system

Abstract

We provide the system. [Solution] A means for acquiring visual and auditory information from the physical environment using an information display device worn by the user, and converting that information into digital text, A means for acquiring location information and usage history from a mobile communication terminal and storing that information as data, A means for integrating the aforementioned digital text information and the aforementioned usage history data and analyzing the user's preference patterns, A means of generating personalized information based on the analysis results and notifying the user, A means of collecting product information in the vicinity based on the user's location within a physical store, and recommending products related to the user's past purchase history and preference patterns, A means of aggregating user evaluation information and distributing rewards to information providers, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, while users are surrounded by a vast amount of information, there is a problem that it is difficult to efficiently obtain information useful for themselves. In addition, information providers and companies are spending a lot of resources because they have limited means to deliver information to the target effectively. In such a situation, there is a demand for a system that provides appropriate information to each user at an appropriate timing and improves the efficiency of information transmission.

Means for Solving the Problems

[0005] This invention provides a means for acquiring visual and auditory information from the physical environment using an information display device worn by the user, and converting that information into digital text. It also includes means for acquiring location information and usage history from a mobile communication device and storing that information as data. Furthermore, it includes means for integrating this digital text information and usage history data to analyze the user's preference patterns. Moreover, it provides means for generating personalized information based on the analysis results and notifying the user. In addition, it includes means for aggregating evaluation information from users and distributing rewards to information providers. Through this system, it is possible to provide information at a timing appropriate to the user and to promote information provision by rewarding information providers.

[0006] An "information presentation device" is a device worn by a user to acquire visual and auditory information from the physical environment, and smart glasses are an example of such a device.

[0007] "Digital text" refers to text data that is created by converting visual and auditory information acquired from a physical environment into written information, saving it in a digital format, and using it for later analysis and notification.

[0008] A "mobile communication device" refers to a device that a user carries with them at all times and that has communication capabilities, such as a smartphone or other mobile device.

[0009] "Location information" refers to data that indicates the user's current location, and is geographical information obtained using GPS or other location measurement technologies.

[0010] "Usage history" refers to records related to a user's digital activities, such as app usage on a mobile communication device and website access history.

[0011] "Preference patterns" refer to the tendencies and characteristics that a user is predicted to like, based on their past behavior and interests.

[0012] "Analysis means" refers to a technology or device that uses acquired digital information to evaluate and analyze user preference patterns and perform processing to provide highly relevant information.

[0013] "Personalized information" refers to recommendations or advertisements that are tailored to the user's individual preferences and current context, making them the most relevant to them.

[0014] A "means of distributing rewards" is a system that provides monetary incentives or other rewards to information providers and related developers based on user evaluation information. [Brief explanation of the drawing]

[0015] [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] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] 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.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention relates to a system in which a user wears an information display device and converts visual and auditory information obtained from the physical environment into digital text. This information display device is implemented, for example, as smart glasses and has the function of automatically capturing surrounding signs and audio guidance and converting them into text.

[0037] Furthermore, mobile communication devices, such as smartphones, acquire the user's location information through GPS sensors and record a history of the user's digital activities, including website and application usage. This information is received and integrated by a server and used as a dataset to analyze the user's preference patterns.

[0038] The server uses machine learning algorithms to analyze user preferences from integrated data. For example, if a user has a history of frequently visiting a particular restaurant, it can prioritize displaying this information when it finds new promotions related to that restaurant.

[0039] Once the analysis is complete, the server generates personalized recommendations and sends a notification to the user's mobile device or information display device. The notification may include links to relevant information and call to action to help the user take action.

[0040] When a user receives a notification and highly rates its content, the server compiles these ratings and distributes rewards to the relevant parties and developers who provided the information. This reward system further encourages information sharing and makes it possible to build an ecosystem where diverse information circulates.

[0041] This format allows users to receive useful information at the right time in their daily lives, while information providers can achieve both the promotion of information circulation in the market and the acquisition of compensation.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The terminal (information display device) uses its built-in camera and microphone to capture information that the user sees and hears while on the move. The captured images and audio data are analyzed in real time and converted into digital text using OCR and speech recognition technologies.

[0045] Step 2:

[0046] The device (mobile communication device) continuously records location information using GPS, and also collects app usage data and browsing history. This data is temporarily stored in local storage for later analysis.

[0047] Step 3:

[0048] The terminals (information display devices and mobile communication devices) periodically transmit collected digital text and usage history data to the server. The transmitted data is encrypted in accordance with security protocols.

[0049] Step 4:

[0050] The server integrates all received data and updates each user's database. The integrated data is then input into a machine learning model to analyze user preference patterns.

[0051] Step 5:

[0052] Based on the analysis results, the server generates personalized information and advertisements tailored to each user. The generated information is sent as push notifications at the appropriate time based on the user's location.

[0053] Step 6:

[0054] The device (mobile communication device or information display device) receives push notifications from the server and makes them visible to the user. The notified information includes relevant links and buttons prompting action.

[0055] Step 7:

[0056] Users review the notified information and evaluate its usefulness. This evaluation is then sent to the server via the platform.

[0057] Step 8:

[0058] The server analyzes the aggregated evaluation data and calculates rewards for information providers or application developers who receive high ratings. Rewards are distributed based on predetermined criteria and notified to the providers.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, users are exposed to a vast amount of information, making it difficult to receive useful information at the right time in their daily lives. Therefore, there is a need to provide personalized information based on user preferences and location to reduce stress caused by information overload. Furthermore, there is a lack of adequate incentives for information providers, resulting in insufficient distribution of high-quality information.

[0062] 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.

[0063] In this invention, the server includes means for acquiring visual and audio data from the external environment using an information display device worn by the user and converting that data into text information; means for acquiring location data and digital activity history from a mobile terminal and storing that data; and means for integrating the text information and the digital activity history and analyzing the user's preference patterns. This makes it possible to provide useful information to the user in a timely manner and to distribute appropriate rewards to information providers.

[0064] A "user" is a person who receives information, is the entity that wears an information display device, and uses a mobile terminal.

[0065] An "information display device" is a device that acquires visual and auditory data from the external environment and converts it into digital text.

[0066] A "mobile device" is a communication-enabled device carried by a user, and is a device used to acquire location data and digital activity history.

[0067] "Location data" refers to information that indicates the user's current location using GPS or other technologies.

[0068] "Digital activity history" refers to data that shows a user's online behavior history, such as records of applications used and websites visited.

[0069] "Preference patterns" refer to information that indicates preferences and tendencies derived from a user's behavior and tastes.

[0070] "Analysis means" refers to technical methods and devices used to analyze user data and identify their behavior and preferences.

[0071] A "notification means" is a function or device used to transmit information or messages to a user.

[0072] "Reward distribution" refers to the process of providing fair compensation to information providers and other stakeholders.

[0073] This invention is a system that uses an information display device worn by the user and a mobile terminal to collect information from the physical environment and digital activity areas, and to provide the user with personalized information.

[0074] The user wears an information display device such as smart glasses, captures visual information of the surroundings with a built-in camera, and records voice guidance with a built-in microphone. This information is sent to a cloud-based character recognition service via a data transmission function and converted into text information.

[0075] The smartphone, as the device, uses its GPS sensor to obtain the user's location data and also records the history of installed applications and visited websites. This information is sent to a server at regular intervals and stored in a database.

[0076] The server centrally manages all received data and uses machine learning models to analyze user preference patterns. For example, the server uses Python®-based libraries to perform data analysis and extract user behavioral trends. Based on the analysis results, the server determines the information and presentation priorities that are valuable to the user and sends this information to the user as a notification.

[0077] For example, if a user frequently visits a particular location, the generative AI model may suggest new promotional information related to that location. For instance, a prompt such as, "Explain how to recommend new promotional information based on the user's past behavioral data," can be used to provide specific analytical instructions to the AI ​​model.

[0078] In this way, an ecosystem is built where users can receive important information in their daily lives at the right time, and information providers are rewarded according to their contributions.

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

[0080] Step 1:

[0081] The user wears an information display device, capturing surrounding visual information with its built-in camera and recording voice guidance with its built-in microphone. Visual and audio data are input, which is temporarily stored within the device. This data is then transmitted to a cloud service via a communication module. The output here is the transmitted data itself.

[0082] Step 2:

[0083] The server acquires visual and audio data received from cloud services. The input data consists of raw visual images and audio files. The server extracts text from the visual data using character recognition software and converts the audio data to text using speech recognition software. The output is the converted text information.

[0084] Step 3:

[0085] The device uses a GPS sensor to determine the user's location. The input is obtained as the current location information. The device also retrieves the usage history of simultaneously installed applications and website visit history from a local database. This data forms the user's activity history and is sent to the server. The output is the user's location data and activity history.

[0086] Step 4:

[0087] The server integrates text information, location data, and behavioral history and stores them in a database. The input is all data collected to date. The server runs machine learning algorithms to analyze user preference patterns. This analysis is used to identify promotions and information that users prefer. The output is the user's personalized preference pattern.

[0088] Step 5:

[0089] The server generates personalized information based on the analysis results. The input is the user's preference patterns. Specifically, it selects information and suggestions that are likely to interest the user and prepares them as notification messages. The output is the generated notification data.

[0090] Step 6:

[0091] The server sends the generated notification to the terminal or information display device. The input is the prepared notification data. The terminal receives this and displays the information to the user. The output is the notification information displayed to the user.

[0092] Step 7:

[0093] The user reviews the notification and provides an evaluation. The input is the displayed notification information. The user sends the evaluation information to the server via their device. The server aggregates these evaluations and distributes appropriate rewards to the information providers. The output is the aggregated evaluation information and the results of the reward distribution.

[0094] (Application Example 1)

[0095] 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."

[0096] Modern consumers demand faster and more appropriate purchasing decisions from a diverse range of product options. However, traditional information systems have struggled to provide personalized recommendations based on individual consumer preferences and past purchase history in real time within physical stores, posing a challenge to improving the shopping experience. Furthermore, there has been a lack of mechanisms to appropriately reflect consumer evaluations of the information received and provide feedback to information providers.

[0097] 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.

[0098] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting it into digital text; means for acquiring location information and usage history from a mobile communication terminal and storing it as data; and means for collecting surrounding product information based on the user's location within a physical store and recommending products related to the user's past purchase history and preference patterns. This enables consumers to receive personalized product information in real time, improving their shopping experience. Furthermore, appropriate feedback and reward distribution are provided to information providers through consumer evaluations, promoting the flow of information in the market.

[0099] An "information presentation device" is a device worn by a user that converts visual and auditory information acquired from the physical environment into digital text.

[0100] A "mobile communication terminal" is a communication device that acquires a user's location information and usage history and stores this information as data.

[0101] "Location information" refers to data that indicates the user's current location when visiting a physical store.

[0102] "Usage history" refers to a record of a user's past digital activities and purchasing behavior.

[0103] A "preference pattern" is the result of an analysis of a user's preferences and tendencies based on their past behavioral data.

[0104] "Product information" refers to data about the details and characteristics of products available in a physical store.

[0105] "Recommendations" refer to information about products and services suggested to users based on their preference patterns.

[0106] "Evaluation information" refers to feedback data that shows users' satisfaction levels and opinions regarding the information they received.

[0107] "Reward distribution" refers to the allocation of rewards to information providers based on user evaluation information.

[0108] The system for carrying out this invention includes an information display device such as smart glasses, a mobile communication terminal such as a smartphone, and a server for processing this data. The information display device can capture surrounding visual and auditory information using a camera and microphone and convert it into digital text. Specifically, it uses an image and speech recognition API to convert environmental information into text and generate information that reflects the user's current situation.

[0109] Mobile communication devices utilize GPS sensors to acquire user location information and store this data along with past usage history. This information is transmitted to a server and analyzed using machine learning models. Machine learning libraries such as TENSORFLOW® are used in this process to perform analysis based on user preference patterns.

[0110] The server provides real-time relevant product information within physical stores based on the user's location and preference data. For example, when a user wearing smart glasses moves around a fashion store, the device identifies specific items through its camera. This information is then used to recommend products that match the user's preferences and history.

[0111] Furthermore, if a user rates the recommended information, the system collects those ratings and distributes the rewards to the information providers. This promotes information flow and builds an ecosystem. The prompt message to the generated AI model is something like, "Based on the product information scanned by the user in the physical store they are visiting, please generate offers tailored to their past purchase history and preference data."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user acquires visual and auditory information from the physical environment through the camera and microphone of the smart glasses. The input consists of camera video and audio signals, which are captured by the information display device. The information display device uses image recognition APIs and speech recognition APIs to convert the visual and auditory information into digital text and generate the output.

[0115] Step 2:

[0116] The device uses a GPS sensor to obtain the user's current location information. The input is location sensor data, which the device analyzes to determine the user's current location. In addition, it retrieves past usage history data from a database and sends it to the server as output along with the location information.

[0117] Step 3:

[0118] The server integrates received visual, auditory, location, and usage history data to form a dataset. The input is the output dataset from the previous step. The server uses a machine learning model to analyze user preference patterns and generate product recommendations. A generative AI model then outputs personalized product information based on these analysis results.

[0119] Step 4:

[0120] The server transmits the generated product recommendation information to the information display device, notifying the user in real time. The input is personalized information generated by the server, and the output is provided as the display on the information display device.

[0121] Step 5:

[0122] The user evaluates the presented information and sends the evaluation data to the server via their terminal. The input is user feedback data. The server aggregates this evaluation information, calculates and distributes rewards to the relevant information providers. The output is provided as a reward distribution list.

[0123] 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.

[0124] This invention is a system that uses an information display device and a mobile communication device worn by the user to digitize information obtained from the physical environment, and further analyzes the user's emotional state by combining it with an emotion engine.

[0125] The information display device is equipped with a camera and microphone, capturing information that the user sees and hears in real time. This information is converted into digital text using OCR and speech recognition technologies. In addition, an emotion engine is used to extract emotional data based on the user's visual and auditory perception from the captured data. This emotion engine can identify emotions using a combination of algorithms, including facial expression recognition and voice tone analysis.

[0126] Mobile communication devices acquire location information using GPS sensors and record the user's app usage history and internet activity history. This information is periodically sent to a server and stored in a database. The server integrates the collected digital text, sentiment data, location, and usage history, and analyzes the user's preference patterns using machine learning models.

[0127] Based on analyzed preference patterns and emotional data, the server generates personalized information for the user. For example, if a user is feeling stressed in a particular location, it can notify them of nearby relaxation spots or cafes. Conversely, when a user is feeling joy or excitement, it can provide information about events or offers that amplify those emotions.

[0128] The generated information is sent to a mobile communication device or information display device. Users can take action based on the notification and provide feedback to the server as an evaluation of the result. The server aggregates this evaluation data and distributes rewards to information providers and app developers who receive high ratings.

[0129] This invention aims to improve user satisfaction by providing information that resonates with users' emotions and offering a more personalized experience. This approach allows users to receive optimal information tailored to their current emotional state, while information providers can build a system that increases their market influence and earns rewards.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The terminal (information display device) uses its built-in camera and microphone to acquire the user's visual and auditory information. The acquired data is converted into digital text using OCR technology and speech recognition technology.

[0133] Step 2:

[0134] The terminal (information display device) uses an emotion engine to analyze the user's emotions from acquired visual and auditory information. This includes facial expression recognition processing to capture changes in facial expressions and voice analysis processing to analyze the tone of voice.

[0135] Step 3:

[0136] The device (mobile communication device) uses GPS to obtain the user's current location and records app usage history and web activity history. This information is encrypted and transmitted to the server based on appropriate security protocols, with privacy in mind.

[0137] Step 4:

[0138] The server integrates digital text, sentiment data, location information, and usage history sent from the terminal. Based on this, machine learning models are used to analyze the individual user's preference patterns and sentiment tendencies.

[0139] Step 5:

[0140] Based on the analysis results, the server generates personalized information that is most relevant to the user. For example, if it detects that the user is feeling tired or stressed, it will recommend information about nearby relaxation facilities or cafes.

[0141] Step 6:

[0142] The server sends the generated personalized information as a push notification to the user's mobile device or information display device. The notification may include details about the suggested facilities or coupons.

[0143] Step 7:

[0144] The user considers their actions based on the received notification and evaluates the usefulness of the notification content. The evaluation results are then sent back to the server via the device.

[0145] Step 8:

[0146] The server collects user rating data and distributes rewards to information providers and content creators who receive high ratings. This reward system provides information providers with an incentive to offer more comprehensive information.

[0147] (Example 2)

[0148] 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".

[0149] Modern information display devices and mobile communication devices do not adequately consider the emotional state and preferences of individual users when providing information. As a result, the information provided may not match the user's current situation or mood, leading to decreased user satisfaction.

[0150] 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.

[0151] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting the information into digital text; means for using a composite algorithm to analyze emotional data from the extracted visual and auditory information; and means for generating appropriate information based on the analysis results using a generative AI model and notifying the user. This makes it possible to provide personalized information according to the user's emotional state and preferences.

[0152] An "information display device" is a device worn by the user to acquire visual and auditory information from the physical environment.

[0153] "Digital text" refers to visual and auditory information that has been electronically converted and expressed as character data.

[0154] "Emotional data" refers to data extracted from visual and auditory information that indicates the user's emotional state.

[0155] A "combined algorithm" is an algorithm that combines multiple methods and techniques used to analyze visual and auditory information.

[0156] A "mobile communication device" is a portable communication device used to acquire a user's location data and usage history.

[0157] A "preference pattern" is a data pattern that indicates a user's preferences and behavioral tendencies.

[0158] A "generative AI model" is an artificial intelligence model that generates information based on analyzed data and provides suggestions tailored to the user.

[0159] "Feedback information" refers to information about users' usage results and evaluations.

[0160] In order to implement this invention, it is necessary to use an information display device, a portable communication device, and a server in conjunction with each other.

[0161] The user wears an information display device that acquires visual and auditory information from the physical environment in real time. This information display device is equipped with a camera and microphone to capture the user's visual and auditory experiences. The acquired visual information is converted into digital text using optical character recognition (OCR) technology, and the auditory information is transcribed into text using speech recognition technology. In addition, a composite algorithm is applied to analyze the content of the video and audio and extract emotional data. In this process, facial expression recognition and voice tone analysis are performed to identify the user's emotional state.

[0162] The device utilizes GPS functionality via a mobile communication device to acquire the user's location data. It also records the user's application usage history and internet activity, transferring this data to a database. This information is aggregated on a server and used to understand the user's behavior patterns and preferences.

[0163] The server integrates accumulated digital text, sentiment data, location information, and usage history, and uses a generative AI model to analyze user preference patterns. This analysis generates personalized information based on the user's emotional state and behavioral characteristics.

[0164] For example, if a user feels fatigued while hiking, this emotional data can be used to provide information about the nearest rest stop. Furthermore, when a user visits a new area, it's possible to suggest recommended tourist spots and restaurants based on their past preferences.

[0165] An example of a prompt sentence for input to the generating AI model is, "Please tell me some recommended relaxation spots that the user should visit." In this way, the aim of the present invention is to provide valuable information to the user and improve their satisfaction.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The user wears an information display device and acquires visual and auditory information from the physical environment. Video data from the camera and audio data from the microphone are used as input. This information is saved to the device as video and audio files as output. Specifically, the camera and microphone are always active, capturing data in real time.

[0169] Step 2:

[0170] The terminal converts the video information saved in Step 1 into digital text using OCR technology. The input is video data, which is processed by optical character recognition technology. The output is the content digitized as text information. Specifically, the process involves detecting text areas in the video and extracting the text.

[0171] Step 3:

[0172] The device applies speech recognition technology to stored audio data to convert speech into text. The input is audio data, which is processed using a speech analysis algorithm. The output is the speech recognition result in text format. Specifically, this process involves analyzing the audio waveform and converting it into a format understandable as human language.

[0173] Step 4:

[0174] The device uses a complex algorithm to analyze video and audio data extracted from visual data and extract emotional data. Input consists of digital text and voice tone data, and an emotion analysis algorithm is used. Output is emotional data indicating the user's emotional state. Specifically, it evaluates emotions through micro-expression analysis using facial recognition and changes in voice tone.

[0175] Step 5:

[0176] The device uses a mobile communication device to obtain location data from a GPS sensor. It also records application usage history and internet browsing history. Inputs are location information and history data, and it performs location acquisition and generates history logs. Outputs are user location data and activity history data. Specifically, it periodically updates location information and accumulates history data.

[0177] Step 6:

[0178] The server integrates digital text, sentiment data, location information, and usage history sent from the terminal. Input is informational data from the terminal, stored in a database. Output is an integrated dataset suitable for analysis. Specifically, data cleaning and standardization of the data structure are performed.

[0179] Step 7:

[0180] The server analyzes integrated data using a generative AI model to predict user preference patterns. The input is an integrated dataset, and the AI ​​model performs data analysis. The output is recommendation information based on user preferences. Specifically, it extracts preference patterns from the data and generates appropriate suggestions based on the results.

[0181] Step 8:

[0182] The server generates personalized information based on the user's emotional state and preference patterns, and notifies the terminal. The input is the analyzed preference patterns, and an information generation algorithm is used. The output is adaptive information notified to the user. Specifically, the notification content is customized and sent to the user's terminal.

[0183] (Application Example 2)

[0184] 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".

[0185] In modern living environments, it is difficult to automatically adjust the home environment according to the user's emotional state. In particular, the lack of control and optimization of electronic devices based on the user's emotions poses a challenge in adequately reducing stress and providing a relaxing space. Furthermore, if such automatic adjustments to the home environment were possible, users could live a more comfortable life.

[0186] 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.

[0187] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting that information into digital text; means for acquiring location information and usage history from a mobile communication device and storing that information as data; and means for controlling surrounding electronic devices to optimize the environment based on analyzed user emotion data. This makes it possible to improve the home environment in accordance with the user's emotions.

[0188] An "information presentation device" is a device worn by a user to acquire visual and auditory information from the physical environment.

[0189] "Digital text" refers to visual and auditory information that has been converted into digital data and made into an analyzable format.

[0190] A "mobile communication device" is a portable electronic device that has the function of acquiring location information and usage history and storing it as data.

[0191] "Preference patterns" refer to data that shows an individual's preferences and habits, analyzed based on user behavior and emotions.

[0192] "Controlling peripheral electronic devices based on emotional data" is a process that analyzes the user's emotional state and adjusts electronic devices within the home accordingly.

[0193] "Personalized information" refers to information that is individually tailored to a user's preferences and emotional state.

[0194] "Evaluation information" refers to feedback provided by users regarding the information and controls they have been given, and is used to improve the system.

[0195] To implement this invention, it is necessary to construct a system that utilizes an information display device worn by the user and a portable communication device. In this system, the camera and microphone mounted on the information display device are used to collect the user's visual and auditory information. This information is converted into digital text in real time. Optical character recognition (OCR) technology and speech recognition technology are used for this conversion. Furthermore, the portable communication device acquires the user's location information using a GPS sensor and collects usage history.

[0196] The server integrates the collected information and uses machine learning algorithms to analyze user preference patterns. Based on the analyzed data, the server determines the user's emotional state and generates commands to control surrounding electronic devices. These controlled devices include lighting, sound systems, and temperature control devices. Smart hobs using Raspberry Pi or Arduino are used as the platform.

[0197] For example, if a user wants to relax at home, the system analyzes the user's emotional data and instructs the system to change the room lighting to a warmer color and play relaxation music. This entire process is automated, reducing the user's burden.

[0198] Examples of prompts include, "How would you optimize the home environment when the user is tired after returning home?" and "Based on the user's current emotions, suggest actions to promote relaxation." This allows the system to utilize generative AI models to derive even more optimal solutions.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The information display device captures the user's visual and auditory information using a camera and microphone. The input is real-time collected audio and video data, and the output is a digitized version of this data. The data is then converted to text using OCR and speech recognition technologies.

[0202] Step 2:

[0203] The mobile communication device acquires the user's location information using a GPS sensor and also records usage history data. The input is location data and app usage history, and the output is the storage of these datasets into a database.

[0204] Step 3:

[0205] The server integrates digital text, location information, and usage history data. The input is the data obtained in steps 1 and 2, and the output is the data analyzed as user preference patterns. Machine learning algorithms are used to analyze the data patterns.

[0206] Step 4:

[0207] The server analyzes the data to determine the user's emotional state and generates real-time control commands for corresponding electronic devices. The input is the analyzed emotional data, and the output is a list of electronic devices to be controlled and the commands for each device.

[0208] Step 5:

[0209] The terminal receives control commands from the server and controls surrounding electronic devices. For example, it can adjust lighting colors or activate sound systems. The input is the command from the server, and the output is the action that optimizes the user's environment.

[0210] Step 6:

[0211] Users can experience an automatically optimized environment and provide feedback under those conditions. The input is the optimized environment, and the output is the user's feedback. This feedback is sent to the server and used for future improvements.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention relates to a system in which a user wears an information display device and converts visual and auditory information obtained from the physical environment into digital text. This information display device is implemented, for example, as smart glasses and has the function of automatically capturing surrounding signs and audio guidance and converting them into text.

[0229] Furthermore, mobile communication devices, such as smartphones, acquire the user's location information through GPS sensors and record a history of the user's digital activities, including website and application usage. This information is received and integrated by a server and used as a dataset to analyze the user's preference patterns.

[0230] The server uses machine learning algorithms to analyze user preferences from integrated data. For example, if a user has a history of frequently visiting a particular restaurant, it can prioritize displaying this information when it finds new promotions related to that restaurant.

[0231] Once the analysis is complete, the server generates personalized recommendations and sends a notification to the user's mobile device or information display device. The notification may include links to relevant information and call to action to help the user take action.

[0232] When a user receives a notification and highly rates its content, the server compiles these ratings and distributes rewards to the relevant parties and developers who provided the information. This reward system further encourages information sharing and makes it possible to build an ecosystem where diverse information circulates.

[0233] This format allows users to receive useful information at the right time in their daily lives, while information providers can achieve both the promotion of information circulation in the market and the acquisition of compensation.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The terminal (information display device) uses its built-in camera and microphone to capture information that the user sees and hears while on the move. The captured images and audio data are analyzed in real time and converted into digital text using OCR and speech recognition technologies.

[0237] Step 2:

[0238] The device (mobile communication device) continuously records location information using GPS, and also collects app usage data and browsing history. This data is temporarily stored in local storage for later analysis.

[0239] Step 3:

[0240] The terminals (information display devices and mobile communication devices) periodically transmit collected digital text and usage history data to the server. The transmitted data is encrypted in accordance with security protocols.

[0241] Step 4:

[0242] The server integrates all received data and updates each user's database. The integrated data is then input into a machine learning model to analyze user preference patterns.

[0243] Step 5:

[0244] Based on the analysis results, the server generates personalized information and advertisements tailored to each user. The generated information is sent as push notifications at the appropriate time based on the user's location.

[0245] Step 6:

[0246] The device (mobile communication device or information display device) receives push notifications from the server and makes them visible to the user. The notified information includes relevant links and buttons prompting action.

[0247] Step 7:

[0248] Users review the notified information and evaluate its usefulness. This evaluation is then sent to the server via the platform.

[0249] Step 8:

[0250] The server analyzes the aggregated evaluation data and calculates rewards for information providers or application developers who receive high ratings. Rewards are distributed based on predetermined criteria and notified to the providers.

[0251] (Example 1)

[0252] 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."

[0253] In modern society, users are exposed to a vast amount of information, making it difficult to receive useful information at the right time in their daily lives. Therefore, there is a need to provide personalized information based on user preferences and location to reduce stress caused by information overload. Furthermore, there is a lack of adequate incentives for information providers, resulting in insufficient distribution of high-quality information.

[0254] 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.

[0255] In this invention, the server includes means for acquiring visual and audio data from the external environment using an information display device worn by the user and converting that data into text information; means for acquiring location data and digital activity history from a mobile terminal and storing that data; and means for integrating the text information and the digital activity history and analyzing the user's preference patterns. This makes it possible to provide useful information to the user in a timely manner and to distribute appropriate rewards to information providers.

[0256] A "user" is a person who receives information, is the entity that wears an information display device, and uses a mobile terminal.

[0257] An "information display device" is a device that acquires visual and auditory data from the external environment and converts it into digital text.

[0258] A "mobile device" is a communication-enabled device carried by a user, and is a device used to acquire location data and digital activity history.

[0259] "Location data" refers to information that indicates the user's current location using GPS or other technologies.

[0260] "Digital activity history" refers to data that shows a user's online behavior history, such as records of applications used and websites visited.

[0261] "Preference patterns" refer to information that indicates preferences and tendencies derived from a user's behavior and tastes.

[0262] "Analysis means" refers to technical methods and devices used to analyze user data and identify their behavior and preferences.

[0263] A "notification means" is a function or device used to transmit information or messages to a user.

[0264] "Reward distribution" refers to the process of providing fair compensation to information providers and other stakeholders.

[0265] This invention is a system that uses an information display device worn by the user and a mobile terminal to collect information from the physical environment and digital activity areas, and to provide the user with personalized information.

[0266] The user wears an information display device such as smart glasses, captures visual information of the surroundings with a built-in camera, and records voice guidance with a built-in microphone. This information is sent to a cloud-based character recognition service via a data transmission function and converted into text information.

[0267] The smartphone, as the device, uses its GPS sensor to obtain the user's location data and also records the history of installed applications and visited websites. This information is sent to a server at regular intervals and stored in a database.

[0268] The server centrally manages all received data and uses machine learning models to analyze user preference patterns. For example, the server uses Python-based libraries to perform data analysis and extract user behavioral trends. Based on the analysis results, the server determines the information and presentation priorities that are valuable to the user and sends this information to the user as a notification.

[0269] For example, if a user frequently visits a particular location, the generative AI model may suggest new promotional information related to that location. For instance, a prompt such as, "Explain how to recommend new promotional information based on the user's past behavioral data," can be used to provide specific analytical instructions to the AI ​​model.

[0270] In this way, an ecosystem is built where users can receive important information in their daily lives at the right time, and information providers are rewarded according to their contributions.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user wears an information display device, capturing surrounding visual information with its built-in camera and recording voice guidance with its built-in microphone. Visual and audio data are input, which is temporarily stored within the device. This data is then transmitted to a cloud service via a communication module. The output here is the transmitted data itself.

[0274] Step 2:

[0275] The server acquires visual and audio data received from cloud services. The input data consists of raw visual images and audio files. The server extracts text from the visual data using character recognition software and converts the audio data to text using speech recognition software. The output is the converted text information.

[0276] Step 3:

[0277] The device uses a GPS sensor to determine the user's location. The input is obtained as the current location information. The device also retrieves the usage history of simultaneously installed applications and website visit history from a local database. This data forms the user's activity history and is sent to the server. The output is the user's location data and activity history.

[0278] Step 4:

[0279] The server integrates text information, location data, and behavioral history and stores them in a database. The input is all data collected to date. The server runs machine learning algorithms to analyze user preference patterns. This analysis is used to identify promotions and information that users prefer. The output is the user's personalized preference pattern.

[0280] Step 5:

[0281] The server generates individualized information based on the analysis results. The input is the user's preference pattern. Specifically, it selects information and proposals that the user is likely to be interested in and prepares them as notification messages. The output is the generated notification data.

[0282] Step 6:

[0283] The server transmits the generated notification to the terminal or information display device. The input is the prepared notification data. The terminal receives this and displays the information to the user. The output is the notification information displayed to the user.

[0284] Step 7:

[0285] The user checks the notification and makes an evaluation. The input is the displayed notification information. The user transmits the evaluation information to the server through the terminal. The server aggregates these evaluations and distributes appropriate rewards to the information providers. The output is the aggregated evaluation information and the result of the reward distribution.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] Modern consumers are seeking to make purchasing decisions more quickly and appropriately for the diverse product options. However, in conventional information - providing systems, it has been difficult to provide personalized proposals based on individual consumers' preferences and past purchase histories in real - time within a physical store, and there have been challenges in improving the purchasing experience. Also, there has been a lack of a mechanism to appropriately reflect the evaluation of the information received by consumers and provide feedback to the information providers.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting it into digital text; means for acquiring location information and usage history from a mobile communication terminal and storing it as data; and means for collecting surrounding product information based on the user's location within a physical store and recommending products related to the user's past purchase history and preference patterns. This enables consumers to receive personalized product information in real time, improving their shopping experience. Furthermore, appropriate feedback and reward distribution are provided to information providers through consumer evaluations, promoting the flow of information in the market.

[0291] An "information presentation device" is a device worn by a user that converts visual and auditory information acquired from the physical environment into digital text.

[0292] A "mobile communication terminal" is a communication device that acquires a user's location information and usage history and stores this information as data.

[0293] "Location information" refers to data that indicates the user's current location when visiting a physical store.

[0294] "Usage history" refers to a record of a user's past digital activities and purchasing behavior.

[0295] A "preference pattern" is the result of an analysis of a user's preferences and tendencies based on their past behavioral data.

[0296] "Product information" refers to data about the details and characteristics of products available in a physical store.

[0297] "Recommendations" refer to information about products and services suggested to users based on their preference patterns.

[0298] "Evaluation information" refers to feedback data that shows users' satisfaction levels and opinions regarding the information they received.

[0299] "Reward distribution" refers to the allocation of rewards to information providers based on user evaluation information.

[0300] The system for carrying out this invention includes an information display device such as smart glasses, a mobile communication terminal such as a smartphone, and a server for processing this data. The information display device can capture surrounding visual and auditory information using a camera and microphone and convert it into digital text. Specifically, it uses an image and speech recognition API to convert environmental information into text and generate information that reflects the user's current situation.

[0301] Mobile communication devices use GPS sensors to acquire user location information and store this data along with past usage history. This information is sent to a server and analyzed using machine learning models. Machine learning libraries such as TensorFlow are used in this process to perform analysis based on user preference patterns.

[0302] The server provides real-time relevant product information within physical stores based on the user's location and preference data. For example, when a user wearing smart glasses moves around a fashion store, the device identifies specific items through its camera. This information is then used to recommend products that match the user's preferences and history.

[0303] Furthermore, if a user rates the recommended information, the system collects those ratings and distributes the rewards to the information providers. This promotes information flow and builds an ecosystem. The prompt message to the generated AI model is something like, "Based on the product information scanned by the user in the physical store they are visiting, please generate offers tailored to their past purchase history and preference data."

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The user obtains visual information and auditory information from the physical environment through the camera and microphone of the smart glasses. The input is the camera video and the audio signal, which are captured by the information presentation device. The information presentation device uses the image recognition API and the speech recognition API to convert the visual and auditory information into digital text and generate its output.

[0307] Step 2:

[0308] The terminal uses the GPS sensor to obtain the user's current location information. The input is the position sensor data, which is analyzed by the terminal to identify the user's current location. In addition, the past usage history data is retrieved from the database and sent to the server as output together with the location information.

[0309] Step 3:

[0310] The server integrates the received visual information, auditory information, location information, and usage history to form a dataset. The input is the output dataset from the previous step. The server uses a machine learning model to analyze the user's preference pattern and generate product recommendation information. Based on these analysis results, the generated AI model outputs individualized product information.

[0311] Step 4:

[0312] The server sends the generated product recommendation information to the information presentation device to notify the user in real time. The input is the personalized information generated by the server, and the display on the information presentation device is provided as the output.

[0313] Step 5:

[0314] The user evaluates the presented information and sends the evaluation data to the server through the terminal. The input is the user's feedback data. The server aggregates this evaluation information and calculates and distributes rewards to the relevant information providers. The output is provided as a reward distribution list.

[0315] 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.

[0316] This invention is a system that uses an information display device and a mobile communication device worn by the user to digitize information obtained from the physical environment, and further analyzes the user's emotional state by combining it with an emotion engine.

[0317] The information display device is equipped with a camera and microphone, capturing information that the user sees and hears in real time. This information is converted into digital text using OCR and speech recognition technologies. In addition, an emotion engine is used to extract emotional data based on the user's visual and auditory perception from the captured data. This emotion engine can identify emotions using a combination of algorithms, including facial expression recognition and voice tone analysis.

[0318] Mobile communication devices acquire location information using GPS sensors and record the user's app usage history and internet activity history. This information is periodically sent to a server and stored in a database. The server integrates the collected digital text, sentiment data, location, and usage history, and analyzes the user's preference patterns using machine learning models.

[0319] Based on analyzed preference patterns and emotional data, the server generates personalized information for the user. For example, if a user is feeling stressed in a particular location, it can notify them of nearby relaxation spots or cafes. Conversely, when a user is feeling joy or excitement, it can provide information about events or offers that amplify those emotions.

[0320] The generated information is sent to a mobile communication device or information display device. Users can take action based on the notification and provide feedback to the server as an evaluation of the result. The server aggregates this evaluation data and distributes rewards to information providers and app developers who receive high ratings.

[0321] This invention aims to improve user satisfaction by providing information that resonates with users' emotions and offering a more personalized experience. This approach allows users to receive optimal information tailored to their current emotional state, while information providers can build a system that increases their market influence and earns rewards.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The terminal (information display device) uses its built-in camera and microphone to acquire the user's visual and auditory information. The acquired data is converted into digital text using OCR technology and speech recognition technology.

[0325] Step 2:

[0326] The terminal (information display device) uses an emotion engine to analyze the user's emotions from acquired visual and auditory information. This includes facial expression recognition processing to capture changes in facial expressions and voice analysis processing to analyze the tone of voice.

[0327] Step 3:

[0328] The device (mobile communication device) uses GPS to obtain the user's current location and records app usage history and web activity history. This information is encrypted and transmitted to the server based on appropriate security protocols, with privacy in mind.

[0329] Step 4:

[0330] The server integrates digital text, sentiment data, location information, and usage history sent from the terminal. Based on this, machine learning models are used to analyze the individual user's preference patterns and sentiment tendencies.

[0331] Step 5:

[0332] Based on the analysis results, the server generates personalized information that is most relevant to the user. For example, if it detects that the user is feeling tired or stressed, it will recommend information about nearby relaxation facilities or cafes.

[0333] Step 6:

[0334] The server sends the generated personalized information as a push notification to the user's mobile device or information display device. The notification may include details about the suggested facilities or coupons.

[0335] Step 7:

[0336] The user considers their actions based on the received notification and evaluates the usefulness of the notification content. The evaluation results are then sent back to the server via the device.

[0337] Step 8:

[0338] The server collects user rating data and distributes rewards to information providers and content creators who receive high ratings. This reward system provides information providers with an incentive to offer more comprehensive information.

[0339] (Example 2)

[0340] 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".

[0341] Modern information display devices and mobile communication devices do not adequately consider the emotional state and preferences of individual users when providing information. As a result, the information provided may not match the user's current situation or mood, leading to decreased user satisfaction.

[0342] 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.

[0343] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting the information into digital text; means for using a composite algorithm to analyze emotional data from the extracted visual and auditory information; and means for generating appropriate information based on the analysis results using a generative AI model and notifying the user. This makes it possible to provide personalized information according to the user's emotional state and preferences.

[0344] An "information display device" is a device worn by the user to acquire visual and auditory information from the physical environment.

[0345] "Digital text" refers to visual and auditory information that has been electronically converted and expressed as character data.

[0346] "Emotional data" refers to data extracted from visual and auditory information that indicates the user's emotional state.

[0347] A "combined algorithm" is an algorithm that combines multiple methods and techniques used to analyze visual and auditory information.

[0348] A "mobile communication device" is a portable communication device used to acquire a user's location data and usage history.

[0349] A "preference pattern" is a data pattern that indicates a user's preferences and behavioral tendencies.

[0350] A "generative AI model" is an artificial intelligence model that generates information based on analyzed data and provides suggestions tailored to the user.

[0351] "Feedback information" refers to information about users' usage results and evaluations.

[0352] In order to implement this invention, it is necessary to use an information display device, a portable communication device, and a server in conjunction with each other.

[0353] The user wears an information display device that acquires visual and auditory information from the physical environment in real time. This information display device is equipped with a camera and microphone to capture the user's visual and auditory experiences. The acquired visual information is converted into digital text using optical character recognition (OCR) technology, and the auditory information is transcribed into text using speech recognition technology. In addition, a composite algorithm is applied to analyze the content of the video and audio and extract emotional data. In this process, facial expression recognition and voice tone analysis are performed to identify the user's emotional state.

[0354] The device utilizes GPS functionality via a mobile communication device to acquire the user's location data. It also records the user's application usage history and internet activity, transferring this data to a database. This information is aggregated on a server and used to understand the user's behavior patterns and preferences.

[0355] The server integrates accumulated digital text, sentiment data, location information, and usage history, and uses a generative AI model to analyze user preference patterns. This analysis generates personalized information based on the user's emotional state and behavioral characteristics.

[0356] For example, if a user feels fatigued while hiking, this emotional data can be used to provide information about the nearest rest stop. Furthermore, when a user visits a new area, it's possible to suggest recommended tourist spots and restaurants based on their past preferences.

[0357] An example of a prompt sentence for input to the generating AI model is, "Please tell me some recommended relaxation spots that the user should visit." In this way, the aim of the present invention is to provide valuable information to the user and improve their satisfaction.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The user wears an information display device and acquires visual and auditory information from the physical environment. Video data from the camera and audio data from the microphone are used as input. This information is saved to the device as video and audio files as output. Specifically, the camera and microphone are always active, capturing data in real time.

[0361] Step 2:

[0362] The terminal converts the video information saved in Step 1 into digital text using OCR technology. The input is video data, which is processed by optical character recognition technology. The output is the content digitized as text information. Specifically, the process involves detecting text areas in the video and extracting the text.

[0363] Step 3:

[0364] The device applies speech recognition technology to stored audio data to convert speech into text. The input is audio data, which is processed using a speech analysis algorithm. The output is the speech recognition result in text format. Specifically, this process involves analyzing the audio waveform and converting it into a format understandable as human language.

[0365] Step 4:

[0366] The device uses a complex algorithm to analyze video and audio data extracted from visual data and extract emotional data. Input consists of digital text and voice tone data, and an emotion analysis algorithm is used. Output is emotional data indicating the user's emotional state. Specifically, it evaluates emotions through micro-expression analysis using facial recognition and changes in voice tone.

[0367] Step 5:

[0368] The device uses a mobile communication device to obtain location data from a GPS sensor. It also records application usage history and internet browsing history. Inputs are location information and history data, and it performs location acquisition and generates history logs. Outputs are user location data and activity history data. Specifically, it periodically updates location information and accumulates history data.

[0369] Step 6:

[0370] The server integrates digital text, sentiment data, location information, and usage history sent from the terminal. Input is informational data from the terminal, stored in a database. Output is an integrated dataset suitable for analysis. Specifically, data cleaning and standardization of the data structure are performed.

[0371] Step 7:

[0372] The server analyzes integrated data using a generative AI model to predict user preference patterns. The input is an integrated dataset, and the AI ​​model performs data analysis. The output is recommendation information based on user preferences. Specifically, it extracts preference patterns from the data and generates appropriate suggestions based on the results.

[0373] Step 8:

[0374] The server generates personalized information based on the user's emotional state and preference patterns, and notifies the terminal. The input is the analyzed preference patterns, and an information generation algorithm is used. The output is adaptive information notified to the user. Specifically, the notification content is customized and sent to the user's terminal.

[0375] (Application Example 2)

[0376] 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."

[0377] In modern living environments, it is difficult to automatically adjust the home environment according to the user's emotional state. In particular, the lack of control and optimization of electronic devices based on the user's emotions poses a challenge in adequately reducing stress and providing a relaxing space. Furthermore, if such automatic adjustments to the home environment were possible, users could live a more comfortable life.

[0378] 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.

[0379] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting that information into digital text; means for acquiring location information and usage history from a mobile communication device and storing that information as data; and means for controlling surrounding electronic devices to optimize the environment based on analyzed user emotion data. This makes it possible to improve the home environment in accordance with the user's emotions.

[0380] An "information presentation device" is a device worn by a user to acquire visual and auditory information from the physical environment.

[0381] "Digital text" refers to visual and auditory information that has been converted into digital data and made into an analyzable format.

[0382] A "mobile communication device" is a portable electronic device that has the function of acquiring location information and usage history and storing it as data.

[0383] "Preference patterns" refer to data that shows an individual's preferences and habits, analyzed based on user behavior and emotions.

[0384] "Controlling peripheral electronic devices based on emotional data" is a process that analyzes the user's emotional state and adjusts electronic devices within the home accordingly.

[0385] "Personalized information" refers to information that is individually tailored to a user's preferences and emotional state.

[0386] "Evaluation information" refers to feedback provided by users regarding the information and controls they have been given, and is used to improve the system.

[0387] To implement this invention, it is necessary to construct a system that utilizes an information display device worn by the user and a portable communication device. In this system, the camera and microphone mounted on the information display device are used to collect the user's visual and auditory information. This information is converted into digital text in real time. Optical character recognition (OCR) technology and speech recognition technology are used for this conversion. Furthermore, the portable communication device acquires the user's location information using a GPS sensor and collects usage history.

[0388] The server integrates the collected information and uses machine learning algorithms to analyze user preference patterns. Based on the analyzed data, the server determines the user's emotional state and generates commands to control surrounding electronic devices. These controlled devices include lighting, sound systems, and temperature control devices. Smart hobs using Raspberry Pi or Arduino are used as the platform.

[0389] For example, if a user wants to relax at home, the system analyzes the user's emotional data and instructs the system to change the room lighting to a warmer color and play relaxation music. This entire process is automated, reducing the user's burden.

[0390] Examples of prompts include, "How would you optimize the home environment when the user is tired after returning home?" and "Based on the user's current emotions, suggest actions to promote relaxation." This allows the system to utilize generative AI models to derive even more optimal solutions.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The information display device captures the user's visual and auditory information using a camera and microphone. The input is real-time collected audio and video data, and the output is a digitized version of this data. The data is then converted to text using OCR and speech recognition technologies.

[0394] Step 2:

[0395] The mobile communication device acquires the user's location information using a GPS sensor and also records usage history data. The input is location data and app usage history, and the output is the storage of these datasets into a database.

[0396] Step 3:

[0397] The server integrates digital text, location information, and usage history data. The input is the data obtained in steps 1 and 2, and the output is the data analyzed as user preference patterns. Machine learning algorithms are used to analyze the data patterns.

[0398] Step 4:

[0399] The server analyzes the data to determine the user's emotional state and generates real-time control commands for corresponding electronic devices. The input is the analyzed emotional data, and the output is a list of electronic devices to be controlled and the commands for each device.

[0400] Step 5:

[0401] The terminal receives control commands from the server and controls surrounding electronic devices. For example, it can adjust lighting colors or activate sound systems. The input is the command from the server, and the output is the action that optimizes the user's environment.

[0402] Step 6:

[0403] Users can experience an automatically optimized environment and provide feedback under those conditions. The input is the optimized environment, and the output is the user's feedback. This feedback is sent to the server and used for future improvements.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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".

[0420] This invention relates to a system in which a user wears an information display device and converts visual and auditory information obtained from the physical environment into digital text. This information display device is implemented, for example, as smart glasses and has the function of automatically capturing surrounding signs and audio guidance and converting them into text.

[0421] Furthermore, mobile communication devices, such as smartphones, acquire the user's location information through GPS sensors and record a history of the user's digital activities, including website and application usage. This information is received and integrated by a server and used as a dataset to analyze the user's preference patterns.

[0422] The server uses machine learning algorithms to analyze user preferences from integrated data. For example, if a user has a history of frequently visiting a particular restaurant, it can prioritize displaying this information when it finds new promotions related to that restaurant.

[0423] Once the analysis is complete, the server generates personalized recommendations and sends a notification to the user's mobile device or information display device. The notification may include links to relevant information and call to action to help the user take action.

[0424] When a user receives a notification and highly rates its content, the server compiles these ratings and distributes rewards to the relevant parties and developers who provided the information. This reward system further encourages information sharing and makes it possible to build an ecosystem where diverse information circulates.

[0425] This format allows users to receive useful information at the right time in their daily lives, while information providers can achieve both the promotion of information circulation in the market and the acquisition of compensation.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The terminal (information display device) uses its built-in camera and microphone to capture information that the user sees and hears while on the move. The captured images and audio data are analyzed in real time and converted into digital text using OCR and speech recognition technologies.

[0429] Step 2:

[0430] The device (mobile communication device) continuously records location information using GPS, and also collects app usage data and browsing history. This data is temporarily stored in local storage for later analysis.

[0431] Step 3:

[0432] The terminals (information display devices and mobile communication devices) periodically transmit collected digital text and usage history data to the server. The transmitted data is encrypted in accordance with security protocols.

[0433] Step 4:

[0434] The server integrates all received data and updates each user's database. The integrated data is then input into a machine learning model to analyze user preference patterns.

[0435] Step 5:

[0436] Based on the analysis results, the server generates personalized information and advertisements tailored to each user. The generated information is sent as push notifications at the appropriate time based on the user's location.

[0437] Step 6:

[0438] The device (mobile communication device or information display device) receives push notifications from the server and makes them visible to the user. The notified information includes relevant links and buttons prompting action.

[0439] Step 7:

[0440] Users review the notified information and evaluate its usefulness. This evaluation is then sent to the server via the platform.

[0441] Step 8:

[0442] The server analyzes the aggregated evaluation data and calculates rewards for information providers or application developers who receive high ratings. Rewards are distributed based on predetermined criteria and notified to the providers.

[0443] (Example 1)

[0444] 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."

[0445] In modern society, users are exposed to a vast amount of information, making it difficult to receive useful information at the right time in their daily lives. Therefore, there is a need to provide personalized information based on user preferences and location to reduce stress caused by information overload. Furthermore, there is a lack of adequate incentives for information providers, resulting in insufficient distribution of high-quality information.

[0446] 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.

[0447] In this invention, the server includes means for acquiring visual and audio data from the external environment using an information display device worn by the user and converting that data into text information; means for acquiring location data and digital activity history from a mobile terminal and storing that data; and means for integrating the text information and the digital activity history and analyzing the user's preference patterns. This makes it possible to provide useful information to the user in a timely manner and to distribute appropriate rewards to information providers.

[0448] A "user" is a person who receives information, is the entity that wears an information display device, and uses a mobile terminal.

[0449] An "information display device" is a device that acquires visual and auditory data from the external environment and converts it into digital text.

[0450] A "mobile device" is a communication-enabled device carried by a user, and is a device used to acquire location data and digital activity history.

[0451] "Location data" refers to information that indicates the user's current location using GPS or other technologies.

[0452] "Digital activity history" refers to data that shows a user's online behavior history, such as records of applications used and websites visited.

[0453] "Preference patterns" refer to information that indicates preferences and tendencies derived from a user's behavior and tastes.

[0454] "Analysis means" refers to technical methods and devices used to analyze user data and identify their behavior and preferences.

[0455] A "notification means" is a function or device used to transmit information or messages to a user.

[0456] "Reward distribution" refers to the process of providing fair compensation to information providers and other stakeholders.

[0457] This invention is a system that uses an information display device worn by the user and a mobile terminal to collect information from the physical environment and digital activity areas, and to provide the user with personalized information.

[0458] The user wears an information display device such as smart glasses, captures visual information of the surroundings with a built-in camera, and records voice guidance with a built-in microphone. This information is sent to a cloud-based character recognition service via a data transmission function and converted into text information.

[0459] The smartphone, as the device, uses its GPS sensor to obtain the user's location data and also records the history of installed applications and visited websites. This information is sent to a server at regular intervals and stored in a database.

[0460] The server centrally manages all received data and uses machine learning models to analyze user preference patterns. For example, the server uses Python-based libraries to perform data analysis and extract user behavioral trends. Based on the analysis results, the server determines the information and presentation priorities that are valuable to the user and sends this information to the user as a notification.

[0461] For example, if a user frequently visits a particular location, the generative AI model may suggest new promotional information related to that location. For instance, a prompt such as, "Explain how to recommend new promotional information based on the user's past behavioral data," can be used to provide specific analytical instructions to the AI ​​model.

[0462] In this way, an ecosystem is built where users can receive important information in their daily lives at the right time, and information providers are rewarded according to their contributions.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The user wears an information display device, capturing surrounding visual information with its built-in camera and recording voice guidance with its built-in microphone. Visual and audio data are input, which is temporarily stored within the device. This data is then transmitted to a cloud service via a communication module. The output here is the transmitted data itself.

[0466] Step 2:

[0467] The server acquires visual and audio data received from cloud services. The input data consists of raw visual images and audio files. The server extracts text from the visual data using character recognition software and converts the audio data to text using speech recognition software. The output is the converted text information.

[0468] Step 3:

[0469] The device uses a GPS sensor to determine the user's location. The input is obtained as the current location information. The device also retrieves the usage history of simultaneously installed applications and website visit history from a local database. This data forms the user's activity history and is sent to the server. The output is the user's location data and activity history.

[0470] Step 4:

[0471] The server integrates text information, location data, and behavioral history and stores them in a database. The input is all data collected to date. The server runs machine learning algorithms to analyze user preference patterns. This analysis is used to identify promotions and information that users prefer. The output is the user's personalized preference pattern.

[0472] Step 5:

[0473] The server generates personalized information based on the analysis results. The input is the user's preference patterns. Specifically, it selects information and suggestions that are likely to interest the user and prepares them as notification messages. The output is the generated notification data.

[0474] Step 6:

[0475] The server sends the generated notification to the terminal or information display device. The input is the prepared notification data. The terminal receives this and displays the information to the user. The output is the notification information displayed to the user.

[0476] Step 7:

[0477] The user reviews the notification and provides an evaluation. The input is the displayed notification information. The user sends the evaluation information to the server via their device. The server aggregates these evaluations and distributes appropriate rewards to the information providers. The output is the aggregated evaluation information and the results of the reward distribution.

[0478] (Application Example 1)

[0479] 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."

[0480] Modern consumers demand faster and more appropriate purchasing decisions from a diverse range of product options. However, traditional information systems have struggled to provide personalized recommendations based on individual consumer preferences and past purchase history in real time within physical stores, posing a challenge to improving the shopping experience. Furthermore, there has been a lack of mechanisms to appropriately reflect consumer evaluations of the information received and provide feedback to information providers.

[0481] 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.

[0482] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting it into digital text; means for acquiring location information and usage history from a mobile communication terminal and storing it as data; and means for collecting surrounding product information based on the user's location within a physical store and recommending products related to the user's past purchase history and preference patterns. This enables consumers to receive personalized product information in real time, improving their shopping experience. Furthermore, appropriate feedback and reward distribution are provided to information providers through consumer evaluations, promoting the flow of information in the market.

[0483] An "information presentation device" is a device worn by a user that converts visual and auditory information acquired from the physical environment into digital text.

[0484] A "mobile communication terminal" is a communication device that acquires a user's location information and usage history and stores this information as data.

[0485] "Location information" refers to data that indicates the user's current location when visiting a physical store.

[0486] "Usage history" refers to a record of a user's past digital activities and purchasing behavior.

[0487] A "preference pattern" is the result of an analysis of a user's preferences and tendencies based on their past behavioral data.

[0488] "Product information" refers to data about the details and characteristics of products available in a physical store.

[0489] "Recommendations" refer to information about products and services suggested to users based on their preference patterns.

[0490] "Evaluation information" refers to feedback data that shows users' satisfaction levels and opinions regarding the information they received.

[0491] "Reward distribution" refers to the allocation of rewards to information providers based on user evaluation information.

[0492] The system for carrying out this invention includes an information display device such as smart glasses, a mobile communication terminal such as a smartphone, and a server for processing this data. The information display device can capture surrounding visual and auditory information using a camera and microphone and convert it into digital text. Specifically, it uses an image and speech recognition API to convert environmental information into text and generate information that reflects the user's current situation.

[0493] Mobile communication devices use GPS sensors to acquire user location information and store this data along with past usage history. This information is sent to a server and analyzed using machine learning models. Machine learning libraries such as TensorFlow are used in this process to perform analysis based on user preference patterns.

[0494] The server provides real-time relevant product information within physical stores based on the user's location and preference data. For example, when a user wearing smart glasses moves around a fashion store, the device identifies specific items through its camera. This information is then used to recommend products that match the user's preferences and history.

[0495] Furthermore, if a user rates the recommended information, the system collects those ratings and distributes the rewards to the information providers. This promotes information flow and builds an ecosystem. The prompt message to the generated AI model is something like, "Based on the product information scanned by the user in the physical store they are visiting, please generate offers tailored to their past purchase history and preference data."

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The user acquires visual and auditory information from the physical environment through the camera and microphone of the smart glasses. The input consists of camera video and audio signals, which are captured by the information display device. The information display device uses image recognition APIs and speech recognition APIs to convert the visual and auditory information into digital text and generate the output.

[0499] Step 2:

[0500] The device uses a GPS sensor to obtain the user's current location information. The input is location sensor data, which the device analyzes to determine the user's current location. In addition, it retrieves past usage history data from a database and sends it to the server as output along with the location information.

[0501] Step 3:

[0502] The server integrates received visual, auditory, location, and usage history data to form a dataset. The input is the output dataset from the previous step. The server uses a machine learning model to analyze user preference patterns and generate product recommendations. A generative AI model then outputs personalized product information based on these analysis results.

[0503] Step 4:

[0504] The server transmits the generated product recommendation information to the information display device, notifying the user in real time. The input is personalized information generated by the server, and the output is provided as the display on the information display device.

[0505] Step 5:

[0506] The user evaluates the presented information and sends the evaluation data to the server via their terminal. The input is user feedback data. The server aggregates this evaluation information, calculates and distributes rewards to the relevant information providers. The output is provided as a reward distribution list.

[0507] 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.

[0508] This invention is a system that uses an information display device and a mobile communication device worn by the user to digitize information obtained from the physical environment, and further analyzes the user's emotional state by combining it with an emotion engine.

[0509] The information display device is equipped with a camera and microphone, capturing information that the user sees and hears in real time. This information is converted into digital text using OCR and speech recognition technologies. In addition, an emotion engine is used to extract emotional data based on the user's visual and auditory perception from the captured data. This emotion engine can identify emotions using a combination of algorithms, including facial expression recognition and voice tone analysis.

[0510] Mobile communication devices acquire location information using GPS sensors and record the user's app usage history and internet activity history. This information is periodically sent to a server and stored in a database. The server integrates the collected digital text, sentiment data, location, and usage history, and analyzes the user's preference patterns using machine learning models.

[0511] Based on analyzed preference patterns and emotional data, the server generates personalized information for the user. For example, if a user is feeling stressed in a particular location, it can notify them of nearby relaxation spots or cafes. Conversely, when a user is feeling joy or excitement, it can provide information about events or offers that amplify those emotions.

[0512] The generated information is sent to a mobile communication device or information display device. Users can take action based on the notification and provide feedback to the server as an evaluation of the result. The server aggregates this evaluation data and distributes rewards to information providers and app developers who receive high ratings.

[0513] This invention aims to improve user satisfaction by providing information that resonates with users' emotions and offering a more personalized experience. This approach allows users to receive optimal information tailored to their current emotional state, while information providers can build a system that increases their market influence and earns rewards.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The terminal (information display device) uses its built-in camera and microphone to acquire the user's visual and auditory information. The acquired data is converted into digital text using OCR technology and speech recognition technology.

[0517] Step 2:

[0518] The terminal (information display device) uses an emotion engine to analyze the user's emotions from acquired visual and auditory information. This includes facial expression recognition processing to capture changes in facial expressions and voice analysis processing to analyze the tone of voice.

[0519] Step 3:

[0520] The device (mobile communication device) uses GPS to obtain the user's current location and records app usage history and web activity history. This information is encrypted and transmitted to the server based on appropriate security protocols, with privacy in mind.

[0521] Step 4:

[0522] The server integrates digital text, sentiment data, location information, and usage history sent from the terminal. Based on this, machine learning models are used to analyze the individual user's preference patterns and sentiment tendencies.

[0523] Step 5:

[0524] Based on the analysis results, the server generates personalized information that is most relevant to the user. For example, if it detects that the user is feeling tired or stressed, it will recommend information about nearby relaxation facilities or cafes.

[0525] Step 6:

[0526] The server sends the generated personalized information as a push notification to the user's mobile device or information display device. The notification may include details about the suggested facilities or coupons.

[0527] Step 7:

[0528] The user considers their actions based on the received notification and evaluates the usefulness of the notification content. The evaluation results are then sent back to the server via the device.

[0529] Step 8:

[0530] The server collects user rating data and distributes rewards to information providers and content creators who receive high ratings. This reward system provides information providers with an incentive to offer more comprehensive information.

[0531] (Example 2)

[0532] 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."

[0533] Modern information display devices and mobile communication devices do not adequately consider the emotional state and preferences of individual users when providing information. As a result, the information provided may not match the user's current situation or mood, leading to decreased user satisfaction.

[0534] 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.

[0535] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting the information into digital text; means for using a composite algorithm to analyze emotional data from the extracted visual and auditory information; and means for generating appropriate information based on the analysis results using a generative AI model and notifying the user. This makes it possible to provide personalized information according to the user's emotional state and preferences.

[0536] An "information display device" is a device worn by the user to acquire visual and auditory information from the physical environment.

[0537] "Digital text" refers to visual and auditory information that has been electronically converted and expressed as character data.

[0538] "Emotional data" refers to data extracted from visual and auditory information that indicates the user's emotional state.

[0539] A "combined algorithm" is an algorithm that combines multiple methods and techniques used to analyze visual and auditory information.

[0540] A "mobile communication device" is a portable communication device used to acquire a user's location data and usage history.

[0541] A "preference pattern" is a data pattern that indicates a user's preferences and behavioral tendencies.

[0542] A "generative AI model" is an artificial intelligence model that generates information based on analyzed data and provides suggestions tailored to the user.

[0543] "Feedback information" refers to information about users' usage results and evaluations.

[0544] In order to implement this invention, it is necessary to use an information display device, a portable communication device, and a server in conjunction with each other.

[0545] The user wears an information display device that acquires visual and auditory information from the physical environment in real time. This information display device is equipped with a camera and microphone to capture the user's visual and auditory experiences. The acquired visual information is converted into digital text using optical character recognition (OCR) technology, and the auditory information is transcribed into text using speech recognition technology. In addition, a composite algorithm is applied to analyze the content of the video and audio and extract emotional data. In this process, facial expression recognition and voice tone analysis are performed to identify the user's emotional state.

[0546] The device utilizes GPS functionality via a mobile communication device to acquire the user's location data. It also records the user's application usage history and internet activity, transferring this data to a database. This information is aggregated on a server and used to understand the user's behavior patterns and preferences.

[0547] The server integrates accumulated digital text, sentiment data, location information, and usage history, and uses a generative AI model to analyze user preference patterns. This analysis generates personalized information based on the user's emotional state and behavioral characteristics.

[0548] For example, if a user feels fatigued while hiking, this emotional data can be used to provide information about the nearest rest stop. Furthermore, when a user visits a new area, it's possible to suggest recommended tourist spots and restaurants based on their past preferences.

[0549] An example of a prompt sentence for input to the generating AI model is, "Please tell me some recommended relaxation spots that the user should visit." In this way, the aim of the present invention is to provide valuable information to the user and improve their satisfaction.

[0550] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0551] Step 1:

[0552] The user wears an information display device and acquires visual and auditory information from the physical environment. Video data from the camera and audio data from the microphone are used as input. This information is saved to the device as video and audio files as output. Specifically, the camera and microphone are always active, capturing data in real time.

[0553] Step 2:

[0554] The terminal converts the video information saved in Step 1 into digital text using OCR technology. The input is video data, which is processed by optical character recognition technology. The output is the content digitized as text information. Specifically, the process involves detecting text areas in the video and extracting the text.

[0555] Step 3:

[0556] The device applies speech recognition technology to stored audio data to convert speech into text. The input is audio data, which is processed using a speech analysis algorithm. The output is the speech recognition result in text format. Specifically, this process involves analyzing the audio waveform and converting it into a format understandable as human language.

[0557] Step 4:

[0558] The device uses a complex algorithm to analyze video and audio data extracted from visual data and extract emotional data. Input consists of digital text and voice tone data, and an emotion analysis algorithm is used. Output is emotional data indicating the user's emotional state. Specifically, it evaluates emotions through micro-expression analysis using facial recognition and changes in voice tone.

[0559] Step 5:

[0560] The device uses a mobile communication device to obtain location data from a GPS sensor. It also records application usage history and internet browsing history. Inputs are location information and history data, and it performs location acquisition and generates history logs. Outputs are user location data and activity history data. Specifically, it periodically updates location information and accumulates history data.

[0561] Step 6:

[0562] The server integrates digital text, sentiment data, location information, and usage history sent from the terminal. Input is informational data from the terminal, stored in a database. Output is an integrated dataset suitable for analysis. Specifically, data cleaning and standardization of the data structure are performed.

[0563] Step 7:

[0564] The server analyzes integrated data using a generative AI model to predict user preference patterns. The input is an integrated dataset, and the AI ​​model performs data analysis. The output is recommendation information based on user preferences. Specifically, it extracts preference patterns from the data and generates appropriate suggestions based on the results.

[0565] Step 8:

[0566] The server generates personalized information based on the user's emotional state and preference patterns, and notifies the terminal. The input is the analyzed preference patterns, and an information generation algorithm is used. The output is adaptive information notified to the user. Specifically, the notification content is customized and sent to the user's terminal.

[0567] (Application Example 2)

[0568] 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."

[0569] In modern living environments, it is difficult to automatically adjust the home environment according to the user's emotional state. In particular, the lack of control and optimization of electronic devices based on the user's emotions poses a challenge in adequately reducing stress and providing a relaxing space. Furthermore, if such automatic adjustments to the home environment were possible, users could live a more comfortable life.

[0570] 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.

[0571] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting that information into digital text; means for acquiring location information and usage history from a mobile communication device and storing that information as data; and means for controlling surrounding electronic devices to optimize the environment based on analyzed user emotion data. This makes it possible to improve the home environment in accordance with the user's emotions.

[0572] An "information presentation device" is a device worn by a user to acquire visual and auditory information from the physical environment.

[0573] "Digital text" refers to visual and auditory information that has been converted into digital data and made into an analyzable format.

[0574] A "mobile communication device" is a portable electronic device that has the function of acquiring location information and usage history and storing it as data.

[0575] "Preference patterns" refer to data that shows an individual's preferences and habits, analyzed based on user behavior and emotions.

[0576] "Controlling peripheral electronic devices based on emotional data" is a process that analyzes the user's emotional state and adjusts electronic devices within the home accordingly.

[0577] "Personalized information" refers to information that is individually tailored to a user's preferences and emotional state.

[0578] "Evaluation information" refers to feedback provided by users regarding the information and controls they have been given, and is used to improve the system.

[0579] To implement this invention, it is necessary to construct a system that utilizes an information display device worn by the user and a portable communication device. In this system, the camera and microphone mounted on the information display device are used to collect the user's visual and auditory information. This information is converted into digital text in real time. Optical character recognition (OCR) technology and speech recognition technology are used for this conversion. Furthermore, the portable communication device acquires the user's location information using a GPS sensor and collects usage history.

[0580] The server integrates the collected information and uses machine learning algorithms to analyze user preference patterns. Based on the analyzed data, the server determines the user's emotional state and generates commands to control surrounding electronic devices. These controlled devices include lighting, sound systems, and temperature control devices. Smart hobs using Raspberry Pi or Arduino are used as the platform.

[0581] For example, if a user wants to relax at home, the system analyzes the user's emotional data and instructs the system to change the room lighting to a warmer color and play relaxation music. This entire process is automated, reducing the user's burden.

[0582] Examples of prompts include, "How would you optimize the home environment when the user is tired after returning home?" and "Based on the user's current emotions, suggest actions to promote relaxation." This allows the system to utilize generative AI models to derive even more optimal solutions.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The information display device captures the user's visual and auditory information using a camera and microphone. The input is real-time collected audio and video data, and the output is a digitized version of this data. The data is then converted to text using OCR and speech recognition technologies.

[0586] Step 2:

[0587] The mobile communication device acquires the user's location information using a GPS sensor and also records usage history data. The input is location data and app usage history, and the output is the storage of these datasets into a database.

[0588] Step 3:

[0589] The server integrates digital text, location information, and usage history data. The input is the data obtained in steps 1 and 2, and the output is the data analyzed as user preference patterns. Machine learning algorithms are used to analyze the data patterns.

[0590] Step 4:

[0591] The server analyzes the data to determine the user's emotional state and generates real-time control commands for corresponding electronic devices. The input is the analyzed emotional data, and the output is a list of electronic devices to be controlled and the commands for each device.

[0592] Step 5:

[0593] The terminal receives control commands from the server and controls surrounding electronic devices. For example, it can adjust lighting colors or activate sound systems. The input is the command from the server, and the output is the action that optimizes the user's environment.

[0594] Step 6:

[0595] Users can experience an automatically optimized environment and provide feedback under those conditions. The input is the optimized environment, and the output is the user's feedback. This feedback is sent to the server and used for future improvements.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] 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.

[0602] 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).

[0603] 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.

[0604] 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.

[0605] 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).

[0606] 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.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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".

[0613] This invention relates to a system in which a user wears an information display device and converts visual and auditory information obtained from the physical environment into digital text. This information display device is implemented, for example, as smart glasses and has the function of automatically capturing surrounding signs and audio guidance and converting them into text.

[0614] Furthermore, mobile communication devices, such as smartphones, acquire the user's location information through GPS sensors and record a history of the user's digital activities, including website and application usage. This information is received and integrated by a server and used as a dataset to analyze the user's preference patterns.

[0615] The server uses machine learning algorithms to analyze user preferences from integrated data. For example, if a user has a history of frequently visiting a particular restaurant, it can prioritize displaying this information when it finds new promotions related to that restaurant.

[0616] Once the analysis is complete, the server generates personalized recommendations and sends a notification to the user's mobile device or information display device. The notification may include links to relevant information and call to action to help the user take action.

[0617] When a user receives a notification and highly rates its content, the server compiles these ratings and distributes rewards to the relevant parties and developers who provided the information. This reward system further encourages information sharing and makes it possible to build an ecosystem where diverse information circulates.

[0618] This format allows users to receive useful information at the right time in their daily lives, while information providers can achieve both the promotion of information circulation in the market and the acquisition of compensation.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The terminal (information display device) uses its built-in camera and microphone to capture information that the user sees and hears while on the move. The captured images and audio data are analyzed in real time and converted into digital text using OCR and speech recognition technologies.

[0622] Step 2:

[0623] The device (mobile communication device) continuously records location information using GPS, and also collects app usage data and browsing history. This data is temporarily stored in local storage for later analysis.

[0624] Step 3:

[0625] The terminals (information display devices and mobile communication devices) periodically transmit collected digital text and usage history data to the server. The transmitted data is encrypted in accordance with security protocols.

[0626] Step 4:

[0627] The server integrates all received data and updates each user's database. The integrated data is then input into a machine learning model to analyze user preference patterns.

[0628] Step 5:

[0629] Based on the analysis results, the server generates personalized information and advertisements tailored to each user. The generated information is sent as push notifications at the appropriate time based on the user's location.

[0630] Step 6:

[0631] The device (mobile communication device or information display device) receives push notifications from the server and makes them visible to the user. The notified information includes relevant links and buttons prompting action.

[0632] Step 7:

[0633] Users review the notified information and evaluate its usefulness. This evaluation is then sent to the server via the platform.

[0634] Step 8:

[0635] The server analyzes the aggregated evaluation data and calculates rewards for information providers or application developers who receive high ratings. Rewards are distributed based on predetermined criteria and notified to the providers.

[0636] (Example 1)

[0637] 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".

[0638] In modern society, users are exposed to a vast amount of information, making it difficult to receive useful information at the right time in their daily lives. Therefore, there is a need to provide personalized information based on user preferences and location to reduce stress caused by information overload. Furthermore, there is a lack of adequate incentives for information providers, resulting in insufficient distribution of high-quality information.

[0639] 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.

[0640] In this invention, the server includes means for acquiring visual and audio data from the external environment using an information display device worn by the user and converting that data into text information; means for acquiring location data and digital activity history from a mobile terminal and storing that data; and means for integrating the text information and the digital activity history and analyzing the user's preference patterns. This makes it possible to provide useful information to the user in a timely manner and to distribute appropriate rewards to information providers.

[0641] A "user" is a person who receives information, is the entity that wears an information display device, and uses a mobile terminal.

[0642] An "information display device" is a device that acquires visual and auditory data from the external environment and converts it into digital text.

[0643] A "mobile device" is a communication-enabled device carried by a user, and is a device used to acquire location data and digital activity history.

[0644] "Location data" refers to information that indicates the user's current location using GPS or other technologies.

[0645] "Digital activity history" refers to data that shows a user's online behavior history, such as records of applications used and websites visited.

[0646] "Preference patterns" refer to information that indicates preferences and tendencies derived from a user's behavior and tastes.

[0647] "Analysis means" refers to technical methods and devices used to analyze user data and identify their behavior and preferences.

[0648] A "notification means" is a function or device used to transmit information or messages to a user.

[0649] "Reward distribution" refers to the process of providing fair compensation to information providers and other stakeholders.

[0650] This invention is a system that uses an information display device worn by the user and a mobile terminal to collect information from the physical environment and digital activity areas, and to provide the user with personalized information.

[0651] The user wears an information display device such as smart glasses, captures visual information of the surroundings with a built-in camera, and records voice guidance with a built-in microphone. This information is sent to a cloud-based character recognition service via a data transmission function and converted into text information.

[0652] The smartphone, as the device, uses its GPS sensor to obtain the user's location data and also records the history of installed applications and visited websites. This information is sent to a server at regular intervals and stored in a database.

[0653] The server centrally manages all received data and uses machine learning models to analyze user preference patterns. For example, the server uses Python-based libraries to perform data analysis and extract user behavioral trends. Based on the analysis results, the server determines the information and presentation priorities that are valuable to the user and sends this information to the user as a notification.

[0654] For example, if a user frequently visits a particular location, the generative AI model may suggest new promotional information related to that location. For instance, a prompt such as, "Explain how to recommend new promotional information based on the user's past behavioral data," can be used to provide specific analytical instructions to the AI ​​model.

[0655] In this way, an ecosystem is built where users can receive important information in their daily lives at the right time, and information providers are rewarded according to their contributions.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The user wears an information display device, capturing surrounding visual information with its built-in camera and recording voice guidance with its built-in microphone. Visual and audio data are input, which is temporarily stored within the device. This data is then transmitted to a cloud service via a communication module. The output here is the transmitted data itself.

[0659] Step 2:

[0660] The server acquires visual and audio data received from cloud services. The input data consists of raw visual images and audio files. The server extracts text from the visual data using character recognition software and converts the audio data to text using speech recognition software. The output is the converted text information.

[0661] Step 3:

[0662] The device uses a GPS sensor to determine the user's location. The input is obtained as the current location information. The device also retrieves the usage history of simultaneously installed applications and website visit history from a local database. This data forms the user's activity history and is sent to the server. The output is the user's location data and activity history.

[0663] Step 4:

[0664] The server integrates text information, location data, and behavioral history and stores them in a database. The input is all data collected to date. The server runs machine learning algorithms to analyze user preference patterns. This analysis is used to identify promotions and information that users prefer. The output is the user's personalized preference pattern.

[0665] Step 5:

[0666] The server generates personalized information based on the analysis results. The input is the user's preference patterns. Specifically, it selects information and suggestions that are likely to interest the user and prepares them as notification messages. The output is the generated notification data.

[0667] Step 6:

[0668] The server sends the generated notification to the terminal or information display device. The input is the prepared notification data. The terminal receives this and displays the information to the user. The output is the notification information displayed to the user.

[0669] Step 7:

[0670] The user reviews the notification and provides an evaluation. The input is the displayed notification information. The user sends the evaluation information to the server via their device. The server aggregates these evaluations and distributes appropriate rewards to the information providers. The output is the aggregated evaluation information and the results of the reward distribution.

[0671] (Application Example 1)

[0672] 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".

[0673] Modern consumers demand faster and more appropriate purchasing decisions from a diverse range of product options. However, traditional information systems have struggled to provide personalized recommendations based on individual consumer preferences and past purchase history in real time within physical stores, posing a challenge to improving the shopping experience. Furthermore, there has been a lack of mechanisms to appropriately reflect consumer evaluations of the information received and provide feedback to information providers.

[0674] 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.

[0675] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting it into digital text; means for acquiring location information and usage history from a mobile communication terminal and storing it as data; and means for collecting surrounding product information based on the user's location within a physical store and recommending products related to the user's past purchase history and preference patterns. This enables consumers to receive personalized product information in real time, improving their shopping experience. Furthermore, appropriate feedback and reward distribution are provided to information providers through consumer evaluations, promoting the flow of information in the market.

[0676] An "information presentation device" is a device worn by a user that converts visual and auditory information acquired from the physical environment into digital text.

[0677] A "mobile communication terminal" is a communication device that acquires a user's location information and usage history and stores this information as data.

[0678] "Location information" refers to data that indicates the user's current location when visiting a physical store.

[0679] "Usage history" refers to a record of a user's past digital activities and purchasing behavior.

[0680] A "preference pattern" is the result of an analysis of a user's preferences and tendencies based on their past behavioral data.

[0681] "Product information" refers to data about the details and characteristics of products available in a physical store.

[0682] "Recommendations" refer to information about products and services suggested to users based on their preference patterns.

[0683] "Evaluation information" refers to feedback data that shows users' satisfaction levels and opinions regarding the information they received.

[0684] "Reward distribution" refers to the allocation of rewards to information providers based on user evaluation information.

[0685] The system for carrying out this invention includes an information display device such as smart glasses, a mobile communication terminal such as a smartphone, and a server for processing this data. The information display device can capture surrounding visual and auditory information using a camera and microphone and convert it into digital text. Specifically, it uses an image and speech recognition API to convert environmental information into text and generate information that reflects the user's current situation.

[0686] Mobile communication devices use GPS sensors to acquire user location information and store this data along with past usage history. This information is sent to a server and analyzed using machine learning models. Machine learning libraries such as TensorFlow are used in this process to perform analysis based on user preference patterns.

[0687] The server provides real-time relevant product information within physical stores based on the user's location and preference data. For example, when a user wearing smart glasses moves around a fashion store, the device identifies specific items through its camera. This information is then used to recommend products that match the user's preferences and history.

[0688] Furthermore, if a user rates the recommended information, the system collects those ratings and distributes the rewards to the information providers. This promotes information flow and builds an ecosystem. The prompt message to the generated AI model is something like, "Based on the product information scanned by the user in the physical store they are visiting, please generate offers tailored to their past purchase history and preference data."

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The user acquires visual and auditory information from the physical environment through the camera and microphone of the smart glasses. The input consists of camera video and audio signals, which are captured by the information display device. The information display device uses image recognition APIs and speech recognition APIs to convert the visual and auditory information into digital text and generate the output.

[0692] Step 2:

[0693] The device uses a GPS sensor to obtain the user's current location information. The input is location sensor data, which the device analyzes to determine the user's current location. In addition, it retrieves past usage history data from a database and sends it to the server as output along with the location information.

[0694] Step 3:

[0695] The server integrates received visual, auditory, location, and usage history data to form a dataset. The input is the output dataset from the previous step. The server uses a machine learning model to analyze user preference patterns and generate product recommendations. A generative AI model then outputs personalized product information based on these analysis results.

[0696] Step 4:

[0697] The server transmits the generated product recommendation information to the information display device, notifying the user in real time. The input is personalized information generated by the server, and the output is provided as the display on the information display device.

[0698] Step 5:

[0699] The user evaluates the presented information and sends the evaluation data to the server via their terminal. The input is user feedback data. The server aggregates this evaluation information, calculates and distributes rewards to the relevant information providers. The output is provided as a reward distribution list.

[0700] 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.

[0701] This invention is a system that uses an information display device and a mobile communication device worn by the user to digitize information obtained from the physical environment, and further analyzes the user's emotional state by combining it with an emotion engine.

[0702] The information display device is equipped with a camera and microphone, capturing information that the user sees and hears in real time. This information is converted into digital text using OCR and speech recognition technologies. In addition, an emotion engine is used to extract emotional data based on the user's visual and auditory perception from the captured data. This emotion engine can identify emotions using a combination of algorithms, including facial expression recognition and voice tone analysis.

[0703] Mobile communication devices acquire location information using GPS sensors and record the user's app usage history and internet activity history. This information is periodically sent to a server and stored in a database. The server integrates the collected digital text, sentiment data, location, and usage history, and analyzes the user's preference patterns using machine learning models.

[0704] Based on analyzed preference patterns and emotional data, the server generates personalized information for the user. For example, if a user is feeling stressed in a particular location, it can notify them of nearby relaxation spots or cafes. Conversely, when a user is feeling joy or excitement, it can provide information about events or offers that amplify those emotions.

[0705] The generated information is sent to a mobile communication device or information display device. Users can take action based on the notification and provide feedback to the server as an evaluation of the result. The server aggregates this evaluation data and distributes rewards to information providers and app developers who receive high ratings.

[0706] This invention aims to improve user satisfaction by providing information that resonates with users' emotions and offering a more personalized experience. This approach allows users to receive optimal information tailored to their current emotional state, while information providers can build a system that increases their market influence and earns rewards.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The terminal (information display device) uses its built-in camera and microphone to acquire the user's visual and auditory information. The acquired data is converted into digital text using OCR technology and speech recognition technology.

[0710] Step 2:

[0711] The terminal (information display device) uses an emotion engine to analyze the user's emotions from acquired visual and auditory information. This includes facial expression recognition processing to capture changes in facial expressions and voice analysis processing to analyze the tone of voice.

[0712] Step 3:

[0713] The device (mobile communication device) uses GPS to obtain the user's current location and records app usage history and web activity history. This information is encrypted and transmitted to the server based on appropriate security protocols, with privacy in mind.

[0714] Step 4:

[0715] The server integrates digital text, sentiment data, location information, and usage history sent from the terminal. Based on this, machine learning models are used to analyze the individual user's preference patterns and sentiment tendencies.

[0716] Step 5:

[0717] Based on the analysis results, the server generates personalized information that is most relevant to the user. For example, if it detects that the user is feeling tired or stressed, it will recommend information about nearby relaxation facilities or cafes.

[0718] Step 6:

[0719] The server sends the generated personalized information as a push notification to the user's mobile device or information display device. The notification may include details about the suggested facilities or coupons.

[0720] Step 7:

[0721] The user considers their actions based on the received notification and evaluates the usefulness of the notification content. The evaluation results are then sent back to the server via the device.

[0722] Step 8:

[0723] The server collects user rating data and distributes rewards to information providers and content creators who receive high ratings. This reward system provides information providers with an incentive to offer more comprehensive information.

[0724] (Example 2)

[0725] 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".

[0726] Modern information display devices and mobile communication devices do not adequately consider the emotional state and preferences of individual users when providing information. As a result, the information provided may not match the user's current situation or mood, leading to decreased user satisfaction.

[0727] 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.

[0728] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting the information into digital text; means for using a composite algorithm to analyze emotional data from the extracted visual and auditory information; and means for generating appropriate information based on the analysis results using a generative AI model and notifying the user. This makes it possible to provide personalized information according to the user's emotional state and preferences.

[0729] An "information display device" is a device worn by the user to acquire visual and auditory information from the physical environment.

[0730] "Digital text" refers to visual and auditory information that has been electronically converted and expressed as character data.

[0731] "Emotional data" refers to data extracted from visual and auditory information that indicates the user's emotional state.

[0732] A "combined algorithm" is an algorithm that combines multiple methods and techniques used to analyze visual and auditory information.

[0733] A "mobile communication device" is a portable communication device used to acquire a user's location data and usage history.

[0734] A "preference pattern" is a data pattern that indicates a user's preferences and behavioral tendencies.

[0735] A "generative AI model" is an artificial intelligence model that generates information based on analyzed data and provides suggestions tailored to the user.

[0736] "Feedback information" refers to information about users' usage results and evaluations.

[0737] In order to implement this invention, it is necessary to use an information display device, a portable communication device, and a server in conjunction with each other.

[0738] The user wears an information display device that acquires visual and auditory information from the physical environment in real time. This information display device is equipped with a camera and microphone to capture the user's visual and auditory experiences. The acquired visual information is converted into digital text using optical character recognition (OCR) technology, and the auditory information is transcribed into text using speech recognition technology. In addition, a composite algorithm is applied to analyze the content of the video and audio and extract emotional data. In this process, facial expression recognition and voice tone analysis are performed to identify the user's emotional state.

[0739] The device utilizes GPS functionality via a mobile communication device to acquire the user's location data. It also records the user's application usage history and internet activity, transferring this data to a database. This information is aggregated on a server and used to understand the user's behavior patterns and preferences.

[0740] The server integrates accumulated digital text, sentiment data, location information, and usage history, and uses a generative AI model to analyze user preference patterns. This analysis generates personalized information based on the user's emotional state and behavioral characteristics.

[0741] For example, if a user feels fatigued while hiking, this emotional data can be used to provide information about the nearest rest stop. Furthermore, when a user visits a new area, it's possible to suggest recommended tourist spots and restaurants based on their past preferences.

[0742] An example of a prompt sentence for input to the generating AI model is, "Please tell me some recommended relaxation spots that the user should visit." In this way, the aim of the present invention is to provide valuable information to the user and improve their satisfaction.

[0743] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0744] Step 1:

[0745] The user wears an information display device and acquires visual and auditory information from the physical environment. Video data from the camera and audio data from the microphone are used as input. This information is saved to the device as video and audio files as output. Specifically, the camera and microphone are always active, capturing data in real time.

[0746] Step 2:

[0747] The terminal converts the video information saved in Step 1 into digital text using OCR technology. The input is video data, which is processed by optical character recognition technology. The output is the content digitized as text information. Specifically, the process involves detecting text areas in the video and extracting the text.

[0748] Step 3:

[0749] The device applies speech recognition technology to stored audio data to convert speech into text. The input is audio data, which is processed using a speech analysis algorithm. The output is the speech recognition result in text format. Specifically, this process involves analyzing the audio waveform and converting it into a format understandable as human language.

[0750] Step 4:

[0751] The device uses a complex algorithm to analyze video and audio data extracted from visual data and extract emotional data. Input consists of digital text and voice tone data, and an emotion analysis algorithm is used. Output is emotional data indicating the user's emotional state. Specifically, it evaluates emotions through micro-expression analysis using facial recognition and changes in voice tone.

[0752] Step 5:

[0753] The device uses a mobile communication device to obtain location data from a GPS sensor. It also records application usage history and internet browsing history. Inputs are location information and history data, and it performs location acquisition and generates history logs. Outputs are user location data and activity history data. Specifically, it periodically updates location information and accumulates history data.

[0754] Step 6:

[0755] The server integrates digital text, sentiment data, location information, and usage history sent from the terminal. Input is informational data from the terminal, stored in a database. Output is an integrated dataset suitable for analysis. Specifically, data cleaning and standardization of the data structure are performed.

[0756] Step 7:

[0757] The server analyzes integrated data using a generative AI model to predict user preference patterns. The input is an integrated dataset, and the AI ​​model performs data analysis. The output is recommendation information based on user preferences. Specifically, it extracts preference patterns from the data and generates appropriate suggestions based on the results.

[0758] Step 8:

[0759] The server generates personalized information based on the user's emotional state and preference patterns, and notifies the terminal. The input is the analyzed preference patterns, and an information generation algorithm is used. The output is adaptive information notified to the user. Specifically, the notification content is customized and sent to the user's terminal.

[0760] (Application Example 2)

[0761] 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".

[0762] In modern living environments, it is difficult to automatically adjust the home environment according to the user's emotional state. In particular, the lack of control and optimization of electronic devices based on the user's emotions poses a challenge in adequately reducing stress and providing a relaxing space. Furthermore, if such automatic adjustments to the home environment were possible, users could live a more comfortable life.

[0763] 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.

[0764] In this invention, the server includes means for acquiring visual and auditory information from the physical environment using an information display device worn by the user and converting that information into digital text; means for acquiring location information and usage history from a mobile communication device and storing that information as data; and means for controlling surrounding electronic devices to optimize the environment based on analyzed user emotion data. This makes it possible to improve the home environment in accordance with the user's emotions.

[0765] An "information presentation device" is a device worn by a user to acquire visual and auditory information from the physical environment.

[0766] "Digital text" refers to visual and auditory information that has been converted into digital data and made into an analyzable format.

[0767] A "mobile communication device" is a portable electronic device that has the function of acquiring location information and usage history and storing it as data.

[0768] "Preference patterns" refer to data that shows an individual's preferences and habits, analyzed based on user behavior and emotions.

[0769] "Controlling peripheral electronic devices based on emotional data" is a process that analyzes the user's emotional state and adjusts electronic devices within the home accordingly.

[0770] "Personalized information" refers to information that is individually tailored to a user's preferences and emotional state.

[0771] "Evaluation information" refers to feedback provided by users regarding the information and controls they have been given, and is used to improve the system.

[0772] To implement this invention, it is necessary to construct a system that utilizes an information display device worn by the user and a portable communication device. In this system, the camera and microphone mounted on the information display device are used to collect the user's visual and auditory information. This information is converted into digital text in real time. Optical character recognition (OCR) technology and speech recognition technology are used for this conversion. Furthermore, the portable communication device acquires the user's location information using a GPS sensor and collects usage history.

[0773] The server integrates the collected information and uses machine learning algorithms to analyze user preference patterns. Based on the analyzed data, the server determines the user's emotional state and generates commands to control surrounding electronic devices. These controlled devices include lighting, sound systems, and temperature control devices. Smart hobs using Raspberry Pi or Arduino are used as the platform.

[0774] For example, if a user wants to relax at home, the system analyzes the user's emotional data and instructs the system to change the room lighting to a warmer color and play relaxation music. This entire process is automated, reducing the user's burden.

[0775] Examples of prompts include, "How would you optimize the home environment when the user is tired after returning home?" and "Based on the user's current emotions, suggest actions to promote relaxation." This allows the system to utilize generative AI models to derive even more optimal solutions.

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The information display device captures the user's visual and auditory information using a camera and microphone. The input is real-time collected audio and video data, and the output is a digitized version of this data. The data is then converted to text using OCR and speech recognition technologies.

[0779] Step 2:

[0780] The mobile communication device acquires the user's location information using a GPS sensor and also records usage history data. The input is location data and app usage history, and the output is the storage of these datasets into a database.

[0781] Step 3:

[0782] The server integrates digital text, location information, and usage history data. The input is the data obtained in steps 1 and 2, and the output is the data analyzed as user preference patterns. Machine learning algorithms are used to analyze the data patterns.

[0783] Step 4:

[0784] The server analyzes the data to determine the user's emotional state and generates real-time control commands for corresponding electronic devices. The input is the analyzed emotional data, and the output is a list of electronic devices to be controlled and the commands for each device.

[0785] Step 5:

[0786] The terminal receives control commands from the server and controls surrounding electronic devices. For example, it can adjust lighting colors or activate sound systems. The input is the command from the server, and the output is the action that optimizes the user's environment.

[0787] Step 6:

[0788] Users can experience an automatically optimized environment and provide feedback under those conditions. The input is the optimized environment, and the output is the user's feedback. This feedback is sent to the server and used for future improvements.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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."

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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 as being incorporated by reference.

[0810] The following is further disclosed regarding the embodiments described above.

[0811] (Claim 1)

[0812] A means for acquiring visual and auditory information from the physical environment using an information display device worn by the user, and converting that information into digital text,

[0813] A means for acquiring location information and usage history from a mobile communication device and storing that information as data,

[0814] A means for integrating the aforementioned digital text information and the aforementioned usage history data and analyzing the user's preference patterns,

[0815] A means of generating personalized information based on the analysis results and notifying the user,

[0816] A means of aggregating user evaluation information and distributing rewards to information providers,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the analysis means predicts user behavior patterns using a machine learning algorithm.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the notification means provides information at an appropriate time based on the user's current location.

[0822] "Example 1"

[0823] (Claim 1)

[0824] A means for acquiring visual and auditory data from the external environment using an information display device worn by the user, and converting that data into text information,

[0825] A means for acquiring location data and digital activity history from a mobile device and storing that data,

[0826] A means for integrating the aforementioned text information and the aforementioned digital activity history to analyze the user's preference patterns,

[0827] A means of generating personalized information based on the analysis results and notifying the user,

[0828] A means of aggregating user feedback information and distributing rewards to information providers,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the analysis means predicts user behavior patterns using an artificial intelligence algorithm.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the notification means provides information at an appropriate time based on the user's current location.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] A means for acquiring visual and auditory information from the physical environment using an information display device worn by the user, and converting that information into digital text,

[0837] A means for acquiring location information and usage history from a mobile communication terminal and storing that information as data,

[0838] A means for integrating the aforementioned digital text information and the aforementioned usage history data and analyzing the user's preference patterns,

[0839] A means of generating personalized information based on the analysis results and notifying the user,

[0840] A means of collecting product information in the vicinity based on the user's location within a physical store, and recommending products related to the user's past purchase history and preference patterns,

[0841] A means of aggregating user evaluation information and distributing rewards to information providers,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, wherein the analysis means predicts user behavior patterns using a machine learning algorithm and improves personalized product recommendations.

[0845] (Claim 3)

[0846] The system according to claim 1, wherein the notification means provides information at an appropriate time based on the user's current location and movement within the store.

[0847] "Example 2 of combining an emotion engine"

[0848] (Claim 1)

[0849] A means for acquiring visual and auditory information from the physical environment using an information display device worn by the user, and converting said information into digital text,

[0850] A means of using a composite algorithm to analyze emotional data from extracted visual and auditory information,

[0851] A means for acquiring location data and usage history from a mobile communication device and storing such information as data,

[0852] A means for integrating the aforementioned digital text information, emotional data, and usage history to analyze the user's preference patterns,

[0853] A means of generating relevant information based on analysis results using a generative AI model and notifying the user accordingly.

[0854] A means of aggregating user feedback information and distributing rewards to the information sources,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the preference pattern analysis means predicts user behavior patterns using a machine learning algorithm.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the information notification means provides information at an appropriate time based on the user's current location.

[0860] "Application example 2 when combining with an emotional engine"

[0861] (Claim 1)

[0862] A means for acquiring visual and auditory information from the physical environment using an information display device worn by the user, and converting that information into digital text,

[0863] A means for acquiring location information and usage history from a mobile communication device and storing that information as data,

[0864] A means for integrating the aforementioned digital text information and the aforementioned usage history data and analyzing the user's preference patterns,

[0865] A means of optimizing the environment by controlling surrounding electronic devices based on analyzed user emotion data,

[0866] A means of generating personalized information based on the analysis results and notifying the user,

[0867] A means of aggregating user evaluation information and distributing rewards to information providers,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, wherein the analysis means predicts user behavior patterns and emotional states using a machine learning algorithm.

[0871] (Claim 3)

[0872] The system according to claim 1, wherein the notification means provides information at an appropriate time based on the user's current location and emotional state, and proposes actions to improve the environment. [Explanation of Symbols]

[0873] 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 for acquiring visual and auditory information from the physical environment using an information display device worn by the user, and converting that information into digital text, A means for acquiring location information and usage history from a mobile communication terminal and storing that information as data, A means for integrating the aforementioned digital text information and the aforementioned usage history data and analyzing the user's preference patterns, A means of generating personalized information based on the analysis results and notifying the user, A means of collecting product information in the vicinity based on the user's location within a physical store, and recommending products related to the user's past purchase history and preference patterns, A means of aggregating user evaluation information and distributing rewards to information providers, A system that includes this.

2. The system according to claim 1, wherein the analysis means predicts user behavior patterns using a machine learning algorithm and improves personalized product recommendations.

3. The system according to claim 1, wherein the notification means provides information at an appropriate time based on the user's current location and movement within the store.

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