system

Abstract

We provide the system. [Solution] A means of collecting user data from multiple electronic services, A means of integrating collected data to generate a single user profile, A means of analyzing user behavior patterns and generating optimal suggestions, A means of displaying the generated suggestions on the user's terminal, A means to collect user selection data again and improve the accuracy of the algorithm, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, users' digital information is dispersed in various electronic services and needs to be used individually, so it is difficult to make optimal selections and action proposals in an integrated form. For this reason, information cannot be utilized efficiently, and users may suffer in terms of convenience and time. Furthermore, due to the lack of cooperation between different services, the resulting information duplication and redundancy also pose a problem of degrading the user experience.

Means for Solving the Problems

[0005] This invention provides a system that collects user data from multiple electronic services and integrates it to generate a single user profile. Based on this integrated data, it analyzes user behavior patterns and generates optimal suggestions. The generated suggestions are displayed on the user's terminal, allowing the user to easily select them. Furthermore, a feedback loop is established to collect user selection data again and use it to improve the accuracy of the algorithm. This enables users to receive consistent information and suggestions, increasing convenience and efficiency.

[0006] "User data" refers to electronic information about individual users, including purchase history, search history, and location information.

[0007] "Electronic services" refer to applications, systems, and associated functions provided on digital platforms, including online shopping and messaging services.

[0008] A "user profile" is a collection of information that integrates user data gathered from multiple electronic services and brings it together into a single entity.

[0009] "Behavioral patterns" refer to a series of tendencies and habits that users exhibit when taking action, and include their preferences and selection tendencies.

[0010] A "suggestion" refers to the options or behavioral recommendations provided to users based on the analyzed user data.

[0011] A "user terminal" is a hardware device that a user can directly operate, and includes smartphones and personal computers.

[0012] A "feedback loop" is a cyclical process that incorporates user selection data to improve the system's algorithms and suggestions. [Brief explanation of the drawing]

[0013] [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]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that efficiently collects and analyzes user data from a wide variety of electronic services to propose the optimal options for the user. The aim of this system is to improve user convenience and enable a more efficient digital life.

[0035] The server initially begins collecting user data using the APIs of each electronic service. This data includes online transaction history, search history, and location information. The server ensures a secure connection and utilizes encryption technologies to protect user privacy.

[0036] The collected data is integrated by the server into a single user profile. This profile is stored in a database and prepared for later analysis. Here, data duplication is removed, ensuring consistency and accuracy of the information.

[0037] Next, the server analyzes the integrated data. Machine learning algorithms are used for data analysis to predict user behavior patterns and preferences. This makes it possible to identify products and services that users are likely to be interested in.

[0038] Based on the analysis results, the server generates optimal suggestions for the user. These include useful information for everyday life and travel plans tailored to the user's schedule. The suggestions are prioritized according to the user's interests.

[0039] The device features an interface for presenting generated suggestions to the user. Its simple and intuitive design allows users to easily review and select the suggested options.

[0040] For example, suppose a user is planning a trip. The server analyzes the user's past travel history and current vacation plans, and suggests recommended travel plans based on the user's interests, including destinations they haven't visited yet. These suggestions may include special discounts on specific hotels and flights, and the suggestions are available immediately.

[0041] In this way, users can receive fragments of information in a unified format, enabling them to make more efficient choices. The system can ultimately collect user feedback and use it to improve accuracy in future iterations.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server collects user data via the APIs of each electronic service. This includes, for example, financial transaction data, search history, and location information, and verifies that the data is up-to-date.

[0045] Step 2:

[0046] The server temporarily stores the collected data and cleanses it of duplicates and redundancies before storing it in the database. After cleansing, the data is properly organized and indexes are set up for efficient access.

[0047] Step 3:

[0048] The server integrates the organized data and generates user profiles. These profiles are designed to provide consistent information across different data sources.

[0049] Step 4:

[0050] The server analyzes user profiles using machine learning algorithms. Here, collaborative filtering and clustering techniques are utilized to build models that predict user behavior patterns and preferences.

[0051] Step 5:

[0052] The server uses the analysis results to suggest the best choices and actions for the user. These suggestions are prioritized and may include, for example, special sale information or travel guidance based on a personalized schedule.

[0053] Step 6:

[0054] The device displays suggestions received from the server to the user. The display is designed to be user-friendly, allowing users to easily understand the suggestions and take action.

[0055] Step 7:

[0056] When a user makes a choice or takes action based on the information provided, the server collects that data again. This choice data is used as feedback to improve the accuracy of the system's algorithms and suggestions.

[0057] (Example 1)

[0058] 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."

[0059] In today's digital society, users generate vast amounts of information through various electronic services. However, efficiently collecting this dispersed information and automatically providing useful suggestions based on user interests and behaviors is a challenging task. Traditional systems often suffer from data fragmentation and information overload, hindering user decision-making, thus necessitating improvements to the user experience.

[0060] 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.

[0061] In this invention, the server includes means for collecting user data from multiple information provision means, means for integrating the collected data to generate a single user information record, and means for analyzing the user's behavior patterns to generate optimal suggestions. This enables the provision of integrated and personalized information to the user.

[0062] "User data" refers to all information generated in relation to a user's activities, including online interactions, location information, and search history.

[0063] "Information provision means" refers to a medium or system for providing information to users, such as online services or digital platforms.

[0064] A "user information record" is a dataset that compiles and records user-related data collected and integrated from multiple electronic services.

[0065] "Behavioral patterns" refer to a user's past behavioral patterns, preferences, and selection tendencies, and are information used to predict the user's future behavior.

[0066] An "information display device" is a device or interface used to visually display generated proposals and information to the user.

[0067] "Selection information" refers to data that shows the result of a user's choice from among several options presented.

[0068] "Predictive methods" are techniques used to analyze user behavior patterns and predict future actions and interests, and are usually based on algorithms.

[0069] A "storage device" is a hardware or software storage system used to securely and efficiently store collected data.

[0070] "Organization" is the process of cleansing collected data, removing duplicates, and ensuring data consistency and accuracy.

[0071] A "learning algorithm" is a computational method used to analyze large amounts of data, extract features from it, and perform predictions and classifications.

[0072] This invention relates to a system that collects, integrates, and analyzes user data and provides individually optimized suggestions to users. Specifically, the system functions based on the roles of the server, terminal, and user.

[0073] The server initially collects user data from multiple online services configured as means of providing information. For example, the server uses API services to obtain the user's location information and also collects web history and search query information that suggests the user's interests. Encryption technologies such as SSL / TLS are used to ensure secure data transfer during this collection process.

[0074] The collected data is integrated by the server and stored in a database as user information records. Here, data cleansing tools are used to remove duplicates and improve information integrity. For example, using a database like MySQL® enables efficient data management.

[0075] Furthermore, the server executes learning algorithms to analyze the integrated data. Specifically, it uses libraries such as scikit-learn and TENSORFLOW® to perform clustering and predictive analysis based on the user's past behavior patterns. This analysis identifies options that the user may be interested in and automatically generates suggestions.

[0076] The generated suggestions are displayed to the user via the device. The device utilizes application frameworks like React Native to provide an intuitive and user-friendly interface. Users can easily review the suggested information and make selections.

[0077] For example, if a user wants to decide on a destination for their next vacation, the system analyzes the user's past travel information and related interests to suggest a new destination and travel plan. This plan might include booking information for specific hotels or special discounts on transportation. Another example of a prompt when using a generative AI model is, "Based on the user's past behavior, suggest a travel destination that they might be interested in next."

[0078] In this way, the system can improve the user experience by providing personalized information tailored to the user in a timely manner.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server collects user data through APIs of various online services. The input consists of digital information derived from the user's online activities, specifically location information, browsing history, and search history. To securely obtain this information, the server establishes an encrypted connection using SSL / TLS and makes API requests. The output is the collected raw data.

[0082] Step 2:

[0083] The server integrates the collected data to generate a user information record. The input for this step is the multiple datasets obtained in step 1. To remove data duplication and integrate different formats, the server uses data cleansing tools and stores the data in a database such as MySQL. The output is an organized and consistent user information record.

[0084] Step 3:

[0085] The server analyzes user behavior patterns based on integrated data. The input is the user information record generated in step 2. The server uses scikit-learn or TensorFlow to implement a learning algorithm and predict user interests and behavioral patterns. Specifically, it performs clustering to identify user groups. The output is the characterized user profile and predicted interests.

[0086] Step 4:

[0087] The server generates optimal suggestions for the user based on the analysis results. Using the user profile from Step 3 as input, it creates suggestions in natural language using NLP. For example, suggestions might include "It would be good to visit a specific tourist destination next weekend." The output is a specific suggestion message presented to the user.

[0088] Step 5:

[0089] The terminal displays suggestions received from the server to the user. The input is the suggestion message generated in step 4. The terminal uses a framework such as React Native to provide an intuitive user interface. The user can make a selection based on the displayed information. The output is the screen showing the suggestions and the user's selection result.

[0090] Step 6:

[0091] The user reviews the suggestions via their device and decides whether to use them. The input is the suggestion information displayed in step 5. The user makes selections using actions such as swiping or tapping, and the results are sent back to the server and collected as selection information. The output is the user's selection data, which helps improve the accuracy of the prediction method.

[0092] (Application Example 1)

[0093] 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."

[0094] Traditionally, when users searched for and purchased products online, they faced the problem of difficulty in quickly finding the most suitable option from a vast amount of information. Furthermore, effectively implementing systems to analyze user behavior and provide personalized product recommendations remained a challenge.

[0095] 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.

[0096] In this invention, the server includes means for collecting user data from multiple sources, means for integrating the collected data to generate a single user profile, means for analyzing the user's behavioral characteristics to generate optimal suggestions, and means for analyzing product information to suggest products that match the user's preferences. As a result, the user can receive highly accurate product suggestions based on their own preferences.

[0097] "User data" refers to information collected from a user's online activities, such as transaction history, search history, and location information.

[0098] "Information sources" refer to digital platforms from which user data can be obtained, such as APIs and databases of online service providers.

[0099] A "user profile" is a collection of information, including the characteristics and preferences of individual users, built upon integrated user data.

[0100] "Behavioral characteristics" refer to a user's past behavioral patterns and selection tendencies, and analyzing these provides fundamental data for predicting the user's future behavior.

[0101] "Product information" refers to data that includes information about the characteristics, price, and reviews of a product provided to consumers.

[0102] A "server" refers to computing resources used to collect, integrate, analyze, and generate suggestions from data, and then provides the results to the user.

[0103] A "learning algorithm" refers to a set of methods used to analyze user data, learn its patterns, and make future predictions.

[0104] This invention is a system designed to effectively collect and analyze user data. The system is server-centric, and the server collects user data from external sources via APIs. This data includes user transaction history, search history, and location information, all protected using encryption technology for privacy. The collected data is integrated into a single user profile by the server, and duplicate data is removed through data cleaning.

[0105] Based on integrated data, the server uses machine learning algorithms to analyze user behavior and generate highly personalized product recommendations. These recommendations are displayed in the user interface, allowing users to easily make the best choices for themselves. User interactions and selection information are also collected again as data to improve the accuracy of the algorithms.

[0106] For example, if a user has a history of frequently purchasing outdoor equipment online, notifications about new camping gear releases and related sales will be displayed on the user interface. This allows users to quickly and effectively receive information tailored to their interests. By using prompts for the generative AI model such as, "Based on the user's purchase history, please tell me about products that might influence their next purchase," more accurate suggestions can be made.

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

[0108] Step 1:

[0109] The server collects user data from multiple sources via APIs. This includes user transaction history, search history, and location information. Input data is obtained from external APIs, and encrypted user data is returned as output. Specifically, API requests are sent and responses are received.

[0110] Step 2:

[0111] The server integrates the collected user data to generate a single user profile. This process removes duplicate data and organizes it into a well-formed format. The input is the raw data collected in step 1, and the output is a consistent and complete user profile. Specific operations include data streaming and storage to a database.

[0112] Step 3:

[0113] The server analyzes behavioral characteristics using machine learning algorithms based on integrated user profiles. The input is the user profile, and the output is user preference and behavioral prediction data. Specific operations include loading and running the predictive model.

[0114] Step 4:

[0115] The server generates optimal product suggestions for the user based on the analysis results. This includes suggesting product information that matches the user's preferences. The input is behavioral prediction data, and the output is product suggestion information. Specifically, suggestion text and prompts are generated using a generative AI model.

[0116] Step 5:

[0117] The server displays the generated suggestions on the terminal's user interface. Product suggestion information is the input, and the output is a visual suggestion screen, allowing the user to review the suggestions through the interface. Specifically, the server updates UI elements and presents them to the user.

[0118] Step 6:

[0119] The user makes a selection based on the suggested product information, and this selection data is sent back to the server. The input is the user's selection action, and the output is the selected product data. Specific operations include recording user input and transmitting data.

[0120] Step 7:

[0121] The server re-analyzes the user's selection data and uses it to improve the accuracy of the learning algorithm. The input is the selected product data, and the output is the updated predictive model. Specifically, the model is retrained and evaluated.

[0122] 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.

[0123] This invention is a system that collects user data from multiple electronic services and combines it with an emotion engine to provide optimal suggestions tailored to the user's emotions. Through the recognition and analysis of emotions, this system enables more personalized responses compared to conventional suggestions.

[0124] The server collects user data from various electronic services and integrates this data to generate a user profile. In this process, an emotion engine estimates the user's emotional state from their text input, voice, facial expressions, and other data. The server uses this multimodal data to improve the accuracy of its emotional assessment.

[0125] Subsequently, the server analyzes the collected data in combination with emotional information from the emotion engine to determine the user's behavioral patterns and emotions. This analysis ensures that suggested content and products are tailored to the user's current emotional state and have a higher receptiveness.

[0126] The server generates personalized suggestions based on the analysis results. This involves reflecting the user's emotional state, customizing the content according to their mood; for example, suggesting refreshing plans when the user is relaxed, and products with relaxing effects when they are stressed.

[0127] The generated suggestions are displayed on the terminal and are designed to allow the user to directly interact with, review, and select them. When the user selects a suggestion, that selection data is sent back to the server and used as feedback to improve the system's algorithms.

[0128] For example, when a user is planning a leisure trip on a holiday, the server analyzes their past travel history and current emotional state (e.g., anticipation or anxiety) to suggest a suitable travel plan. By quickly understanding the user's emotional state, the emotion engine provides more appropriate options, improving the user experience.

[0129] By using this system, users can enjoy the integrated use of fragments of digital data and gain maximum convenience and satisfaction from emotionally resonant suggestions.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server collects user data through the APIs of each electronic service. This includes purchase history, search queries, location information, etc., and is kept as up-to-date data.

[0133] Step 2:

[0134] The server uses an emotion engine to estimate the user's emotional state. The emotion engine determines the emotion based on the user's text input, voice data, facial expression analysis, etc., and reflects it in the database.

[0135] Step 3:

[0136] The server integrates the collected data and generates organized user profiles. Duplicate data is removed to improve data consistency.

[0137] Step 4:

[0138] The server uses integrated data and sentiment information to perform analysis using machine learning algorithms. This analysis builds a model based on the user's behavior patterns and current emotional state.

[0139] Step 5:

[0140] The server generates personalized recommendations based on an analysis model. These recommendations include products and services that reflect emotional information and resonate with the user's mood.

[0141] Step 6:

[0142] The device displays the generated suggestions to the user. The displayed suggestions are presented in a relaxed layout, designed for easy user selection.

[0143] Step 7:

[0144] Users interact with the terminal to review suggestions and make selections or take actions. This selection data is sent to the server and used as feedback to improve the accuracy of the system's suggestions.

[0145] (Example 2)

[0146] 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".

[0147] In modern society, users acquire vast amounts of information from various information-providing devices, but it is difficult for them to obtain suggestions optimized for their own psychological state and behavioral patterns. Conventional systems lack sufficient personalization that takes emotional states into account, resulting in a problem where truly valuable information is not being provided to users.

[0148] 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.

[0149] In this invention, the server includes means for collecting user information from multiple information providing devices, means for analyzing the user's emotional state using an emotion analysis device, and means for generating optimal suggestions based on the user's behavioral patterns and emotional state. This makes it possible to accurately grasp the user's psychological state and provide appropriate and personalized suggestions according to that state.

[0150] "Information provision device" is a general term for electronic devices that have the function of transmitting and receiving user information.

[0151] A "user profile" is data that integrates collected user information and summarizes information about individual users.

[0152] An "emotion analysis device" is a device or software that has the function of analyzing a user's emotional state.

[0153] "Behavioral patterns" refer to the collective behavioral patterns and habits expressed by users, and are recorded as digital data.

[0154] An "inference algorithm" is a computational method or procedure for generating optimal suggestions based on collected information and analysis results.

[0155] An "output device" refers to a screen or device used by the user to review and operate the proposed content.

[0156] This invention is a system for providing personalized suggestions that take into account the user's emotional state. The server collects information from multiple information providers from the user, integrates this information into a database, and generates a user profile. The information providers include various digital devices and online services.

[0157] Emotion analysis devices are used to analyze a user's emotional state from collected information. Specifically, they use libraries with natural language processing technology, voice analysis tools, and facial recognition technology to process text, voice, and visual data and identify emotional states.

[0158] The server generates optimal suggestions based on the analyzed emotional state and behavioral patterns. This suggestion generation uses machine learning algorithms to select content and products that match the user's needs from the collected information. A generative AI model is utilized, and prompts can be used to provide more detailed and personalized suggestions. For example, the prompt "Suggest music suitable for when the user is relaxed" might be sent to the generative AI model.

[0159] The generated suggestions are displayed on the terminal. The terminal has a user interface for the user to review and select from the suggestions. The user interface is built using a web framework and is designed to be intuitive for the user to operate.

[0160] After a user selects a suggestion, that selection information is sent back to the server. This feedback information is used to improve the inference algorithm. This improves the accuracy of subsequent suggestions, allowing users to have an increasingly personalized experience.

[0161] This system allows users to receive optimal suggestions tailored to their emotional state, greatly increasing the value of digital data.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server collects user information from information providers. Inputs include user text, voice, and image information. The data is collected using a streaming platform and stored in a database. The output is an integrated digital profile.

[0165] Step 2:

[0166] The server uses an emotion analysis device to analyze the emotional state of the collected data. Inputs include user text, voice, and image data. The data is processed using natural language processing, speech recognition, and facial expression analysis tools to evaluate the emotional state. The output is data indicating the user's emotional state.

[0167] Step 3:

[0168] The server analyzes user behavior patterns based on analysis results and integrated digital profiles, and generates appropriate suggestions. Input data includes emotional data and past behavioral history. Machine learning algorithms are applied to perform data calculations and derive the most suitable suggestions for the user. The output is the generated suggestions.

[0169] Step 4:

[0170] The terminal displays the generated suggestions to the user. The input is the suggestion content sent from the server. The information is displayed in a user-friendly format via the terminal's user interface. The output is the suggestion information viewable by the user.

[0171] Step 5:

[0172] When a user selects a suggested option, that selection information is sent back from the terminal to the server. The input is the user's selected option. The server receives this feedback and improves the inference algorithm. The output is the improved state of the inference algorithm.

[0173] (Application Example 2)

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

[0175] Traditional e-commerce has not adequately considered the emotional state of users when suggesting products. As a result, it has been difficult for users to find the optimal product that suits their situation, making it difficult to increase their desire to purchase. This has led to a decline in the user experience and a loss of purchasing opportunities. The present invention aims to solve this problem.

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

[0177] In this invention, the server includes means for collecting user data from multiple electronic services, means for integrating the collected data to generate a single user profile, means for analyzing the user's facial expressions, voice, and text input to estimate their emotional state, and means for suggesting products corresponding to the estimated emotional state. This enables product suggestions optimized for the user's emotions, significantly improving the purchasing experience.

[0178] "User data" refers to all information collected from users through electronic services, and includes a wide range of data such as text, audio, behavioral history, and purchase history.

[0179] "Electronic services" refer to digital services provided through online platforms and applications, and include e-commerce, music streaming, and social media.

[0180] A "user profile" is a collection of information that shows the characteristics and tendencies of a user, generated by integrating and analyzing collected user data.

[0181] "Emotional state" refers to the user's current psychological and emotional condition, which is estimated through methods such as facial expression and voice analysis.

[0182] "Product recommendations" refer to recommendations for products and services that the server provides to a user based on the user's profile and emotional state.

[0183] This invention is a system that provides product recommendations tailored to the user's emotional state based on diverse user data. This system consists of the following elements:

[0184] The server collects user data from multiple electronic services. This data includes text, voice, and behavioral history. This generates a comprehensive user profile. A database management system is used for data processing, and necessary data manipulation is performed using Python and related machine learning libraries.

[0185] The user's device is primarily a smartphone, and its built-in camera and microphone are used to capture facial expressions and voice data in real time. The captured data is analyzed using the OpenCV library and Google's Speech-to-Text API. This allows for highly accurate estimation of the user's emotional state.

[0186] Sentiment analysis uses sentiment analysis APIs such as IBM Watson®. Generative AI models are then used to provide optimal product recommendations based on this analysis. These recommendations are displayed immediately on the user's device and can be purchased directly. The user interface is intuitive, allowing users to easily save or purchase recommendations with clicks and taps.

[0187] For example, when a user is using their smartphone in the evening, the system can sense that they are in a relaxed state. This leads to suggestions such as aromatherapy candles or refreshing beverages. This interaction is monitored by the system, and the selection results are sent to the server to improve the accuracy of future suggestions. An example of a prompt provided to the generating AI model is, "Generate the most suitable recommendations based on the user's current state of relaxation."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server collects user data from multiple electronic services. Inputs include text, voice, and behavioral history data provided by each electronic service. The server integrates this data and stores it in a database. It then cleanses the data to remove duplicates and generates clean user profiles, preparing it for subsequent processing.

[0191] Step 2:

[0192] The user's device captures real-time facial expressions and audio through its camera and microphone. The input for this step is raw data obtained from a smartphone or related device. The device analyzes facial expressions using the OpenCV library and converts audio to text using Google's Speech-to-Text API. This result is then sent to the server for subsequent sentiment analysis.

[0193] Step 3:

[0194] The server passes facial expression data and voice text sent from the terminal to an emotion analysis API to estimate the user's emotional state. The input for this step is the facial expression analysis result and voice text. The output is data representing the user's emotional state, which the server uses with a generative AI model to generate suitable product suggestions.

[0195] Step 4:

[0196] The server utilizes a generative AI model to generate a list of products best suited to the user's emotional state. The input consists of the estimated emotional state and user profile. The server uses product consideration prompts to generate a suggestion list and sends it to the terminal. This list is customized based on the user's current emotional state.

[0197] Step 5:

[0198] The user's terminal displays a list of product suggestions received from the server. The input is suggestion data from the server. The output allows the user to view product details and make a purchase decision through an intuitive interface. The user directly selects from the list, and the system feeds this selection data back to the server.

[0199] Step 6:

[0200] The server collects user selection data again and analyzes it using machine learning algorithms to improve the accuracy of the algorithms. The input here is the user selection data. The server uses this to improve the accuracy of future suggestions, enabling more personalized product recommendations.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

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

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] This invention is a system that efficiently collects and analyzes user data from a wide variety of electronic services to propose the optimal options for the user. The aim of this system is to improve user convenience and enable a more efficient digital life.

[0218] The server initially begins collecting user data using the APIs of each electronic service. This data includes online transaction history, search history, and location information. The server ensures a secure connection and utilizes encryption technologies to protect user privacy.

[0219] The collected data is integrated by the server into a single user profile. This profile is stored in a database and prepared for later analysis. Here, data duplication is removed, ensuring consistency and accuracy of the information.

[0220] Next, the server analyzes the integrated data. Machine learning algorithms are used for data analysis to predict user behavior patterns and preferences. This makes it possible to identify products and services that users are likely to be interested in.

[0221] Based on the analysis results, the server generates optimal suggestions for the user. These include useful information for everyday life and travel plans tailored to the user's schedule. The suggestions are prioritized according to the user's interests.

[0222] The device features an interface for presenting generated suggestions to the user. Its simple and intuitive design allows users to easily review and select the suggested options.

[0223] For example, suppose a user is planning a trip. The server analyzes the user's past travel history and current vacation plans, and suggests recommended travel plans based on the user's interests, including destinations they haven't visited yet. These suggestions may include special discounts on specific hotels and flights, and the suggestions are available immediately.

[0224] In this way, users can receive fragments of information in a unified format, enabling them to make more efficient choices. The system can ultimately collect user feedback and use it to improve accuracy in future iterations.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server collects user data via the APIs of each electronic service. This includes, for example, financial transaction data, search history, and location information, and verifies that the data is up-to-date.

[0228] Step 2:

[0229] The server temporarily stores the collected data and cleanses it of duplicates and redundancies before storing it in the database. After cleansing, the data is properly organized and indexes are set up for efficient access.

[0230] Step 3:

[0231] The server integrates the organized data and generates user profiles. These profiles are designed to provide consistent information across different data sources.

[0232] Step 4:

[0233] The server analyzes user profiles using machine learning algorithms. Here, collaborative filtering and clustering techniques are utilized to build models that predict user behavior patterns and preferences.

[0234] Step 5:

[0235] The server uses the analysis results to suggest the best choices and actions for the user. These suggestions are prioritized and may include, for example, special sale information or travel guidance based on a personalized schedule.

[0236] Step 6:

[0237] The device displays suggestions received from the server to the user. The display is designed to be user-friendly, allowing users to easily understand the suggestions and take action.

[0238] Step 7:

[0239] When a user makes a choice or takes action based on the information provided, the server collects that data again. This choice data is used as feedback to improve the accuracy of the system's algorithms and suggestions.

[0240] (Example 1)

[0241] 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."

[0242] In today's digital society, users generate vast amounts of information through various electronic services. However, efficiently collecting this dispersed information and automatically providing useful suggestions based on user interests and behaviors is a challenging task. Traditional systems often suffer from data fragmentation and information overload, hindering user decision-making, thus necessitating improvements to the user experience.

[0243] 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.

[0244] In this invention, the server includes means for collecting user data from multiple information provision means, means for integrating the collected data to generate a single user information record, and means for analyzing the user's behavior patterns to generate optimal suggestions. This enables the provision of integrated and personalized information to the user.

[0245] "User data" refers to all information generated in relation to a user's activities, including online interactions, location information, and search history.

[0246] "Information provision means" refers to a medium or system for providing information to users, such as online services or digital platforms.

[0247] A "user information record" is a dataset that compiles and records user-related data collected and integrated from multiple electronic services.

[0248] "Behavioral patterns" refer to a user's past behavioral patterns, preferences, and selection tendencies, and are information used to predict the user's future behavior.

[0249] An "information display device" is a device or interface used to visually display generated proposals and information to the user.

[0250] "Selection information" refers to data that shows the result of a user's choice from among several options presented.

[0251] "Predictive methods" are techniques used to analyze user behavior patterns and predict future actions and interests, and are usually based on algorithms.

[0252] A "storage device" is a hardware or software storage system used to securely and efficiently store collected data.

[0253] "Organization" is the process of cleansing collected data, removing duplicates, and ensuring data consistency and accuracy.

[0254] A "learning algorithm" is a computational method used to analyze large amounts of data, extract features from it, and perform predictions and classifications.

[0255] This invention relates to a system that collects, integrates, and analyzes user data and provides individually optimized suggestions to users. Specifically, the system functions based on the roles of the server, terminal, and user.

[0256] The server initially collects user data from multiple online services configured as means of providing information. For example, the server uses API services to obtain the user's location information and also collects web history and search query information that suggests the user's interests. Encryption technologies such as SSL / TLS are used to ensure secure data transfer during this collection process.

[0257] The collected data is integrated by the server and stored in a database as user information records. Here, data cleansing tools are used to remove duplicates and improve information integrity. For example, using a MySQL database enables efficient data management.

[0258] Furthermore, the server executes learning algorithms to analyze the integrated data. Specifically, it uses libraries such as scikit-learn and TensorFlow to perform clustering and predictive analysis based on the user's past behavior patterns. This analysis identifies options that the user may be interested in and automatically generates suggestions.

[0259] The generated suggestions are displayed to the user via the device. The device utilizes application frameworks like React Native to provide an intuitive and user-friendly interface. Users can easily review the suggested information and make selections.

[0260] For example, if a user wants to decide on a destination for their next vacation, the system analyzes the user's past travel information and related interests to suggest a new destination and travel plan. This plan might include booking information for specific hotels or special discounts on transportation. Another example of a prompt when using a generative AI model is, "Based on the user's past behavior, suggest a travel destination that they might be interested in next."

[0261] In this way, the system can improve the user experience by providing personalized information tailored to the user in a timely manner.

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

[0263] Step 1:

[0264] The server collects user data through APIs of various online services. The input consists of digital information derived from the user's online activities, specifically location information, browsing history, and search history. To securely obtain this information, the server establishes an encrypted connection using SSL / TLS and makes API requests. The output is the collected raw data.

[0265] Step 2:

[0266] The server integrates the collected data to generate a user information record. The input for this step is the multiple datasets obtained in step 1. To remove data duplication and integrate different formats, the server uses data cleansing tools and stores the data in a database such as MySQL. The output is an organized and consistent user information record.

[0267] Step 3:

[0268] The server analyzes user behavior patterns based on integrated data. The input is the user information record generated in step 2. The server uses scikit-learn or TensorFlow to implement a learning algorithm and predict user interests and behavioral patterns. Specifically, it performs clustering to identify user groups. The output is the characterized user profile and predicted interests.

[0269] Step 4:

[0270] The server generates optimal suggestions for the user based on the analysis results. Using the user profile from Step 3 as input, it creates suggestions in natural language using NLP. For example, suggestions might include "It would be good to visit a specific tourist destination next weekend." The output is a specific suggestion message presented to the user.

[0271] Step 5:

[0272] The terminal displays suggestions received from the server to the user. The input is the suggestion message generated in step 4. The terminal uses a framework such as React Native to provide an intuitive user interface. The user can make a selection based on the displayed information. The output is the screen showing the suggestions and the user's selection result.

[0273] Step 6:

[0274] The user reviews the suggestions via their device and decides whether to use them. The input is the suggestion information displayed in step 5. The user makes selections using actions such as swiping or tapping, and the results are sent back to the server and collected as selection information. The output is the user's selection data, which helps improve the accuracy of the prediction method.

[0275] (Application Example 1)

[0276] 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."

[0277] Traditionally, when users searched for and purchased products online, they faced the problem of difficulty in quickly finding the most suitable option from a vast amount of information. Furthermore, effectively implementing systems to analyze user behavior and provide personalized product recommendations remained a challenge.

[0278] 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.

[0279] In this invention, the server includes means for collecting user data from multiple information sources, means for integrating the collected data to generate a single user profile, means for analyzing the user's behavioral characteristics to generate an optimal proposal, and means for analyzing product information to propose products that match the user's preferences. As a result, the user can receive highly accurate product proposals based on their preferences.

[0280] "User data" means information such as transaction history, search history, and location information collected from the user's online activities.

[0281] "Information source" refers to a digital platform from which user data can be obtained, such as the API or database of an online service provider.

[0282] "User profile" is an aggregate of information that includes the characteristics and preferences of individual users, constructed based on integrated user data.

[0283] "Behavioral characteristics" refer to the user's past behavioral patterns and selection tendencies, which serve as basic data for predicting the user's future behavior by analyzing them.

[0284] "Product information" refers to data that includes information about the characteristics, prices, reviews, etc. of products provided to consumers.

[0285] "Server" refers to the computer resources for collecting, integrating, analyzing, and generating proposals for data, and has the role of providing the results to the user.

[0286] "Learning algorithm" means a series of methods used to analyze user data, learn its patterns, and make future predictions.

[0287] This invention is a system designed to effectively collect and analyze user data. The system is server-centric, and the server collects user data from external sources via APIs. This data includes user transaction history, search history, and location information, all protected using encryption technology for privacy. The collected data is integrated into a single user profile by the server, and duplicate data is removed through data cleaning.

[0288] Based on integrated data, the server uses machine learning algorithms to analyze user behavior and generate highly personalized product recommendations. These recommendations are displayed in the user interface, allowing users to easily make the best choices for themselves. User interactions and selection information are also collected again as data to improve the accuracy of the algorithms.

[0289] For example, if a user has a history of frequently purchasing outdoor equipment online, notifications about new camping gear releases and related sales will be displayed on the user interface. This allows users to quickly and effectively receive information tailored to their interests. By using prompts for the generative AI model such as, "Based on the user's purchase history, please tell me about products that might influence their next purchase," more accurate suggestions can be made.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The server collects user data from multiple sources via APIs. This includes user transaction history, search history, and location information. Input data is obtained from external APIs, and encrypted user data is returned as output. Specifically, API requests are sent and responses are received.

[0293] Step 2:

[0294] The server integrates the collected user data to generate a single user profile. This process removes duplicate data and organizes it into a well-formed format. The input is the raw data collected in step 1, and the output is a consistent and complete user profile. Specific operations include data streaming and storage to a database.

[0295] Step 3:

[0296] The server analyzes behavioral characteristics using machine learning algorithms based on integrated user profiles. The input is the user profile, and the output is user preference and behavioral prediction data. Specific operations include loading and running the predictive model.

[0297] Step 4:

[0298] The server generates optimal product suggestions for the user based on the analysis results. This includes suggesting product information that matches the user's preferences. The input is behavioral prediction data, and the output is product suggestion information. Specifically, suggestion text and prompts are generated using a generative AI model.

[0299] Step 5:

[0300] The server displays the generated suggestions on the terminal's user interface. Product suggestion information is the input, and the output is a visual suggestion screen, allowing the user to review the suggestions through the interface. Specifically, the server updates UI elements and presents them to the user.

[0301] Step 6:

[0302] The user makes a selection based on the suggested product information, and this selection data is sent back to the server. The input is the user's selection action, and the output is the selected product data. Specific operations include recording user input and transmitting data.

[0303] Step 7:

[0304] The server analyzes the user's selection data again and uses it to improve the accuracy of the learning algorithm. The input is the selected product data, and the output is the updated prediction model. As specific operations, re-learning and evaluation of the model are performed.

[0305] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0306] The present invention is a system that collects user data from a plurality of electronic services and combines an emotion engine to make an optimal proposal according to the user's emotion. Through the recognition and analysis of emotions, this system enables a more personalized response compared to conventional proposals.

[0307] The server collects user data from each electronic service, integrates the data, and generates a user profile. In this process, the emotion engine estimates the emotional state from the user's text input, voice, expression, etc. The server uses these multimodal data to improve the accuracy of emotions.

[0308] After that, the server combines and analyzes the collected data and the emotion information from the emotion engine to judge the user's behavior pattern and emotion. This analysis ensures that the proposed content and products match the user's current emotional state and have higher acceptance.

[0309] The server generates a proposal suitable for the user based on the analysis results. Here, reflecting the emotion recognition results, when the user is relaxed, a plan useful for refreshment is proposed, and when the user feels stressed, a product with a relaxation effect is proposed, etc., and the content is customized according to the emotion.

[0310] The generated suggestions are displayed on the terminal and are designed to allow the user to directly interact with, review, and select them. When the user selects a suggestion, that selection data is sent back to the server and used as feedback to improve the system's algorithms.

[0311] For example, when a user is planning a leisure trip on a holiday, the server analyzes their past travel history and current emotional state (e.g., anticipation or anxiety) to suggest a suitable travel plan. By quickly understanding the user's emotional state, the emotion engine provides more appropriate options, improving the user experience.

[0312] By using this system, users can enjoy the integrated use of fragments of digital data and gain maximum convenience and satisfaction from emotionally resonant suggestions.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The server collects user data through the APIs of each electronic service. This includes purchase history, search queries, location information, etc., and is kept as up-to-date data.

[0316] Step 2:

[0317] The server uses an emotion engine to estimate the user's emotional state. The emotion engine determines the emotion based on the user's text input, voice data, facial expression analysis, etc., and reflects it in the database.

[0318] Step 3:

[0319] The server integrates the collected data and generates organized user profiles. Duplicate data is removed to improve data consistency.

[0320] Step 4:

[0321] The server uses integrated data and sentiment information to perform analysis using machine learning algorithms. This analysis builds a model based on the user's behavior patterns and current emotional state.

[0322] Step 5:

[0323] The server generates personalized recommendations based on an analysis model. These recommendations include products and services that reflect emotional information and resonate with the user's mood.

[0324] Step 6:

[0325] The device displays the generated suggestions to the user. The displayed suggestions are presented in a relaxed layout, designed for easy user selection.

[0326] Step 7:

[0327] Users interact with the terminal to review suggestions and make selections or take actions. This selection data is sent to the server and used as feedback to improve the accuracy of the system's suggestions.

[0328] (Example 2)

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

[0330] In modern society, users acquire vast amounts of information from various information-providing devices, but it is difficult for them to obtain suggestions optimized for their own psychological state and behavioral patterns. Conventional systems lack sufficient personalization that takes emotional states into account, resulting in a problem where truly valuable information is not being provided to users.

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

[0332] In this invention, the server includes means for collecting user information from multiple information providing devices, means for analyzing the user's emotional state using an emotion analysis device, and means for generating optimal suggestions based on the user's behavioral patterns and emotional state. This makes it possible to accurately grasp the user's psychological state and provide appropriate and personalized suggestions according to that state.

[0333] "Information provision device" is a general term for electronic devices that have the function of transmitting and receiving user information.

[0334] A "user profile" is data that integrates collected user information and summarizes information about individual users.

[0335] An "emotion analysis device" is a device or software that has the function of analyzing a user's emotional state.

[0336] "Behavioral patterns" refer to the collective behavioral patterns and habits expressed by users, and are recorded as digital data.

[0337] An "inference algorithm" is a computational method or procedure for generating optimal suggestions based on collected information and analysis results.

[0338] An "output device" refers to a screen or device used by the user to review and operate the proposed content.

[0339] This invention is a system for providing personalized suggestions that take into account the user's emotional state. The server collects information from multiple information providers from the user, integrates this information into a database, and generates a user profile. The information providers include various digital devices and online services.

[0340] Emotion analysis devices are used to analyze a user's emotional state from collected information. Specifically, they use libraries with natural language processing technology, voice analysis tools, and facial recognition technology to process text, voice, and visual data and identify emotional states.

[0341] The server generates optimal suggestions based on the analyzed emotional state and behavioral patterns. This suggestion generation uses machine learning algorithms to select content and products that match the user's needs from the collected information. A generative AI model is utilized, and prompts can be used to provide more detailed and personalized suggestions. For example, the prompt "Suggest music suitable for when the user is relaxed" might be sent to the generative AI model.

[0342] The generated suggestions are displayed on the terminal. The terminal has a user interface for the user to review and select from the suggestions. The user interface is built using a web framework and is designed to be intuitive for the user to operate.

[0343] After a user selects a suggestion, that selection information is sent back to the server. This feedback information is used to improve the inference algorithm. This improves the accuracy of subsequent suggestions, allowing users to have an increasingly personalized experience.

[0344] This system allows users to receive optimal suggestions tailored to their emotional state, greatly increasing the value of digital data.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The server collects user information from information providers. Inputs include user text, voice, and image information. The data is collected using a streaming platform and stored in a database. The output is an integrated digital profile.

[0348] Step 2:

[0349] The server uses an emotion analysis device to analyze the emotional state of the collected data. Inputs include user text, voice, and image data. The data is processed using natural language processing, speech recognition, and facial expression analysis tools to evaluate the emotional state. The output is data indicating the user's emotional state.

[0350] Step 3:

[0351] The server analyzes user behavior patterns based on analysis results and integrated digital profiles, and generates appropriate suggestions. Input data includes emotional data and past behavioral history. Machine learning algorithms are applied to perform data calculations and derive the most suitable suggestions for the user. The output is the generated suggestions.

[0352] Step 4:

[0353] The terminal displays the generated suggestions to the user. The input is the suggestion content sent from the server. The information is displayed in a user-friendly format via the terminal's user interface. The output is the suggestion information viewable by the user.

[0354] Step 5:

[0355] When a user selects a suggested option, that selection information is sent back from the terminal to the server. The input is the user's selected option. The server receives this feedback and improves the inference algorithm. The output is the improved state of the inference algorithm.

[0356] (Application Example 2)

[0357] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0358] Traditional e-commerce has not adequately considered the emotional state of users when suggesting products. As a result, it has been difficult for users to find the optimal product that suits their situation, making it difficult to increase their desire to purchase. This has led to a decline in the user experience and a loss of purchasing opportunities. The present invention aims to solve this problem.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0360] In this invention, the server includes means for collecting user data from multiple electronic services, means for integrating the collected data to generate a single user profile, means for analyzing the user's facial expressions, voice, and text input to estimate their emotional state, and means for suggesting products corresponding to the estimated emotional state. This enables product suggestions optimized for the user's emotions, significantly improving the purchasing experience.

[0361] "User data" refers to all information collected from users through electronic services, and includes a wide range of data such as text, audio, behavioral history, and purchase history.

[0362] "Electronic services" refer to digital services provided through online platforms and applications, and include e-commerce, music streaming, and social media.

[0363] A "user profile" is a collection of information that shows the characteristics and tendencies of a user, generated by integrating and analyzing collected user data.

[0364] "Emotional state" refers to the user's current psychological and emotional condition, which is estimated through methods such as facial expression and voice analysis.

[0365] "Product recommendations" refer to recommendations for products and services that the server provides to a user based on the user's profile and emotional state.

[0366] This invention is a system that provides product recommendations tailored to the user's emotional state based on diverse user data. This system consists of the following elements:

[0367] The server collects user data from multiple electronic services. This data includes text, voice, and behavioral history. This generates a comprehensive user profile. A database management system is used for data processing, and necessary data manipulation is performed using Python and related machine learning libraries.

[0368] The user's device is primarily a smartphone, and its built-in camera and microphone are used to capture facial expressions and voice data in real time. The captured data is analyzed using the OpenCV library and Google's Speech-to-Text API. This allows for highly accurate estimation of the user's emotional state.

[0369] Sentiment analysis uses sentiment analysis APIs such as IBM Watson. Generative AI models are then used to provide optimal product recommendations based on this analysis. These recommendations are displayed immediately on the user's device and can be purchased directly. The user interface is intuitive, allowing users to easily save or purchase recommendations with clicks and taps.

[0370] For example, when a user is using their smartphone in the evening, the system can sense that they are in a relaxed state. This leads to suggestions such as aromatherapy candles or refreshing beverages. This interaction is monitored by the system, and the selection results are sent to the server to improve the accuracy of future suggestions. An example of a prompt provided to the generating AI model is, "Generate the most suitable recommendations based on the user's current state of relaxation."

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server collects user data from multiple electronic services. Inputs include text, voice, and behavioral history data provided by each electronic service. The server integrates this data and stores it in a database. It then cleanses the data to remove duplicates and generates clean user profiles, preparing it for subsequent processing.

[0374] Step 2:

[0375] The user's device captures real-time facial expressions and audio through its camera and microphone. The input for this step is raw data obtained from a smartphone or related device. The device analyzes facial expressions using the OpenCV library and converts audio to text using Google's Speech-to-Text API. This result is then sent to the server for subsequent sentiment analysis.

[0376] Step 3:

[0377] The server passes facial expression data and voice text sent from the terminal to an emotion analysis API to estimate the user's emotional state. The input for this step is the facial expression analysis result and voice text. The output is data representing the user's emotional state, which the server uses with a generative AI model to generate suitable product suggestions.

[0378] Step 4:

[0379] The server utilizes a generative AI model to generate a list of products best suited to the user's emotional state. The input consists of the estimated emotional state and user profile. The server uses product consideration prompts to generate a suggestion list and sends it to the terminal. This list is customized based on the user's current emotional state.

[0380] Step 5:

[0381] The user's terminal displays a list of product suggestions received from the server. The input is suggestion data from the server. The output allows the user to view product details and make a purchase decision through an intuitive interface. The user directly selects from the list, and the system feeds this selection data back to the server.

[0382] Step 6:

[0383] The server collects user selection data again and analyzes it using machine learning algorithms to improve the accuracy of the algorithms. The input here is the user selection data. The server uses this to improve the accuracy of future suggestions, enabling more personalized product recommendations.

[0384] 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.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] 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.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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".

[0400] This invention is a system that efficiently collects and analyzes user data from a wide variety of electronic services to propose the optimal options for the user. The aim of this system is to improve user convenience and enable a more efficient digital life.

[0401] The server initially begins collecting user data using the APIs of each electronic service. This data includes online transaction history, search history, and location information. The server ensures a secure connection and utilizes encryption technologies to protect user privacy.

[0402] The collected data is integrated by the server into a single user profile. This profile is stored in a database and prepared for later analysis. Here, data duplication is removed, ensuring consistency and accuracy of the information.

[0403] Next, the server analyzes the integrated data. Machine learning algorithms are used for data analysis to predict user behavior patterns and preferences. This makes it possible to identify products and services that users are likely to be interested in.

[0404] Based on the analysis results, the server generates optimal suggestions for the user. These include useful information for everyday life and travel plans tailored to the user's schedule. The suggestions are prioritized according to the user's interests.

[0405] The device features an interface for presenting generated suggestions to the user. Its simple and intuitive design allows users to easily review and select the suggested options.

[0406] For example, suppose a user is planning a trip. The server analyzes the user's past travel history and current vacation plans, and suggests recommended travel plans based on the user's interests, including destinations they haven't visited yet. These suggestions may include special discounts on specific hotels and flights, and the suggestions are available immediately.

[0407] In this way, users can receive fragments of information in a unified format, enabling them to make more efficient choices. The system can ultimately collect user feedback and use it to improve accuracy in future iterations.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The server collects user data via the APIs of each electronic service. This includes, for example, financial transaction data, search history, and location information, and verifies that the data is up-to-date.

[0411] Step 2:

[0412] The server temporarily stores the collected data and cleanses it of duplicates and redundancies before storing it in the database. After cleansing, the data is properly organized and indexes are set up for efficient access.

[0413] Step 3:

[0414] The server integrates the organized data and generates user profiles. These profiles are designed to provide consistent information across different data sources.

[0415] Step 4:

[0416] The server analyzes user profiles using machine learning algorithms. Here, collaborative filtering and clustering techniques are utilized to build models that predict user behavior patterns and preferences.

[0417] Step 5:

[0418] The server uses the analysis results to suggest the best choices and actions for the user. These suggestions are prioritized and may include, for example, special sale information or travel guidance based on a personalized schedule.

[0419] Step 6:

[0420] The device displays suggestions received from the server to the user. The display is designed to be user-friendly, allowing users to easily understand the suggestions and take action.

[0421] Step 7:

[0422] When a user makes a choice or takes action based on the information provided, the server collects that data again. This choice data is used as feedback to improve the accuracy of the system's algorithms and suggestions.

[0423] (Example 1)

[0424] 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."

[0425] In today's digital society, users generate vast amounts of information through various electronic services. However, efficiently collecting this dispersed information and automatically providing useful suggestions based on user interests and behaviors is a challenging task. Traditional systems often suffer from data fragmentation and information overload, hindering user decision-making, thus necessitating improvements to the user experience.

[0426] 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.

[0427] In this invention, the server includes means for collecting user data from multiple information provision means, means for integrating the collected data to generate a single user information record, and means for analyzing the user's behavior patterns to generate optimal suggestions. This enables the provision of integrated and personalized information to the user.

[0428] "User data" refers to all information generated in relation to a user's activities, including online interactions, location information, and search history.

[0429] "Information provision means" refers to a medium or system for providing information to users, such as online services or digital platforms.

[0430] A "user information record" is a dataset that compiles and records user-related data collected and integrated from multiple electronic services.

[0431] "Behavioral patterns" refer to a user's past behavioral patterns, preferences, and selection tendencies, and are information used to predict the user's future behavior.

[0432] An "information display device" is a device or interface used to visually display generated proposals and information to the user.

[0433] "Selection information" refers to data that shows the result of a user's choice from among several options presented.

[0434] "Predictive methods" are techniques used to analyze user behavior patterns and predict future actions and interests, and are usually based on algorithms.

[0435] A "storage device" is a hardware or software storage system used to securely and efficiently store collected data.

[0436] "Organization" is the process of cleansing collected data, removing duplicates, and ensuring data consistency and accuracy.

[0437] A "learning algorithm" is a computational method used to analyze large amounts of data, extract features from it, and perform predictions and classifications.

[0438] This invention relates to a system that collects, integrates, and analyzes user data and provides individually optimized suggestions to users. Specifically, the system functions based on the roles of the server, terminal, and user.

[0439] The server initially collects user data from multiple online services configured as means of providing information. For example, the server uses API services to obtain the user's location information and also collects web history and search query information that suggests the user's interests. Encryption technologies such as SSL / TLS are used to ensure secure data transfer during this collection process.

[0440] The collected data is integrated by the server and stored in a database as user information records. Here, data cleansing tools are used to remove duplicates and improve information integrity. For example, using a MySQL database enables efficient data management.

[0441] Furthermore, the server executes learning algorithms to analyze the integrated data. Specifically, it uses libraries such as scikit-learn and TensorFlow to perform clustering and predictive analysis based on the user's past behavior patterns. This analysis identifies options that the user may be interested in and automatically generates suggestions.

[0442] The generated suggestions are displayed to the user via the device. The device utilizes application frameworks like React Native to provide an intuitive and user-friendly interface. Users can easily review the suggested information and make selections.

[0443] For example, if a user wants to decide on a destination for their next vacation, the system analyzes the user's past travel information and related interests to suggest a new destination and travel plan. This plan might include booking information for specific hotels or special discounts on transportation. Another example of a prompt when using a generative AI model is, "Based on the user's past behavior, suggest a travel destination that they might be interested in next."

[0444] In this way, the system can improve the user experience by providing personalized information tailored to the user in a timely manner.

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] The server collects user data through APIs of various online services. The input consists of digital information derived from the user's online activities, specifically location information, browsing history, and search history. To securely obtain this information, the server establishes an encrypted connection using SSL / TLS and makes API requests. The output is the collected raw data.

[0448] Step 2:

[0449] The server integrates the collected data to generate a user information record. The input for this step is the multiple datasets obtained in step 1. To remove data duplication and integrate different formats, the server uses data cleansing tools and stores the data in a database such as MySQL. The output is an organized and consistent user information record.

[0450] Step 3:

[0451] The server analyzes user behavior patterns based on integrated data. The input is the user information record generated in step 2. The server uses scikit-learn or TensorFlow to implement a learning algorithm and predict user interests and behavioral patterns. Specifically, it performs clustering to identify user groups. The output is the characterized user profile and predicted interests.

[0452] Step 4:

[0453] The server generates optimal suggestions for the user based on the analysis results. Using the user profile from Step 3 as input, it creates suggestions in natural language using NLP. For example, suggestions might include "It would be good to visit a specific tourist destination next weekend." The output is a specific suggestion message presented to the user.

[0454] Step 5:

[0455] The terminal displays suggestions received from the server to the user. The input is the suggestion message generated in step 4. The terminal uses a framework such as React Native to provide an intuitive user interface. The user can make a selection based on the displayed information. The output is the screen showing the suggestions and the user's selection result.

[0456] Step 6:

[0457] The user reviews the suggestions via their device and decides whether to use them. The input is the suggestion information displayed in step 5. The user makes selections using actions such as swiping or tapping, and the results are sent back to the server and collected as selection information. The output is the user's selection data, which helps improve the accuracy of the prediction method.

[0458] (Application Example 1)

[0459] 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."

[0460] Traditionally, when users searched for and purchased products online, they faced the problem of difficulty in quickly finding the most suitable option from a vast amount of information. Furthermore, effectively implementing systems to analyze user behavior and provide personalized product recommendations remained a challenge.

[0461] 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.

[0462] In this invention, the server includes means for collecting user data from multiple sources, means for integrating the collected data to generate a single user profile, means for analyzing the user's behavioral characteristics to generate optimal suggestions, and means for analyzing product information to suggest products that match the user's preferences. As a result, the user can receive highly accurate product suggestions based on their own preferences.

[0463] "User data" refers to information collected from a user's online activities, such as transaction history, search history, and location information.

[0464] "Information sources" refer to digital platforms from which user data can be obtained, such as APIs and databases of online service providers.

[0465] A "user profile" is a collection of information, including the characteristics and preferences of individual users, built upon integrated user data.

[0466] "Behavioral characteristics" refer to a user's past behavioral patterns and selection tendencies, and analyzing these provides fundamental data for predicting the user's future behavior.

[0467] "Product information" refers to data that includes information about the characteristics, price, and reviews of a product provided to consumers.

[0468] A "server" refers to computing resources used to collect, integrate, analyze, and generate suggestions from data, and then provides the results to the user.

[0469] A "learning algorithm" refers to a set of methods used to analyze user data, learn its patterns, and make future predictions.

[0470] This invention is a system designed to effectively collect and analyze user data. The system is server-centric, and the server collects user data from external sources via APIs. This data includes user transaction history, search history, and location information, all protected using encryption technology for privacy. The collected data is integrated into a single user profile by the server, and duplicate data is removed through data cleaning.

[0471] Based on integrated data, the server uses machine learning algorithms to analyze user behavior and generate highly personalized product recommendations. These recommendations are displayed in the user interface, allowing users to easily make the best choices for themselves. User interactions and selection information are also collected again as data to improve the accuracy of the algorithms.

[0472] For example, if a user has a history of frequently purchasing outdoor equipment online, notifications about new camping gear releases and related sales will be displayed on the user interface. This allows users to quickly and effectively receive information tailored to their interests. By using prompts for the generative AI model such as, "Based on the user's purchase history, please tell me about products that might influence their next purchase," more accurate suggestions can be made.

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The server collects user data from multiple sources via APIs. This includes user transaction history, search history, and location information. Input data is obtained from external APIs, and encrypted user data is returned as output. Specifically, API requests are sent and responses are received.

[0476] Step 2:

[0477] The server integrates the collected user data to generate a single user profile. This process removes duplicate data and organizes it into a well-formed format. The input is the raw data collected in step 1, and the output is a consistent and complete user profile. Specific operations include data streaming and storage to a database.

[0478] Step 3:

[0479] The server analyzes behavioral characteristics using machine learning algorithms based on integrated user profiles. The input is the user profile, and the output is user preference and behavioral prediction data. Specific operations include loading and running the predictive model.

[0480] Step 4:

[0481] The server generates optimal product suggestions for the user based on the analysis results. This includes suggesting product information that matches the user's preferences. The input is behavioral prediction data, and the output is product suggestion information. Specifically, suggestion text and prompts are generated using a generative AI model.

[0482] Step 5:

[0483] The server displays the generated suggestions on the terminal's user interface. Product suggestion information is the input, and the output is a visual suggestion screen, allowing the user to review the suggestions through the interface. Specifically, the server updates UI elements and presents them to the user.

[0484] Step 6:

[0485] The user makes a selection based on the suggested product information, and this selection data is sent back to the server. The input is the user's selection action, and the output is the selected product data. Specific operations include recording user input and transmitting data.

[0486] Step 7:

[0487] The server re-analyzes the user's selection data and uses it to improve the accuracy of the learning algorithm. The input is the selected product data, and the output is the updated predictive model. Specifically, the model is retrained and evaluated.

[0488] 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.

[0489] This invention is a system that collects user data from multiple electronic services and combines it with an emotion engine to provide optimal suggestions tailored to the user's emotions. Through the recognition and analysis of emotions, this system enables more personalized responses compared to conventional suggestions.

[0490] The server collects user data from various electronic services and integrates this data to generate a user profile. In this process, an emotion engine estimates the user's emotional state from their text input, voice, facial expressions, and other data. The server uses this multimodal data to improve the accuracy of its emotional assessment.

[0491] Subsequently, the server analyzes the collected data in combination with emotional information from the emotion engine to determine the user's behavioral patterns and emotions. This analysis ensures that suggested content and products are tailored to the user's current emotional state and have a higher receptiveness.

[0492] The server generates personalized suggestions based on the analysis results. This involves reflecting the user's emotional state, customizing the content according to their mood; for example, suggesting refreshing plans when the user is relaxed, and products with relaxing effects when they are stressed.

[0493] The generated suggestions are displayed on the terminal and are designed to allow the user to directly interact with, review, and select them. When the user selects a suggestion, that selection data is sent back to the server and used as feedback to improve the system's algorithms.

[0494] For example, when a user is planning a leisure trip on a holiday, the server analyzes their past travel history and current emotional state (e.g., anticipation or anxiety) to suggest a suitable travel plan. By quickly understanding the user's emotional state, the emotion engine provides more appropriate options, improving the user experience.

[0495] By using this system, users can enjoy the integrated use of fragments of digital data and gain maximum convenience and satisfaction from emotionally resonant suggestions.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The server collects user data through the APIs of each electronic service. This includes purchase history, search queries, location information, etc., and is kept as up-to-date data.

[0499] Step 2:

[0500] The server uses an emotion engine to estimate the user's emotional state. The emotion engine determines the emotion based on the user's text input, voice data, facial expression analysis, etc., and reflects it in the database.

[0501] Step 3:

[0502] The server integrates the collected data and generates organized user profiles. Duplicate data is removed to improve data consistency.

[0503] Step 4:

[0504] The server uses integrated data and sentiment information to perform analysis using machine learning algorithms. This analysis builds a model based on the user's behavior patterns and current emotional state.

[0505] Step 5:

[0506] The server generates personalized recommendations based on an analysis model. These recommendations include products and services that reflect emotional information and resonate with the user's mood.

[0507] Step 6:

[0508] The device displays the generated suggestions to the user. The displayed suggestions are presented in a relaxed layout, designed for easy user selection.

[0509] Step 7:

[0510] Users interact with the terminal to review suggestions and make selections or take actions. This selection data is sent to the server and used as feedback to improve the accuracy of the system's suggestions.

[0511] (Example 2)

[0512] 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."

[0513] In modern society, users acquire vast amounts of information from various information-providing devices, but it is difficult for them to obtain suggestions optimized for their own psychological state and behavioral patterns. Conventional systems lack sufficient personalization that takes emotional states into account, resulting in a problem where truly valuable information is not being provided to users.

[0514] 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.

[0515] In this invention, the server includes means for collecting user information from multiple information providing devices, means for analyzing the user's emotional state using an emotion analysis device, and means for generating optimal suggestions based on the user's behavioral patterns and emotional state. This makes it possible to accurately grasp the user's psychological state and provide appropriate and personalized suggestions according to that state.

[0516] "Information provision device" is a general term for electronic devices that have the function of transmitting and receiving user information.

[0517] A "user profile" is data that integrates collected user information and summarizes information about individual users.

[0518] An "emotion analysis device" is a device or software that has the function of analyzing a user's emotional state.

[0519] "Behavioral patterns" refer to the collective behavioral patterns and habits expressed by users, and are recorded as digital data.

[0520] An "inference algorithm" is a computational method or procedure for generating optimal suggestions based on collected information and analysis results.

[0521] An "output device" refers to a screen or device used by the user to review and operate the proposed content.

[0522] This invention is a system for providing personalized suggestions that take into account the user's emotional state. The server collects information from multiple information providers from the user, integrates this information into a database, and generates a user profile. The information providers include various digital devices and online services.

[0523] Emotion analysis devices are used to analyze a user's emotional state from collected information. Specifically, they use libraries with natural language processing technology, voice analysis tools, and facial recognition technology to process text, voice, and visual data and identify emotional states.

[0524] The server generates optimal suggestions based on the analyzed emotional state and behavioral patterns. This suggestion generation uses machine learning algorithms to select content and products that match the user's needs from the collected information. A generative AI model is utilized, and prompts can be used to provide more detailed and personalized suggestions. For example, the prompt "Suggest music suitable for when the user is relaxed" might be sent to the generative AI model.

[0525] The generated suggestions are displayed on the terminal. The terminal has a user interface for the user to review and select from the suggestions. The user interface is built using a web framework and is designed to be intuitive for the user to operate.

[0526] After a user selects a suggestion, that selection information is sent back to the server. This feedback information is used to improve the inference algorithm. This improves the accuracy of subsequent suggestions, allowing users to have an increasingly personalized experience.

[0527] This system allows users to receive optimal suggestions tailored to their emotional state, greatly increasing the value of digital data.

[0528] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0529] Step 1:

[0530] The server collects user information from information providers. Inputs include user text, voice, and image information. The data is collected using a streaming platform and stored in a database. The output is an integrated digital profile.

[0531] Step 2:

[0532] The server uses an emotion analysis device to analyze the emotional state of the collected data. Inputs include user text, voice, and image data. The data is processed using natural language processing, speech recognition, and facial expression analysis tools to evaluate the emotional state. The output is data indicating the user's emotional state.

[0533] Step 3:

[0534] The server analyzes user behavior patterns based on analysis results and integrated digital profiles, and generates appropriate suggestions. Input data includes emotional data and past behavioral history. Machine learning algorithms are applied to perform data calculations and derive the most suitable suggestions for the user. The output is the generated suggestions.

[0535] Step 4:

[0536] The terminal displays the generated suggestions to the user. The input is the suggestion content sent from the server. The information is displayed in a user-friendly format via the terminal's user interface. The output is the suggestion information viewable by the user.

[0537] Step 5:

[0538] When a user selects a suggested option, that selection information is sent back from the terminal to the server. The input is the user's selected option. The server receives this feedback and improves the inference algorithm. The output is the improved state of the inference algorithm.

[0539] (Application Example 2)

[0540] 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."

[0541] Traditional e-commerce has not adequately considered the emotional state of users when suggesting products. As a result, it has been difficult for users to find the optimal product that suits their situation, making it difficult to increase their desire to purchase. This has led to a decline in the user experience and a loss of purchasing opportunities. The present invention aims to solve this problem.

[0542] 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.

[0543] In this invention, the server includes means for collecting user data from multiple electronic services, means for integrating the collected data to generate a single user profile, means for analyzing the user's facial expressions, voice, and text input to estimate their emotional state, and means for suggesting products corresponding to the estimated emotional state. This enables product suggestions optimized for the user's emotions, significantly improving the purchasing experience.

[0544] "User data" refers to all information collected from users through electronic services, and includes a wide range of data such as text, audio, behavioral history, and purchase history.

[0545] "Electronic services" refer to digital services provided through online platforms and applications, and include e-commerce, music streaming, and social media.

[0546] A "user profile" is a collection of information that shows the characteristics and tendencies of a user, generated by integrating and analyzing collected user data.

[0547] "Emotional state" refers to the user's current psychological and emotional condition, which is estimated through methods such as facial expression and voice analysis.

[0548] "Product recommendations" refer to recommendations for products and services that the server provides to a user based on the user's profile and emotional state.

[0549] This invention is a system that provides product recommendations tailored to the user's emotional state based on diverse user data. This system consists of the following elements:

[0550] The server collects user data from multiple electronic services. This data includes text, voice, and behavioral history. This generates a comprehensive user profile. A database management system is used for data processing, and necessary data manipulation is performed using Python and related machine learning libraries.

[0551] The user's device is primarily a smartphone, and its built-in camera and microphone are used to capture facial expressions and voice data in real time. The captured data is analyzed using the OpenCV library and Google's Speech-to-Text API. This allows for highly accurate estimation of the user's emotional state.

[0552] Sentiment analysis uses sentiment analysis APIs such as IBM Watson. Generative AI models are then used to provide optimal product recommendations based on this analysis. These recommendations are displayed immediately on the user's device and can be purchased directly. The user interface is intuitive, allowing users to easily save or purchase recommendations with clicks and taps.

[0553] For example, when a user is using their smartphone in the evening, the system can sense that they are in a relaxed state. This leads to suggestions such as aromatherapy candles or refreshing beverages. This interaction is monitored by the system, and the selection results are sent to the server to improve the accuracy of future suggestions. An example of a prompt provided to the generating AI model is, "Generate the most suitable recommendations based on the user's current state of relaxation."

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

[0555] Step 1:

[0556] The server collects user data from multiple electronic services. Inputs include text, voice, and behavioral history data provided by each electronic service. The server integrates this data and stores it in a database. It then cleanses the data to remove duplicates and generates clean user profiles, preparing it for subsequent processing.

[0557] Step 2:

[0558] The user's device captures real-time facial expressions and audio through its camera and microphone. The input for this step is raw data obtained from a smartphone or related device. The device analyzes facial expressions using the OpenCV library and converts audio to text using Google's Speech-to-Text API. This result is then sent to the server for subsequent sentiment analysis.

[0559] Step 3:

[0560] The server passes facial expression data and voice text sent from the terminal to an emotion analysis API to estimate the user's emotional state. The input for this step is the facial expression analysis result and voice text. The output is data representing the user's emotional state, which the server uses with a generative AI model to generate suitable product suggestions.

[0561] Step 4:

[0562] The server utilizes a generative AI model to generate a list of products best suited to the user's emotional state. The input consists of the estimated emotional state and user profile. The server uses product consideration prompts to generate a suggestion list and sends it to the terminal. This list is customized based on the user's current emotional state.

[0563] Step 5:

[0564] The user's terminal displays a list of product suggestions received from the server. The input is suggestion data from the server. The output allows the user to view product details and make a purchase decision through an intuitive interface. The user directly selects from the list, and the system feeds this selection data back to the server.

[0565] Step 6:

[0566] The server collects user selection data again and analyzes it using machine learning algorithms to improve the accuracy of the algorithms. The input here is the user selection data. The server uses this to improve the accuracy of future suggestions, enabling more personalized product recommendations.

[0567] 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.

[0568] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0569] 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.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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".

[0584] This invention is a system that efficiently collects and analyzes user data from a wide variety of electronic services to propose the optimal options for the user. The aim of this system is to improve user convenience and enable a more efficient digital life.

[0585] The server initially begins collecting user data using the APIs of each electronic service. This data includes online transaction history, search history, and location information. The server ensures a secure connection and utilizes encryption technologies to protect user privacy.

[0586] The collected data is integrated by the server into a single user profile. This profile is stored in a database and prepared for later analysis. Here, data duplication is removed, ensuring consistency and accuracy of the information.

[0587] Next, the server analyzes the integrated data. Machine learning algorithms are used for data analysis to predict user behavior patterns and preferences. This makes it possible to identify products and services that users are likely to be interested in.

[0588] Based on the analysis results, the server generates optimal suggestions for the user. These include useful information for everyday life and travel plans tailored to the user's schedule. The suggestions are prioritized according to the user's interests.

[0589] The device features an interface for presenting generated suggestions to the user. Its simple and intuitive design allows users to easily review and select the suggested options.

[0590] For example, suppose a user is planning a trip. The server analyzes the user's past travel history and current vacation plans, and suggests recommended travel plans based on the user's interests, including destinations they haven't visited yet. These suggestions may include special discounts on specific hotels and flights, and the suggestions are available immediately.

[0591] In this way, users can receive fragments of information in a unified format, enabling them to make more efficient choices. The system can ultimately collect user feedback and use it to improve accuracy in future iterations.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] The server collects user data via the APIs of each electronic service. This includes, for example, financial transaction data, search history, and location information, and verifies that the data is up-to-date.

[0595] Step 2:

[0596] The server temporarily stores the collected data and cleanses it of duplicates and redundancies before storing it in the database. After cleansing, the data is properly organized and indexes are set up for efficient access.

[0597] Step 3:

[0598] The server integrates the organized data and generates user profiles. These profiles are designed to provide consistent information across different data sources.

[0599] Step 4:

[0600] The server analyzes user profiles using machine learning algorithms. Here, collaborative filtering and clustering techniques are utilized to build models that predict user behavior patterns and preferences.

[0601] Step 5:

[0602] The server uses the analysis results to suggest the best choices and actions for the user. These suggestions are prioritized and may include, for example, special sale information or travel guidance based on a personalized schedule.

[0603] Step 6:

[0604] The device displays suggestions received from the server to the user. The display is designed to be user-friendly, allowing users to easily understand the suggestions and take action.

[0605] Step 7:

[0606] When a user makes a choice or takes action based on the information provided, the server collects that data again. This choice data is used as feedback to improve the accuracy of the system's algorithms and suggestions.

[0607] (Example 1)

[0608] 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".

[0609] In today's digital society, users generate vast amounts of information through various electronic services. However, efficiently collecting this dispersed information and automatically providing useful suggestions based on user interests and behaviors is a challenging task. Traditional systems often suffer from data fragmentation and information overload, hindering user decision-making, thus necessitating improvements to the user experience.

[0610] 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.

[0611] In this invention, the server includes means for collecting user data from multiple information provision means, means for integrating the collected data to generate a single user information record, and means for analyzing the user's behavior patterns to generate optimal suggestions. This enables the provision of integrated and personalized information to the user.

[0612] "User data" refers to all information generated in relation to a user's activities, including online interactions, location information, and search history.

[0613] "Information provision means" refers to a medium or system for providing information to users, such as online services or digital platforms.

[0614] A "user information record" is a dataset that compiles and records user-related data collected and integrated from multiple electronic services.

[0615] "Behavioral patterns" refer to a user's past behavioral patterns, preferences, and selection tendencies, and are information used to predict the user's future behavior.

[0616] An "information display device" is a device or interface used to visually display generated proposals and information to the user.

[0617] "Selection information" refers to data that shows the result of a user's choice from among several options presented.

[0618] "Predictive methods" are techniques used to analyze user behavior patterns and predict future actions and interests, and are usually based on algorithms.

[0619] A "storage device" is a hardware or software storage system used to securely and efficiently store collected data.

[0620] "Organization" is the process of cleansing collected data, removing duplicates, and ensuring data consistency and accuracy.

[0621] A "learning algorithm" is a computational method used to analyze large amounts of data, extract features from it, and perform predictions and classifications.

[0622] This invention relates to a system that collects, integrates, and analyzes user data and provides individually optimized suggestions to users. Specifically, the system functions based on the roles of the server, terminal, and user.

[0623] The server initially collects user data from multiple online services configured as means of providing information. For example, the server uses API services to obtain the user's location information and also collects web history and search query information that suggests the user's interests. Encryption technologies such as SSL / TLS are used to ensure secure data transfer during this collection process.

[0624] The collected data is integrated by the server and stored in a database as user information records. Here, data cleansing tools are used to remove duplicates and improve information integrity. For example, using a MySQL database enables efficient data management.

[0625] Furthermore, the server executes learning algorithms to analyze the integrated data. Specifically, it uses libraries such as scikit-learn and TensorFlow to perform clustering and predictive analysis based on the user's past behavior patterns. This analysis identifies options that the user may be interested in and automatically generates suggestions.

[0626] The generated suggestions are displayed to the user via the device. The device utilizes application frameworks like React Native to provide an intuitive and user-friendly interface. Users can easily review the suggested information and make selections.

[0627] For example, if a user wants to decide on a destination for their next vacation, the system analyzes the user's past travel information and related interests to suggest a new destination and travel plan. This plan might include booking information for specific hotels or special discounts on transportation. Another example of a prompt when using a generative AI model is, "Based on the user's past behavior, suggest a travel destination that they might be interested in next."

[0628] In this way, the system can improve the user experience by providing personalized information tailored to the user in a timely manner.

[0629] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0630] Step 1:

[0631] The server collects user data through APIs of various online services. The input consists of digital information derived from the user's online activities, specifically location information, browsing history, and search history. To securely obtain this information, the server establishes an encrypted connection using SSL / TLS and makes API requests. The output is the collected raw data.

[0632] Step 2:

[0633] The server integrates the collected data to generate a user information record. The input for this step is the multiple datasets obtained in step 1. To remove data duplication and integrate different formats, the server uses data cleansing tools and stores the data in a database such as MySQL. The output is an organized and consistent user information record.

[0634] Step 3:

[0635] The server analyzes user behavior patterns based on integrated data. The input is the user information record generated in step 2. The server uses scikit-learn or TensorFlow to implement a learning algorithm and predict user interests and behavioral patterns. Specifically, it performs clustering to identify user groups. The output is the characterized user profile and predicted interests.

[0636] Step 4:

[0637] The server generates optimal suggestions for the user based on the analysis results. Using the user profile from Step 3 as input, it creates suggestions in natural language using NLP. For example, suggestions might include "It would be good to visit a specific tourist destination next weekend." The output is a specific suggestion message presented to the user.

[0638] Step 5:

[0639] The terminal displays suggestions received from the server to the user. The input is the suggestion message generated in step 4. The terminal uses a framework such as React Native to provide an intuitive user interface. The user can make a selection based on the displayed information. The output is the screen showing the suggestions and the user's selection result.

[0640] Step 6:

[0641] The user reviews the suggestions via their device and decides whether to use them. The input is the suggestion information displayed in step 5. The user makes selections using actions such as swiping or tapping, and the results are sent back to the server and collected as selection information. The output is the user's selection data, which helps improve the accuracy of the prediction method.

[0642] (Application Example 1)

[0643] 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".

[0644] Traditionally, when users searched for and purchased products online, they faced the problem of difficulty in quickly finding the most suitable option from a vast amount of information. Furthermore, effectively implementing systems to analyze user behavior and provide personalized product recommendations remained a challenge.

[0645] 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.

[0646] In this invention, the server includes means for collecting user data from multiple sources, means for integrating the collected data to generate a single user profile, means for analyzing the user's behavioral characteristics to generate optimal suggestions, and means for analyzing product information to suggest products that match the user's preferences. As a result, the user can receive highly accurate product suggestions based on their own preferences.

[0647] "User data" refers to information collected from a user's online activities, such as transaction history, search history, and location information.

[0648] "Information sources" refer to digital platforms from which user data can be obtained, such as APIs and databases of online service providers.

[0649] A "user profile" is a collection of information, including the characteristics and preferences of individual users, built upon integrated user data.

[0650] "Behavioral characteristics" refer to a user's past behavioral patterns and selection tendencies, and analyzing these provides fundamental data for predicting the user's future behavior.

[0651] "Product information" refers to data that includes information about the characteristics, price, and reviews of a product provided to consumers.

[0652] A "server" refers to computing resources used to collect, integrate, analyze, and generate suggestions from data, and then provides the results to the user.

[0653] A "learning algorithm" refers to a set of methods used to analyze user data, learn its patterns, and make future predictions.

[0654] This invention is a system designed to effectively collect and analyze user data. The system is server-centric, and the server collects user data from external sources via APIs. This data includes user transaction history, search history, and location information, all protected using encryption technology for privacy. The collected data is integrated into a single user profile by the server, and duplicate data is removed through data cleaning.

[0655] Based on integrated data, the server uses machine learning algorithms to analyze user behavior and generate highly personalized product recommendations. These recommendations are displayed in the user interface, allowing users to easily make the best choices for themselves. User interactions and selection information are also collected again as data to improve the accuracy of the algorithms.

[0656] For example, if a user has a history of frequently purchasing outdoor equipment online, notifications about new camping gear releases and related sales will be displayed on the user interface. This allows users to quickly and effectively receive information tailored to their interests. By using prompts for the generative AI model such as, "Based on the user's purchase history, please tell me about products that might influence their next purchase," more accurate suggestions can be made.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The server collects user data from multiple sources via APIs. This includes user transaction history, search history, and location information. Input data is obtained from external APIs, and encrypted user data is returned as output. Specifically, API requests are sent and responses are received.

[0660] Step 2:

[0661] The server integrates the collected user data to generate a single user profile. This process removes duplicate data and organizes it into a well-formed format. The input is the raw data collected in step 1, and the output is a consistent and complete user profile. Specific operations include data streaming and storage to a database.

[0662] Step 3:

[0663] The server analyzes behavioral characteristics using machine learning algorithms based on integrated user profiles. The input is the user profile, and the output is user preference and behavioral prediction data. Specific operations include loading and running the predictive model.

[0664] Step 4:

[0665] The server generates optimal product suggestions for the user based on the analysis results. This includes suggesting product information that matches the user's preferences. The input is behavioral prediction data, and the output is product suggestion information. Specifically, suggestion text and prompts are generated using a generative AI model.

[0666] Step 5:

[0667] The server displays the generated suggestions on the terminal's user interface. Product suggestion information is the input, and the output is a visual suggestion screen, allowing the user to review the suggestions through the interface. Specifically, the server updates UI elements and presents them to the user.

[0668] Step 6:

[0669] The user makes a selection based on the suggested product information, and this selection data is sent back to the server. The input is the user's selection action, and the output is the selected product data. Specific operations include recording user input and transmitting data.

[0670] Step 7:

[0671] The server re-analyzes the user's selection data and uses it to improve the accuracy of the learning algorithm. The input is the selected product data, and the output is the updated predictive model. Specifically, the model is retrained and evaluated.

[0672] 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.

[0673] This invention is a system that collects user data from multiple electronic services and combines it with an emotion engine to provide optimal suggestions tailored to the user's emotions. Through the recognition and analysis of emotions, this system enables more personalized responses compared to conventional suggestions.

[0674] The server collects user data from various electronic services and integrates this data to generate a user profile. In this process, an emotion engine estimates the user's emotional state from their text input, voice, facial expressions, and other data. The server uses this multimodal data to improve the accuracy of its emotional assessment.

[0675] Subsequently, the server analyzes the collected data in combination with emotional information from the emotion engine to determine the user's behavioral patterns and emotions. This analysis ensures that suggested content and products are tailored to the user's current emotional state and have a higher receptiveness.

[0676] The server generates personalized suggestions based on the analysis results. This involves reflecting the user's emotional state, customizing the content according to their mood; for example, suggesting refreshing plans when the user is relaxed, and products with relaxing effects when they are stressed.

[0677] The generated suggestions are displayed on the terminal and are designed to allow the user to directly interact with, review, and select them. When the user selects a suggestion, that selection data is sent back to the server and used as feedback to improve the system's algorithms.

[0678] For example, when a user is planning a leisure trip on a holiday, the server analyzes their past travel history and current emotional state (e.g., anticipation or anxiety) to suggest a suitable travel plan. By quickly understanding the user's emotional state, the emotion engine provides more appropriate options, improving the user experience.

[0679] By using this system, users can enjoy the integrated use of fragments of digital data and gain maximum convenience and satisfaction from emotionally resonant suggestions.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The server collects user data through the APIs of each electronic service. This includes purchase history, search queries, location information, etc., and is kept as up-to-date data.

[0683] Step 2:

[0684] The server uses an emotion engine to estimate the user's emotional state. The emotion engine determines the emotion based on the user's text input, voice data, facial expression analysis, etc., and reflects it in the database.

[0685] Step 3:

[0686] The server integrates the collected data and generates organized user profiles. Duplicate data is removed to improve data consistency.

[0687] Step 4:

[0688] The server uses integrated data and sentiment information to perform analysis using machine learning algorithms. This analysis builds a model based on the user's behavior patterns and current emotional state.

[0689] Step 5:

[0690] The server generates personalized recommendations based on an analysis model. These recommendations include products and services that reflect emotional information and resonate with the user's mood.

[0691] Step 6:

[0692] The device displays the generated suggestions to the user. The displayed suggestions are presented in a relaxed layout, designed for easy user selection.

[0693] Step 7:

[0694] Users interact with the terminal to review suggestions and make selections or take actions. This selection data is sent to the server and used as feedback to improve the accuracy of the system's suggestions.

[0695] (Example 2)

[0696] 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".

[0697] In modern society, users acquire vast amounts of information from various information-providing devices, but it is difficult for them to obtain suggestions optimized for their own psychological state and behavioral patterns. Conventional systems lack sufficient personalization that takes emotional states into account, resulting in a problem where truly valuable information is not being provided to users.

[0698] 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.

[0699] In this invention, the server includes means for collecting user information from multiple information providing devices, means for analyzing the user's emotional state using an emotion analysis device, and means for generating optimal suggestions based on the user's behavioral patterns and emotional state. This makes it possible to accurately grasp the user's psychological state and provide appropriate and personalized suggestions according to that state.

[0700] "Information provision device" is a general term for electronic devices that have the function of transmitting and receiving user information.

[0701] A "user profile" is data that integrates collected user information and summarizes information about individual users.

[0702] An "emotion analysis device" is a device or software that has the function of analyzing a user's emotional state.

[0703] "Behavioral patterns" refer to the collective behavioral patterns and habits expressed by users, and are recorded as digital data.

[0704] An "inference algorithm" is a computational method or procedure for generating optimal suggestions based on collected information and analysis results.

[0705] An "output device" refers to a screen or device used by the user to review and operate the proposed content.

[0706] This invention is a system for providing personalized suggestions that take into account the user's emotional state. The server collects information from multiple information providers from the user, integrates this information into a database, and generates a user profile. The information providers include various digital devices and online services.

[0707] Emotion analysis devices are used to analyze a user's emotional state from collected information. Specifically, they use libraries with natural language processing technology, voice analysis tools, and facial recognition technology to process text, voice, and visual data and identify emotional states.

[0708] The server generates optimal suggestions based on the analyzed emotional state and behavioral patterns. This suggestion generation uses machine learning algorithms to select content and products that match the user's needs from the collected information. A generative AI model is utilized, and prompts can be used to provide more detailed and personalized suggestions. For example, the prompt "Suggest music suitable for when the user is relaxed" might be sent to the generative AI model.

[0709] The generated suggestions are displayed on the terminal. The terminal has a user interface for the user to review and select from the suggestions. The user interface is built using a web framework and is designed to be intuitive for the user to operate.

[0710] After a user selects a suggestion, that selection information is sent back to the server. This feedback information is used to improve the inference algorithm. This improves the accuracy of subsequent suggestions, allowing users to have an increasingly personalized experience.

[0711] This system allows users to receive optimal suggestions tailored to their emotional state, greatly increasing the value of digital data.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The server collects user information from information providers. Inputs include user text, voice, and image information. The data is collected using a streaming platform and stored in a database. The output is an integrated digital profile.

[0715] Step 2:

[0716] The server uses an emotion analysis device to analyze the emotional state of the collected data. Inputs include user text, voice, and image data. The data is processed using natural language processing, speech recognition, and facial expression analysis tools to evaluate the emotional state. The output is data indicating the user's emotional state.

[0717] Step 3:

[0718] The server analyzes user behavior patterns based on analysis results and integrated digital profiles, and generates appropriate suggestions. Input data includes emotional data and past behavioral history. Machine learning algorithms are applied to perform data calculations and derive the most suitable suggestions for the user. The output is the generated suggestions.

[0719] Step 4:

[0720] The terminal displays the generated suggestions to the user. The input is the suggestion content sent from the server. The information is displayed in a user-friendly format via the terminal's user interface. The output is the suggestion information viewable by the user.

[0721] Step 5:

[0722] When a user selects a suggested option, that selection information is sent back from the terminal to the server. The input is the user's selected option. The server receives this feedback and improves the inference algorithm. The output is the improved state of the inference algorithm.

[0723] (Application Example 2)

[0724] 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".

[0725] Traditional e-commerce has not adequately considered the emotional state of users when suggesting products. As a result, it has been difficult for users to find the optimal product that suits their situation, making it difficult to increase their desire to purchase. This has led to a decline in the user experience and a loss of purchasing opportunities. The present invention aims to solve this problem.

[0726] 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.

[0727] In this invention, the server includes means for collecting user data from multiple electronic services, means for integrating the collected data to generate a single user profile, means for analyzing the user's facial expressions, voice, and text input to estimate their emotional state, and means for suggesting products corresponding to the estimated emotional state. This enables product suggestions optimized for the user's emotions, significantly improving the purchasing experience.

[0728] "User data" refers to all information collected from users through electronic services, and includes a wide range of data such as text, audio, behavioral history, and purchase history.

[0729] "Electronic services" refer to digital services provided through online platforms and applications, and include e-commerce, music streaming, and social media.

[0730] A "user profile" is a collection of information that shows the characteristics and tendencies of a user, generated by integrating and analyzing collected user data.

[0731] "Emotional state" refers to the user's current psychological and emotional condition, which is estimated through methods such as facial expression and voice analysis.

[0732] "Product recommendations" refer to recommendations for products and services that the server provides to a user based on the user's profile and emotional state.

[0733] This invention is a system that provides product recommendations tailored to the user's emotional state based on diverse user data. This system consists of the following elements:

[0734] The server collects user data from multiple electronic services. This data includes text, voice, and behavioral history. This generates a comprehensive user profile. A database management system is used for data processing, and necessary data manipulation is performed using Python and related machine learning libraries.

[0735] The user's device is primarily a smartphone, and its built-in camera and microphone are used to capture facial expressions and voice data in real time. The captured data is analyzed using the OpenCV library and Google's Speech-to-Text API. This allows for highly accurate estimation of the user's emotional state.

[0736] Sentiment analysis uses sentiment analysis APIs such as IBM Watson. Generative AI models are then used to provide optimal product recommendations based on this analysis. These recommendations are displayed immediately on the user's device and can be purchased directly. The user interface is intuitive, allowing users to easily save or purchase recommendations with clicks and taps.

[0737] For example, when a user is using their smartphone in the evening, the system can sense that they are in a relaxed state. This leads to suggestions such as aromatherapy candles or refreshing beverages. This interaction is monitored by the system, and the selection results are sent to the server to improve the accuracy of future suggestions. An example of a prompt provided to the generating AI model is, "Generate the most suitable recommendations based on the user's current state of relaxation."

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The server collects user data from multiple electronic services. Inputs include text, voice, and behavioral history data provided by each electronic service. The server integrates this data and stores it in a database. It then cleanses the data to remove duplicates and generates clean user profiles, preparing it for subsequent processing.

[0741] Step 2:

[0742] The user's device captures real-time facial expressions and audio through its camera and microphone. The input for this step is raw data obtained from a smartphone or related device. The device analyzes facial expressions using the OpenCV library and converts audio to text using Google's Speech-to-Text API. This result is then sent to the server for subsequent sentiment analysis.

[0743] Step 3:

[0744] The server passes facial expression data and voice text sent from the terminal to an emotion analysis API to estimate the user's emotional state. The input for this step is the facial expression analysis result and voice text. The output is data representing the user's emotional state, which the server uses with a generative AI model to generate suitable product suggestions.

[0745] Step 4:

[0746] The server utilizes a generative AI model to generate a list of products best suited to the user's emotional state. The input consists of the estimated emotional state and user profile. The server uses product consideration prompts to generate a suggestion list and sends it to the terminal. This list is customized based on the user's current emotional state.

[0747] Step 5:

[0748] The user's terminal displays a list of product suggestions received from the server. The input is suggestion data from the server. The output allows the user to view product details and make a purchase decision through an intuitive interface. The user directly selects from the list, and the system feeds this selection data back to the server.

[0749] Step 6:

[0750] The server collects user selection data again and analyzes it using machine learning algorithms to improve the accuracy of the algorithms. The input here is the user selection data. The server uses this to improve the accuracy of future suggestions, enabling more personalized product recommendations.

[0751] 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.

[0752] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0758] 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.

[0759] 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."

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] The following is further disclosed regarding the embodiments described above.

[0773] (Claim 1)

[0774] A means of collecting user data from multiple electronic services,

[0775] A means of integrating collected data to generate a single user profile,

[0776] A means of analyzing user behavior patterns and generating optimal suggestions,

[0777] A means of displaying the generated suggestions on the user's terminal,

[0778] A means to collect user selection data again and improve the accuracy of the algorithm,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising means for storing the collected data in a database and for cleaning up duplicate data.

[0782] (Claim 3)

[0783] The system according to claim 1, wherein the means for analyzing the user's behavior patterns further includes means for using a machine learning algorithm.

[0784] "Example 1"

[0785] (Claim 1)

[0786] A means of collecting user data from multiple information provision means,

[0787] A means of integrating collected data to generate a single user information record,

[0788] A means of analyzing user behavior patterns and generating optimal suggestions,

[0789] A means for displaying the generated proposal on an information display device,

[0790] A means to collect user selection information again and improve the accuracy of the prediction method,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, further comprising means for storing the collected data in a storage device and for organizing duplicate data.

[0794] (Claim 3)

[0795] The system according to claim 1, wherein the means for analyzing the user's behavior pattern further includes means for using a learning algorithm.

[0796] "Application Example 1"

[0797] (Claim 1)

[0798] Means for collecting user data from multiple sources,

[0799] A means of integrating collected data to generate a single user profile,

[0800] A means of analyzing user behavioral characteristics and generating optimal suggestions,

[0801] A means of displaying the generated suggestions in the user interface,

[0802] A means of collecting user selection information again to improve the accuracy of the learning algorithm,

[0803] A method for analyzing product information and suggesting products that match the user's preferences,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for storing the collected data in an information management system and removing duplicate data.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the means for analyzing the user's behavioral characteristics further includes means for using machine learning techniques.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] A means for collecting user information from multiple information providing devices,

[0812] A means of integrating the collected information and generating a single user profile,

[0813] A means of analyzing a user's emotional state using an emotion analysis device,

[0814] A means for generating optimal suggestions based on the user's behavioral patterns and emotional state,

[0815] A means for displaying the generated proposal on an output device that can be operated by the user,

[0816] A means to collect user selection information again and improve the accuracy of the inference algorithm,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising means for storing the collected information in a recording device and removing duplicate information.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the means for analyzing the user's behavioral patterns and emotional state further includes means for using a learning algorithm.

[0822] "Application example 2 when combining with an emotional engine"

[0823] (Claim 1)

[0824] A means of collecting user data from multiple electronic services,

[0825] A means of integrating collected data to generate a single user profile,

[0826] A means for analyzing a user's facial expressions, voice, and text input to estimate their emotional state,

[0827] A means of suggesting products that correspond to an estimated emotional state,

[0828] A means of displaying the generated suggestions on the user's terminal,

[0829] A means to collect user selection data again and improve the accuracy of the algorithm,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising means for storing the collected data in a database and for cleaning up duplicate data.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the means for analyzing the user's behavioral patterns and emotional state further includes means for using a machine learning algorithm. [Explanation of symbols]

[0835] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting user data from multiple electronic services, A means of integrating collected data to generate a single user profile, A means of analyzing user behavior patterns and generating optimal suggestions, A means of displaying the generated suggestions on the user's terminal, A means to collect user selection data again and improve the accuracy of the algorithm, A system that includes this.

2. The system according to claim 1, further comprising means for storing the collected data in a database and for cleaning up duplicate data.

3. The system according to claim 1, wherein the means for analyzing the user's behavioral patterns further includes means for using a machine learning algorithm.

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