system

Abstract

We provide the system. [Solution] The primary means of gathering information about user preferences and lifestyles, An information integration means that integrates information obtained from multiple generative artificial intelligence models and generates optimized proposals based on that information, An information presentation means that outputs the generated suggestions to the user, A system that includes means of communication with public systems and provides a customized interface for each user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003] [[ID=​​​​​​​​​​​​​​​​​​This invention solves the above problems by providing a first information gathering means for collecting user preferences and lifestyle information, an information integration means for integrating information obtained from multiple generative artificial intelligence models and generating optimized suggestions based on that information, and an information presentation means for outputting the generated suggestions to the user. Furthermore, by including a cooperation means for communicating with public systems and providing a customized interface for each user, convenience in public spaces is improved. In addition, by including a learning means for collecting user feedback information and updating the parameters of the generative artificial intelligence model for subsequent uses, it is possible to continuously improve the accuracy of the service.

[0006] "Information gathering methods" refer to means of collecting user preferences and lifestyle information.

[0007] A "generative artificial intelligence model" is an artificial intelligence model that generates suggestions tailored to the user's specific needs based on diverse information.

[0008] "Information integration means" refers to a means for integrating information obtained from multiple generative artificial intelligence models and generating optimized proposals.

[0009] An "information presentation means" is a means of outputting the generated proposal to the user.

[0010] "Integration means" refers to a means of providing a customized interface for each user through communication with public systems.

[0011] "Learning methods" refer to means of continuously improving the accuracy of a service by collecting feedback information from users and updating the parameters of a generative artificial intelligence model. [Brief explanation of the drawing]

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

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

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

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

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

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

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] In an embodiment of the present invention, the system first involves the user's terminal collecting user preferences and lifestyle information. Specifically, the user inputs data such as the type of meal they want, their budget, and their current location into the terminal. This information is then transmitted from the terminal to the server.

[0034] The server receives information from multiple users and performs integrated processing using a generative artificial intelligence model. This integrated processing generates suggestions that optimally reflect each user's preferences and conditions. Machine learning algorithms are used to generate suggestions, analyzing similarities and commonalities to derive options that satisfy everyone.

[0035] The generated suggestions are sent from the server to the terminal, which then presents the information to the user in a visualized form. The user can choose the most appealing option from the presented choices. Based on this choice, the user's decision is fed back to the server.

[0036] Furthermore, in integration with public systems, the server uses collected information to provide customized user interfaces to public equipment. This dynamic interface aims to improve ease of use for users.

[0037] Furthermore, the server utilizes feedback information to continuously learn the parameters of the generative artificial intelligence model. This makes it possible to further improve the accuracy of subsequent proposals.

[0038] As a concrete example, when considering lunch choices for multiple people, the terminal sends each user's food preferences to the server. Based on this, the server lists restaurants that everyone can enjoy and returns them to the terminal. Users select their preferred restaurants from the presented information, and the selection results are sent back to the server to help improve the overall accuracy of the system.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device collects user preferences and lifestyle information. Users input their desired restaurant criteria, budget, and current location into the device. This information is organized within the device and prepared for transmission to the server.

[0042] Step 2:

[0043] The server aggregates user information received from terminals. Information from multiple users is collected, and the server organizes and stores each user's preference data. This information is later used as input data for a generative artificial intelligence model.

[0044] Step 3:

[0045] The server integrates information using a generative artificial intelligence model and generates optimized suggestions. Using machine learning algorithms, it clusters information from users with similar preferences and selects restaurant candidates that will satisfy everyone.

[0046] Step 4:

[0047] The server sends the generated suggestions to the terminal. The terminal displays a list of suggested restaurants and their details to the user, presenting the options in a visually clear manner.

[0048] Step 5:

[0049] The user selects their preferred restaurant from the presented options via their device. The selection result is fed back from the device to the server and used to generate suggestions for future visits.

[0050] Step 6:

[0051] The server uses feedback information as training data to improve the accuracy of the generative artificial intelligence model. It continuously updates the model, adjusting parameters and making further optimizations for future suggestions.

[0052] Through these steps, the system provides users with highly accurate decision-making support.

[0053] (Example 1)

[0054] 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."

[0055] In today's world, there is a demand for quickly providing optimal suggestions tailored to the individual preferences and conditions of diverse users. However, conventional systems have difficulty efficiently acquiring and analyzing detailed information for each user, and the means to continuously improve the accuracy of those suggestions have been limited. As a result, it was often impossible to provide suggestions that satisfied each user.

[0056] 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.

[0057] In this invention, the server includes a first information acquisition means for acquiring user preferences and movement information, a transmission means for transmitting the acquired information to a central processing unit, and an information generation means in which multiple information processing devices cooperate to perform analysis and generate suggestions that satisfy the user's conditions. This makes it possible to quickly and accurately provide optimal suggestions that meet the detailed requirements of each user.

[0058] "User preferences and movement information" refers to data about specific conditions and settings that users prefer, as well as their current location and movement patterns.

[0059] "Primary information acquisition means" refers to functions and processes for acquiring information directly input by the user or sensor data.

[0060] "Transmission means" refers to the functions or processes used to communicate collected information to other elements or devices.

[0061] The "Central Processing Unit" is the main computing unit responsible for analyzing and processing received data and generating results.

[0062] An "information processing device" is a device or software used to analyze, process, and make decisions based on data.

[0063] "Information generation means" refers to functions or processes that create optimal suggestions for the user from processed data.

[0064] "Display means" refers to functions or devices that visually convey generated information or suggestions to the user.

[0065] "Evaluation methods" refer to functions and processes for collecting and analyzing user choices and responses.

[0066] "Means of collaboration" refers to the processes and functions by which a system communicates with other external systems or infrastructure and exchanges information.

[0067] "Feedback information" refers to data based on user selections and operation history, which is used to improve the system.

[0068] "Correction mechanisms" refer to functions or processes for adjusting and improving system or model parameters based on feedback.

[0069] "Optimization methods" refer to processes and functions that utilize collected data to improve the accuracy of proposals.

[0070] The system of this invention generates optimal suggestions tailored to the user's needs. The user utilizes the system by inputting their preferences and conditions (e.g., type of meal, budget, current location) into a terminal. The terminal then transmits this input information to the server.

[0071] The server uses a generative AI model to analyze the received user data. This analysis employs a deep learning framework to analyze the similarities and commonalities of the information, thereby establishing suggestions that will satisfy the user. Specific software used includes TENSORFLOW® and PyTorch.

[0072] The suggestions generated by the server are sent to the terminal in a visualized format. The terminal presents this information to the user, who can choose the most appealing option from the presented choices. The user's selection is sent back to the server and used to train the generative AI model.

[0073] As a concrete example, when a user chooses a lunch spot, they input their current location and preferences, and the device transmits this information to the server. Based on this, the server generates a selection of nearby restaurants that will satisfy everyone and returns it to the device. Through this process, the user can choose an attractive option, and that choice will contribute to better suggestions for future visits.

[0074] An example of a prompt might be a question like, "Please tell me about bakeries that are easily accessible from my current location." Such text-based prompts allow the system to accurately analyze the target content and provide appropriate suggestions.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The device collects user preferences and location information. The user inputs information about their preferences (e.g., type of food and budget) into the device, and the device simultaneously obtains the current location information using its built-in GPS. The input at this stage consists of preference data entered by the user and location information obtained by the device. This data is prepared as packets to be sent to the server.

[0078] Step 2:

[0079] The terminal sends the collected information to the server. The terminal constructs user preferences and location information as a transmission packet and sends it to the server using a secure communication protocol (e.g., SSL / TLS). In this process, the input data is encrypted and guaranteed to be sent securely to the server. The output is the encrypted packet that the server successfully received.

[0080] Step 3:

[0081] The server analyzes the received data and inputs it into a generative AI model. The server decodes the received data packets and retrieves relevant information from the database based on the acquired user preferences and location information. This information is then input into the generative AI model, and machine learning algorithms are applied to generate the most suitable suggestions for the user. In this process, specific data about the user's preferences and location is taken as input, and optimized suggestion data is obtained as output.

[0082] Step 4:

[0083] The server sends the generated suggestions to the terminal. Based on the optimal suggestions obtained from the generated AI model, the server sends them to the terminal in a format that can be presented to the user (e.g., data packets in JSON or XML format). From this transmission, the terminal used by the user receives the suggestions in a state where they can be displayed. The final output of this step is data that can be visualized on the terminal side.

[0084] Step 5:

[0085] The terminal displays the received suggestions to the user. The terminal parses the data sent from the server and presents the suggestions to the user in a visualized format. Typically, this display is done via an application or web interface. In this step, the input data is integrated into the user interface and output in an easy-to-use format.

[0086] Step 6:

[0087] The user makes a selection based on the suggestions and inputs the result into the terminal. The user evaluates the suggestions displayed on the terminal and expresses their opinion through the interface by choosing the option they find most appealing. The output is the information selected by the user.

[0088] Step 7:

[0089] The device feeds back the user's selections to the server, contributing to the improvement of the generative AI model. The device sends data about the user's selections back to the server, which incorporates this into the next learning phase. Upon receiving this feedback information, the server adjusts the parameters of the generative AI model to improve the accuracy of subsequent suggestions. In this step, the input is new user selection data, and the output is the improvement of the generative AI model.

[0090] (Application Example 1)

[0091] 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."

[0092] Currently, there is a lack of methods to quickly and accurately provide suggestions optimized to individual user preferences and conditions. Furthermore, there is a need for systems to continuously improve through user interaction, thereby enhancing the quality of suggestions. This invention aims to solve these problems and realize a system that provides users with better choices.

[0093] 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.

[0094] In this invention, the server includes information acquisition means, information generation means, information display means, coordination means, and information visualization means. This enables the generation and display of optimal suggestions based on user preferences.

[0095] "Information acquisition means" refers to a device or function for collecting information from users regarding their food preferences and lifestyle.

[0096] "Information generation means" refers to a device or function that creates optimized proposals using multiple artificial intelligence models based on acquired information.

[0097] "Information display means" refers to a device or function for visually providing the generated proposal to the user.

[0098] "Cooperative means" refers to a device or function that shares data with public devices via a communication network and provides a customized interface for each user.

[0099] "Information visualization means" refers to a visualization device or function that displays content according to user requests and enables interaction.

[0100] In an embodiment for carrying out the present invention, the system mainly consists of three components: a server, a terminal, and a user.

[0101] The server functions as an information generation tool, collecting information such as food preferences, budget, and location transmitted from users via their terminals. The collected data is integrated and analyzed using multiple generative artificial intelligence models. Specifically, these models compare similarities and conditions among users to generate optimized suggestions that satisfy everyone. The server accesses public devices through collaborative means and provides users with personalized interfaces.

[0102] The terminal functions as both an information display and visualization tool. It receives suggestions sent from the server and visualizes them using smart glasses or other display devices. Through this interface, users can intuitively understand and select suggestions. The selected information is then fed back to the server to help train the model and improve the suggestions.

[0103] A concrete example is a system that helps businessmen wearing smart glasses easily find an Italian restaurant suitable for lunch that day. Users can operate the interface through their glasses to select the optimal restaurant.

[0104] Example of a prompt:

[0105] "Create an AI prompt that generates optimal lunch suggestions based on user preferences and effectively communicates with the user visually. Please consider the following conditions: Italian cuisine, budget under 1500 yen, and location in Minato Ward, Tokyo."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The device collects input from the user. The user inputs their food preferences, budget, and current location information through a device such as smart glasses. The device collects this data and sends it to the server. The input data is in text and numerical format and includes food category, price, latitude and longitude, etc.

[0109] Step 2:

[0110] The server receives information and processes the data using a generative AI model. The server uses the received data as input to analyze the similarity between users using a machine learning algorithm. This analysis generates optimized meal suggestions that take each user's conditions into account. The output is a personalized list of restaurants for each user.

[0111] Step 3:

[0112] The server sends the generated suggestions to the terminal. The server organizes the optimized suggestions and converts them into a visually easy-to-read format. This data is sent to the terminal and is ready for the user to select through the interface.

[0113] Step 4:

[0114] The device visualizes and presents suggestions to the user. The device displays the received list of restaurants on the smart glasses' screen, providing an interactive interface for easy selection. The user can choose their preferred option from the displayed choices.

[0115] Step 5:

[0116] The user makes a selection and provides feedback. The user makes a decision through their device and sends their selection to the server. This feedback is used for the continuous learning of the generative AI model, contributing to improved suggestion accuracy. The user's selection patterns are reflected in subsequent data processing.

[0117] 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.

[0118] In an embodiment of the present invention, the system collects information via the user's terminal and performs integrated processing on a server to provide optimized suggestions. By incorporating an emotion engine, it becomes possible to take the user's emotional data into consideration. First, when the terminal inputs the user's preferences and lifestyle information, the built-in emotion engine analyzes the user's voice and facial expressions. This analysis allows for the real-time estimation of the user's emotional state.

[0119] The collected emotional data is sent to the server along with other user information. The server uses a generative artificial intelligence model to integrate the emotional data with conventional data. Based on this integrated data, the server performs the usual suggestion process, but it can dynamically adjust the suggestions based on the emotional information. For example, if the user is experiencing some kind of stress, the server will suggest restaurants that would help alleviate that stress.

[0120] Furthermore, the server continuously updates the parameters of its generative artificial intelligence model based on the feedback received, further improving the accuracy of future suggestions. This allows the system to provide flexible suggestions that are tailored to the user's needs and emotions.

[0121] As a concrete example, suppose a user inputs into their terminal that they want to relax after work. At this point, the emotion engine detects the user's stress level from the tone of their voice. When the server selects a suitable restaurant, it incorporates this emotional information and presents a restaurant with a relaxing atmosphere. This entire process allows the user to smoothly make the choice that best suits their emotional state.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The device collects user preferences and lifestyle information, and uses a built-in emotion engine to analyze the user's voice and facial expressions. This estimates the user's emotional state, and this information is stored along with other data.

[0125] Step 2:

[0126] The device sends all collected information to the server, including user sentiment data, selection data, and location information. This allows the server to receive a comprehensive dataset.

[0127] Step 3:

[0128] The server analyzes the received information and integrates the data using a generative artificial intelligence model. The server then generates optimal suggestions based on the user's preferences and emotions, taking individual sentiment data into consideration.

[0129] Step 4:

[0130] The server sends the generated suggestions back to the terminal. The terminal displays the suggestions to the user in a visually easy-to-understand manner and presents the available options.

[0131] Step 5:

[0132] The user uses their device to select their preferred suggestion from the presented options. Once the selection is complete, the result is sent back from the device to the server.

[0133] Step 6:

[0134] The server receives user selections and feedback, and uses this information to improve the accuracy of the generative artificial intelligence model. In particular, it analyzes the effect of suggestions based on emotional data and reflects this in the generation of suggestions for the next time.

[0135] (Example 2)

[0136] 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".

[0137] In modern information systems, it is difficult to provide suggestions that take into account the user's emotional state, and traditional methods have failed to offer flexible suggestions that meet user needs. Therefore, there is a need for optimized information delivery that takes emotional data into account.

[0138] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0139] In this invention, the server includes emotion analysis means for estimating the user's emotional state from facial expressions and voice data, information gathering means, and information integration means for integrating and analyzing information using a generative artificial intelligence model. This enables flexible and optimized suggestions based on the user's emotional state.

[0140] "Information gathering means" refers to devices and functions that effectively collect user preferences and lifestyle information.

[0141] "Emotional analysis means" refers to a function that analyzes the user's facial expressions and voice data to estimate their emotional state in real time.

[0142] "Information integration means" refers to a processing function that integrates information generated by multiple generative artificial intelligence models and generates optimized suggestions based on user needs.

[0143] "Information presentation means" refers to devices or interfaces for displaying or presenting generated proposals to users in an easily understandable manner.

[0144] "Integration means" refers to a function that communicates with public systems and provides users with customized information and interfaces.

[0145] A "learning method" is a mechanism for updating the parameters of a generative artificial intelligence model using user feedback information to improve the accuracy of its suggestions.

[0146] "Improvement measures" refer to a function that records the results of multiple users' suggestion selections and uses them as feedback to improve the performance of the generative artificial intelligence model.

[0147] This invention is an information system that provides suggestions that take into account the user's emotions and preferences. Specifically, the user inputs information about their likes and daily life via their own terminal. The terminal has a built-in emotion analysis engine that uses voice recognition and a camera to analyze the user's voice tone and facial expressions in real time and estimate their emotional state.

[0148] The collected emotional data and user information are transmitted to the server using secure communication methods. The server integrates this data using a generative AI model. This AI model analyzes the existing data and newly acquired emotional data together to create optimal suggestions for the user. Based on the integrated data, the suggestions are dynamically adjusted and output in a way that is sensitive to the user's emotions.

[0149] For example, if a user inputs "I want to relax" into the device, the device senses stress from that voice. Based on a generative AI model, the server then presents the user with suggestions for quiet, relaxing cafes. In this way, the system can smoothly suggest appropriate options according to the user's needs and emotional state.

[0150] An example of a prompt might be, "Tell me a place where I can relax today." The system analyzes the user's emotions from this input, and a generative AI model assists in making appropriate choices. The system has the ability to leverage these prompts to provide information optimized for each user.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The user enters information into the device.

[0154] Specifically, users operate the device to input their preferences and current emotional state. Input can be done via touch or voice, and the entered information is temporarily stored in the device's memory. For example, if a user inputs "I want to relax," the device obtains data to understand this intention.

[0155] Step 2:

[0156] The device collects and analyzes emotional data.

[0157] The device uses a built-in emotion analysis engine to collect the user's facial expressions and voice. The data collected through the camera and microphone is processed by an emotion analysis algorithm and output as emotional states such as stress and relaxation. These analysis results, along with the user's input data, are then used in the next step.

[0158] Step 3:

[0159] The device sends user data to the server.

[0160] The terminal packets the collected emotional data and user input information and sends it to the server using a secure protocol. The transmitted data is then used by the server for further processing.

[0161] Step 4:

[0162] The server integrates and analyzes the data.

[0163] The server integrates and processes the received user data using a generative AI model. First, the data is normalized and converted into a unified format through preprocessing. Then, the generative AI model analyzes the sentiment data and conventional user data to generate possible suggestion options.

[0164] Step 5:

[0165] The server generates and outputs the suggested content.

[0166] The server generates optimized suggestions based on the analysis results. These suggestions are tailored to the user's emotional state at the time and are prioritized and organized. This ensures that effective suggestions are provided to the user for use in the next step.

[0167] Step 6:

[0168] The proposed results are returned to the user's terminal and presented to the user.

[0169] The server sends the generated suggestions to the terminal, which then presents them to the user. The terminal visually displays the suggestions using its screen or verbally communicates them through a voice assistant. Based on this information, the user can make a selection that suits their needs.

[0170] (Application Example 2)

[0171] 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".

[0172] The challenge lies in providing an e-commerce system that can quickly deliver optimal suggestions based on the user's emotional state and preferences. Conventional systems have struggled to adequately reflect user preferences and emotions, highlighting the need for improved user experience.

[0173] 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.

[0174] In this invention, the server includes a first information gathering means for collecting user preferences and lifestyle-related information, an emotion analysis means for analyzing the user's voice and visual expressions to evaluate their emotional state, and an information integration means for integrating information obtained from multiple generative artificial intelligence computation elements together with emotion data and generating optimized suggestions based on this. This makes it possible to generate optimized suggestions based on the user's emotional state and preferences.

[0175] "User preferences" refer to the personal tastes and interests that a user has based on specific conditions.

[0176] "Lifestyle-related information" refers to information collected about the user's daily life, including, for example, lifestyle habits and behavioral patterns.

[0177] "Emotional analysis means" refers to a technical method or device for evaluating a user's emotional state by analyzing their voice and visual expressions.

[0178] "Generative artificial intelligence computation elements" are algorithms and computational processes used to generate new information and results from large amounts of data and past experience.

[0179] "Information integration means" refers to a method for centrally combining emotional data and other information to generate suggestions optimized for the user.

[0180] An "e-commerce service" is an online platform for buying and selling goods and services via the internet.

[0181] A "proposal" is the act of presenting options for products, services, etc., that are expected to be beneficial to the user.

[0182] To implement this invention, a system is needed to provide optimized suggestions based on the user's emotions and preferences. First, the terminal collects user preferences and lifestyle-related information. This includes text, voice, and image data entered by the user using a mobile device or other interface. The terminal incorporates emotion analysis means to analyze the user's voice tone and visual expressions in real time and estimate their emotional state.

[0183] The analyzed emotional data is sent to the server along with the user's preferences and lifestyle-related information. The server uses information integration means to combine this data with stored data and dynamically generates optimal suggestions using generative artificial intelligence computation elements. In this process, a generative AI model is used to suggest appropriate products or services that correspond to the user's emotional state and preferences.

[0184] As a concrete example, suppose a user inputs into the app that they want to relax after work. If the emotion analysis system detects stress from the user's tone of voice, the server will suggest products that are highly effective for relaxation. For example, it could suggest calming music or aromatherapy oils. This series of suggestions allows the user to smoothly make the choice that best suits their emotional state.

[0185] An example of a prompt would be, "Please suggest products based on the user's stress level. Optimize this by including emotional data analyzed from the user's tone of voice and facial expressions, as well as their past purchase history." This prompt ensures that the generative AI model functions correctly and provides the most relevant suggestions for the user.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The device collects user preferences and lifestyle-related information. It receives text, voice, and image data entered by the user and stores them in an internal database. The entered data is then prepared for analysis by sentiment analysis tools.

[0189] Step 2:

[0190] The emotion analysis system built into the device analyzes the user's voice and visual expressions in real time. The input is collected voice and image data, and the output is numerical and categorical information indicating the user's emotional state. This analysis estimates the user's stress level and mood.

[0191] Step 3:

[0192] The analyzed sentiment data and user preference information are sent to the server. The server receives this data and performs data integration processing using generative artificial intelligence computation elements. Here, calculations are performed to generate optimal suggestions based on the sentiment data and past usage history.

[0193] Step 4:

[0194] The server sends prompts to a generative AI model, which generates suggestions based on the user's emotions and preferences. The input consists of prompts and user data, and the output is a list of optimal products and services. The generative AI model dynamically generates optimized choices tailored to the user.

[0195] Step 5:

[0196] Optimized suggestions are sent from the server to the terminal, which then presents the information to the user. This allows the user to smoothly select products that match their emotional state. Based on the information provided in this process, the user is assisted in making a product selection decision.

[0197] 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.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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 shown 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.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] In an embodiment of the present invention, the system first involves the user's terminal collecting user preferences and lifestyle information. Specifically, the user inputs data such as the type of meal they want, their budget, and their current location into the terminal. This information is then transmitted from the terminal to the server.

[0214] The server receives information from multiple users and performs integrated processing using a generative artificial intelligence model. This integrated processing generates suggestions that optimally reflect each user's preferences and conditions. Machine learning algorithms are used to generate suggestions, analyzing similarities and commonalities to derive options that satisfy everyone.

[0215] The generated suggestions are sent from the server to the terminal, which then presents the information to the user in a visualized form. The user can choose the most appealing option from the presented choices. Based on this choice, the user's decision is fed back to the server.

[0216] Furthermore, in integration with public systems, the server uses collected information to provide customized user interfaces to public equipment. This dynamic interface aims to improve ease of use for users.

[0217] Furthermore, the server utilizes feedback information to continuously learn the parameters of the generative artificial intelligence model. This makes it possible to further improve the accuracy of subsequent proposals.

[0218] As a concrete example, when considering lunch choices for multiple people, the terminal sends each user's food preferences to the server. Based on this, the server lists restaurants that everyone can enjoy and returns them to the terminal. Users select their preferred restaurants from the presented information, and the selection results are sent back to the server to help improve the overall accuracy of the system.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The device collects user preferences and lifestyle information. Users input their desired restaurant criteria, budget, and current location into the device. This information is organized within the device and prepared for transmission to the server.

[0222] Step 2:

[0223] The server aggregates user information received from terminals. Information from multiple users is collected, and the server organizes and stores each user's preference data. This information is later used as input data for a generative artificial intelligence model.

[0224] Step 3:

[0225] The server integrates information using a generative artificial intelligence model and generates optimized suggestions. Using machine learning algorithms, it clusters information from users with similar preferences and selects restaurant candidates that will satisfy everyone.

[0226] Step 4:

[0227] The server sends the generated suggestions to the terminal. The terminal displays a list of suggested restaurants and their details to the user, presenting the options in a visually clear manner.

[0228] Step 5:

[0229] The user selects their preferred restaurant from the presented options via their device. The selection result is fed back from the device to the server and used to generate suggestions for future visits.

[0230] Step 6:

[0231] The server uses feedback information as training data to improve the accuracy of the generative artificial intelligence model. It continuously updates the model, adjusting parameters and making further optimizations for future suggestions.

[0232] Through these steps, the system provides users with highly accurate decision-making support.

[0233] (Example 1)

[0234] 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."

[0235] In today's world, there is a demand for quickly providing optimal suggestions tailored to the individual preferences and conditions of diverse users. However, conventional systems have difficulty efficiently acquiring and analyzing detailed information for each user, and the means to continuously improve the accuracy of those suggestions have been limited. As a result, it was often impossible to provide suggestions that satisfied each user.

[0236] 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.

[0237] In this invention, the server includes a first information acquisition means for acquiring user preferences and movement information, a transmission means for transmitting the acquired information to a central processing unit, and an information generation means in which multiple information processing devices cooperate to perform analysis and generate suggestions that satisfy the user's conditions. This makes it possible to quickly and accurately provide optimal suggestions that meet the detailed requirements of each user.

[0238] "User preferences and movement information" refers to data about specific conditions and settings that users prefer, as well as their current location and movement patterns.

[0239] "Primary information acquisition means" refers to functions and processes for acquiring information directly input by the user or sensor data.

[0240] "Transmission means" refers to the functions or processes used to communicate collected information to other elements or devices.

[0241] The "Central Processing Unit" is the main computing unit responsible for analyzing and processing received data and generating results.

[0242] An "information processing device" is a device or software used to analyze, process, and make decisions based on data.

[0243] "Information generation means" refers to functions or processes that create optimal suggestions for the user from processed data.

[0244] "Display means" refers to functions or devices that visually convey generated information or suggestions to the user.

[0245] "Evaluation methods" refer to functions and processes for collecting and analyzing user choices and responses.

[0246] "Means of collaboration" refers to the processes and functions by which a system communicates with other external systems or infrastructure and exchanges information.

[0247] "Feedback information" refers to data based on user selections and operation history, which is used to improve the system.

[0248] "Correction mechanisms" refer to functions or processes for adjusting and improving system or model parameters based on feedback.

[0249] "Optimization methods" refer to processes and functions that utilize collected data to improve the accuracy of proposals.

[0250] The system of this invention generates optimal suggestions tailored to the user's needs. The user utilizes the system by inputting their preferences and conditions (e.g., type of meal, budget, current location) into a terminal. The terminal then transmits this input information to a server.

[0251] The server uses a generative AI model to analyze the received user data. This analysis employs a deep learning framework to analyze the similarities and commonalities of the information, thereby establishing suggestions that will satisfy the user. Specific software used includes TensorFlow and PyTorch.

[0252] The suggestions generated by the server are sent to the terminal in a visualized format. The terminal presents this information to the user, who can choose the most appealing option from the presented choices. The user's selection is sent back to the server and used to train the generative AI model.

[0253] As a concrete example, when a user chooses a lunch spot, they input their current location and preferences, and the device transmits this information to the server. Based on this, the server generates a selection of nearby restaurants that will satisfy everyone and returns it to the device. Through this process, the user can choose an attractive option, and that choice will contribute to better suggestions for future visits.

[0254] An example of a prompt might be a question like, "Please tell me about bakeries that are easily accessible from my current location." Such text-based prompts allow the system to accurately analyze the target content and provide appropriate suggestions.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The device collects user preferences and location information. The user inputs information about their preferences (e.g., type of food and budget) into the device, and the device simultaneously obtains the current location information using its built-in GPS. The input at this stage consists of preference data entered by the user and location information obtained by the device. This data is prepared as packets to be sent to the server.

[0258] Step 2:

[0259] The terminal sends the collected information to the server. The terminal constructs user preferences and location information as a transmission packet and sends it to the server using a secure communication protocol (e.g., SSL / TLS). In this process, the input data is encrypted and guaranteed to be sent securely to the server. The output is the encrypted packet that the server successfully received.

[0260] Step 3:

[0261] The server analyzes the received data and inputs it into a generative AI model. The server decodes the received data packets and retrieves relevant information from the database based on the acquired user preferences and location information. This information is then input into the generative AI model, and machine learning algorithms are applied to generate the most suitable suggestions for the user. In this process, specific data about the user's preferences and location is taken as input, and optimized suggestion data is obtained as output.

[0262] Step 4:

[0263] The server sends the generated suggestions to the terminal. Based on the optimal suggestions obtained from the generated AI model, the server sends them to the terminal in a format that can be presented to the user (e.g., data packets in JSON or XML format). From this transmission, the terminal used by the user receives the suggestions in a state where they can be displayed. The final output of this step is data that can be visualized on the terminal side.

[0264] Step 5:

[0265] The terminal displays the received suggestions to the user. The terminal parses the data sent from the server and presents the suggestions to the user in a visualized format. Typically, this display is done via an application or web interface. In this step, the input data is integrated into the user interface and output in an easy-to-use format.

[0266] Step 6:

[0267] The user makes a selection based on the suggestions and inputs the result into the terminal. The user evaluates the suggestions displayed on the terminal and expresses their opinion through the interface by choosing the option they find most appealing. The output is the information selected by the user.

[0268] Step 7:

[0269] The device feeds back the user's selections to the server, contributing to the improvement of the generative AI model. The device sends data about the user's selections back to the server, which incorporates this into the next learning phase. Upon receiving this feedback information, the server adjusts the parameters of the generative AI model to improve the accuracy of subsequent suggestions. In this step, the input is new user selection data, and the output is the improvement of the generative AI model.

[0270] (Application Example 1)

[0271] 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."

[0272] Currently, there is a lack of methods to quickly and accurately provide suggestions optimized to individual user preferences and conditions. Furthermore, there is a need for systems to continuously improve through user interaction, thereby enhancing the quality of suggestions. This invention aims to solve these problems and realize a system that provides users with better choices.

[0273] 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.

[0274] In this invention, the server includes information acquisition means, information generation means, information display means, coordination means, and information visualization means. This enables the generation and display of optimal suggestions based on user preferences.

[0275] "Information acquisition means" refers to a device or function for collecting information from users regarding their food preferences and lifestyle.

[0276] "Information generation means" refers to a device or function that creates optimized proposals using multiple artificial intelligence models based on acquired information.

[0277] "Information display means" refers to a device or function for visually providing the generated proposal to the user.

[0278] "Cooperative means" refers to a device or function that shares data with public devices via a communication network and provides a customized interface for each user.

[0279] "Information visualization means" refers to a visualization device or function that displays content according to user requests and enables interaction.

[0280] In an embodiment for carrying out the present invention, the system mainly consists of three components: a server, a terminal, and a user.

[0281] The server functions as an information generation tool, collecting information such as food preferences, budget, and location transmitted from users via their terminals. The collected data is integrated and analyzed using multiple generative artificial intelligence models. Specifically, these models compare similarities and conditions among users to generate optimized suggestions that satisfy everyone. The server accesses public devices through collaborative means and provides users with personalized interfaces.

[0282] The terminal functions as both an information display and visualization tool. It receives suggestions sent from the server and visualizes them using smart glasses or other display devices. Through this interface, users can intuitively understand and select suggestions. The selected information is then fed back to the server to help train the model and improve the suggestions.

[0283] As a specific example, there is a system that supports a businessman wearing smart glasses to easily find an Italian restaurant suitable for lunch that day. The user can operate the interface through the glasses and select the optimal restaurant.

[0284] Example of prompt sentence:

[0285] "Generate an optimal lunch proposal based on user preferences and create an AI prompt that can visually communicate well with the user. As a condition, consider an Italian budget within 1,500 yen and the location in Minato Ward, Tokyo."

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The terminal collects the input from the user. The user inputs their meal preferences, budget, and current location information through a terminal such as smart glasses. The terminal collects this data and transmits it to the server. The input data is in text or numerical form and includes meal categories, amounts, latitude and longitude, etc.

[0289] Step 2:

[0290] The server receives the information and processes the data using the generated AI model. The server analyzes the similarity between users using a machine learning algorithm with the data received as input. Through this analysis, an optimized meal proposal considering the conditions of each user is generated. The output is a list of restaurants customized for each user.

[0291] Step 3:

[0292] The server transmits the proposal generated to the terminal. The server organizes the optimized proposal and converts it into a visually easy-to-see format. This data is transmitted to the terminal, preparing for the user to select through the interface.

[0293] Step 4:

[0294] The device visualizes and presents suggestions to the user. The device displays the received list of restaurants on the smart glasses' screen, providing an interactive interface for easy selection. The user can choose their preferred option from the displayed choices.

[0295] Step 5:

[0296] The user makes a selection and provides feedback. The user makes a decision through their device and sends their selection to the server. This feedback is used for the continuous learning of the generative AI model, contributing to improved suggestion accuracy. The user's selection patterns are reflected in subsequent data processing.

[0297] 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.

[0298] In an embodiment of the present invention, the system collects information via the user's terminal and performs integrated processing on a server to provide optimized suggestions. By incorporating an emotion engine, it becomes possible to take the user's emotional data into consideration. First, when the terminal inputs the user's preferences and lifestyle information, the built-in emotion engine analyzes the user's voice and facial expressions. This analysis allows for the real-time estimation of the user's emotional state.

[0299] The collected emotional data is sent to the server along with other user information. The server uses a generative artificial intelligence model to integrate the emotional data with conventional data. Based on this integrated data, the server performs the usual suggestion process, but it can dynamically adjust the suggestions based on the emotional information. For example, if the user is experiencing some kind of stress, the server will suggest restaurants that would help alleviate that stress.

[0300] Furthermore, the server continuously updates the parameters of its generative artificial intelligence model based on the feedback received, further improving the accuracy of future suggestions. This allows the system to provide flexible suggestions that are tailored to the user's needs and emotions.

[0301] As a concrete example, suppose a user inputs into their terminal that they want to relax after work. At this point, the emotion engine detects the user's stress level from the tone of their voice. When the server selects a suitable restaurant, it incorporates this emotional information and presents a restaurant with a relaxing atmosphere. This entire process allows the user to smoothly make the choice that best suits their emotional state.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The device collects user preferences and lifestyle information, and uses a built-in emotion engine to analyze the user's voice and facial expressions. This estimates the user's emotional state, and this information is stored along with other data.

[0305] Step 2:

[0306] The device sends all collected information to the server, including user sentiment data, selection data, and location information. This allows the server to receive a comprehensive dataset.

[0307] Step 3:

[0308] The server analyzes the received information and integrates the data using a generative artificial intelligence model. The server generates an optimal proposal based on the user's preferences and emotions while considering individual emotion data.

[0309] Step 4:

[0310] The server sends back the proposal generated by the server to the terminal. The terminal displays the proposal to the user in a visually understandable manner and presents selectable options.

[0311] Step 5:

[0312] The user uses the terminal to select a desired proposal from the presented options. When the selection is complete, the result is sent from the terminal back to the server.

[0313] Step 6:

[0314] The server receives the user's selection result and feedback and uses it to improve the accuracy of the generative artificial intelligence model. In particular, it analyzes the effect of the proposal based on emotion data and reflects it in the next proposal generation.

[0315] (Example 2)

[0316] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0317] In modern information systems, it is difficult to make proposals considering the user's emotional state, and conventional methods have not been able to provide flexible proposals that meet the user's needs. Therefore, optimized information provision considering emotion data is required.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0319] In this invention, the server includes emotion analysis means for estimating the user's emotional state from facial expressions and voice data, information gathering means, and information integration means for integrating and analyzing information using a generative artificial intelligence model. This enables flexible and optimized suggestions based on the user's emotional state.

[0320] "Information gathering means" refers to devices and functions that effectively collect user preferences and lifestyle information.

[0321] "Emotional analysis means" refers to a function that analyzes the user's facial expressions and voice data to estimate their emotional state in real time.

[0322] "Information integration means" refers to a processing function that integrates information generated by multiple generative artificial intelligence models and generates optimized suggestions based on user needs.

[0323] "Information presentation means" refers to devices or interfaces for displaying or presenting generated proposals to users in an easily understandable manner.

[0324] "Integration means" refers to a function that communicates with public systems and provides users with customized information and interfaces.

[0325] A "learning method" is a mechanism for updating the parameters of a generative artificial intelligence model using user feedback information to improve the accuracy of its suggestions.

[0326] "Improvement measures" refer to a function that records the results of multiple users' suggestion selections and uses them as feedback to improve the performance of the generative artificial intelligence model.

[0327] This invention is an information system that provides suggestions that take into account the user's emotions and preferences. Specifically, the user inputs information about their likes and daily life via their own terminal. The terminal has a built-in emotion analysis engine that uses voice recognition and a camera to analyze the user's voice tone and facial expressions in real time and estimate their emotional state.

[0328] The collected emotional data and user information are transmitted to the server using secure communication methods. The server integrates this data using a generative AI model. This AI model analyzes the existing data and newly acquired emotional data together to create optimal suggestions for the user. Based on the integrated data, the suggestions are dynamically adjusted and output in a way that is sensitive to the user's emotions.

[0329] For example, if a user inputs "I want to relax" into the device, the device senses stress from that voice. Based on a generative AI model, the server then presents the user with suggestions for quiet, relaxing cafes. In this way, the system can smoothly suggest appropriate options according to the user's needs and emotional state.

[0330] An example of a prompt might be, "Tell me a place where I can relax today." The system analyzes the user's emotions from this input, and a generative AI model assists in making appropriate choices. The system has the ability to leverage these prompts to provide information optimized for each user.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] The user enters information into the device.

[0334] Specifically, users operate the device to input their preferences and current emotional state. Input can be done via touch or voice, and the entered information is temporarily stored in the device's memory. For example, if a user inputs "I want to relax," the device obtains data to understand this intention.

[0335] Step 2:

[0336] The device collects and analyzes emotional data.

[0337] The device uses a built-in emotion analysis engine to collect the user's facial expressions and voice. The data collected through the camera and microphone is processed by an emotion analysis algorithm and output as emotional states such as stress and relaxation. These analysis results, along with the user's input data, are then used in the next step.

[0338] Step 3:

[0339] The device sends user data to the server.

[0340] The terminal packets the collected emotional data and user input information and sends it to the server using a secure protocol. The transmitted data is then used by the server for further processing.

[0341] Step 4:

[0342] The server integrates and analyzes the data.

[0343] The server integrates and processes the received user data using a generative AI model. First, the data is normalized and converted into a unified format through preprocessing. Then, the generative AI model analyzes the sentiment data and conventional user data to generate possible suggestion options.

[0344] Step 5:

[0345] The server generates and outputs the suggested content.

[0346] The server generates optimized suggestions based on the analysis results. These suggestions are tailored to the user's emotional state at the time and are prioritized and organized. This ensures that effective suggestions are provided to the user for use in the next step.

[0347] Step 6:

[0348] The proposed results are returned to the user's terminal and presented to the user.

[0349] The server sends the generated suggestions to the terminal, which then presents them to the user. The terminal visually displays the suggestions using its screen or verbally communicates them through a voice assistant. Based on this information, the user can make a selection that suits their needs.

[0350] (Application Example 2)

[0351] 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 as the "terminal".

[0352] The challenge lies in providing an e-commerce system that can quickly deliver optimal suggestions based on the user's emotional state and preferences. Conventional systems have struggled to adequately reflect user preferences and emotions, highlighting the need for improved user experience.

[0353] 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.

[0354] In this invention, the server includes a first information gathering means for collecting user preferences and lifestyle-related information, an emotion analysis means for analyzing the user's voice and visual expressions to evaluate their emotional state, and an information integration means for integrating information obtained from multiple generative artificial intelligence computation elements together with emotion data and generating optimized suggestions based on this. This makes it possible to generate optimized suggestions based on the user's emotional state and preferences.

[0355] "User preferences" refer to the personal tastes and interests that a user has based on specific conditions.

[0356] "Lifestyle-related information" refers to information collected about the user's daily life, including, for example, lifestyle habits and behavioral patterns.

[0357] "Emotional analysis means" refers to a technical method or device for evaluating a user's emotional state by analyzing their voice and visual expressions.

[0358] "Generative artificial intelligence computation elements" are algorithms and computational processes used to generate new information and results from large amounts of data and past experience.

[0359] "Information integration means" refers to a method for centrally combining emotional data and other information to generate suggestions optimized for the user.

[0360] An "e-commerce service" is an online platform for buying and selling goods and services via the internet.

[0361] A "proposal" is the act of presenting options for products, services, etc., that are expected to be beneficial to the user.

[0362] To implement this invention, a system is needed to provide optimized suggestions based on the user's emotions and preferences. First, the terminal collects user preferences and lifestyle-related information. This includes text, voice, and image data entered by the user using a mobile device or other interface. The terminal incorporates emotion analysis means to analyze the user's voice tone and visual expressions in real time and estimate their emotional state.

[0363] The analyzed emotional data is sent to the server along with the user's preferences and lifestyle-related information. The server uses information integration means to combine this data with stored data and dynamically generates optimal suggestions using generative artificial intelligence computation elements. In this process, a generative AI model is used to suggest appropriate products or services that correspond to the user's emotional state and preferences.

[0364] As a concrete example, suppose a user inputs into the app that they want to relax after work. If the emotion analysis system detects stress from the user's tone of voice, the server will suggest products that are highly effective for relaxation. For example, it could suggest calming music or aromatherapy oils. This series of suggestions allows the user to smoothly make the choice that best suits their emotional state.

[0365] An example of a prompt would be, "Please suggest products based on the user's stress level. Optimize this by including emotional data analyzed from the user's tone of voice and facial expressions, as well as their past purchase history." This prompt ensures that the generative AI model functions correctly and provides the most relevant suggestions for the user.

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The device collects user preferences and lifestyle-related information. It receives text, voice, and image data entered by the user and stores them in an internal database. The entered data is then prepared for analysis by sentiment analysis tools.

[0369] Step 2:

[0370] The emotion analysis system built into the device analyzes the user's voice and visual expressions in real time. The input is collected voice and image data, and the output is numerical and categorical information indicating the user's emotional state. This analysis estimates the user's stress level and mood.

[0371] Step 3:

[0372] The analyzed sentiment data and user preference information are sent to the server. The server receives this data and performs data integration processing using generative artificial intelligence computation elements. Here, calculations are performed to generate optimal suggestions based on the sentiment data and past usage history.

[0373] Step 4:

[0374] The server sends prompts to a generative AI model, which generates suggestions based on the user's emotions and preferences. The input consists of prompts and user data, and the output is a list of optimal products and services. The generative AI model dynamically generates optimized choices tailored to the user.

[0375] Step 5:

[0376] Optimized suggestions are sent from the server to the terminal, which then presents the information to the user. This allows the user to smoothly select products that match their emotional state. Based on the information provided in this process, the user is assisted in making a product selection decision.

[0377] 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.

[0378] 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 those described above. 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 shown 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.

[0379] 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.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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".

[0393] In an embodiment of the present invention, the system first involves the user's terminal collecting user preferences and lifestyle information. Specifically, the user inputs data such as the type of meal they want, their budget, and their current location into the terminal. This information is then transmitted from the terminal to the server.

[0394] The server receives information from multiple users and performs integrated processing using a generative artificial intelligence model. This integrated processing generates suggestions that optimally reflect each user's preferences and conditions. Machine learning algorithms are used to generate suggestions, analyzing similarities and commonalities to derive options that satisfy everyone.

[0395] The generated suggestions are sent from the server to the terminal, which then presents the information to the user in a visualized form. The user can choose the most appealing option from the presented choices. Based on this choice, the user's decision is fed back to the server.

[0396] Furthermore, in integration with public systems, the server uses collected information to provide customized user interfaces to public equipment. This dynamic interface aims to improve ease of use for users.

[0397] Furthermore, the server utilizes feedback information to continuously learn the parameters of the generative artificial intelligence model. This makes it possible to further improve the accuracy of subsequent proposals.

[0398] As a concrete example, when considering lunch choices for multiple people, the terminal sends each user's food preferences to the server. Based on this, the server lists restaurants that everyone can enjoy and returns them to the terminal. Users select their preferred restaurants from the presented information, and the selection results are sent back to the server to help improve the overall accuracy of the system.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] The device collects user preferences and lifestyle information. Users input their desired restaurant criteria, budget, and current location into the device. This information is organized within the device and prepared for transmission to the server.

[0402] Step 2:

[0403] The server aggregates user information received from terminals. Information from multiple users is collected, and the server organizes and stores each user's preference data. This information is later used as input data for a generative artificial intelligence model.

[0404] Step 3:

[0405] The server integrates information using a generative artificial intelligence model and generates optimized suggestions. Using machine learning algorithms, it clusters information from users with similar preferences and selects restaurant candidates that will satisfy everyone.

[0406] Step 4:

[0407] The server sends the generated suggestions to the terminal. The terminal displays a list of suggested restaurants and their details to the user, presenting the options in a visually clear manner.

[0408] Step 5:

[0409] The user selects their preferred restaurant from the presented options via their device. The selection result is fed back from the device to the server and used to generate suggestions for future visits.

[0410] Step 6:

[0411] The server uses feedback information as training data to improve the accuracy of the generative artificial intelligence model. It continuously updates the model, adjusting parameters and making further optimizations for future suggestions.

[0412] Through these steps, the system provides users with highly accurate decision-making support.

[0413] (Example 1)

[0414] 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."

[0415] In today's world, there is a demand for quickly providing optimal suggestions tailored to the individual preferences and conditions of diverse users. However, conventional systems have difficulty efficiently acquiring and analyzing detailed information for each user, and the means to continuously improve the accuracy of those suggestions have been limited. As a result, it was often impossible to provide suggestions that satisfied each user.

[0416] 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.

[0417] In this invention, the server includes a first information acquisition means for acquiring user preferences and movement information, a transmission means for transmitting the acquired information to a central processing unit, and an information generation means in which multiple information processing devices cooperate to perform analysis and generate suggestions that satisfy the user's conditions. This makes it possible to quickly and accurately provide optimal suggestions that meet the detailed requirements of each user.

[0418] "User preferences and movement information" refers to data about specific conditions and settings that users prefer, as well as their current location and movement patterns.

[0419] "Primary information acquisition means" refers to functions and processes for acquiring information directly input by the user or sensor data.

[0420] "Transmission means" refers to the functions or processes used to communicate collected information to other elements or devices.

[0421] The "Central Processing Unit" is the main computing unit responsible for analyzing and processing received data and generating results.

[0422] An "information processing device" is a device or software used to analyze, process, and make decisions based on data.

[0423] "Information generation means" refers to functions or processes that create optimal suggestions for the user from processed data.

[0424] "Display means" refers to functions or devices that visually convey generated information or suggestions to the user.

[0425] "Evaluation methods" refer to functions and processes for collecting and analyzing user choices and responses.

[0426] "Means of collaboration" refers to the processes and functions by which a system communicates with other external systems or infrastructure and exchanges information.

[0427] "Feedback information" refers to data based on user selections and operation history, which is used to improve the system.

[0428] "Correction mechanisms" refer to functions or processes for adjusting and improving system or model parameters based on feedback.

[0429] "Optimization methods" refer to processes and functions that utilize collected data to improve the accuracy of proposals.

[0430] The system of this invention generates optimal suggestions tailored to the user's needs. The user utilizes the system by inputting their preferences and conditions (e.g., type of meal, budget, current location) into a terminal. The terminal then transmits this input information to a server.

[0431] The server uses a generative AI model to analyze the received user data. This analysis employs a deep learning framework to analyze the similarities and commonalities of the information, thereby establishing suggestions that will satisfy the user. Specific software used includes TensorFlow and PyTorch.

[0432] The suggestions generated by the server are sent to the terminal in a visualized format. The terminal presents this information to the user, who can choose the most appealing option from the presented choices. The user's selection is sent back to the server and used to train the generative AI model.

[0433] As a concrete example, when a user chooses a lunch spot, they input their current location and preferences, and the device transmits this information to the server. Based on this, the server generates a selection of nearby restaurants that will satisfy everyone and returns it to the device. Through this process, the user can choose an attractive option, and that choice will contribute to better suggestions for future visits.

[0434] An example of a prompt might be a question like, "Please tell me about bakeries that are easily accessible from my current location." Such text-based prompts allow the system to accurately analyze the target content and provide appropriate suggestions.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] The device collects user preferences and location information. The user inputs information about their preferences (e.g., type of food and budget) into the device, and the device simultaneously obtains the current location information using its built-in GPS. The input at this stage consists of preference data entered by the user and location information obtained by the device. This data is prepared as packets to be sent to the server.

[0438] Step 2:

[0439] The terminal sends the collected information to the server. The terminal constructs user preferences and location information as a transmission packet and sends it to the server using a secure communication protocol (e.g., SSL / TLS). In this process, the input data is encrypted and guaranteed to be sent securely to the server. The output is the encrypted packet that the server successfully received.

[0440] Step 3:

[0441] The server analyzes the received data and inputs it into a generative AI model. The server decodes the received data packets and retrieves relevant information from the database based on the acquired user preferences and location information. This information is then input into the generative AI model, and machine learning algorithms are applied to generate the most suitable suggestions for the user. In this process, specific data about the user's preferences and location is taken as input, and optimized suggestion data is obtained as output.

[0442] Step 4:

[0443] The server sends the generated suggestions to the terminal. Based on the optimal suggestions obtained from the generated AI model, the server sends them to the terminal in a format that can be presented to the user (e.g., data packets in JSON or XML format). From this transmission, the terminal used by the user receives the suggestions in a state where they can be displayed. The final output of this step is data that can be visualized on the terminal side.

[0444] Step 5:

[0445] The terminal displays the received suggestions to the user. The terminal parses the data sent from the server and presents the suggestions to the user in a visualized format. Typically, this display is done via an application or web interface. In this step, the input data is integrated into the user interface and output in an easy-to-use format.

[0446] Step 6:

[0447] The user makes a selection based on the suggestions and inputs the result into the terminal. The user evaluates the suggestions displayed on the terminal and expresses their opinion through the interface by choosing the option they find most appealing. The output is the information selected by the user.

[0448] Step 7:

[0449] The device feeds back the user's selections to the server, contributing to the improvement of the generative AI model. The device sends data about the user's selections back to the server, which incorporates this into the next learning phase. Upon receiving this feedback information, the server adjusts the parameters of the generative AI model to improve the accuracy of subsequent suggestions. In this step, the input is new user selection data, and the output is the improvement of the generative AI model.

[0450] (Application Example 1)

[0451] 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."

[0452] Currently, there is a lack of methods to quickly and accurately provide suggestions optimized to individual user preferences and conditions. Furthermore, there is a need for systems to continuously improve through user interaction, thereby enhancing the quality of suggestions. This invention aims to solve these problems and realize a system that provides users with better choices.

[0453] 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.

[0454] In this invention, the server includes information acquisition means, information generation means, information display means, coordination means, and information visualization means. This enables the generation and display of optimal suggestions based on user preferences.

[0455] "Information acquisition means" refers to a device or function for collecting information from users regarding their food preferences and lifestyle.

[0456] "Information generation means" refers to a device or function that creates optimized proposals using multiple artificial intelligence models based on acquired information.

[0457] "Information display means" refers to a device or function for visually providing the generated proposal to the user.

[0458] "Cooperative means" refers to a device or function that shares data with public devices via a communication network and provides a customized interface for each user.

[0459] "Information visualization means" refers to a visualization device or function that displays content according to user requests and enables interaction.

[0460] In an embodiment for carrying out the present invention, the system mainly consists of three components: a server, a terminal, and a user.

[0461] The server functions as an information generation tool, collecting information such as food preferences, budget, and location transmitted from users via their terminals. The collected data is integrated and analyzed using multiple generative artificial intelligence models. Specifically, these models compare similarities and conditions among users to generate optimized suggestions that satisfy everyone. The server accesses public devices through collaborative means and provides users with personalized interfaces.

[0462] The terminal functions as both an information display and visualization tool. It receives suggestions sent from the server and visualizes them using smart glasses or other display devices. Through this interface, users can intuitively understand and select suggestions. The selected information is then fed back to the server to help train the model and improve the suggestions.

[0463] A concrete example is a system that helps businessmen wearing smart glasses easily find an Italian restaurant suitable for lunch that day. Users can operate the interface through their glasses to select the optimal restaurant.

[0464] Example of a prompt:

[0465] "Create an AI prompt that generates optimal lunch suggestions based on user preferences and effectively communicates with the user visually. Please consider the following conditions: Italian cuisine, budget under 1500 yen, and location in Minato Ward, Tokyo."

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The device collects input from the user. The user inputs their food preferences, budget, and current location information through a device such as smart glasses. The device collects this data and sends it to the server. The input data is in text and numerical format and includes food category, price, latitude and longitude, etc.

[0469] Step 2:

[0470] The server receives information and processes the data using a generative AI model. The server uses the received data as input to analyze the similarity between users using a machine learning algorithm. This analysis generates optimized meal suggestions that take each user's conditions into account. The output is a personalized list of restaurants for each user.

[0471] Step 3:

[0472] The server sends the generated suggestions to the terminal. The server organizes the optimized suggestions and converts them into a visually easy-to-read format. This data is sent to the terminal and is ready for the user to select through the interface.

[0473] Step 4:

[0474] The device visualizes and presents suggestions to the user. The device displays the received list of restaurants on the smart glasses' screen, providing an interactive interface for easy selection. The user can choose their preferred option from the displayed choices.

[0475] Step 5:

[0476] The user makes a selection and provides feedback. The user makes a decision through their device and sends their selection to the server. This feedback is used for the continuous learning of the generative AI model, contributing to improved suggestion accuracy. The user's selection patterns are reflected in subsequent data processing.

[0477] 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.

[0478] In an embodiment of the present invention, the system collects information via the user's terminal and performs integrated processing on a server to provide optimized suggestions. By incorporating an emotion engine, it becomes possible to take the user's emotional data into consideration. First, when the terminal inputs the user's preferences and lifestyle information, the built-in emotion engine analyzes the user's voice and facial expressions. This analysis allows for the real-time estimation of the user's emotional state.

[0479] The collected emotional data is sent to the server along with other user information. The server uses a generative artificial intelligence model to integrate the emotional data with conventional data. Based on this integrated data, the server performs the usual suggestion process, but it can dynamically adjust the suggestions based on the emotional information. For example, if the user is experiencing some kind of stress, the server will suggest restaurants that would help alleviate that stress.

[0480] Furthermore, the server continuously updates the parameters of its generative artificial intelligence model based on the feedback received, further improving the accuracy of future suggestions. This allows the system to provide flexible suggestions that are tailored to the user's needs and emotions.

[0481] As a concrete example, suppose a user inputs into their terminal that they want to relax after work. At this point, the emotion engine detects the user's stress level from the tone of their voice. When the server selects a suitable restaurant, it incorporates this emotional information and presents a restaurant with a relaxing atmosphere. This entire process allows the user to smoothly make the choice that best suits their emotional state.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The device collects user preferences and lifestyle information, and uses a built-in emotion engine to analyze the user's voice and facial expressions. This estimates the user's emotional state, and this information is stored together with other data.

[0485] Step 2:

[0486] The device sends all collected information to the server, including user sentiment data, selection data, and location information. This allows the server to receive a comprehensive dataset.

[0487] Step 3:

[0488] The server analyzes the received information and integrates the data using a generative artificial intelligence model. The server then generates optimal suggestions based on the user's preferences and emotions, taking individual sentiment data into consideration.

[0489] Step 4:

[0490] The server sends the generated suggestions back to the terminal. The terminal displays the suggestions to the user in a visually easy-to-understand manner and presents the available options.

[0491] Step 5:

[0492] The user uses their device to select their preferred suggestion from the presented options. Once the selection is complete, the result is sent back from the device to the server.

[0493] Step 6:

[0494] The server receives user selections and feedback, and uses this information to improve the accuracy of the generative artificial intelligence model. In particular, it analyzes the effect of suggestions based on emotional data and reflects this in the generation of suggestions for the next time.

[0495] (Example 2)

[0496] 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."

[0497] In modern information systems, it is difficult to provide suggestions that take into account the user's emotional state, and traditional methods have failed to offer flexible suggestions that meet user needs. Therefore, there is a need for optimized information delivery that takes emotional data into account.

[0498] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0499] In this invention, the server includes emotion analysis means for estimating the user's emotional state from facial expressions and voice data, information gathering means, and information integration means for integrating and analyzing information using a generative artificial intelligence model. This enables flexible and optimized suggestions based on the user's emotional state.

[0500] "Information gathering means" refers to devices and functions that effectively collect user preferences and lifestyle information.

[0501] "Emotional analysis means" refers to a function that analyzes the user's facial expressions and voice data to estimate their emotional state in real time.

[0502] "Information integration means" refers to a processing function that integrates information generated by multiple generative artificial intelligence models and generates optimized suggestions based on user needs.

[0503] "Information presentation means" refers to devices or interfaces for displaying or presenting generated proposals to users in an easily understandable manner.

[0504] "Integration means" refers to a function that communicates with public systems and provides users with customized information and interfaces.

[0505] A "learning method" is a mechanism for updating the parameters of a generative artificial intelligence model using user feedback information to improve the accuracy of its suggestions.

[0506] "Improvement measures" refer to a function that records the results of multiple users' suggestion selections and uses them as feedback to improve the performance of the generative artificial intelligence model.

[0507] This invention is an information system that provides suggestions that take into account the user's emotions and preferences. Specifically, the user inputs information about their likes and daily life via their own terminal. The terminal has a built-in emotion analysis engine that uses voice recognition and a camera to analyze the user's voice tone and facial expressions in real time and estimate their emotional state.

[0508] The collected emotional data and user information are transmitted to the server using secure communication methods. The server integrates this data using a generative AI model. This AI model analyzes the existing data and newly acquired emotional data together to create optimal suggestions for the user. Based on the integrated data, the suggestions are dynamically adjusted and output in a way that is sensitive to the user's emotions.

[0509] For example, if a user inputs "I want to relax" into the device, the device senses stress from that voice. Based on a generative AI model, the server then presents the user with suggestions for quiet, relaxing cafes. In this way, the system can smoothly suggest appropriate options according to the user's needs and emotional state.

[0510] An example of a prompt might be, "Tell me a place where I can relax today." The system analyzes the user's emotions from this input, and a generative AI model assists in making appropriate choices. The system has the ability to leverage these prompts to provide information optimized for each user.

[0511] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0512] Step 1:

[0513] The user enters information into the device.

[0514] Specifically, users operate the device to input their preferences and current emotional state. Input can be done via touch or voice, and the entered information is temporarily stored in the device's memory. For example, if a user inputs "I want to relax," the device obtains data to understand this intention.

[0515] Step 2:

[0516] The device collects and analyzes emotional data.

[0517] The device uses a built-in emotion analysis engine to collect the user's facial expressions and voice. The data collected through the camera and microphone is processed by an emotion analysis algorithm and output as emotional states such as stress and relaxation. These analysis results, along with the user's input data, are then used in the next step.

[0518] Step 3:

[0519] The device sends user data to the server.

[0520] The terminal packets the collected emotional data and user input information and sends it to the server using a secure protocol. The transmitted data is then used by the server for further processing.

[0521] Step 4:

[0522] The server integrates and analyzes the data.

[0523] The server integrates the received user data using a generative AI model. First, the data is normalized and converted into a unified format through preprocessing. Then, the generative AI model analyzes the sentiment data and conventional user data to generate possible suggestion options.

[0524] Step 5:

[0525] The server generates and outputs the suggested content.

[0526] The server generates optimized suggestions based on the analysis results. These suggestions are tailored to the user's emotional state at the time and are prioritized and organized. This ensures that effective suggestions are provided to the user for use in the next step.

[0527] Step 6:

[0528] The proposed results are returned to the user's terminal and presented to the user.

[0529] The server sends the generated suggestions to the terminal, which then presents them to the user. The terminal visually displays the suggestions using its screen or verbally communicates them through a voice assistant. Based on this information, the user can make a selection that suits their needs.

[0530] (Application Example 2)

[0531] 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."

[0532] The challenge lies in providing an e-commerce system that can quickly deliver optimal suggestions based on the user's emotional state and preferences. Conventional systems have struggled to adequately reflect user preferences and emotions, highlighting the need for improved user experience.

[0533] 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.

[0534] In this invention, the server includes a first information gathering means for collecting user preferences and lifestyle-related information, an emotion analysis means for analyzing the user's voice and visual expressions to evaluate their emotional state, and an information integration means for integrating information obtained from multiple generative artificial intelligence computation elements together with emotion data and generating optimized suggestions based on this. This makes it possible to generate optimized suggestions based on the user's emotional state and preferences.

[0535] "User preferences" refer to the personal tastes and interests that a user has based on specific conditions.

[0536] "Lifestyle-related information" refers to information collected about the user's daily life, including, for example, lifestyle habits and behavioral patterns.

[0537] "Emotional analysis means" refers to a technical method or device for evaluating a user's emotional state by analyzing their voice and visual expressions.

[0538] "Generative artificial intelligence computation elements" are algorithms and computational processes used to generate new information and results from large amounts of data and past experience.

[0539] "Information integration means" refers to a method for centrally combining emotional data and other information to generate suggestions optimized for the user.

[0540] An "e-commerce service" is an online platform for buying and selling goods and services via the internet.

[0541] A "proposal" is the act of presenting options for products, services, etc., that are expected to be beneficial to the user.

[0542] To implement this invention, a system is needed to provide optimized suggestions based on the user's emotions and preferences. First, the terminal collects user preferences and lifestyle-related information. This includes text, voice, and image data entered by the user using a mobile device or other interface. The terminal incorporates emotion analysis means to analyze the user's voice tone and visual expressions in real time and estimate their emotional state.

[0543] The analyzed emotional data is sent to the server along with the user's preferences and lifestyle-related information. The server uses information integration means to combine this data with stored data and dynamically generates optimal suggestions using generative artificial intelligence computation elements. In this process, a generative AI model is used to suggest appropriate products or services that correspond to the user's emotional state and preferences.

[0544] As a concrete example, suppose a user inputs into the app that they want to relax after work. If the emotion analysis system detects stress from the user's tone of voice, the server will suggest products that are highly effective for relaxation. For example, it could suggest calming music or aromatherapy oils. This series of suggestions allows the user to smoothly make the choice that best suits their emotional state.

[0545] An example of a prompt would be, "Please suggest products based on the user's stress level. Optimize this by including emotional data analyzed from the user's tone of voice and facial expressions, as well as their past purchase history." This prompt ensures that the generative AI model functions correctly and provides the most relevant suggestions for the user.

[0546] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0547] Step 1:

[0548] The device collects user preferences and lifestyle-related information. It receives text, voice, and image data entered by the user and stores them in an internal database. The entered data is then prepared for analysis by sentiment analysis tools.

[0549] Step 2:

[0550] The emotion analysis system built into the device analyzes the user's voice and visual expressions in real time. The input is collected voice and image data, and the output is numerical and categorical information indicating the user's emotional state. This analysis estimates the user's stress level and mood.

[0551] Step 3:

[0552] The analyzed sentiment data and user preference information are sent to the server. The server receives this data and performs data integration processing using generative artificial intelligence computation elements. Here, calculations are performed to generate optimal suggestions based on the sentiment data and past usage history.

[0553] Step 4:

[0554] The server sends prompts to a generative AI model, which generates suggestions based on the user's emotions and preferences. The input consists of prompts and user data, and the output is a list of optimal products and services. The generative AI model dynamically generates optimized choices tailored to the user.

[0555] Step 5:

[0556] Optimized suggestions are sent from the server to the terminal, which then presents the information to the user. This allows the user to smoothly select products that match their emotional state. Based on the information provided in this process, the user is assisted in making a product selection decision.

[0557] 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.

[0558] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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 shown 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.

[0559] 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.

[0560] [Fourth Embodiment]

[0561] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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".

[0574] In an embodiment of the present invention, the system first involves the user's terminal collecting user preferences and lifestyle information. Specifically, the user inputs data such as the type of meal they want, their budget, and their current location into the terminal. This information is then transmitted from the terminal to the server.

[0575] The server receives information from multiple users and performs integrated processing using a generative artificial intelligence model. This integrated processing generates suggestions that optimally reflect each user's preferences and conditions. Machine learning algorithms are used to generate suggestions, analyzing similarities and commonalities to derive options that satisfy everyone.

[0576] The generated suggestions are sent from the server to the terminal, which then presents the information to the user in a visualized form. The user can choose the most appealing option from the presented choices. Based on this choice, the user's decision is fed back to the server.

[0577] Furthermore, in integration with public systems, the server uses collected information to provide customized user interfaces to public equipment. This dynamic interface aims to improve ease of use for users.

[0578] Furthermore, the server utilizes feedback information to continuously learn the parameters of the generative artificial intelligence model. This makes it possible to further improve the accuracy of subsequent proposals.

[0579] As a concrete example, when considering lunch choices for multiple people, the terminal sends each user's food preferences to the server. Based on this, the server lists restaurants that everyone can enjoy and returns them to the terminal. Users select their preferred restaurants from the presented information, and the selection results are sent back to the server to help improve the overall accuracy of the system.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] The device collects user preferences and lifestyle information. Users input their desired restaurant criteria, budget, and current location into the device. This information is organized within the device and prepared for transmission to the server.

[0583] Step 2:

[0584] The server aggregates user information received from terminals. Information from multiple users is collected, and the server organizes and stores each user's preference data. This information is later used as input data for a generative artificial intelligence model.

[0585] Step 3:

[0586] The server integrates information using a generative artificial intelligence model and generates optimized suggestions. Using machine learning algorithms, it clusters information from users with similar preferences and selects restaurant candidates that will satisfy everyone.

[0587] Step 4:

[0588] The server sends the generated suggestions to the terminal. The terminal displays a list of suggested restaurants and their details to the user, presenting the options in a visually clear manner.

[0589] Step 5:

[0590] The user selects their preferred restaurant from the presented options via their device. The selection result is fed back from the device to the server and used to generate suggestions for future visits.

[0591] Step 6:

[0592] The server uses feedback information as training data to improve the accuracy of the generative artificial intelligence model. It continuously updates the model, adjusting parameters and making further optimizations for future suggestions.

[0593] Through these steps, the system provides users with highly accurate decision-making support.

[0594] (Example 1)

[0595] 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".

[0596] In today's world, there is a demand for quickly providing optimal suggestions tailored to the individual preferences and conditions of diverse users. However, conventional systems have difficulty efficiently acquiring and analyzing detailed information for each user, and the means to continuously improve the accuracy of those suggestions have been limited. As a result, it was often impossible to provide suggestions that satisfied each user.

[0597] 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.

[0598] In this invention, the server includes a first information acquisition means for acquiring user preferences and movement information, a transmission means for transmitting the acquired information to a central processing unit, and an information generation means in which multiple information processing devices cooperate to perform analysis and generate suggestions that satisfy the user's conditions. This makes it possible to quickly and accurately provide optimal suggestions that meet the detailed requirements of each user.

[0599] "User preferences and movement information" refers to data about specific conditions and settings that users prefer, as well as their current location and movement patterns.

[0600] "Primary information acquisition means" refers to functions and processes for acquiring information directly input by the user or sensor data.

[0601] "Transmission means" refers to the functions or processes used to communicate collected information to other elements or devices.

[0602] The "Central Processing Unit" is the main computing unit responsible for analyzing and processing received data and generating results.

[0603] An "information processing device" is a device or software used to analyze, process, and make decisions based on data.

[0604] "Information generation means" refers to functions or processes that create optimal suggestions for the user from processed data.

[0605] "Display means" refers to functions or devices that visually convey generated information or suggestions to the user.

[0606] "Evaluation methods" refer to functions and processes for collecting and analyzing user choices and responses.

[0607] "Means of collaboration" refers to the processes and functions by which a system communicates with other external systems or infrastructure and exchanges information.

[0608] "Feedback information" refers to data based on user selections and operation history, which is used to improve the system.

[0609] "Correction mechanisms" refer to functions or processes for adjusting and improving system or model parameters based on feedback.

[0610] "Optimization methods" refer to processes and functions that utilize collected data to improve the accuracy of proposals.

[0611] The system of this invention generates optimal suggestions tailored to the user's needs. The user utilizes the system by inputting their preferences and conditions (e.g., type of meal, budget, current location) into a terminal. The terminal then transmits this input information to a server.

[0612] The server uses a generative AI model to analyze the received user data. This analysis employs a deep learning framework to analyze the similarities and commonalities of the information, thereby establishing suggestions that will satisfy the user. Specific software used includes TensorFlow and PyTorch.

[0613] The suggestions generated by the server are sent to the terminal in a visualized format. The terminal presents this information to the user, who can choose the most appealing option from the presented choices. The user's selection is sent back to the server and used to train the generative AI model.

[0614] As a concrete example, when a user chooses a lunch spot, they input their current location and preferences, and the device transmits this information to the server. Based on this, the server generates a selection of nearby restaurants that will satisfy everyone and returns it to the device. Through this process, the user can choose an attractive option, and that choice will contribute to better suggestions for future visits.

[0615] An example of a prompt might be a question like, "Please tell me about bakeries that are easily accessible from my current location." Such text-based prompts allow the system to accurately analyze the target content and provide appropriate suggestions.

[0616] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0617] Step 1:

[0618] The device collects user preferences and location information. The user inputs information about their preferences (e.g., type of food and budget) into the device, and the device simultaneously obtains the current location information using its built-in GPS. The input at this stage consists of preference data entered by the user and location information obtained by the device. This data is prepared as packets to be sent to the server.

[0619] Step 2:

[0620] The terminal sends the collected information to the server. The terminal constructs user preferences and location information as a transmission packet and sends it to the server using a secure communication protocol (e.g., SSL / TLS). In this process, the input data is encrypted and guaranteed to be sent securely to the server. The output is the encrypted packet that the server successfully received.

[0621] Step 3:

[0622] The server analyzes the received data and inputs it into a generative AI model. The server decodes the received data packets and retrieves relevant information from the database based on the acquired user preferences and location information. This information is then input into the generative AI model, and machine learning algorithms are applied to generate the most suitable suggestions for the user. In this process, specific data about the user's preferences and location is taken as input, and optimized suggestion data is obtained as output.

[0623] Step 4:

[0624] The server sends the generated suggestions to the terminal. Based on the optimal suggestions obtained from the generated AI model, the server sends them to the terminal in a format that can be presented to the user (e.g., data packets in JSON or XML format). From this transmission, the terminal used by the user receives the suggestions in a state where they can be displayed. The final output of this step is data that can be visualized on the terminal side.

[0625] Step 5:

[0626] The terminal displays the received suggestions to the user. The terminal parses the data sent from the server and presents the suggestions to the user in a visualized format. Typically, this display is done via an application or web interface. In this step, the input data is integrated into the user interface and output in an easy-to-use format.

[0627] Step 6:

[0628] The user makes a selection based on the suggestions and inputs the result into the terminal. The user evaluates the suggestions displayed on the terminal and expresses their opinion through the interface by choosing the option they find most appealing. The output is the information selected by the user.

[0629] Step 7:

[0630] The device feeds back the user's selections to the server, contributing to the improvement of the generative AI model. The device sends data about the user's selections back to the server, which incorporates this into the next learning phase. Upon receiving this feedback information, the server adjusts the parameters of the generative AI model to improve the accuracy of subsequent suggestions. In this step, the input is new user selection data, and the output is the improvement of the generative AI model.

[0631] (Application Example 1)

[0632] 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".

[0633] Currently, there is a lack of methods to quickly and accurately provide suggestions optimized to individual user preferences and conditions. Furthermore, there is a need for systems to continuously improve through user interaction, thereby enhancing the quality of suggestions. This invention aims to solve these problems and realize a system that provides users with better choices.

[0634] 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.

[0635] In this invention, the server includes information acquisition means, information generation means, information display means, coordination means, and information visualization means. This enables the generation and display of optimal suggestions based on user preferences.

[0636] "Information acquisition means" refers to a device or function for collecting information from users regarding their food preferences and lifestyle.

[0637] "Information generation means" refers to a device or function that creates optimized proposals using multiple artificial intelligence models based on acquired information.

[0638] "Information display means" refers to a device or function for visually providing the generated proposal to the user.

[0639] "Cooperative means" refers to a device or function that shares data with public devices via a communication network and provides a customized interface for each user.

[0640] "Information visualization means" refers to a visualization device or function that displays content according to user requests and enables interaction.

[0641] In an embodiment for carrying out the present invention, the system mainly consists of three components: a server, a terminal, and a user.

[0642] The server functions as an information generation tool, collecting information such as food preferences, budget, and location transmitted from users via their terminals. The collected data is integrated and analyzed using multiple generative artificial intelligence models. Specifically, these models compare similarities and conditions among users to generate optimized suggestions that satisfy everyone. The server accesses public devices through collaborative means and provides users with personalized interfaces.

[0643] The terminal functions as both an information display and visualization tool. It receives suggestions sent from the server and visualizes them using smart glasses or other display devices. Through this interface, users can intuitively understand and select suggestions. The selected information is then fed back to the server to help train the model and improve the suggestions.

[0644] A concrete example is a system that helps businessmen wearing smart glasses easily find an Italian restaurant suitable for lunch that day. Users can operate the interface through their glasses to select the optimal restaurant.

[0645] Example of a prompt:

[0646] "Create an AI prompt that generates optimal lunch suggestions based on user preferences and effectively communicates with the user visually. Please consider the following conditions: Italian cuisine, budget under 1500 yen, and location in Minato Ward, Tokyo."

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] The device collects input from the user. The user inputs their food preferences, budget, and current location information through a device such as smart glasses. The device collects this data and sends it to the server. The input data is in text and numerical format and includes food category, price, latitude and longitude, etc.

[0650] Step 2:

[0651] The server receives information and processes the data using a generative AI model. The server uses the received data as input to analyze the similarity between users using a machine learning algorithm. This analysis generates optimized meal suggestions that take each user's conditions into account. The output is a personalized list of restaurants for each user.

[0652] Step 3:

[0653] The server sends the generated suggestions to the terminal. The server organizes the optimized suggestions and converts them into a visually easy-to-read format. This data is sent to the terminal and is ready for the user to select through the interface.

[0654] Step 4:

[0655] The device visualizes and presents suggestions to the user. The device displays the received list of restaurants on the smart glasses' screen, providing an interactive interface for easy selection. The user can choose their preferred option from the displayed choices.

[0656] Step 5:

[0657] The user makes a selection and provides feedback. The user makes a decision through their device and sends their selection to the server. This feedback is used for the continuous learning of the generative AI model, contributing to improved suggestion accuracy. The user's selection patterns are reflected in subsequent data processing.

[0658] 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.

[0659] In an embodiment of the present invention, the system collects information via the user's terminal and performs integrated processing on a server to provide optimized suggestions. By incorporating an emotion engine, it becomes possible to take the user's emotional data into consideration. First, when the terminal inputs the user's preferences and lifestyle information, the built-in emotion engine analyzes the user's voice and facial expressions. This analysis allows for the real-time estimation of the user's emotional state.

[0660] The collected emotional data is sent to the server along with other user information. The server uses a generative artificial intelligence model to integrate the emotional data with conventional data. Based on this integrated data, the server performs the usual suggestion process, but it can dynamically adjust the suggestions based on the emotional information. For example, if the user is experiencing some kind of stress, the server will suggest restaurants that would help alleviate that stress.

[0661] Furthermore, the server continuously updates the parameters of its generative artificial intelligence model based on the feedback received, further improving the accuracy of future suggestions. This allows the system to provide flexible suggestions that are tailored to the user's needs and emotions.

[0662] As a concrete example, suppose a user inputs into their terminal that they want to relax after work. At this point, the emotion engine detects the user's stress level from the tone of their voice. When the server selects a suitable restaurant, it incorporates this emotional information and presents a restaurant with a relaxing atmosphere. This entire process allows the user to smoothly make the choice that best suits their emotional state.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The device collects user preferences and lifestyle information, and uses a built-in emotion engine to analyze the user's voice and facial expressions. This estimates the user's emotional state, and this information is stored together with other data.

[0666] Step 2:

[0667] The device sends all collected information to the server, including user sentiment data, selection data, and location information. This allows the server to receive a comprehensive dataset.

[0668] Step 3:

[0669] The server analyzes the received information and integrates the data using a generative artificial intelligence model. The server then generates optimal suggestions based on the user's preferences and emotions, taking individual sentiment data into consideration.

[0670] Step 4:

[0671] The server sends the generated suggestions back to the terminal. The terminal displays the suggestions to the user in a visually easy-to-understand manner and presents the available options.

[0672] Step 5:

[0673] The user uses their device to select their preferred suggestion from the presented options. Once the selection is complete, the result is sent back from the device to the server.

[0674] Step 6:

[0675] The server receives user selections and feedback, and uses this information to improve the accuracy of the generative artificial intelligence model. In particular, it analyzes the effect of suggestions based on emotional data and reflects this in the generation of suggestions for the next time.

[0676] (Example 2)

[0677] 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".

[0678] In modern information systems, it is difficult to provide suggestions that take into account the user's emotional state, and traditional methods have failed to offer flexible suggestions that meet user needs. Therefore, there is a need for optimized information delivery that takes emotional data into account.

[0679] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0680] In this invention, the server includes emotion analysis means for estimating the user's emotional state from facial expressions and voice data, information gathering means, and information integration means for integrating and analyzing information using a generative artificial intelligence model. This enables flexible and optimized suggestions based on the user's emotional state.

[0681] "Information gathering means" refers to devices and functions that effectively collect user preferences and lifestyle information.

[0682] "Emotional analysis means" refers to a function that analyzes the user's facial expressions and voice data to estimate their emotional state in real time.

[0683] "Information integration means" refers to a processing function that integrates information generated by multiple generative artificial intelligence models and generates optimized suggestions based on user needs.

[0684] "Information presentation means" refers to devices or interfaces for displaying or presenting generated proposals to users in an easily understandable manner.

[0685] "Integration means" refers to a function that communicates with public systems and provides users with customized information and interfaces.

[0686] A "learning method" is a mechanism for updating the parameters of a generative artificial intelligence model using user feedback information to improve the accuracy of its suggestions.

[0687] "Improvement measures" refer to a function that records the results of multiple users' suggestion selections and uses them as feedback to improve the performance of the generative artificial intelligence model.

[0688] This invention is an information system that provides suggestions that take into account the user's emotions and preferences. Specifically, the user inputs information about their likes and daily life via their own terminal. The terminal has a built-in emotion analysis engine that uses voice recognition and a camera to analyze the user's voice tone and facial expressions in real time and estimate their emotional state.

[0689] The collected emotional data and user information are transmitted to the server using secure communication methods. The server integrates this data using a generative AI model. This AI model analyzes the existing data and newly acquired emotional data together to create optimal suggestions for the user. Based on the integrated data, the suggestions are dynamically adjusted and output in a way that is sensitive to the user's emotions.

[0690] For example, if a user inputs "I want to relax" into the device, the device senses stress from that voice. Based on a generative AI model, the server then presents the user with suggestions for quiet, relaxing cafes. In this way, the system can smoothly suggest appropriate options according to the user's needs and emotional state.

[0691] An example of a prompt might be, "Tell me a place where I can relax today." The system analyzes the user's emotions from this input, and a generative AI model assists in making appropriate choices. The system has the ability to leverage these prompts to provide information optimized for each user.

[0692] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0693] Step 1:

[0694] The user enters information into the device.

[0695] Specifically, users operate the device to input their preferences and current emotional state. Input can be done via touch or voice, and the entered information is temporarily stored in the device's memory. For example, if a user inputs "I want to relax," the device obtains data to understand this intention.

[0696] Step 2:

[0697] The device collects and analyzes emotional data.

[0698] The device uses a built-in emotion analysis engine to collect the user's facial expressions and voice. The data collected through the camera and microphone is processed by an emotion analysis algorithm and output as emotional states such as stress and relaxation. These analysis results, along with the user's input data, are then used in the next step.

[0699] Step 3:

[0700] The device sends user data to the server.

[0701] The terminal packets the collected emotional data and user input information and sends it to the server using a secure protocol. The transmitted data is then used by the server for further processing.

[0702] Step 4:

[0703] The server integrates and analyzes the data.

[0704] The server integrates the received user data using a generative AI model. First, the data is normalized and converted into a unified format through preprocessing. Then, the generative AI model analyzes the sentiment data and conventional user data to generate possible suggestion options.

[0705] Step 5:

[0706] The server generates and outputs the suggested content.

[0707] The server generates optimized suggestions based on the analysis results. These suggestions are tailored to the user's emotional state at the time and are prioritized and organized. This ensures that effective suggestions are provided to the user for use in the next step.

[0708] Step 6:

[0709] The proposed results are returned to the user's terminal and presented to the user.

[0710] The server sends the generated suggestions to the terminal, which then presents them to the user. The terminal visually displays the suggestions using its screen or verbally communicates them through a voice assistant. Based on this information, the user can make a selection that suits their needs.

[0711] (Application Example 2)

[0712] 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".

[0713] The challenge lies in providing an e-commerce system that can quickly deliver optimal suggestions based on the user's emotional state and preferences. Conventional systems have struggled to adequately reflect user preferences and emotions, highlighting the need for improved user experience.

[0714] 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.

[0715] In this invention, the server includes a first information gathering means for collecting user preferences and lifestyle-related information, an emotion analysis means for analyzing the user's voice and visual expressions to evaluate their emotional state, and an information integration means for integrating information obtained from multiple generative artificial intelligence computation elements together with emotion data and generating optimized suggestions based on this. This makes it possible to generate optimized suggestions based on the user's emotional state and preferences.

[0716] "User preferences" refer to the personal tastes and interests that a user has based on specific conditions.

[0717] "Lifestyle-related information" refers to information collected about the user's daily life, including, for example, lifestyle habits and behavioral patterns.

[0718] "Emotional analysis means" refers to a technical method or device for evaluating a user's emotional state by analyzing their voice and visual expressions.

[0719] "Generative artificial intelligence computation elements" are algorithms and computational processes used to generate new information and results from large amounts of data and past experience.

[0720] "Information integration means" refers to a method for centrally combining emotional data and other information to generate suggestions optimized for the user.

[0721] An "e-commerce service" is an online platform for buying and selling goods and services via the internet.

[0722] A "proposal" is the act of presenting options for products, services, etc., that are expected to be beneficial to the user.

[0723] To implement this invention, a system is needed to provide optimized suggestions based on the user's emotions and preferences. First, the terminal collects user preferences and lifestyle-related information. This includes text, voice, and image data entered by the user using a mobile device or other interface. The terminal incorporates emotion analysis means to analyze the user's voice tone and visual expressions in real time and estimate their emotional state.

[0724] The analyzed emotional data is sent to the server along with the user's preferences and lifestyle-related information. The server uses information integration means to combine this data with stored data and dynamically generates optimal suggestions using generative artificial intelligence computation elements. In this process, a generative AI model is used to suggest appropriate products or services that correspond to the user's emotional state and preferences.

[0725] As a concrete example, suppose a user inputs into the app that they want to relax after work. If the emotion analysis system detects stress from the user's tone of voice, the server will suggest products that are highly effective for relaxation. For example, it could suggest calming music or aromatherapy oils. This series of suggestions allows the user to smoothly make the choice that best suits their emotional state.

[0726] An example of a prompt would be, "Please suggest products based on the user's stress level. Optimize this by including emotional data analyzed from the user's tone of voice and facial expressions, as well as their past purchase history." This prompt ensures that the generative AI model functions correctly and provides the most relevant suggestions for the user.

[0727] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0728] Step 1:

[0729] The device collects user preferences and lifestyle-related information. It receives text, voice, and image data entered by the user and stores them in an internal database. The entered data is then prepared for analysis by sentiment analysis tools.

[0730] Step 2:

[0731] The emotion analysis system built into the device analyzes the user's voice and visual expressions in real time. The input is collected voice and image data, and the output is numerical and categorical information indicating the user's emotional state. This analysis estimates the user's stress level and mood.

[0732] Step 3:

[0733] The analyzed sentiment data and user preference information are sent to the server. The server receives this data and performs data integration processing using generative artificial intelligence computation elements. Here, calculations are performed to generate optimal suggestions based on the sentiment data and past usage history.

[0734] Step 4:

[0735] The server sends prompts to a generative AI model, which generates suggestions based on the user's emotions and preferences. The input consists of prompts and user data, and the output is a list of optimal products and services. The generative AI model dynamically generates optimized choices tailored to the user.

[0736] Step 5:

[0737] Optimized suggestions are sent from the server to the terminal, which then presents the information to the user. This allows the user to smoothly select products that match their emotional state. Based on the information provided in this process, the user is assisted in making a product selection decision.

[0738] 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.

[0739] The data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. 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 shown 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] The inside of the Emotion Map 400 represents what's in your mind, while the outside represents what you're doing. Therefore, the further you go out the 400-coordinate scale, the more visible your emotions become (the more they manifest in your actions).

[0745] 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.

[0746] 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."

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] The following is further disclosed regarding the embodiments described above.

[0760] (Claim 1)

[0761] The primary means of gathering information about user preferences and lifestyles,

[0762] An information integration means that integrates information obtained from multiple generative artificial intelligence models and generates optimized proposals based on that information,

[0763] An information presentation means that outputs the generated suggestions to the user,

[0764] A system that includes means of communication with public systems and provides a customized interface for each user.

[0765] (Claim 2)

[0766] The system according to claim 1, comprising a learning means for collecting user feedback information and updating the parameters of the generative artificial intelligence model for subsequent uses.

[0767] (Claim 3)

[0768] The system according to claim 1, further comprising an improvement means for recording the results of multiple users' selection of suggestions and feeding them back to a generative artificial intelligence model to improve the accuracy of the suggestions.

[0769] "Example 1"

[0770] (Claim 1)

[0771] A first information acquisition means for acquiring user preferences and movement information,

[0772] A transmission means for transmitting acquired information to the central processing unit,

[0773] An information generation means in which multiple information processing devices cooperate to perform analysis and generate proposals that satisfy the user's conditions,

[0774] A display method that visualizes the generated proposals and presents them to the user,

[0775] An evaluation means that evaluates the user's selection regarding the proposal and transmits the selection result back to the information processing device,

[0776] A system that includes integration methods to provide a user-specific operation screen in conjunction with public infrastructure.

[0777] (Claim 2)

[0778] The system according to claim 1, further comprising a modification means for receiving feedback information and adjusting the operating settings of the generated AI model.

[0779] (Claim 3)

[0780] The system according to claim 1, further comprising an optimization means for saving the selection history of multiple users and returning it to the information processing device to improve the suggestions.

[0781] "Application Example 1"

[0782] (Claim 1)

[0783] Information acquisition methods for obtaining user preferences and lifestyle information,

[0784] Information generation means that integrates information obtained from multiple artificial intelligence models and generates optimized proposals for decision support based on that information,

[0785] An information display means that visually presents the generated proposals to the user,

[0786] A collaborative means for accessing public devices via a communication network and realizing user-specific interfaces,

[0787] Information visualization means that interactively displays content that conforms to the user's requirements using a visualization device,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, comprising a learning algorithm that acquires user feedback information and updates the parameters of a generative artificial intelligence model.

[0791] (Claim 3)

[0792] The system according to claim 1, comprising an improvement module that records the proposal selection results of multiple users and uses those results to provide feedback to a generative artificial intelligence model in order to improve the accuracy of the proposals.

[0793] "Example 2 of combining an emotion engine"

[0794] (Claim 1)

[0795] The primary means of gathering information about user preferences and lifestyles,

[0796] A means of emotion analysis for estimating the emotional state from the user's facial expressions and voice data,

[0797] An information integration means that integrates information obtained from multiple generative artificial intelligence models, incorporates emotional data, and generates optimized suggestions.

[0798] An information presentation means that outputs the generated suggestions to the user,

[0799] A system that includes means of communication with public systems and provides a customized interface for each user.

[0800] (Claim 2)

[0801] The system according to claim 1, comprising a learning means for collecting user feedback information and updating the parameters of the generative artificial intelligence model for subsequent uses.

[0802] (Claim 3)

[0803] The system according to claim 1, further comprising an improvement means for recording the results of multiple users' selection of suggestions and feeding them back to a generative artificial intelligence model to improve the accuracy of the suggestions.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] The primary means of information gathering is to collect user preferences and lifestyle-related information,

[0807] A means of sentiment analysis that analyzes the user's voice and visual expressions to evaluate their emotional state,

[0808] An information integration means that integrates information obtained from multiple generative artificial intelligence computation elements together with emotional data, and generates optimized proposals based on that information,

[0809] An information presentation means that outputs the generated suggestions to the user,

[0810] A system that includes means for communication with e-commerce services and for providing customized interfaces for each user.

[0811] (Claim 2)

[0812] The system according to claim 1, comprising a learning means for collecting user feedback information and updating the parameters of the generative artificial intelligence computation elements for subsequent uses.

[0813] (Claim 3)

[0814] The system according to claim 1, further comprising an improvement means for recording the results of multiple users' selection of suggestions and feeding them back to a generative artificial intelligence computation element to improve the accuracy of the suggestions. [Explanation of symbols]

[0815] 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. The primary means of gathering information about user preferences and lifestyles, An information integration means that integrates information obtained from multiple generative artificial intelligence models and generates optimized proposals based on that information, An information presentation means that outputs the generated suggestions to the user, A system that includes means of communication with public systems and provides a customized interface for each user.

2. The system according to claim 1, comprising a learning means for collecting user feedback information and updating the parameters of the generative artificial intelligence model for subsequent uses.

3. The system according to claim 1, further comprising an improvement means for recording the results of multiple users' selection of suggestions and feeding them back to a generative artificial intelligence model to improve the accuracy of the suggestions.

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