior or emotions. Furthermore, they lack mechanisms for effectively utilizing user feedback to improve the system. Therefore, there is a need for more personalized praise (acknowledgment) that resonates with user emotions.

[1340] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting and managing user data, means for analyzing the user's emotional data using an emotion engine, means for training a generative AI model for generating compliments or compliment songs based on the user data and emotional data, means for providing the generated compliments or compliment songs to the user, means for receiving user feedback on the provided compliments or compliment songs, and means for improving the generative AI model based on the feedback. This makes it possible to provide personalized compliments (approvals) that take into account the user's emotional state, and also enables continuous system improvement based on feedback.

[1341] "User data" is a general term for data related to individual users, including basic user information, behavioral history, and feedback.

[1342] An "emotion engine" is a general term for software or hardware used to analyze a user's emotional state from their voice, facial expressions, text, etc.

[1343] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to learn user behavior and emotional states, and then generates appropriate compliments and compliment songs.

[1344] A "praise" is a sentence or message used to acknowledge a user's actions or achievements.

[1345] "Praise songs" are audio or musical content created to praise users.

[1346] "Generating" means creating new data or information based on input data.

[1347] "Training" refers to the process of using collected data to train a generative AI model and improve its performance.

[1348] "Feedback" refers to the opinions and evaluations that users provide regarding compliments or songs that praise others.

[1349] "To analyze" means to examine data and find meaning or trends.

[1350] "Personalized" means that it is tailored to the specific needs and preferences of each individual user.

[1351] A "system" is a collection of devices or programs in which multiple components work together to achieve a specific function.

[1352] To implement this invention, four elements are necessary: ​​a terminal that provides a user interface, a server that manages and generates data and possesses an AI model, an emotion engine that recognizes the user's emotions, and a user who makes a request for praise (approval).

[1353] First, the user uses their device to enter a request for praise (approval). Input methods include text boxes and speech recognition. For example, the user might enter a request such as, "I want you to praise my presentation." The device then sends the entered request to the server. A secure protocol such as HTTPS is used for this communication.

[1354] Next, the server works with the emotion engine to acquire and analyze the user's voice and facial expression data. The emotion engine sends the analysis results to the server, and based on these results, it determines the user's emotional state. The server also collects the user's behavior history and past feedback and stores it in a database.

[1355] The server uses this data to train a generative AI model. Machine learning techniques are used for training, learning user behavior patterns and emotional states. This allows the generative AI model to generate the most appropriate compliments and praise songs for each user.

[1356] For example, it can generate specific compliments such as, "Your presentation was fantastic! Your passion really came through," or an original compliment song that includes the user's name. The generated compliments and songs are sent from the server to the terminal, which then displays or plays them for the user. Possible display methods include text display, audio output, and video playback.

[1357] Users who receive praise (approval) enter feedback on the content into their device. This feedback is sent to the server and used to improve the generative AI model. The server uses this feedback to retrain the generative AI model and improve the quality of future praise messages and praise songs.

[1358] As a concrete example, in the business world, a user might request "praise for a proposal made in a meeting," and an emotion engine would analyze their emotions at that time. The server would generate a compliment based on the analysis results and the user's behavioral history, and send it to the device. The device would then display the compliment, and the user would provide feedback in response.

[1359] In the private area, the user can request to be praised for creating a new dish, and the emotion engine analyzes their facial expressions. Similarly, the server generates compliments and praise songs based on the analysis results and the user's behavioral history, and provides them to the user through the device.

[1360] Example of a prompt

[1361] Please tell me what you would like to be praised for. For example, "I made a great suggestion at today's meeting, so I'd like to be praised for it." Also, please tell me how you felt at the time.

[1362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1363] Step 1:

[1364] Users use their devices to enter requests for praise (approval). Input methods include text boxes and voice recognition. For example, a user might say, "I'd like to be praised for my proposal at the meeting." The input from the device is saved as text data in its internal memory.

[1365] Step 2:

[1366] The terminal sends the entered request to the server via a secure protocol such as HTTPS. The terminal's input data is transmitted through network components, and the server receives the request. The input data is sent in text format, and the server receives it.

[1367] Step 3:

[1368] The server uses an emotion engine to analyze the user's voice and facial expression data. It takes voice and image data as input, and the emotion engine analyzes this data to output the emotional state. For example, emotion tags such as "joy," "surprise," and "sadness" are output.

[1369] Step 4:

[1370] The server collects user behavior history and past feedback and stores it in a database. By taking past behavioral data and feedback data as input and storing them in the database, it builds a user model. For example, evaluations of past presentations and past emotional states are recorded.

[1371] Step 5:

[1372] The server trains a generative AI model based on collected user data and sentiment data. Behavioral history, emotional states, and feedback are used as training data, which the generative AI model learns from. Through training, the generative AI model understands user behavior patterns and emotional tendencies.

[1373] Step 6:

[1374] Using a generative AI model, the server generates appropriate compliments or compliment songs. Training data is used as input, and compliments or compliment songs are generated as output. For example, a compliment such as "Your presentation was fantastic! Your passion came through!" might be generated.

[1375] Step 7:

[1376] The server sends the generated compliments and compliment songs to the device. The data output by the generating AI model is sent in text or audio format and received by the device. The output data is securely transferred using the HTTPS protocol.

[1377] Step 8:

[1378] The device displays or plays received compliments and compliment songs to the user through its user interface. It receives data sent from the server as input and outputs screen displays and audio playback. Text may be displayed on the screen, or audio may play from the speaker.

[1379] Step 9:

[1380] Users input feedback on the praise (acknowledgment) they receive into their device. They use text boxes or rating buttons to enter their feedback. For example, they might write, "This praise was very encouraging."

[1381] Step 10:

[1382] The device sends feedback to the server via the HTTPS protocol. Input data is sent in text format, and the server receives the feedback data. This feedback is then stored in a database and used as training data for the next session.

[1383] Step 11:

[1384] The server improves the generative AI model based on the feedback. It analyzes the feedback data and uses it as training data for the generative AI model. This improves the quality of the praise and compliment songs generated in the future.

[1385] The above describes the specific processing steps of the program in this system.

[1386] (Application Example 2)

[1387] 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."

[1388] Traditional praise (acknowledgment) systems primarily generate compliments and praise songs based on user input, without considering the user's real-time emotional state. Therefore, providing appropriate praise (acknowledgment) tailored to the user's emotional state was difficult. Furthermore, there were no systems in place to offer real-time, emotionally appropriate compliments during face-to-face interactions with customers in physical stores.

[1389] 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.

[1390] In this invention, the server includes means for receiving requests for praise (approval) from users, means for transmitting such requests to the server, means for collecting and managing user data, means for training a generative AI model for generating praise words or praise songs based on the user data, means for providing the generated praise words or praise songs to the user, means for acquiring customer facial expression data and voice data and analyzing them using an emotion engine, and means for using the generative AI model based on the emotion analysis results to display appropriate praise words on the user terminal in real time. This enables the provision of personalized praise (approval) that takes into account the user's real-time emotional state, and the presentation of emotion-appropriate praise words in real time when interacting with customers in physical stores.

[1391] "A means of receiving requests for praise (approval) from users" refers to an interface that receives information via the device about what the user wants to be praised for.

[1392] "Means for sending the aforementioned request to the server" refers to a function that sends a request for praise (approval) received from the user terminal to the server via the network.

[1393] "Means for collecting and managing user data" refers to a system that collects and stores user basic information, behavioral history, feedback, etc., in a database for management.

[1394] "Means for training a generative AI model to generate compliments or compliment songs based on user data" refers to a method of training a generative AI model to generate compliments or compliment songs using a machine learning algorithm with collected user data.

[1395] "Means of providing users with generated compliments or compliment songs" refers to a function that presents users with compliments or compliment songs generated by a trained generative AI model through their device.

[1396] "Means for acquiring customer facial expression data and voice data and analyzing it using an emotion engine" refers to a system that captures customer facial expressions and voices using a camera and microphone, and analyzes that data using an emotion engine.

[1397] "A method for displaying appropriate compliments on the user's device in real time using a generative AI model based on emotion analysis results" refers to a method in which a generative AI model generates compliments tailored to the user in real time based on data analyzed by an emotion engine, and displays them immediately on the user's device.

[1398] To implement this invention, it is necessary to construct a system that combines a user interface using a smart device, a server, an emotion engine, and a generative AI model. The aim of this system is to provide users wearing smart devices in physical stores with real-time, emotion-responsive compliments.

[1399] Server Processing

[1400] The server uses the following hardware and software:

[1401] Hardware: The server can use an EC2 instance from Amazon Web Services (AWS).

[1402] Software: The necessary software to be installed on the server includes Flask (a Python micro web framework) and the Requests library (for sending HTTP requests).

[1403] The server performs the following operations.

[1404] 1. Collection and management of user data

[1405] The server collects user facial expression and voice data transmitted from smart devices and stores it in a database. This allows for the accumulation of the user's emotional state and behavioral history.

[1406] 2. Emotion analysis

[1407] An emotion engine (for example, Microsoft Azure's Emotion API) is used to analyze acquired facial expression and voice data to identify the user's emotional state.

[1408] 3. Generating compliments

[1409] Based on the analyzed sentiment data, a generative AI model (e.g., OpenAI's GPT-4) generates appropriate compliments. The following prompts are used in this generation process.

[1410] Example of a prompt:

[1411] Based on the customer's facial expression analysis results and emotional state, please create compliments and suggestions appropriate for the following situations.

[1412] Customer's emotional state: {emotional state}

[1413] Customer behavior: {Actions}

[1414] Example: A customer looks a little anxious when choosing new clothes. Compliments and suggestions to reassure them.

[1415] 4. Offering compliments

[1416] The generated compliments are displayed in real time on the smart device's screen. This allows store employees to instantly convey appropriate compliments to customers.

[1417] Terminal processing

[1418] Smart devices have the following functions:

[1419] Providing a user interface

[1420] Smart devices (such as smart glasses and head-mounted displays) provide an interface that allows users to input what they want to be praised for using voice input or touch controls.

[1421] Communication with the server

[1422] Smart devices transmit user input, facial expression data, and voice data to a server. This is done using secure communication protocols such as HTTPS.

[1423] Integration with the emotion engine

[1424] The system uses the camera and microphone built into the smart device to capture the user's facial expressions and voice, and then analyzes them for transmission to a server.

[1425] Compliment display

[1426] Compliments sent from the server are displayed in real time on the smart device's screen. This allows store staff to use that information to give customers appropriate compliments and suggestions.

[1427] User actions

[1428] Information entry

[1429] Users input the behaviors and achievements they want to be praised for in real time via their smart devices. This input is done through voice recognition or touch operation.

[1430] Provide feedback

[1431] After receiving praise (acknowledgment), users provide feedback on the content via their smart devices. This feedback is stored on a server and used to improve the generated AI model.

[1432] Specific example

[1433] In a physical store, if a customer choosing new clothes appears slightly anxious, a salesperson wearing smart glasses can capture that anxious expression. An emotion engine analyzes the expression as "anxiety," and based on this analysis, a generative AI model generates a compliment in real time, such as, "Those clothes look great on you. You've made a really great choice." The salesperson can then view this on the smart glasses' display and convey the compliment to the customer. In this way, the customer's shopping experience can be improved.

[1434] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1435] Step 1:

[1436] Users input the things or actions they want to be praised for through a smart device (such as smart glasses or a head-mounted display). This input is done using voice recognition or touch controls. The input information is temporarily stored as data within the device.

[1437] Input: Information (text or audio) about the user's desired praise or actions.

[1438] Output: Temporarily held input data

[1439] Step 2:

[1440] The terminal sends user input data to the server via a secure communication protocol (e.g., HTTPS). Simultaneously, the user's facial expressions and voice data are also captured and sent to the server.

[1441] Input: Temporarily stored user input data, facial expression data, voice data

[1442] Output: User input data, facial expression data, and voice data sent to the server.

[1443] Step 3:

[1444] The server collects basic user information, behavioral history, and feedback based on the received data and stores it in a database. This allows for the accumulation of user emotional states and behavioral history.

[1445] Input: User input data, facial expression data, and voice data sent from the terminal.

[1446] Output: User information, activity history, and feedback stored in the database.

[1447] Step 4:

[1448] The server uses an emotion engine (for example, Microsoft Azure's Emotion API) to analyze the acquired facial expression and voice data and identify the user's emotional state.

[1449] Input: Saved user facial expression data, voice data

[1450] Output: Analyzed user emotional state data

[1451] Step 5:

[1452] Based on the analyzed sentiment data, the server uses a generative AI model (e.g., OpenAI's GPT-4) to generate appropriate compliments. This generation process is based on prompt statements.

[1453] Example of a prompt:

[1454] Based on the customer's facial expression analysis results and emotional state, please create compliments and suggestions appropriate for the following situations.

[1455] Customer's emotional state: {emotional state}

[1456] Customer behavior: {Actions}

[1457] Example: A customer looks a little anxious when choosing new clothes. Compliments and suggestions to reassure them.

[1458] Input: Analyzed user emotion state data, prompt text

[1459] Output: Generated compliments

[1460] Step 6:

[1461] The server sends the generated compliments to the smart device's display in real time.

[1462] Input: Generated compliments

[1463] Output: Compliments sent to smart devices

[1464] Step 7:

[1465] The terminal displays compliments sent from the server in real time, providing them to the user. This allows users to instantly deliver appropriate compliments when interacting with customers face-to-face in physical stores.

[1466] Input: Compliments sent from the server

[1467] Output: Compliments displayed on the smart device's screen.

[1468] Step 8:

[1469] After receiving praise (acknowledgment), users provide feedback on the content via their device. This feedback is sent to the server and used to improve the generated AI model.

[1470] Input: User feedback data

[1471] Output: Feedback sent to the server, improvements to the generated AI model.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] [Fourth Embodiment]

[1476] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1477] 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.

[1478] 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).

[1479] 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.

[1480] 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.

[1481] 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).

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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".

[1489] To implement this invention, three elements are necessary: ​​a terminal that provides a user interface, a server that manages data and maintains a generation AI model, and a user who makes a request for praise (approval). The system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server. The server collects and analyzes the user's data and uses the generation AI model to generate appropriate words of praise or praise songs. The generated words of praise or praise songs are then provided to the user through the terminal.

[1490] Server Processing

[1491] 1. Collection and management of user data

[1492] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a database. For example, it retrieves data via an API to capture presentations that a user has previously given high ratings to, or specific behavioral history.

[1493] 2. Training the Generative AI Model

[1494] The server trains a generative AI model based on the collected data. Machine learning techniques are used for training, learning user behavior patterns and preferences. This allows for the provision of more personalized praise (affirmation).

[1495] 3. Generating compliments and compliment songs

[1496] The server uses a trained generative AI model to generate personalized compliments and songs for the user. For example, it can create specific messages such as, "Your presentation today was fantastic! Your passion really came through," or original songs that include the user's name.

[1497] Terminal processing

[1498] 1. Providing a user interface

[1499] The terminal provides an interface for the user to enter requests. Possible input methods include text boxes, voice recognition, and selection menus.

[1500] 2. Communication with the server

[1501] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[1502] 3. Providing praise (acknowledgment)

[1503] The terminal provides users with compliments and songs received from the server using text display, audio output, and video playback functions.

[1504] User actions

[1505] 1. Information Entry

[1506] Users input the actions or achievements they want to be praised for through their device. For example, they can even voice-in requests such as, "I'd like to be praised for the proposal I made at today's meeting."

[1507] 2. Providing feedback

[1508] After receiving praise (acknowledgment), the user enters feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[1509] 3. Use both inside and outside the company

[1510] Users who receive praise (acknowledgment) gain a positive experience, which encourages them to praise others. This fosters a new culture of recognition both within and outside the organization.

[1511] Specific example

[1512] The following are specific examples of how this system can be used in both the business and personal spheres.

[1513] Examples of use in the business field

[1514] User A enters a request into the terminal asking for praise for their presentation. The terminal sends this request and User A's ID to the server. The server refers to User A's past presentation evaluation data and uses a generative AI model to generate the most appropriate praise. The generated result is sent to the terminal in text format, and the terminal displays it to User A.

[1515] Examples of use in private areas

[1516] User B enters a request into the device to be praised for making a new dish. The device sends this request to the server. The server uses a generative AI model based on past feedback data about User B's cooking to generate appropriate praise. The generated result is sent to the device in the form of a voice message, which the device then provides to User B via voice.

[1517] In this way, the present invention realizes a system that provides praise (recognition) tailored to the individual circumstances of each user, and can create a new culture of recognition throughout society.

[1518] The following describes the processing flow.

[1519] Program processing flow

[1520] Step 1:

[1521] The user enters a request on the device.

[1522] Users input the behaviors or achievements they want to be praised for into their device as text or voice.

[1523] Example: User A types "I want you to praise my presentation today."

[1524] Step 2:

[1525] The terminal receives the input data.

[1526] The device receives user input and temporarily stores the necessary data (text or voice).

[1527] Example: The device saves data that says, "I want you to praise my presentation today."

[1528] Step 3:

[1529] The device sends the request to the server.

[1530] The terminal sends the user's request and related data, such as the user ID and past activity history, to the server.

[1531] Example: The device sends a request to the server saying "I want to be praised for today's presentation" along with the ID of user A.

[1532] Step 4:

[1533] The server receives the request.

[1534] The server receives requests sent from the terminal and parses the request content.

[1535] Example: The server receives a request to "praise today's presentation" and begins analysis.

[1536] Step 5:

[1537] The server retrieves user data.

[1538] The server retrieves the user's basic information and past activity history from the database.

[1539] Example: The server retrieves evaluations and feedback on user A's past presentations.

[1540] Step 6:

[1541] The server applies the generated AI model.

[1542] The server inputs the acquired user data into an AI model to generate appropriate compliments and praise songs.

[1543] Example: The server uses a generated AI model to produce a compliment such as, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1544] Step 7:

[1545] The server compiles the generated results.

[1546] The server compiles the generated results, such as compliments and compliment songs, and formats them into an appropriate format.

[1547] Example: Format the compliments generated by the server into text format.

[1548] Step 8:

[1549] The server sends the generated results to the terminal.

[1550] The server transfers the generated results to the terminal.

[1551] Example: The server sends a compliment to the terminal saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1552] Step 9:

[1553] The terminal receives the generated result.

[1554] The terminal receives the generated results sent from the server and converts them into the corresponding output format (text, audio, video, etc.).

[1555] Example: The device receives the text message, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1556] Step 10:

[1557] The device provides praise (acknowledgment) to the user.

[1558] The device provides the user with generated compliments and compliment songs using text display, audio playback, and video playback functions.

[1559] Example: The device plays an audio message saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1560] Step 11:

[1561] Users provide feedback

[1562] Users who receive praise (approval) enter feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[1563] Example: User A enters the feedback, "I was so happy to receive that compliment!"

[1564] Step 12:

[1565] The device sends feedback to the server.

[1566] The device sends user feedback to the server.

[1567] Example: The device sends feedback to the server saying, "I was so happy to receive that compliment!"

[1568] Step 13:

[1569] The server receives the feedback and saves it as training data.

[1570] The server receives feedback and stores it as training data. It also uses this data to improve the generative AI model.

[1571] Example: The server receives user A's feedback and adds it to a database to be used for future praise (approval).

[1572] This allows for efficient praise (recognition) through collaboration between users, devices, and servers, gradually fostering a new culture of recognition.

[1573] (Example 1)

[1574] 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".

[1575] The lack of systems that provide appropriate praise and encouragement for individual user actions and achievements hinders the enhancement of user motivation and the fostering of a positive culture. In this situation, it is difficult to create a culture of mutual recognition within organizations and among individuals. Furthermore, existing systems fail to effectively utilize user feedback, making it difficult to continuously improve the quality of praise (recognition). Moreover, the provision of praise (recognition) is limited, lacking broad application in business and personal spheres.

[1576] 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.

[1577] In this invention, the server includes means for collecting and managing user data, means for preprocessing and feature extracting data for use in training, and means for training a generative AI model for generating compliments or compliment songs based on the user data. This makes it possible to provide appropriate compliments or compliment songs based on the user's behavior and achievements. Furthermore, by including means for receiving user feedback and storing it as training data to improve the generative AI model, the quality of compliments (recognition) can be continuously improved. In addition, by including means for providing compliments (recognition) in stages for business, private, and all people, a wide range of applications becomes possible.

[1578] A "user" refers to an individual or legal entity that requests praise (approval).

[1579] A "request" refers to the information a user enters into the system to seek praise (approval).

[1580] A "server" refers to a computer system that collects and manages user data, trains and runs a generation AI model to generate compliments and praise songs.

[1581] "Data" refers to a collection of information such as a user's basic information, behavioral history, and feedback.

[1582] A "generative AI model" refers to an algorithm that uses machine learning techniques to generate compliments and compliment songs based on user data.

[1583] "Training" refers to the learning process in which the server uses collected user data to enable the generating AI model to create appropriate compliments and praise songs.

[1584] "Praise" refers to sentences that acknowledge and encourage a user's actions and achievements.

[1585] "Praise songs" refer to musical content that celebrates and encourages users' actions and achievements.

[1586] "Feedback" refers to information that users provide to the system, such as their evaluations and impressions of praise (approval).

[1587] The "business domain" refers to the environment related to the workplace and business operations.

[1588] "Private space" refers to the environment associated with home and personal activities.

[1589] "Gradual delivery" refers to the process of providing praise (acknowledgment) in a flexible manner, adapting it to various situations and recipients.

[1590] To implement this invention, three elements are necessary: ​​a terminal that provides a user interface, a server that manages data and holds a generating AI model, and a user who makes a request for praise (approval). This system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server.

[1591] Server Processing

[1592] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a MySQL database. This data is used for later analysis and training of generative AI models. Specifically, it uses a RESTful API to retrieve user presentation evaluation data.

[1593] Next, the server preprocesses and extracts features from the collected data. This process uses Python and TensorFlow. Based on the preprocessed data, the server trains a generative AI model. This training improves the model's ability to generate compliments and praise songs that are tailored to the user's actions and achievements.

[1594] A trained generative AI model generates compliments and praise songs in real time in response to user requests. The generated content is converted into text or audio files and provided to the user via a user interface. This process can utilize GPT-3 models or open-source speech synthesis libraries.

[1595] Terminal processing

[1596] The device provides an interface for the user to input requests using HTML / CSS and JavaScript. Examples include web pages with text boxes or voice input functions. When the user inputs a request, the device converts this information into JSON format and sends it to the server using the HTTPS protocol. Specifically, it sends the request data as a POST request to the server's API endpoint.

[1597] The device also plays a role in providing users with compliments and praise songs received from the server. Text is displayed on the web page, and audio is played via the Audio tag.

[1598] User actions

[1599] The user enters what they want to be praised for through the device's interface. For example, they might type "I want to be praised for completing today's project" into the text box. For voice input, they can click the microphone icon to use a dedicated speech recognition API. Once the user sends the request, the device sends it to the server, which then generates appropriate praise or praise songs.

[1600] Users who receive the generated compliments or compliment songs can input feedback on their device. For example, they can enter comments such as "I liked this compliment" or "I would like more specific details" into the text box and click the "Send Feedback" button to send the feedback to the server.

[1601] This system can provide praise (recognition) in the business, personal, and all-encompassing spheres. Specific examples are given below.

[1602] Examples of use in the business field

[1603] User A enters a request into the terminal asking for praise for their presentation. The terminal sends this request and User A's ID to the server. The server refers to User A's past presentation evaluation data and uses a generative AI model to generate the most appropriate praise. The generated result is sent to the terminal in text format, and the terminal displays it to User A.

[1604] Example of a prompt

[1605] "Please praise my presentation today. User ID: 00123"

[1606] Examples of use in private areas

[1607] User B enters a request into the device to be praised for making a new dish. The device sends this request to the server. The server uses a generative AI model based on past feedback data about User B's cooking to generate appropriate praise. The generated result is sent to the device in the form of a voice message, which the device then provides to User B via voice.

[1608] Example of a prompt

[1609] "Please praise me for creating a new dish. User ID: 00456"

[1610] In this way, the present invention can provide praise (recognition) tailored to the individual circumstances of each user, thereby fostering a new culture of recognition throughout society.

[1611] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1612] Step 1: Enter your request

[1613] The user enters the content they want to be praised for into the device's interface. For example, they might type "I'd like to be praised for today's presentation" into the text box. For voice input, they click the microphone icon and use a dedicated speech recognition API.

[1614] Input: A text or voice request entered by the user.

[1615] Output: Request data (text format or audio data)

[1616] Step 2: Sending Request Data

[1617] The terminal converts the user's input request into JSON format and sends it to the server using the HTTPS protocol.

[1618] Input: Request data (text format or audio data)

[1619] Output: Request data in JSON format to be sent to the server

[1620] Step 3: Collecting User Data

[1621] The server collects data such as basic user information, behavioral history, and feedback based on the received request data. Specifically, it uses a RESTful API to obtain user presentation evaluation data.

[1622] Input: User ID included in the request data

[1623] Output: Data set of user basic information, behavioral history, and feedback.

[1624] Step 4: Data Management

[1625] The server saves the collected data to a MySQL database. For example, it executes an SQL query like "INSERT INTO user_data (user_id, presentation_score) VALUES (1, 95);".

[1626] Input: Collected user data

[1627] Output: User data stored in the MySQL database

[1628] Step 5: Data preprocessing and feature extraction

[1629] The server uses Python and TensorFlow to retrieve data from the database and perform preprocessing and feature extraction. Preprocessing includes denoising and data normalization.

[1630] Input: User data stored in a MySQL database

[1631] Output: Preprocessed and feature-extracted data

[1632] Step 6: Training the AI ​​Model

[1633] The server trains a generative AI model based on preprocessed data, using feedforward and backpropagation algorithms.

[1634] Input: Preprocessed and feature-extracted data

[1635] Output: Trained generative AI model

[1636] Step 7: Generating compliments and compliment songs

[1637] The server uses a trained generative AI model to generate personalized compliments and praise songs for each user. For example, it can create a compliment such as "Your presentation today was excellent!" along with an original song that includes the user's name.

[1638] Input: User data and AI model

[1639] Output: Data of the generated compliments or compliment songs

[1640] Step 8: Providing praise (acknowledgment) results

[1641] The server sends the generated compliments and compliment songs to the device. The device receives them and provides them to the user using text display, audio output, or video playback functions.

[1642] Input: Data for generated compliments or compliment songs

[1643] Output: Compliments in text format displayed to the user, or audio / video played.

[1644] Step 9: Entering Feedback

[1645] The user enters feedback on the praise (acknowledgment) provided into the device. For example, they might enter a rating such as, "I liked this compliment."

[1646] Input: Feedback text or voice

[1647] Output: Feedback data

[1648] Step 10: Send and save feedback

[1649] The device sends user feedback to the server, which receives it and stores it as training data to improve the generated AI model. The server stores the feedback data in a MySQL database using INSERT statements.

[1650] Input: User feedback data

[1651] Output: Feedback data stored in the MySQL database

[1652] (Application Example 1)

[1653] 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".

[1654] It is necessary to alleviate the anxiety and uncertainty users feel while shopping in physical stores and to encourage purchasing behavior. In particular, it is crucial that users feel confident in their product selection and purchase decisions. Furthermore, personalized praise is an effective way to boost motivation and improve the user experience in physical stores, thereby increasing customer satisfaction. However, current technology lacks the appropriate systems to achieve this.

[1655] 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.

[1656] In this invention, the server includes means for receiving requests for praise (approval) from users, means for transmitting such requests to the server, means for collecting and managing user data, means for training a generative AI model for generating praise words or praise songs based on the user data, means for providing the generated praise words or praise songs to users, means for providing the generated praise words or praise songs in physical stores, and means for generating words of praise for the user's actions when they purchase or use a product. This makes it possible to improve the user experience in physical stores and increase the user's confidence in selecting and purchasing products.

[1657] 1. "Praise (acknowledgment)" refers to the positive evaluation or recognition that a user receives for a specific action or achievement.

[1658] 2. "Means for receiving requests" refers to interfaces or systems for receiving requests from users for praise (approval).

[1659] 3. "Means for sending requests to a server" refers to a system that has a communication function for sending received user requests to an external server.

[1660] 4. "Means for collecting and managing data" refers to systems and functions for collecting, appropriately storing, and managing user behavior history, feedback information, etc.

[1661] 5. A "generative AI model" refers to a machine learning model that generates compliments and compliment songs tailored to the user based on collected data.

[1662] 6. "Training methods" refer to the process and methods of optimizing and learning a generative AI model using collected data.

[1663] 7. "Means for providing generated compliments or compliment songs" refers to interfaces or output devices for providing generated compliments or compliment songs to users.

[1664] 8. "Means of providing in physical stores" refers to systems and methods for providing generated compliments and compliment songs to users in a physical store environment.

[1665] 9. "Means for generating compliments for user actions when purchasing or using a product" refers to methods and systems for generating appropriate compliments for user actions when selecting, purchasing, or using a product in a physical store.

[1666] To implement this invention, the following elements are necessary to constitute a system that provides praise (approval): a server, a terminal, and a user that communicate with each other and utilize a generative AI model to generate and provide personalized praise messages and praise songs.

[1667] Server Processing

[1668] 1. Collection and management of user data

[1669] The server collects data such as basic user information, behavioral history, and feedback, and stores it in a database. This includes retrieving various user data via APIs. For example, this could include shopping behavior history and past purchase history.

[1670] 2. Training the Generative AI Model

[1671] The server uses machine learning techniques based on the collected data to train a generative AI model. This training allows the model to learn user behavior patterns and preferences, enabling it to provide more personalized praise (affirmation).

[1672] 3. Generating compliments and compliment songs

[1673] The server uses a trained generative AI model to generate compliments and praise songs tailored to the user's behavior and product choices. For example, it can generate specific messages such as, "That's a great choice! Your taste is excellent."

[1674] Terminal processing

[1675] 1. Providing a user interface

[1676] The terminal provides an interface for users to enter requests, including text boxes, voice recognition, and selection menus.

[1677] 2. Communication with the server

[1678] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[1679] 3. Providing praise (acknowledgment)

[1680] The device provides users with compliments and songs received from the server using text display, audio output, and video playback functions. In particular, in physical stores, compliments can be displayed using smart glasses or smartphones.

[1681] User actions

[1682] 1. Information Entry

[1683] Users input the actions or achievements they want to be praised for through their device. For example, they might request praise (approval) for a newly purchased product.

[1684] 2. Providing feedback

[1685] After receiving praise (acknowledgment), users input feedback on the content into their device. This includes whether they were satisfied with the content and suggestions for improvement.

[1686] 3. Use both inside and outside the company

[1687] Users who receive praise (acknowledgment) are more likely to praise others based on that positive experience. This fosters a new culture of recognition both within and outside the organization.

[1688] Specific examples of implementation in physical stores

[1689] Hardware:

[1690] Smart glasses (for example, Google Glass)

[1691] Smartphone (iOS or Android device)

[1692] software:

[1693] Server-side: Python, Flask (as an API server)

[1694] Client-side: Python (for API calls), UI for video display

[1695] The server receives compliment (approval) requests from users, collects and manages user data, and trains a generative AI model. It then provides the user with the generated compliments. For example, when a user picks up an item in a physical store, displaying a compliment such as "You have excellent taste in this item you've chosen!" on smart glasses can improve the user experience.

[1696] Prompt example:

[1697] User ID: user123

[1698] Action: recommended_product_acquired

[1699] Prompt message:

[1700] User123 has decided to purchase the newly recommended item. Let's praise them for choosing this item. This item is based on the latest fashion trends and is a very tasteful choice.

[1701] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1702] Step 1:

[1703] The user enters a praise (approval) request using their device. The user enters the action they want to be praised for, either through text or voice, regarding the product selection or purchase they made while shopping. This becomes the input data.

[1704] Step 2:

[1705] The terminal sends the input request to the server. A secure communication protocol such as HTTPS is used for transmission. The output is the request data sent to the server.

[1706] Step 3:

[1707] Based on the received request data, the server collects the user's past behavior history and feedback information from the database. This becomes the new input data.

[1708] Step 4:

[1709] The server trains a generative AI model using collected user data. It performs data preprocessing, feature extraction, and model retraining to learn user behavior patterns and preferences. The output is the trained generative AI model.

[1710] Step 5:

[1711] The server uses a trained generative AI model to generate praise for the requested action. Here, the prompt text serves as input data, and the corresponding praise is output. Specifically, the prompt text is input to the generative AI model, which then generates appropriate praise.

[1712] Step 6:

[1713] The server sends the generated compliment to the terminal. This becomes the new output data. The terminal sends the received compliment from the server to an output device that provides it to the user.

[1714] Step 7:

[1715] The device displays received compliments using smart glasses or a smartphone. Being complimented increases the user's willingness to purchase, leading to a positive shopping experience. The output is the displayed compliment.

[1716] Step 8:

[1717] Users input feedback on the compliments they receive into their device. This input data includes their satisfaction level with the compliments and areas for improvement.

[1718] Step 9:

[1719] The terminal sends feedback data to the server. The server stores the received feedback in a database and uses it to further improve the generative AI model. The output is an improvement in the performance of the generative AI model.

[1720] This enables a personalized praise (acknowledgment) system that improves the user experience in physical stores by coordinating between users, devices, and servers.

[1721] 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.

[1722] To implement this invention, four elements are required: a terminal that provides a user interface, a server that manages data and houses a generation AI model, an emotion engine that recognizes the user's emotions, and a user who makes a request for praise (approval). This system starts when the user inputs what they want to be praised for into the terminal, and the terminal sends that information to the server. The server collects and analyzes the user's data and the results of the emotion engine's analysis, and uses the generation AI model to generate appropriate words of praise or praise songs. The generated words of praise or praise songs are then provided to the user through the terminal.

[1723] Server Processing

[1724] 1. Collection and management of user data

[1725] The server collects and stores data such as basic user information, behavioral history, feedback, and analysis results from the emotion engine in a database. For example, it retrieves data via API to capture presentations that the user has previously given high ratings to, specific behavioral history, and emotional states.

[1726] 2. Training the Generative AI Model

[1727] The server trains a generative AI model based on the collected data. Machine learning techniques are used for training, learning user behavioral tendencies, preferences, and emotional states. This allows for the provision of more personalized praise (affirmation).

[1728] 3. Generating compliments and compliment songs

[1729] The server uses a trained generative AI model to generate personalized compliments and songs for the user. For example, it can create specific messages such as, "Your presentation today was fantastic! Your passion really came through," or original songs that include the user's name.

[1730] Terminal processing

[1731] 1. Providing a user interface

[1732] The terminal provides an interface for the user to enter requests. Possible input methods include text boxes, voice recognition, and selection menus.

[1733] 2. Communication with the server

[1734] The device sends user requests and feedback to the server. Secure protocols such as HTTPS are used for communication to maintain confidentiality.

[1735] 3. Integration with the emotion engine

[1736] The device works in conjunction with an emotion engine to acquire and analyze the user's voice and facial expression data. The analysis results are sent to the server as data related to the user's emotional state.

[1737] 4. Providing praise (acknowledgment)

[1738] The terminal provides users with compliments and songs received from the server using text display, audio output, and video playback functions.

[1739] User actions

[1740] 1. Information Entry

[1741] Users input the actions or achievements they want to be praised for through their device. For example, they can voice-in requests such as, "I'd like to be praised for the proposal I made at today's meeting."

[1742] 2. Providing feedback

[1743] After receiving praise (acknowledgment), the user enters feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[1744] 3. Use both inside and outside the company

[1745] Users who receive praise (acknowledgment) gain a positive experience, which encourages them to praise others. This fosters a new culture of recognition both within and outside the organization.

[1746] The role of the emotional engine

[1747] 1. Analysis of user sentiment

[1748] The emotion engine analyzes the user's emotional state from their voice, facial expressions, and text, and sends the results to the server. This allows the generative AI model to generate appropriate compliments and praise songs that take the user's emotional state into account.

[1749] 2. Sending emotional data

[1750] The analysis results from the emotion engine are sent to the server as data indicating the user's emotional state. This data is also used as training data for the generative AI model.

[1751] Specific example

[1752] The following are specific examples of how this system can be used in both the business and personal spheres.

[1753] Examples of use in the business field

[1754] User A enters a request into the device asking for praise for their presentation. Simultaneously, the emotion engine analyzes User A's voice and facial expressions and sends their emotional state to the server. The server generates the most appropriate praise based on the request and emotion data, and sends the generated praise to the device. The device displays the praise to User A and also plays it aloud.

[1755] Examples of use in private areas

[1756] User B enters a request into the terminal asking to be praised for making a new dish. Simultaneously, the emotion engine analyzes User B's facial expressions and sends their emotional state to the server. The server generates the most appropriate praise based on the request and emotion data, and sends the generated praise to the terminal. The terminal displays and plays the praise aloud for User B.

[1757] In this way, the present invention realizes a system that can provide praise (acknowledgment) that takes into account the user's emotional state, thereby forming a new, more personalized culture of acknowledgment.

[1758] The following describes the processing flow.

[1759] Program processing flow

[1760] Step 1:

[1761] The user enters a request on the device.

[1762] Users input the behaviors or achievements they want to be praised for into their device as text or voice.

[1763] Example: User A types "I want you to praise my presentation today."

[1764] Step 2:

[1765] The terminal receives the input data.

[1766] The device receives user input and temporarily stores the necessary data (text or voice).

[1767] Example: The device saves data that says, "I want you to praise my presentation today."

[1768] Step 3:

[1769] The device collects emotional data.

[1770] The device activates an emotion engine and collects user voice and facial expression data.

[1771] Example: The device records user A's voice tone and facial expressions using its camera and microphone.

[1772] Step 4:

[1773] The emotion engine analyzes emotional data.

[1774] The emotion engine analyzes the collected data to identify the user's emotional state. For example, it categorizes it into positive, negative, or neutral.

[1775] Example: The emotion engine identifies a "positive" emotional state from user A's facial expressions and voice.

[1776] Step 5:

[1777] The device sends the request and sentiment data to the server.

[1778] The terminal sends the user's request and the results of the emotion engine's analysis to the server.

[1779] Example: The device sends a request to the server saying "I want to be praised for today's presentation" along with "positive" sentiment data.

[1780] Step 6:

[1781] The server receives the request and sentiment data.

[1782] The server receives requests and emotion data sent from the terminal and analyzes the request content and emotion state.

[1783] Example: The server receives a request to "praise today's presentation" and "positive" sentiment data.

[1784] Step 7:

[1785] The server retrieves user data.

[1786] The server retrieves the user's basic information, past behavioral history, and sentiment data from the database.

[1787] Example: The server retrieves user A's past presentation evaluations, behavioral history, and sentiment data.

[1788] Step 8:

[1789] The server applies the generated AI model.

[1790] The server inputs the acquired user data into an AI model to generate appropriate compliments and praise songs. Emotional data is also taken into consideration during the generation process.

[1791] Example: The server uses a generated AI model to produce a compliment such as, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1792] Step 9:

[1793] The server compiles the generated results.

[1794] The server compiles the generated results, such as compliments and compliment songs, and formats them into an appropriate format.

[1795] Example: Format the compliments generated by the server into text format.

[1796] Step 10:

[1797] The server sends the generated results to the terminal.

[1798] The server transfers the generated results to the terminal.

[1799] Example: The server sends a compliment to the terminal saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1800] Step 11:

[1801] The terminal receives the generated result.

[1802] The terminal receives the generated results sent from the server and converts them into the corresponding output format (text, audio, video, etc.).

[1803] Example: The device receives the text message, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1804] Step 12:

[1805] The device provides praise (acknowledgment) to the user.

[1806] The device provides the user with generated compliments and compliment songs using text display, audio playback, and video playback functions.

[1807] Example: The device plays an audio message saying, "Today's presentation was very insightful and made a significant contribution to solving the problem!"

[1808] Step 13:

[1809] Users provide feedback

[1810] Users who receive praise (approval) enter feedback on the content into their device. This feedback can range from positive evaluations to suggestions for improvement.

[1811] Example: User A enters the feedback, "I was so happy to receive that compliment!"

[1812] Step 14:

[1813] The device sends feedback to the server.

[1814] The device sends user feedback to the server.

[1815] Example: The device sends feedback to the server saying, "I was so happy to receive that compliment!"

[1816] Step 15:

[1817] The server receives the feedback and saves it as training data.

[1818] The server receives feedback and stores it as training data. It also uses this data to improve the generative AI model.

[1819] Example: The server receives user A's feedback and adds it to a database to be used for future praise (approval).

[1820] This allows for efficient praise (acknowledgment) through the collaboration of users, devices, servers, and the emotion engine, gradually fostering a new culture of recognition.

[1821] (Example 2)

[1822] 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".

[1823] Traditional praise (acknowledgment) systems often suffer from low user satisfaction because they simply generate compliments and praise songs mechanically, without adequately considering user behavior or emotions. Furthermore, they lack mechanisms for effectively utilizing user feedback to improve the system. Therefore, there is a need for more personalized praise (acknowledgment) that resonates with user emotions.

[1824] In Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting and managing user data, means for analyzing the user's emotional data using an emotion engine, means for training a generative AI model for generating compliments or compliment songs based on the user data and emotional data, means for providing the generated compliments or compliment songs to the user, means for receiving user feedback on the provided compliments or compliment songs, and means for improving the generative AI model based on the feedback. This makes it possible to provide personalized compliments (approvals) that take into account the user's emotional state, and also enables continuous system improvement based on feedback.

[1825] "User data" is a general term for data related to individual users, including basic user information, behavioral history, and feedback.

[1826] An "emotion engine" is a general term for software or hardware used to analyze a user's emotional state from their voice, facial expressions, text, etc.

[1827] A "generative AI model" is an artificial intelligence model that uses machine learning techniques to learn user behavior and emotional states, and then generates appropriate compliments and compliment songs.

[1828] A "praise" is a sentence or message used to acknowledge a user's actions or achievements.

[1829] "Praise songs" are audio or musical content created to praise users.

[1830] "Generating" means creating new data or information based on input data.

[1831] "Training" refers to the process of using collected data to train a generative AI model and improve its performance.

[1832] "Feedback" refers to the opinions and evaluations that users provide regarding compliments or songs that praise others.

[1833] "To analyze" means to examine data and find meaning or trends.

[1834] "Personalized" means that it is tailored to the specific needs and preferences of each individual user.

[1835] A "system" is a collection of devices or programs in which multiple components work together to achieve a specific function.

[1836] To implement this invention, four elements are necessary: ​​a terminal that provides a user interface, a server that manages and generates data and possesses an AI model, an emotion engine that recognizes the user's emotions, and a user who makes a request for praise (approval).

[1837] First, the user uses their device to enter a request for praise (approval). Input methods include text boxes and speech recognition. For example, the user might enter a request such as, "I want you to praise my presentation." The device then sends the entered request to the server. A secure protocol such as HTTPS is used for this communication.

[1838] Next, the server works with the emotion engine to acquire and analyze the user's voice and facial expression data. The emotion engine sends the analysis results to the server, and based on these results, it determines the user's emotional state. The server also collects the user's behavior history and past feedback and stores it in a database.

[1839] The server uses this data to train a generative AI model. Machine learning techniques are used for training, learning user behavior patterns and emotional states. This allows the generative AI model to generate the most appropriate compliments and praise songs for each user.

[1840] For example, it can generate specific compliments such as, "Your presentation was fantastic! Your passion really came through," or an original compliment song that includes the user's name. The generated compliments and songs are sent from the server to the terminal, which then displays or plays them for the user. Possible display methods include text display, audio output, and video playback.

[1841] Users who receive praise (approval) enter feedback on the content into their device. This feedback is sent to the server and used to improve the generative AI model. The server uses this feedback to retrain the generative AI model and improve the quality of future praise messages and praise songs.

[1842] As a concrete example, in the business world, a user might request "praise for a proposal made in a meeting," and an emotion engine would analyze their emotions at that time. The server would generate a compliment based on the analysis results and the user's behavioral history, and send it to the device. The device would then display the compliment, and the user would provide feedback in response.

[1843] In the private area, the user can request to be praised for creating a new dish, and the emotion engine analyzes their facial expressions. Similarly, the server generates compliments and praise songs based on the analysis results and the user's behavioral history, and provides them to the user through the device.

[1844] Example of a prompt

[1845] Please tell me what you would like to be praised for. For example, "I made a great suggestion at today's meeting, so I'd like to be praised for it." Also, please tell me how you felt at the time.

[1846] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1847] Step 1:

[1848] Users use their devices to enter requests for praise (approval). Input methods include text boxes and voice recognition. For example, a user might say, "I'd like to be praised for my proposal at the meeting." The input from the device is saved as text data in its internal memory.

[1849] Step 2:

[1850] The terminal sends the entered request to the server via a secure protocol such as HTTPS. The terminal's input data is transmitted through network components, and the server receives the request. The input data is sent in text format, and the server receives it.

[1851] Step 3:

[1852] The server uses an emotion engine to analyze the user's voice and facial expression data. It takes voice and image data as input, and the emotion engine analyzes this data to output the emotional state. For example, emotion tags such as "joy," "surprise," and "sadness" are output.

[1853] Step 4:

[1854] The server collects user behavior history and past feedback and stores it in a database. By taking past behavioral data and feedback data as input and storing them in the database, it builds a user model. For example, evaluations of past presentations and past emotional states are recorded.

[1855] Step 5:

[1856] The server trains a generative AI model based on collected user data and sentiment data. Behavioral history, emotional states, and feedback are used as training data, which the generative AI model learns from. Through training, the generative AI model understands user behavior patterns and emotional tendencies.

[1857] Step 6:

[1858] Using a generative AI model, the server generates appropriate compliments or compliment songs. Training data is used as input, and compliments or compliment songs are generated as output. For example, a compliment such as "Your presentation was fantastic! Your passion came through!" might be generated.

[1859] Step 7:

[1860] The server sends the generated compliments and compliment songs to the device. The data output by the generating AI model is sent in text or audio format and received by the device. The output data is securely transferred using the HTTPS protocol.

[1861] Step 8:

[1862] The device displays or plays received compliments and compliment songs to the user through its user interface. It receives data sent from the server as input and outputs screen displays and audio playback. Text may be displayed on the screen, or audio may play from the speaker.

[1863] Step 9:

[1864] Users input feedback on the praise (acknowledgment) they receive into their device. They use text boxes or rating buttons to enter their feedback. For example, they might write, "This praise was very encouraging."

[1865] Step 10:

[1866] The device sends feedback to the server via the HTTPS protocol. Input data is sent in text format, and the server receives the feedback data. This feedback is then stored in a database and used as training data for the next session.

[1867] Step 11:

[1868] The server improves the generative AI model based on the feedback. It analyzes the feedback data and uses it as training data for the generative AI model. This improves the quality of the praise and compliment songs generated in the future.

[1869] The above describes the specific processing steps of the program in this system.

[1870] (Application Example 2)

[1871] 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".

[1872] Traditional praise (acknowledgment) systems primarily generate compliments and praise songs based on user input, without considering the user's real-time emotional state. Therefore, providing appropriate praise (acknowledgment) tailored to the user's emotional state was difficult. Furthermore, there were no systems in place to offer real-time, emotionally appropriate compliments during face-to-face interactions with customers in physical stores.

[1873] 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.

[1874] In this invention, the server includes means for receiving requests for praise (approval) from users, means for transmitting such requests to the server, means for collecting and managing user data, means for training a generative AI model for generating praise words or praise songs based on the user data, means for providing the generated praise words or praise songs to the user, means for acquiring customer facial expression data and voice data and analyzing them using an emotion engine, and means for using the generative AI model based on the emotion analysis results to display appropriate praise words on the user terminal in real time. This enables the provision of personalized praise (approval) that takes into account the user's real-time emotional state, and the presentation of emotion-appropriate praise words in real time when interacting with customers in physical stores.

[1875] "A means of receiving requests for praise (approval) from users" refers to an interface that receives information via the device about what the user wants to be praised for.

[1876] "Means for sending the aforementioned request to the server" refers to a function that sends a request for praise (approval) received from the user terminal to the server via the network.

[1877] "Means for collecting and managing user data" refers to a system that collects and stores user basic information, behavioral history, feedback, etc., in a database for management.

[1878] "Means for training a generative AI model to generate compliments or compliment songs based on user data" refers to a method of training a generative AI model to generate compliments or compliment songs using a machine learning algorithm with collected user data.

[1879] "Means of providing users with generated compliments or compliment songs" refers to a function that presents users with compliments or compliment songs generated by a trained generative AI model through their device.

[1880] "Means for acquiring customer facial expression data and voice data and analyzing it using an emotion engine" refers to a system that captures customer facial expressions and voices using a camera and microphone, and analyzes that data using an emotion engine.

[1881] "A method for displaying appropriate compliments on the user's device in real time using a generative AI model based on emotion analysis results" refers to a method in which a generative AI model generates compliments tailored to the user in real time based on data analyzed by an emotion engine, and displays them immediately on the user's device.

[1882] To implement this invention, it is necessary to construct a system that combines a user interface using a smart device, a server, an emotion engine, and a generative AI model. The aim of this system is to provide users wearing smart devices in physical stores with real-time, emotion-responsive compliments.

[1883] Server Processing

[1884] The server uses the following hardware and software:

[1885] Hardware: The server can use an EC2 instance from Amazon Web Services (AWS).

[1886] Software: The necessary software to be installed on the server includes Flask (a Python micro web framework) and the Requests library (for sending HTTP requests).

[1887] The server performs the following operations.

[1888] 1. Collection and management of user data

[1889] The server collects user facial expression and voice data transmitted from smart devices and stores it in a database. This allows for the accumulation of the user's emotional state and behavioral history.

[1890] 2. Emotion analysis

[1891] An emotion engine (for example, Microsoft Azure's Emotion API) is used to analyze acquired facial expression and voice data to identify the user's emotional state.

[1892] 3. Generating compliments

[1893] Based on the analyzed sentiment data, a generative AI model (e.g., OpenAI's GPT-4) generates appropriate compliments. The following prompts are used in this generation process.

[1894] Example of a prompt:

[1895] Based on the customer's facial expression analysis results and emotional state, please create compliments and suggestions appropriate for the following situations.

[1896] Customer's emotional state: {emotional state}

[1897] Customer behavior: {Actions}

[1898] Example: A customer looks a little anxious when choosing new clothes. Compliments and suggestions to reassure them.

[1899] 4. Offering compliments

[1900] The generated compliments are displayed in real time on the smart device's screen. This allows store employees to instantly convey appropriate compliments to customers.

[1901] Terminal processing

[1902] Smart devices have the following functions:

[1903] Providing a user interface

[1904] Smart devices (such as smart glasses and head-mounted displays) provide an interface that allows users to input what they want to be praised for using voice input or touch controls.

[1905] Communication with the server

[1906] Smart devices transmit user input, facial expression data, and voice data to a server. This is done using secure communication protocols such as HTTPS.

[1907] Integration with the emotion engine

[1908] The system uses the camera and microphone built into the smart device to capture the user's facial expressions and voice, and then analyzes them for transmission to a server.

[1909] Compliment display

[1910] Compliments sent from the server are displayed in real time on the smart device's screen. This allows store staff to use that information to give customers appropriate compliments and suggestions.

[1911] User actions

[1912] Information entry

[1913] Users input the behaviors and achievements they want to be praised for in real time via their smart devices. This input is done through voice recognition or touch operation.

[1914] Provide feedback

[1915] After receiving praise (acknowledgment), users provide feedback on the content via their smart devices. This feedback is stored on a server and used to improve the generated AI model.

[1916] Specific example

[1917] In a physical store, if a customer choosing new clothes appears slightly anxious, a salesperson wearing smart glasses can capture that anxious expression. An emotion engine analyzes the expression as "anxiety," and based on this analysis, a generative AI model generates a compliment in real time, such as, "Those clothes look great on you. You've made a really great choice." The salesperson can then view this on the smart glasses' display and convey the compliment to the customer. In this way, the customer's shopping experience can be improved.

[1918] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1919] Step 1:

[1920] Users input the things or actions they want to be praised for through a smart device (such as smart glasses or a head-mounted display). This input is done using voice recognition or touch controls. The input information is temporarily stored as data within the device.

[1921] Input: Information (text or audio) about the user's desired praise or actions.

[1922] Output: Temporarily held input data

[1923] Step 2:

[1924] The terminal sends user input data to the server via a secure communication protocol (e.g., HTTPS). Simultaneously, the user's facial expressions and voice data are also captured and sent to the server.

[1925] Input: Temporarily stored user input data, facial expression data, voice data

[1926] Output: User input data, facial expression data, and voice data sent to the server.

[1927] Step 3:

[1928] The server collects basic user information, behavioral history, and feedback based on the received data and stores it in a database. This allows for the accumulation of user emotional states and behavioral history.

[1929] Input: User input data, facial expression data, and voice data sent from the terminal.

[1930] Output: User information, activity history, and feedback stored in the database.

[1931] Step 4:

[1932] The server uses an emotion engine (for example, Microsoft Azure's Emotion API) to analyze the acquired facial expression and voice data to determine the user's emotional state.

[1933] Input: Saved user facial expression data, voice data

[1934] Output: Analyzed user emotional state data

[1935] Step 5:

[1936] Based on the analyzed sentiment data, the server uses a generative AI model (e.g., OpenAI's GPT-4) to generate appropriate compliments. This generation process is based on prompt statements.

[1937] Example of a prompt:

[1938] Based on the customer's facial expression analysis results and emotional state, please create compliments and suggestions appropriate for the following situations.

[1939] Customer's emotional state: {emotional state}

[1940] Customer behavior: {Actions}

[1941] Example: A customer looks a little anxious when choosing new clothes. Compliments and suggestions to reassure them.

[1942] Input: Analyzed user emotion state data, prompt text

[1943] Output: Generated compliments

[1944] Step 6:

[1945] The server sends the generated compliments to the smart device's display in real time.

[1946] Input: Generated compliments

[1947] Output: Compliments sent to smart devices

[1948] Step 7:

[1949] The terminal displays compliments sent from the server in real time, providing them to the user. This allows users to instantly deliver appropriate compliments when interacting with customers face-to-face in physical stores.

[1950] Input: Compliments sent from the server

[1951] Output: Compliments displayed on the smart device's screen.

[1952] Step 8:

[1953] After receiving praise (acknowledgment), users provide feedback on the content via their device. This feedback is sent to the server and used to improve the generated AI model.

[1954] Input: User feedback data

[1955] Output: Feedback sent to the server, improvements to the generated AI model.

[1956] 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.

[1957] 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.

[1958] 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.

[1959] 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.

[1960] 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.

[1961] 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.

[1962] 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.

[1963] 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.

[1964] 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."

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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.

[1969] 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.

[1970] 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.

[1971] 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.

[1972] 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.

[1973] 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.

[1974] 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.

[1975] 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.

[1976] 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.

[1977] The following is further disclosed regarding the embodiments described above.

[1978] (Claim 1)

[1979] A means of receiving requests for praise (approval) from users,

[1980] Means for sending the aforementioned request to the server,

[1981] Means for collecting and managing user data,

[1982] A means for training a generative AI model to generate compliments or compliment songs based on the user's data,

[1983] A means of providing the user with generated compliments or compliment songs,

[1984] A system that includes this.

[1985] (Claim 2)

[1986] The system according to claim 1, further comprising means for receiving feedback provided by a user and storing it as training data to be used to improve the generated AI model.

[1987] (Claim 3)

[1988] The system according to claim 1, further comprising means for providing a gradual delivery of praise (acknowledgment) in the business sphere, praise (acknowledgment) in the private sphere, and praise (acknowledgment) for all people.

[1989] "Example 1"

[1990] (Claim 1)

[1991] A means of receiving requests for praise (approval) from users,

[1992] Means for sending the aforementioned request to the server,

[1993] Means for collecting and managing user data,

[1994] A means for training a generative AI model to generate compliments or compliment songs based on the user's data,

[1995] Means for preprocessing and feature extracting data for use in training,

[1996] A means of providing the user with generated compliments or compliment songs,

[1997] A means for providing the aforementioned compliments or compliment songs through text display, audio output, or video playback functions,

[1998] A system that includes this.

[1999] (Claim 2)

[2000] The system according to claim 1, further comprising means for receiving feedback provided by a user and storing it as training data to be used to improve the generated AI model.

[2001] (Claim 3)

[2002] The system according to claim 1, further comprising means for providing a gradual delivery of praise (acknowledgment) in the business sphere, praise (acknowledgment) in the private sphere, and praise (acknowledgment) for all people.

[2003] "Application Example 1"

[2004] (Claim 1)

[2005] A means of receiving requests for praise (approval) from users,

[2006] Means for sending the aforementioned request to the server,

[2007] Means for collecting and managing user data,

[2008] A means for training a generative AI model to generate compliments or compliment songs based on the user's data,

[2009] A means of providing the user with generated compliments or compliment songs,

[2010] A means of providing the generated compliments or compliment songs at a physical store,

[2011] A means of generating words of praise for a user's actions when they purchase or use a product,

[2012] A system that includes this.

[2013] (Claim 2)

[2014] The system according to claim 1, further comprising means for receiving feedback provided by a user and storing it as training data to be used to improve the generated AI model.

[2015] (Claim 3)

[2016] The system according to claim 1, further comprising means for providing a gradual delivery of praise (acknowledgment) in the business sphere, praise (acknowledgment) in the private sphere, and praise (acknowledgment) for all people.

[2017] "Example 2 of combining an emotion engine"

[2018] (Claim 1)

[2019] A means of receiving requests for praise (approval) from users,

[2020] Means for sending the aforementioned request to the server,

[2021] Means for collecting and managing user data,

[2022] A means of analyzing user emotional data using an emotion engine,

[2023] A means for training a generative AI model to generate compliments or compliment songs based on the user's data and emotional data,

[2024] A means of providing the user with generated compliments or compliment songs,

[2025] A means of receiving user feedback on the compliments or compliment songs provided,

[2026] A means for improving the generated AI model based on the aforementioned feedback,

[2027] A system that includes this.

[2028] (Claim 2)

[2029] The system according to claim 1, further comprising means for considering the user's past behavioral history and emotional state in the training and generation of the generative AI model.

[2030] (Claim 3)

[2031] The system according to claim 1, further comprising means of providing praise (recognition) in stages, thereby forming a new culture of recognition as users gain positive experiences both inside and outside the company.

[2032] "Application example 2 when combining with an emotional engine"

[2033] (Claim 1)

[2034] A means of receiving requests for praise (approval) from users,

[2035] Means for sending the aforementioned request to the server,

[2036] Means for collecting and managing user data,

[2037] A means for training a generative AI model to generate compliments or compliment songs based on the user's data,

[2038] A means of providing the user with generated compliments or compliment songs,

[2039] A means for acquiring customer facial expression data and voice data and analyzing them using an emotion engine,

[2040] A means of displaying appropriate compliments on the user's terminal in real time using a generation AI model based on emotion analysis results,

[2041] A system that includes this.

[2042] (Claim 2)

[2043] The system according to claim 1, further comprising means for receiving feedback provided by a user and storing it as training data to be used to improve the generated AI model.

[2044] (Claim 3)

[2045] The system according to claim 1, further comprising means for providing a gradual delivery of praise (acknowledgment) in the business sphere, praise (acknowledgment) in the private sphere, and praise (acknowledgment) for all people.

[2046] (Claim 4)

[2047] The system according to claim 1, further comprising means for analyzing the behavior and emotional state of a user wearing a smart device in a physical store in real time, and for a generated AI model to display compliments in real time based on the analysis data. [Explanation of Symbols]

[2048] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving requests for praise (approval) from users, Means for sending the aforementioned request to the server, Means for collecting and managing user data, A means for training a generative AI model to generate compliments or compliment songs based on the user's data, A means of providing the user with generated compliments or compliment songs, A system that includes this.

2. The system according to claim 1, further comprising means for receiving feedback provided by a user and storing it as training data to be used to improve the generated AI model.

3. The system according to claim 1, further comprising means for providing a gradual delivery of praise (acknowledgment) in the business domain, praise (acknowledgment) in the private domain, and praise (acknowledgment) for all people.

Images