system

Abstract

We provide the system. [Solution] A means for collecting visitor location information and generating a visitor behavior database, A means of analyzing visitors' past behavioral history and generating personalized suggestions, A means of presenting the generated proposals to visitors via avatars, A means of collecting feedback from visitors and learning to improve the accuracy of proposals, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 conventional guidance systems, it has been difficult to provide information optimized for the individual interests and behaviors of visitors, and only uniform information could be provided, resulting in a problem of decreasing visitor satisfaction. Also, when providing individual support, there has been an issue of high costs for training and operation of face-to-face staff. Furthermore, there has been no system for effectively collecting and utilizing feedback from visitors, and service improvement has tended to be delayed.

Means for Solving the Problems

[0005] This invention provides a system that collects visitor location information in real time, stores and analyzes their behavioral data. This enables the generation of personalized suggestions based on the visitor's past behavioral history, which are then presented to the visitor via an avatar using a generating AI model. Furthermore, the system identifies the visitor's emotional state through voice and facial expression analysis and optimizes suggestions based on the results. In addition, it collects feedback from visitors and continuously trains the model to improve the accuracy of the suggestions. This enables efficient and personalized information provision, thereby increasing visitor satisfaction.

[0006] "Visitors" refer to people who visit a facility or event, and are the target audience for information provision based on their behavior and interests.

[0007] "Location information" refers to data that shows a visitor's current location and movement route, and is used to understand their positional relationships within the facility.

[0008] A "behavioral database" is a system that stores data such as visitors' past behavior and purchase history, and is used for analysis to create personalized recommendations.

[0009] A "generative AI model" is an artificial intelligence technology that learns from large amounts of data and automatically generates suggestions and conversations for users. It is a program that performs advanced analysis, including natural language processing.

[0010] An "avatar" is a digital character represented by computer graphics, serving as a virtual guide to present information and interact with visitors within the system.

[0011] "Feedback" refers to data on the reactions and opinions that visitors show to suggestions and information provided, and is used to improve the system and enhance the accuracy of suggestions.

[0012] "Voice and facial expression analysis" refers to a technology that processes the voices and facial expressions of visitors as digital data to measure their emotional state and reactions. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system that uses an avatar agent displayed on digital signage to provide personalized information to visitors. Specific embodiments are shown below.

[0035] <Description of Embodiments>

[0036] 1. Data Collection and Analysis

[0037] The device collects location information using Bluetooth beacons and Wi-Fi when visitors enter the facility, and sends this data to a server to obtain visitor behavior data. With the visitor's permission, past purchase history and event participation history are also collected and stored on the server through devices with a dedicated app installed.

[0038] 2. Personalized suggestion generation

[0039] The server analyzes accumulated behavioral and historical data to identify visitors' interests and preferences. Using a generative AI model, it selects the most suitable events and products for each visitor. Specifically, it selects information that is likely to interest them on their next visit, based on the genres of events they have attended in the past and their purchase history.

[0040] 3. Information presentation via avatars

[0041] The terminal displays an avatar on the screen based on the suggestions sent from the server, and conveys information to visitors in a natural conversational format. The avatar uses voice to explain the details of the suggested events and products in a one-on-one dialogue-like manner.

[0042] 4. Utilization of Sentiment Analysis

[0043] The server analyzes visitors' voice data and facial expressions to evaluate their emotions. This allows the server to understand their reactions to suggestions in real time and determine if the suggestions are appropriate. For example, if a visitor smiles, the server can determine that the suggestion was successful and proceed with the guidance further.

[0044] 5. Feedback and Learning

[0045] The terminal sends feedback from visitors to the server. The server uses this feedback data to continuously train its AI model, improving the accuracy of its suggestions. This allows the system to take into account visitors' reactions and interests, enabling it to provide even more accurate information on subsequent visits.

[0046] <Specific example>

[0047] For example, when a visitor enters a specific area of ​​a tourist destination, an avatar placed on the device will guide them to nearby attractions and recommended routes, saying something like, "Here are some convenient spots to visit next." Furthermore, if the visitor's expression shows surprise, the avatar will continue to offer suggestions, providing information as long as their interest remains.

[0048] This system allows visitors to receive personalized information more efficiently and enjoy a special experience. Furthermore, it enables facilities to gain a deeper understanding of visitor behavior and optimize resources for operations and guidance.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The user installs a dedicated app on their smartphone and consents to the collection of location information and behavioral data. At this stage, the user's permission is explicitly obtained.

[0052] Step 2:

[0053] The device uses Bluetooth beacons and Wi-Fi access points placed within the facility to collect location information about the user's movement path. The collected data is used to instantly identify nearby shops and areas.

[0054] Step 3:

[0055] The server receives location information and activity history transmitted from the terminal and stores it in a database. Furthermore, it analyzes this data to extract user behavior patterns and interests.

[0056] Step 4:

[0057] The server uses a generative AI model to create a list of events and products tailored to the user. This list is personalized based on the user's past behavioral data and preferences.

[0058] Step 5:

[0059] The device retrieves a list of recommendations sent from the server and launches an application that displays an avatar on the screen. The avatar guides the user through the recommendations using text and voice.

[0060] Step 6:

[0061] The device captures the user's voice and facial expressions using its camera and microphone, and performs real-time emotion analysis. This information is used to dynamically change the avatar's responses and suggestions.

[0062] Step 7:

[0063] Users provide feedback on the suggestions. For example, they can request additional information if they are interested, or skip it if they are not. This feedback is recorded digitally.

[0064] Step 8:

[0065] The server analyzes user feedback and updates the generated AI model based on it, improving the accuracy of future suggestions. The feedback is stored in a database and used in the continuous learning process.

[0066] (Example 1)

[0067] 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."

[0068] Traditional methods of providing more personalized information to visitors make it difficult to improve the accuracy of suggestions and to grasp visitors' reactions in real time. In addition, it is difficult to adjust the information presented according to the visitor's emotional state, resulting in inefficient information transmission. This led to the problem of a low degree of match between the information visitors seek and the information they receive.

[0069] 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.

[0070] In this invention, the server includes means for acquiring visitor location information and creating visitor behavior information, means for analyzing the visitor's past behavior information and generating personalized suggestions, and means for presenting the generated suggestions to the visitor through a visual character. This makes it possible to provide visitors with optimized information in real time and improve visitor satisfaction and the operational efficiency of the facility.

[0071] A "visitor" is an individual who goes to a facility or a specific place.

[0072] "Location information" refers to information that indicates the geographical location of a specific place.

[0073] "Behavioral information" refers to a collection of data that records the activities and movements of visitors.

[0074] "Personalized suggestions" refer to providing visitors with customized information based on their specific interests and preferences.

[0075] A "visual character" is a person or character that is brought to life through animation or images displayed on a digital screen.

[0076] "Responses" refer to data that includes feedback and reactions from visitors.

[0077] "Methods for continuous learning" refer to the process of updating algorithms using past data to improve performance.

[0078] "Emotions" are expressions that describe the visitor's inner psychology or feelings.

[0079] "Voice and facial analysis" is a technique that evaluates a visitor's tone of voice and facial movements, and infers their emotions and intentions based on that.

[0080] An "artificial intelligence model" is an algorithm or program designed to solve complex problems by incorporating the learning process of computer systems.

[0081] This invention describes a method for specifically implementing a system for providing personalized information to visitors.

[0082] The server stores visitor location and behavior information in a database. This uses location data collection via Bluetooth beacons and Wi-Fi. By installing a dedicated app on the visitor's device, past purchase history and event participation history are also collected.

[0083] The terminal sends the collected data to the server for real-time processing. The server uses programming languages ​​such as Python and R to perform data analysis. This identifies visitors' interests and preferences and generates personalized suggestions.

[0084] The server generates suggestions using a generative AI model. These suggestions are input to the AI ​​model with prompts such as: "The visitor's past purchase history includes products A and B, and their event history includes events X and Y. What products or events would you suggest for the visitor's next visit?"

[0085] The terminal displays an avatar on digital signage based on suggestions sent from the server. Using a visual character, it provides information to visitors in an interactive format via voice and text. It utilizes natural language processing technologies such as Google® Cloud Speech-to-Text API.

[0086] After receiving a suggestion, the user provides feedback to the system through their response. The terminal sends this feedback to the server, which continuously trains the AI ​​model to improve the accuracy of the suggestions. A concrete example of this operation is when a visitor enters a new product section, and the terminal suggests, "Here are our latest recommended products," through an avatar.

[0087] This invention allows visitors to receive information of interest on the spot and have a unique experience, while facility operators can deepen their understanding of visitors and achieve efficient resource management.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The device acquires the visitor's location information using Bluetooth beacons and Wi-Fi. This allows it to identify which area the visitor is in. The input is signal strength from beacons and Wi-Fi connection information, and the output is the visitor's location coordinates. Specifically, the device analyzes the signal strength and determines the visitor's location as coordinates on a map of the facility.

[0091] Step 2:

[0092] The terminal retrieves visitors' past purchase and event participation history through a dedicated app. Input is historical information from the visitor's device, and output is a behavioral history database constructed based on that information. The terminal compiles this historical information into information packets and sends them to the server.

[0093] Step 3:

[0094] The server analyzes the received visitor location information and behavioral history data. Here, it processes the data using Python or similar tools to identify visitors' interests and preferences. The input is location information and behavioral history, and the output is estimated visitor preference data. The server uses statistical analysis techniques to model the data and reveal preference patterns.

[0095] Step 4:

[0096] The server generates suggestions using a generative AI model. The input consists of analyzed preference data and a prompt statement like this: "The visitor's past purchase history includes items A and B, and their event history includes events X and Y. What products or events would you suggest for the visitor's next visit?" The output is the generated personalized suggestions. The AI ​​model processes the input and outputs the suggestions in text format.

[0097] Step 5:

[0098] The terminal displays the suggested content received from the server on digital signage. It uses a visual character to convey the suggested content to visitors via voice. The input is the suggested content from the server, and the output is the presentation of visual and audio information through the avatar. The terminal uses the Google Cloud Speech-to-Text API to convert text to speech.

[0099] Step 6:

[0100] The user provides feedback on the information presented by the device. The server uses this feedback to train an AI model to improve the accuracy of its suggestions. The input is the user's feedback, and the output is an update to the AI ​​model based on the training. The server adds the feedback to a database and performs training to improve the model's performance.

[0101] (Application Example 1)

[0102] 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."

[0103] In today's retail industry, accurately understanding consumer needs and interests and providing personalized recommendations to each individual is becoming increasingly important. However, traditional systems have struggled to provide real-time, personalized recommendations to customers visiting stores, often resulting in missed sales opportunities. Therefore, there is a need for a way to provide individually personalized shopping experiences to customers within the store.

[0104] 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.

[0105] In this invention, the server includes means for collecting visitor location data and generating visitor behavior information, means for analyzing the visitor's past behavior history and generating personalized suggestions, and means for presenting the generated suggestions to the visitor via a virtual avatar. This makes it possible to provide each customer with optimal product information and event announcements in real time.

[0106] "Visitor location data" refers to information indicating the location of visitors within a facility or store, and is collected using communication technologies such as Bluetooth and Wi-Fi.

[0107] "Visitor behavior information" refers to data that includes visitors' past activity history and movement patterns within the facility, and is used to analyze visitors' interests and preferences.

[0108] "Personalized suggestions" refer to information that is individually optimized and presented to visitors based on their behavioral history and preferences, including product information and event announcements.

[0109] A "virtual avatar" is a digital character that is displayed on digital signage or communication terminals and provides information to visitors in a natural conversational format.

[0110] A "communication terminal" is an electronic device used to send and receive information, and specifically includes smartphones and tablets.

[0111] To implement this invention, it is essential to install communication terminals and servers within stores or facilities. The communication terminals mainly consist of visitors' smartphones and digital signage, and collect visitor location data using Bluetooth beacons and Wi-Fi. The server receives this location data and generates and stores behavioral information.

[0112] The server utilizes an AI model to analyze collected behavioral information and visitors' past history. This analysis generates optimal suggestions based on visitors' interests and preferences. These suggestions, which include information on selected products and event announcements, are provided to visitors through their smartphones or virtual avatars displayed on digital signage.

[0113] The virtual avatar is equipped with a generative AI model and uses natural language processing technology to interact with visitors. Based on feedback from visitors and emotional information obtained through voice and facial expression analysis, the avatar adjusts its suggestions in real time to capture visitors' attention.

[0114] For example, when a customer in an apparel shop moves around the store with their smartphone, Bluetooth beacons pinpoint the customer's location. Based on past purchase history and preferences, the server can then have an avatar on the smartphone suggest, "Here's a scarf that matches this coat."

[0115] This allows visitors to enjoy a shopping experience based on their own interests.

[0116] A concrete example of a prompt message would be a request sent to the server in the format of, "A customer has arrived at the apparel shop. Based on items related to recently purchased items, please suggest products that can be promoted." This prompt provides the basis for the generative AI model to make optimal suggestions.

[0117] This system allows stores to increase customers' willingness to buy and respond quickly to individual needs, thereby boosting sales.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The device uses Bluetooth beacons and Wi-Fi to detect the visitor's location data. Based on this, the device determines the visitor's current location and sends that data to the server. The input is location information, and the output generates location data that is sent to the server.

[0121] Step 2:

[0122] The server updates visitor behavior information based on the location data it receives. Furthermore, the server retrieves and analyzes past visitor behavior history and purchase data from a database. This identifies visitors' interests and preferences. The input is visitor location data and history data, and the output generates analysis results of interests and preferences.

[0123] Step 3:

[0124] The server uses the analysis results to activate a generative AI model, which generates product information and event guides best suited to the visitor. The generative AI model receives prompt text and analysis results as input and generates personalized suggestions as output.

[0125] Step 4:

[0126] The generated proposal is sent to the terminal, and a virtual avatar receives it. The avatar performs natural language processing and presents the proposal to the visitor visually and audibly. The input is the generated proposal, and the output is the generation of visual and audio data, which is then presented to the visitor.

[0127] Step 5:

[0128] The terminal uses its emotion analysis function to determine the visitor's reaction and sends it to the server as feedback. The server receives this feedback and uses it as training data for a generating AI model. The input is the visitor's reaction data, and the output is the generation of training data for the model.

[0129] Step 6:

[0130] The server continuously learns from feedback data and improves the accuracy of its suggestions. This allows it to provide more refined suggestions to future visitors. The input is training data, and the output is an improved generative AI model.

[0131] 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.

[0132] This invention is a system that combines an emotion engine to recognize user emotions, aiming to highly personalize the information provided to visitors. This system uses avatars displayed on digital signage to analyze the emotional state of visitors in real time and makes appropriate suggestions based on that information.

[0133] <Description of Embodiments>

[0134] 1. Utilizing the Emotional Engine

[0135] The terminal captures visitors' facial expressions with a camera and collects their voices with a microphone. This data is sent to an emotion engine, where emotion analysis is performed in real time. The emotion engine identifies states such as joy, surprise, and confusion, and sends this information to a server.

[0136] 2. Data collection and personalized suggestions

[0137] The server analyzes data from the emotion engine, combining it with past behavioral data. This analysis generates information and suggestions tailored to the visitor's current emotional state. For example, if a visitor expresses surprise, the server can suggest new products or services.

[0138] 3. Information presentation via avatars

[0139] The terminal displays suggestions sent from the server through an avatar. The avatar reflects the analysis results of the emotion engine and uses appropriate tone of voice and facial expressions to make suggestions to visitors. This enables more natural and approachable communication.

[0140] 4. Continuous feedback and system improvement

[0141] Users provide feedback on the presented content. This feedback is stored in a database on the server and used to improve future suggestions. The system continuously learns from this feedback, improving its coordination with the emotion engine and enabling it to provide more appropriate guidance to each visitor.

[0142] <Specific example>

[0143] For example, when a visitor to a tourist spot stops by an information center, the terminal uses an emotion engine to recognize that the visitor is relaxed based on their facial expression and voice. The server then suggests quiet parks or cafes so that the visitor can enjoy sightseeing while remaining relaxed. The avatar gently guides the visitor in a soft voice, saying, "Why not relax in a nearby park?"

[0144] This system can also help facilities understand the emotional state of visitors and provide more effective customer service. Visitors can maximize their experience within the facility by smoothly receiving information tailored to their individual needs.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] Users launch a dedicated app upon entering the facility and consent to the collection of location and emotional data. During the initial setup, the app requests permission to access the camera and microphone.

[0148] Step 2:

[0149] The device continuously captures the user's facial expressions and voice using cameras and microphones integrated into the facility's digital signage. This data is immediately transmitted to the emotion engine.

[0150] Step 3:

[0151] The emotion engine analyzes captured audio and facial expression data in real time to identify the user's emotional state (excitement, calmness, anxiety, etc.). The identified emotion data is immediately sent to the server.

[0152] Step 4:

[0153] The server receives data from the emotion engine and analyzes it in combination with previously accumulated behavioral history data. This process determines the most appropriate suggestion for the user's current emotional state.

[0154] Step 5:

[0155] The server uses a generative AI model to generate personalized suggestions based on the user's emotions and behavioral history. These suggestions include information on events of interest and recommended products.

[0156] Step 6:

[0157] The terminal presents information to the user through an avatar, based on suggestion data from the server. The avatar adjusts its facial expressions and tone of voice to match the user's emotional state, making suggestions in a friendly manner.

[0158] Step 7:

[0159] Users receive avatar suggestions and can act accordingly if they are interested. They can also skip suggestions if they are not interested.

[0160] Step 8:

[0161] The device records the user's responses and comments to the suggestions and sends them to the server as feedback. This feedback is used as training data to improve the accuracy of future suggestions.

[0162] Step 9:

[0163] The server analyzes the received feedback data and updates the AI ​​model. This enables more precise sentiment analysis and suggestion generation.

[0164] (Example 2)

[0165] 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 will be referred to as the "terminal."

[0166] Instead of providing visitors with uniform information, there is a need to provide personalized information at the appropriate time based on their individual emotional state and past behavioral history. Furthermore, effectively collecting visitor feedback and using it to improve the accuracy of suggestions is also a crucial challenge. Ultimately, this is necessary to optimize the visitor experience and deliver more engaging services.

[0167] 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.

[0168] In this invention, the server includes means for collecting visitor location information and generating visitor behavior data, means for analyzing the visitor's past behavior history and generating personalized suggestions, and means for presenting the generated suggestions to the visitor through a visual character. This enables the provision of timely and personalized information based on the visitor's emotional state. Furthermore, by collecting feedback from visitors and improving the accuracy of suggestions using a generative AI model, the visitor experience can be continuously enhanced.

[0169] "Location information" refers to data that represents the geographical location of an object or person within a specific place or space.

[0170] "Behavioral data" refers to information that records visitors' past movements and behavioral history, revealing their individual lifestyles and interests.

[0171] "Personalized suggestions" refer to suggestions that provide information and services tailored to individual needs based on the visitor's emotional state and past behavioral history.

[0172] A "visual character" is a virtual person or character displayed on digital signage or a terminal that serves to convey emotions or suggestions visually and audibly.

[0173] The "emotion analysis engine" is software that analyzes acquired voice and facial expression data and executes algorithms to identify the emotional state of visitors in real time.

[0174] A "generative AI model" is an artificial intelligence model used to dynamically optimize suggestions by learning from visitors' behavioral history and feedback data.

[0175] "Feedback" refers to evaluations and opinions provided by visitors regarding the information and services they received.

[0176] This invention relates to a system that recognizes the emotions of visitors and provides information tailored to their individual needs. The system includes a terminal equipped with a camera and microphone, software for performing emotion analysis, a server for managing and analyzing data, and a function for presenting information through a visual character.

[0177] The device captures visitors' facial expressions and voices using a camera and microphone. Specifically, it uses a high-resolution camera and a noise-canceling microphone to collect data in real time, effectively blocking out external noise. This data is sent to an emotion analysis engine for emotion analysis.

[0178] The server analyzes emotional data received from the emotion analysis engine, combining it with past behavioral history. This analysis uses a generative AI model based on deep learning algorithms. Based on the analysis results, it generates suggestions that are appropriate to the visitor's current emotional state.

[0179] The generated proposals are presented to visitors via a terminal using a visual character. This visual character uses an animation engine and a speech synthesis engine to express appropriate facial expressions and voice tones according to the emotional state, communicating with the user in a friendly manner.

[0180] Furthermore, users provide feedback on the presented information through input devices. This feedback information is stored in a server database and analyzed by a generating AI model. This makes it possible to improve the accuracy of suggestions and maximize the visitor experience.

[0181] As a concrete example, when a visitor to a tourist destination stops by an information center, the terminal uses emotion analysis to recognize that the visitor is relaxed based on their facial expressions and voice. Based on this information, the server suggests parks or cafes where the visitor can spend time quietly. A visual character, in a gentle voice, guides the visitor by saying, "Why not relax in a nearby park?" In this way, visitors can receive services tailored to their individual needs.

[0182] An example of a prompt to input into a generative AI model would be, "Based on visitor sentiment data, please suggest some activities that would allow visitors to spend their time quietly."

[0183] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0184] Step 1:

[0185] The device uses a camera and microphone to capture visitors' facial expressions and voices. The input is the visitor's face and voice, and the output is their digital data. In this process, a high-resolution camera captures facial expressions, and a microphone with noise cancellation digitizes the voice. This data is then prepared for transmission to an emotion analysis engine.

[0186] Step 2:

[0187] The device transmits the acquired facial and audio data to the emotion analysis engine. The input is digitized facial and audio data, and the output is an analysis result indicating the emotional state. The emotion analysis engine uses a deep learning algorithm to quantify specific emotions. For example, it identifies emotions such as joy, surprise, or confusion.

[0188] Step 3:

[0189] The server analyzes emotional data received from the emotion analysis engine, combining it with past behavioral history data of visitors. The input consists of emotional data and behavioral history, while the output is personalized suggestions. This analysis is performed using a generative AI model to generate optimal suggestions tailored to specific emotional states.

[0190] Step 4:

[0191] The server sends the generated suggestions to the terminal. The input is the generated suggestion data, and the output is the suggestion information for the terminal. The server processes this in real time and prepares to transmit relevant information to the visual character.

[0192] Step 5:

[0193] The terminal presents suggestions received from the server to visitors via a visual character. The input is the suggested information, and the output is the information presented by the visual character. Using an animation engine and a speech synthesis engine, the character guides visitors with the most appropriate facial expressions and voice.

[0194] Step 6:

[0195] Users provide feedback on information presented by animated characters. Input is the presented content and the customer response, while output is feedback data. Users can easily submit their opinions via touch panel or voice input.

[0196] Step 7:

[0197] The server collects user feedback and stores it in a database. The input is feedback data, and the output is training data for improvement. The server utilizes a generative AI model to analyze the feedback, thereby improving the accuracy of suggestions and optimizing the system.

[0198] (Application Example 2)

[0199] 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".

[0200] In modern brick-and-mortar stores, visitors are often offered a uniform service, making it difficult to provide personalized experiences tailored to each visitor's emotions and circumstances. Therefore, there is a need for technology that can provide optimal suggestions and guidance in real time, based on the visitor's emotional state and individual needs.

[0201] 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.

[0202] This invention includes a server that uses a facial expression analysis engine to recognize the emotional state of visitors and transmits emotional data to a large-scale distributed platform; a server that analyzes the information using a cloud service and provides visitors with the most suitable products and information; and a server that displays the generated suggestions to visitors via a digital character. This makes it possible to provide personalized suggestions tailored to the visitor's emotions and situation in real time, thereby improving the visitor's experience.

[0203] "Visitors" refers to individual customers or users who visit a facility or store.

[0204] "Location information" refers to data that indicates a visitor is in a specific location within a facility, and is obtained using GPS or beacon technology.

[0205] A "behavioral information infrastructure" refers to a database or platform for creating a dataset that includes visitor location information and past behavioral history.

[0206] A "facial expression analysis engine" refers to a software or hardware system that detects a visitor's facial expressions and analyzes their emotional state in real time.

[0207] A "large-scale distributed infrastructure" refers to a computing infrastructure that utilizes multiple servers and cloud services to efficiently process and analyze vast amounts of data.

[0208] "Cloud services" refer to services that allow users to remotely access and manage data and applications via the internet.

[0209] A "digital character" is a computer-generated avatar or virtual character used for interaction with visitors and for presenting information.

[0210] The system that implements this application uses multiple hardware and software components to analyze visitors' emotional states and provide personalized information. The server is built on a cloud service infrastructure and utilizes a facial expression analysis engine for emotion analysis. Specifically, services such as Microsoft® Azure® Face API and Amazon Recognition are available.

[0211] The server receives image and audio data of visitors transmitted from terminals. Terminals consist of smartphones, fixed cameras in stores, microphones, etc., and capture visitors' facial expressions and voices in real time. The received data is processed by an emotion analysis engine in the cloud, and the visitor's emotional state is identified. The resulting emotion data is processed using a large-scale distributed infrastructure and analyzed in combination with the visitor's past behavior data.

[0212] The server generates optimal product suggestions and information for visitors based on the analysis results. This generation utilizes a generative AI model using Python and TENSORFLOW®. The generated suggestions are presented to visitors through a digital character. The digital character operates on an application implemented using React Native and provides information by changing its voice tone and facial expressions based on emotional data.

[0213] Users can provide feedback on the information presented by the digital character. This feedback is stored in a database on the server side and used to improve the accuracy of subsequent suggestions. The system inputs this feedback into a generation AI model via prompts, continuously generating optimal suggestions.

[0214] For example, if a visitor who has stopped by a cafe in a shopping mall is seen smiling happily in front of a terminal, the system will pick up on that emotion and suggest that the visitor enjoy a new menu item. A prompt such as "Would you like to try a new dessert?" will be used to stimulate the visitor's desire to purchase.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The device acquires image and audio data when a visitor stands in front of the camera. It captures data in real time using the camera and microphone and sends that data to the server. The input is a facial image captured by the camera and audio captured by the microphone, and the output is a data packet containing this data.

[0218] Step 2:

[0219] The server receives image and audio data transmitted from the terminal and uses a facial expression analysis engine to identify emotional states. Data processing for emotion analysis involves processing the image data and analyzing facial expressions to output emotion labels such as joy or surprise. This processing is carried out on a large-scale distributed infrastructure.

[0220] Step 3:

[0221] The server compares the sentiment analysis results with the visitor's past behavior records stored in a cloud-based database to generate personalized suggestions. A generative AI model is used to take the analysis results as prompts and output the generated suggestions. In this process, the inputs are the sentiment analysis results and past behavior data, and the output data includes the most suitable suggestions based on those results.

[0222] Step 4:

[0223] The server passes the generated suggestions to a digital character, which then displays them to visitors on its screen. The digital character is built using a React Native application and provides information using emotionally responsive voice tones and facial expressions. The input here is the generated suggestions, and the output is the actions and sounds generated by the digital character.

[0224] Step 5:

[0225] Users provide feedback on suggestions offered by digital characters. This feedback is sent to a server and stored in a database. The input is user feedback information, and based on this, data analysis is performed to generate output for improving the suggestions.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention provides a system that uses an avatar agent displayed on digital signage to provide personalized information to visitors. Specific embodiments are shown below.

[0243] <Description of Embodiments>

[0244] 1. Data Collection and Analysis

[0245] The device collects location information using Bluetooth beacons and Wi-Fi when visitors enter the facility, and sends this data to a server to obtain visitor behavior data. With the visitor's permission, past purchase history and event participation history are also collected and stored on the server through devices with a dedicated app installed.

[0246] 2. Personalized suggestion generation

[0247] The server analyzes accumulated behavioral and historical data to identify visitors' interests and preferences. Using a generative AI model, it selects the most suitable events and products for each visitor. Specifically, it selects information that is likely to interest them on their next visit, based on the genres of events they have attended in the past and their purchase history.

[0248] 3. Information presentation via avatars

[0249] The terminal displays an avatar on the screen based on the suggestions sent from the server, and conveys information to visitors in a natural conversational format. The avatar uses voice to explain the details of the suggested events and products in a one-on-one dialogue-like manner.

[0250] 4. Utilization of Sentiment Analysis

[0251] The server analyzes visitors' voice data and facial expressions to evaluate their emotions. This allows the server to understand their reactions to suggestions in real time and determine if the suggestions are appropriate. For example, if a visitor smiles, the server can determine that the suggestion was successful and proceed with the guidance further.

[0252] 5. Feedback and Learning

[0253] The terminal sends feedback from visitors to the server. The server uses this feedback data to continuously train its AI model, improving the accuracy of its suggestions. This allows the system to take into account visitors' reactions and interests, enabling it to provide even more accurate information on subsequent visits.

[0254] <Specific example>

[0255] For example, when a visitor enters a specific area of ​​a tourist destination, an avatar placed on the device will guide them to nearby attractions and recommended routes, saying something like, "Here are some convenient spots to visit next." Furthermore, if the visitor's expression shows surprise, the avatar will continue to offer suggestions, providing information as long as their interest remains.

[0256] This system allows visitors to receive personalized information more efficiently and enjoy a special experience. Furthermore, it enables facilities to gain a deeper understanding of visitor behavior and optimize resources for operations and guidance.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The user installs a dedicated app on their smartphone and consents to the collection of location information and behavioral data. At this stage, the user's permission is explicitly obtained.

[0260] Step 2:

[0261] The device uses Bluetooth beacons and Wi-Fi access points placed within the facility to collect location information about the user's movement path. The collected data is used to instantly identify nearby shops and areas.

[0262] Step 3:

[0263] The server receives location information and activity history transmitted from the terminal and stores it in a database. Furthermore, it analyzes this data to extract user behavior patterns and interests.

[0264] Step 4:

[0265] The server uses a generative AI model to create a list of events and products tailored to the user. This list is personalized based on the user's past behavioral data and preferences.

[0266] Step 5:

[0267] The device retrieves a list of recommendations sent from the server and launches an application that displays an avatar on the screen. The avatar guides the user through the recommendations using text and voice.

[0268] Step 6:

[0269] The device captures the user's voice and facial expressions using its camera and microphone, and performs real-time emotion analysis. This information is used to dynamically change the avatar's responses and suggestions.

[0270] Step 7:

[0271] Users provide feedback on the suggestions. For example, they can request additional information if they are interested, or skip it if they are not. This feedback is recorded digitally.

[0272] Step 8:

[0273] The server analyzes user feedback and updates the generated AI model based on it, improving the accuracy of future suggestions. The feedback is stored in a database and used in the continuous learning process.

[0274] (Example 1)

[0275] 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."

[0276] In conventional methods of providing more personalized information to visitors, it has been difficult to improve the accuracy of proposals and grasp visitors' reactions in real time. In addition, it has been difficult to adjust information presentation according to the emotional state of visitors, and efficient information transmission has not been achieved. As a result, there has been a problem that the degree of match between the information required by visitors and the information provided is low.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0278] In this invention, the server includes means for acquiring the location information of a visitor and creating the behavior information of the visitor, means for analyzing the past behavior information of the visitor and generating a personalized proposal, and means for presenting the generated proposal to the visitor through a visual character. Thereby, it becomes possible to provide optimized information to the visitor in real time and improve the satisfaction of the visitor and the operation efficiency of the facility.

[0279] A "visitor" refers to an individual who visits a facility or a specific location.

[0280] "Location information" refers to information indicating the geographical location at a specific place.

[0281] "Behavior information" is a set of data recording the activities and movements of a visitor.

[0282] "Personalized proposal" refers to providing customized information based on the specific interests and preferences of a visitor.

[0283] A "visual character" is a person or character embodied by animation or an image displayed on a digital display.

[0284] "Response" refers to data indicating feedback or reaction from a visitor.

[0285] The "means of continuous learning" is a process of updating an algorithm while using past data to improve performance.

[0286] "Emotion" is an expression indicating the inner psychology or feelings of the visitor.

[0287] "Analysis of voice and expression" is a technology that evaluates the tone of the visitor's voice and facial movements and infers emotions and intentions based on them.

[0288] An "artificial intelligence model" is an algorithm or program designed to solve complex problems by incorporating the process of learning by a computer system.

[0289] This invention will explain a method for specifically implementing a system for providing personalized information to visitors.

[0290] The server stores the location information and behavior information of the visitor in the database. This uses the collection of location information using Bluetooth beacons or Wi-Fi. By installing a dedicated app on the visitor's device, past purchase history and event participation history are also collected.

[0291] The terminal transmits the collected data to the server to make it processable in real time. The server uses programming languages such as Python and R to perform data analysis. Thereby, the interests and preferences of the visitor are identified, and personalized proposals are generated.

[0292] The server creates a proposal using the generated AI model. This proposal is input into the AI model with the following prompt sentence: "The past purchase history of the visitor is product A, product B, and the event history is event X, event Y. What products or events should be proposed for the next visit of the visitor?"

[0293] The terminal displays an avatar on digital signage based on suggestions sent from the server. Using a visual character, it provides information to visitors in an interactive format via voice and text. It utilizes natural language processing technologies such as the Google Cloud Speech-to-Text API.

[0294] After receiving a suggestion, the user provides feedback to the system through their response. The terminal sends this feedback to the server, which continuously trains the AI ​​model to improve the accuracy of the suggestions. A concrete example of this operation is when a visitor enters a new product section, and the terminal suggests, "Here are our latest recommended products," through an avatar.

[0295] This invention allows visitors to receive information of interest on the spot and have a unique experience, while facility operators can deepen their understanding of visitors and achieve efficient resource management.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The device acquires the visitor's location information using Bluetooth beacons and Wi-Fi. This allows it to identify which area the visitor is in. The input is signal strength from beacons and Wi-Fi connection information, and the output is the visitor's location coordinates. Specifically, the device analyzes the signal strength and determines the visitor's location as coordinates on a map of the facility.

[0299] Step 2:

[0300] The terminal retrieves visitors' past purchase and event participation history through a dedicated app. Input is historical information from the visitor's device, and output is a behavioral history database constructed based on that information. The terminal compiles this historical information into information packets and sends them to the server.

[0301] Step 3:

[0302] The server analyzes the received location information and action history data of the visitor. Here, data is processed using Python or the like to identify the interests and preferences of the visitor. The input is the location information and action history, and the output is the estimated preference data of the visitor. The server models the data using statistical analysis methods to clarify the preference patterns.

[0303] Step 4:

[0304] The server generates proposals using a generative AI model. The input is the analyzed preference data and a prompt sentence like: "The visitor's past purchase history is product A, product B, and the event history is event X, event Y. What products or events should be proposed for the next visit of the visitor?" The output is the generated personalized proposal. The AI model processes the input and outputs the proposal in text format.

[0305] Step 5:

[0306] The terminal displays the proposal content received from the server on the digital signage. Using visual characters, the proposal content is conveyed to the visitor audibly. The input is the proposal content from the server, and the output is the visual and audible information presentation through the avatar. The terminal uses the Google Cloud Speech-to-Text API to perform the conversion from text to voice.

[0307] Step 6:

[0308] The user provides feedback on the information presented by the terminal. The server trains the AI model to improve the accuracy of the proposal based on the feedback. The input is the feedback information from the user, and the output is the update of the AI model based on the learning. The server adds the feedback to the database and conducts training to improve the performance of the model.

[0309] (Application Example 1)

[0310] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0311] In today's retail industry, accurately understanding consumer needs and interests and providing personalized recommendations to each individual is becoming increasingly important. However, traditional systems have struggled to provide real-time, personalized recommendations to customers visiting stores, often resulting in missed sales opportunities. Therefore, there is a need for a way to provide individually personalized shopping experiences to customers within the store.

[0312] 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.

[0313] In this invention, the server includes means for collecting visitor location data and generating visitor behavior information, means for analyzing the visitor's past behavior history and generating personalized suggestions, and means for presenting the generated suggestions to the visitor via a virtual avatar. This makes it possible to provide each customer with optimal product information and event announcements in real time.

[0314] "Visitor location data" refers to information indicating the location of visitors within a facility or store, and is collected using communication technologies such as Bluetooth and Wi-Fi.

[0315] "Visitor behavior information" refers to data that includes visitors' past activity history and movement patterns within the facility, and is used to analyze visitors' interests and preferences.

[0316] "Personalized suggestions" refer to information that is individually optimized and presented to visitors based on their behavioral history and preferences, including product information and event announcements.

[0317] A "virtual avatar" is a digital character that is displayed on digital signage or communication terminals and provides information to visitors in a natural conversational format.

[0318] A "communication terminal" is an electronic device used to send and receive information, and specifically includes smartphones and tablets.

[0319] To implement this invention, it is essential to install communication terminals and servers within stores or facilities. The communication terminals mainly consist of visitors' smartphones and digital signage, and collect visitor location data using Bluetooth beacons and Wi-Fi. The server receives this location data and generates and stores behavioral information.

[0320] The server utilizes an AI model to analyze collected behavioral information and visitors' past history. This analysis generates optimal suggestions based on visitors' interests and preferences. These suggestions, which include information on selected products and event announcements, are provided to visitors through their smartphones or virtual avatars displayed on digital signage.

[0321] The virtual avatar is equipped with a generative AI model and uses natural language processing technology to interact with visitors. Based on feedback from visitors and emotional information obtained through voice and facial expression analysis, the avatar adjusts its suggestions in real time to capture visitors' attention.

[0322] For example, when a customer in an apparel shop moves around the store with their smartphone, Bluetooth beacons pinpoint the customer's location. Based on past purchase history and preferences, the server can then have an avatar on the smartphone suggest, "Here's a scarf that matches this coat."

[0323] This allows visitors to enjoy a shopping experience based on their own interests.

[0324] A concrete example of a prompt message would be a request sent to the server in the format of, "A customer has arrived at the apparel shop. Based on items related to recently purchased items, please suggest products that can be promoted." This prompt provides the basis for the generative AI model to make optimal suggestions.

[0325] This system allows stores to increase customers' willingness to buy and respond quickly to individual needs, thereby boosting sales.

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] The device uses Bluetooth beacons and Wi-Fi to detect the visitor's location data. Based on this, the device determines the visitor's current location and sends that data to the server. The input is location information, and the output generates location data that is sent to the server.

[0329] Step 2:

[0330] The server updates visitor behavior information based on the location data it receives. Furthermore, the server retrieves and analyzes past visitor behavior history and purchase data from a database. This identifies visitors' interests and preferences. The input is visitor location data and history data, and the output generates analysis results of interests and preferences.

[0331] Step 3:

[0332] The server uses the analysis results to activate a generative AI model, which generates product information and event guides best suited to the visitor. The generative AI model receives prompt text and analysis results as input and generates personalized suggestions as output.

[0333] Step 4:

[0334] The generated proposal is sent to the terminal, and a virtual avatar receives it. The avatar performs natural language processing and presents the proposal to the visitor visually and audibly. The input is the generated proposal, and the output is the generation of visual and audio data, which is then presented to the visitor.

[0335] Step 5:

[0336] The terminal uses its emotion analysis function to determine the visitor's reaction and sends it to the server as feedback. The server receives this feedback and uses it as training data for a generating AI model. The input is the visitor's reaction data, and the output is the generation of training data for the model.

[0337] Step 6:

[0338] The server continuously learns from feedback data and improves the accuracy of its suggestions. This allows it to provide more refined suggestions to future visitors. The input is training data, and the output is an improved generative AI model.

[0339] 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.

[0340] This invention is a system that combines an emotion engine to recognize user emotions, aiming to highly personalize the information provided to visitors. This system uses avatars displayed on digital signage to analyze the emotional state of visitors in real time and makes appropriate suggestions based on that information.

[0341] <Description of Embodiments>

[0342] 1. Utilizing the Emotional Engine

[0343] The terminal captures visitors' facial expressions with a camera and collects their voices with a microphone. This data is sent to an emotion engine, where emotion analysis is performed in real time. The emotion engine identifies states such as joy, surprise, and confusion, and sends this information to a server.

[0344] 2. Data collection and personalized suggestions

[0345] The server analyzes data from the emotion engine, combining it with past behavioral data. This analysis generates information and suggestions tailored to the visitor's current emotional state. For example, if a visitor expresses surprise, the server can suggest new products or services.

[0346] 3. Information presentation via avatars

[0347] The terminal displays suggestions sent from the server through an avatar. The avatar reflects the analysis results of the emotion engine and uses appropriate tone of voice and facial expressions to make suggestions to visitors. This enables more natural and approachable communication.

[0348] 4. Continuous feedback and system improvement

[0349] Users provide feedback on the presented content. This feedback is stored in a database on the server and used to improve future suggestions. The system continuously learns from this feedback, improving its coordination with the emotion engine and enabling it to provide more appropriate guidance to each visitor.

[0350] <Specific example>

[0351] For example, when a visitor to a tourist spot stops by an information center, the terminal uses an emotion engine to recognize that the visitor is relaxed based on their facial expression and voice. The server then suggests quiet parks or cafes so that the visitor can enjoy sightseeing while remaining relaxed. The avatar gently guides the visitor in a soft voice, saying, "Why not relax in a nearby park?"

[0352] This system can also help facilities understand the emotional state of visitors and provide more effective customer service. Visitors can maximize their experience within the facility by smoothly receiving information tailored to their individual needs.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] Users launch a dedicated app upon entering the facility and consent to the collection of location and emotional data. During the initial setup, the app requests permission to access the camera and microphone.

[0356] Step 2:

[0357] The device continuously captures the user's facial expressions and voice using cameras and microphones integrated into the facility's digital signage. This data is immediately transmitted to the emotion engine.

[0358] Step 3:

[0359] The emotion engine analyzes captured audio and facial expression data in real time to identify the user's emotional state (excitement, calmness, anxiety, etc.). The identified emotion data is immediately sent to the server.

[0360] Step 4:

[0361] The server receives data from the emotion engine and analyzes it in combination with previously accumulated behavioral history data. This process determines the most appropriate suggestion for the user's current emotional state.

[0362] Step 5:

[0363] The server uses a generative AI model to generate personalized suggestions based on the user's emotions and behavioral history. These suggestions include information on events of interest and recommended products.

[0364] Step 6:

[0365] The terminal presents information to the user through an avatar, based on suggestion data from the server. The avatar adjusts its facial expressions and tone of voice to match the user's emotional state, making suggestions in a friendly manner.

[0366] Step 7:

[0367] Users receive avatar suggestions and can act accordingly if they are interested. They can also skip suggestions if they are not interested.

[0368] Step 8:

[0369] The device records the user's responses and comments to the suggestions and sends them to the server as feedback. This feedback is used as training data to improve the accuracy of future suggestions.

[0370] Step 9:

[0371] The server analyzes the received feedback data and updates the AI ​​model. This enables more precise sentiment analysis and suggestion generation.

[0372] (Example 2)

[0373] 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".

[0374] Instead of providing visitors with uniform information, there is a need to provide personalized information at the appropriate time based on their individual emotional state and past behavioral history. Furthermore, effectively collecting visitor feedback and using it to improve the accuracy of suggestions is also a crucial challenge. Ultimately, this is necessary to optimize the visitor experience and deliver more engaging services.

[0375] 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.

[0376] In this invention, the server includes means for collecting visitor location information and generating visitor behavior data, means for analyzing the visitor's past behavior history and generating personalized suggestions, and means for presenting the generated suggestions to the visitor through a visual character. This enables the provision of timely and personalized information based on the visitor's emotional state. Furthermore, by collecting feedback from visitors and improving the accuracy of suggestions using a generative AI model, the visitor experience can be continuously enhanced.

[0377] "Location information" refers to data that represents the geographical location of an object or person within a specific place or space.

[0378] "Behavioral data" refers to information that records visitors' past movements and behavioral history, revealing their individual lifestyles and interests.

[0379] "Personalized suggestions" refer to suggestions that provide information and services tailored to individual needs based on the visitor's emotional state and past behavioral history.

[0380] A "visual character" is a virtual person or character displayed on digital signage or a terminal that serves to convey emotions or suggestions visually and audibly.

[0381] The "emotion analysis engine" is software that analyzes acquired voice and facial expression data and executes algorithms to identify the emotional state of visitors in real time.

[0382] A "generative AI model" is an artificial intelligence model used to dynamically optimize suggestions by learning from visitors' behavioral history and feedback data.

[0383] "Feedback" refers to evaluations and opinions provided by visitors regarding the information and services they received.

[0384] This invention relates to a system that recognizes the emotions of visitors and provides information tailored to their individual needs. The system includes a terminal equipped with a camera and microphone, software for performing emotion analysis, a server for managing and analyzing data, and a function for presenting information through a visual character.

[0385] The device captures visitors' facial expressions and voices using a camera and microphone. Specifically, it uses a high-resolution camera and a noise-canceling microphone to collect data in real time, effectively blocking out external noise. This data is sent to an emotion analysis engine for emotion analysis.

[0386] The server analyzes emotional data received from the emotion analysis engine, combining it with past behavioral history. This analysis uses a generative AI model based on deep learning algorithms. Based on the analysis results, it generates suggestions that are appropriate to the visitor's current emotional state.

[0387] The generated proposals are presented to visitors via a terminal using a visual character. This visual character uses an animation engine and a speech synthesis engine to express appropriate facial expressions and voice tones according to the emotional state, communicating with the user in a friendly manner.

[0388] Furthermore, users provide feedback on the presented information through input devices. This feedback information is stored in a server database and analyzed by a generating AI model. This makes it possible to improve the accuracy of suggestions and maximize the visitor experience.

[0389] As a concrete example, when a visitor to a tourist destination stops by an information center, the terminal uses emotion analysis to recognize that the visitor is relaxed based on their facial expressions and voice. Based on this information, the server suggests parks or cafes where the visitor can spend time quietly. A visual character, in a gentle voice, guides the visitor by saying, "Why not relax in a nearby park?" In this way, visitors can receive services tailored to their individual needs.

[0390] An example of a prompt to input into a generative AI model would be, "Based on visitor sentiment data, please suggest some activities that would allow visitors to spend their time quietly."

[0391] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0392] Step 1:

[0393] The device uses a camera and microphone to capture visitors' facial expressions and voices. The input is the visitor's face and voice, and the output is their digital data. In this process, a high-resolution camera captures facial expressions, and a microphone with noise cancellation digitizes the voice. This data is then prepared for transmission to an emotion analysis engine.

[0394] Step 2:

[0395] The device transmits the acquired facial and audio data to the emotion analysis engine. The input is digitized facial and audio data, and the output is an analysis result indicating the emotional state. The emotion analysis engine uses a deep learning algorithm to quantify specific emotions. For example, it identifies emotions such as joy, surprise, or confusion.

[0396] Step 3:

[0397] The server analyzes emotional data received from the emotion analysis engine, combining it with past behavioral history data of visitors. The input consists of emotional data and behavioral history, while the output is personalized suggestions. This analysis is performed using a generative AI model to generate optimal suggestions tailored to specific emotional states.

[0398] Step 4:

[0399] The server sends the generated suggestions to the terminal. The input is the generated suggestion data, and the output is the suggestion information for the terminal. The server processes this in real time and prepares to transmit relevant information to the visual character.

[0400] Step 5:

[0401] The terminal presents suggestions received from the server to visitors via a visual character. The input is the suggested information, and the output is the information presented by the visual character. Using an animation engine and a speech synthesis engine, the character guides visitors with the most appropriate facial expressions and voice.

[0402] Step 6:

[0403] Users provide feedback on information presented by animated characters. Input is the presented content and the customer response, while output is feedback data. Users can easily submit their opinions via touch panel or voice input.

[0404] Step 7:

[0405] The server collects user feedback and stores it in a database. The input is feedback data, and the output is training data for improvement. The server utilizes a generative AI model to analyze the feedback, thereby improving the accuracy of suggestions and optimizing the system.

[0406] (Application Example 2)

[0407] 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."

[0408] In modern brick-and-mortar stores, visitors are often offered a uniform service, making it difficult to provide personalized experiences tailored to each visitor's emotions and circumstances. Therefore, there is a need for technology that can provide optimal suggestions and guidance in real time, based on the visitor's emotional state and individual needs.

[0409] 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.

[0410] This invention includes a server that uses a facial expression analysis engine to recognize the emotional state of visitors and transmits emotional data to a large-scale distributed platform; a server that analyzes the information using a cloud service and provides visitors with the most suitable products and information; and a server that displays the generated suggestions to visitors via a digital character. This makes it possible to provide personalized suggestions tailored to the visitor's emotions and situation in real time, thereby improving the visitor's experience.

[0411] "Visitors" refers to individual customers or users who visit a facility or store.

[0412] "Location information" refers to data that indicates a visitor is in a specific location within a facility, and is obtained using GPS or beacon technology.

[0413] A "behavioral information infrastructure" refers to a database or platform for creating a dataset that includes visitor location information and past behavioral history.

[0414] A "facial expression analysis engine" refers to a software or hardware system that detects a visitor's facial expressions and analyzes their emotional state in real time.

[0415] A "large-scale distributed infrastructure" refers to a computing infrastructure that utilizes multiple servers and cloud services to efficiently process and analyze vast amounts of data.

[0416] "Cloud services" refer to services that allow users to remotely access and manage data and applications via the internet.

[0417] A "digital character" is a computer-generated avatar or virtual character used for interaction with visitors and for presenting information.

[0418] The system that implements this application uses multiple hardware and software components to analyze visitors' emotional states and provide personalized information. The server is built on a cloud service infrastructure and utilizes a facial expression analysis engine for emotion analysis. Specifically, services such as Microsoft Azure's Face API and Amazon Recognition are available.

[0419] The server receives image and audio data of visitors transmitted from terminals. Terminals consist of smartphones, fixed cameras in stores, microphones, etc., and capture visitors' facial expressions and voices in real time. The received data is processed by an emotion analysis engine in the cloud, and the visitor's emotional state is identified. The resulting emotion data is processed using a large-scale distributed infrastructure and analyzed in combination with the visitor's past behavior data.

[0420] The server generates optimal product suggestions and information for visitors based on the analysis results. This generation utilizes a generative AI model using Python and TensorFlow. The generated suggestions are presented to visitors through a digital character. The digital character operates on an application implemented using React Native and provides information by changing its voice tone and facial expressions based on emotional data.

[0421] Users can provide feedback on the information presented by the digital character. This feedback is stored in a database on the server side and used to improve the accuracy of subsequent suggestions. The system inputs this feedback into a generation AI model via prompts, continuously generating optimal suggestions.

[0422] For example, if a visitor who has stopped by a cafe in a shopping mall is seen smiling happily in front of a terminal, the system will pick up on that emotion and suggest that the visitor enjoy a new menu item. A prompt such as "Would you like to try a new dessert?" will be used to stimulate the visitor's desire to purchase.

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] The device acquires image and audio data when a visitor stands in front of the camera. It captures data in real time using the camera and microphone and sends that data to the server. The input is a facial image captured by the camera and audio captured by the microphone, and the output is a data packet containing this data.

[0426] Step 2:

[0427] The server receives image and audio data transmitted from the terminal and uses a facial expression analysis engine to identify emotional states. Data processing for emotion analysis involves processing the image data and analyzing facial expressions to output emotion labels such as joy or surprise. This processing is carried out on a large-scale distributed infrastructure.

[0428] Step 3:

[0429] The server compares the sentiment analysis results with the visitor's past behavior records stored in a cloud-based database to generate personalized suggestions. A generative AI model is used to take the analysis results as prompts and output the generated suggestions. In this process, the inputs are the sentiment analysis results and past behavior data, and the output data includes the most suitable suggestions based on those results.

[0430] Step 4:

[0431] The server passes the generated suggestions to a digital character, which then displays them to visitors on its screen. The digital character is built using a React Native application and provides information using emotionally responsive voice tones and facial expressions. The input here is the generated suggestions, and the output is the actions and sounds generated by the digital character.

[0432] Step 5:

[0433] Users provide feedback on suggestions offered by digital characters. This feedback is sent to a server and stored in a database. The input is user feedback information, and based on this, data analysis is performed to generate output for improving the suggestions.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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".

[0450] This invention provides a system that uses an avatar agent displayed on digital signage to provide personalized information to visitors. Specific embodiments are shown below.

[0451] <Description of Embodiments>

[0452] 1. Data Collection and Analysis

[0453] The device collects location information using Bluetooth beacons and Wi-Fi when visitors enter the facility, and sends this data to a server to obtain visitor behavior data. With the visitor's permission, past purchase history and event participation history are also collected and stored on the server through devices with a dedicated app installed.

[0454] 2. Personalized suggestion generation

[0455] The server analyzes accumulated behavioral and historical data to identify visitors' interests and preferences. Using a generative AI model, it selects the most suitable events and products for each visitor. Specifically, it selects information that is likely to interest them on their next visit, based on the genres of events they have attended in the past and their purchase history.

[0456] 3. Information presentation via avatars

[0457] The terminal displays an avatar on the screen based on the suggestions sent from the server, and conveys information to visitors in a natural conversational format. The avatar uses voice to explain the details of the suggested events and products in a one-on-one dialogue-like manner.

[0458] 4. Utilization of Sentiment Analysis

[0459] The server analyzes visitors' voice data and facial expressions to evaluate their emotions. This allows the server to understand their reactions to suggestions in real time and determine if the suggestions are appropriate. For example, if a visitor smiles, the server can determine that the suggestion was successful and proceed with the guidance further.

[0460] 5. Feedback and Learning

[0461] The terminal sends feedback from visitors to the server. The server uses this feedback data to continuously train its AI model, improving the accuracy of its suggestions. This allows the system to take into account visitors' reactions and interests, enabling it to provide even more accurate information on subsequent visits.

[0462] <Specific example>

[0463] For example, when a visitor enters a specific area of ​​a tourist destination, an avatar placed on the device will guide them to nearby attractions and recommended routes, saying something like, "Here are some convenient spots to visit next." Furthermore, if the visitor's expression shows surprise, the avatar will continue to offer suggestions, providing information as long as their interest remains.

[0464] This system allows visitors to receive personalized information more efficiently and enjoy a special experience. Furthermore, it enables facilities to gain a deeper understanding of visitor behavior and optimize resources for operations and guidance.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] The user installs a dedicated app on their smartphone and consents to the collection of location information and behavioral data. At this stage, the user's permission is explicitly obtained.

[0468] Step 2:

[0469] The device uses Bluetooth beacons and Wi-Fi access points placed within the facility to collect location information about the user's movement path. The collected data is used to instantly identify nearby shops and areas.

[0470] Step 3:

[0471] The server receives location information and activity history transmitted from the terminal and stores it in a database. Furthermore, it analyzes this data to extract user behavior patterns and interests.

[0472] Step 4:

[0473] The server uses a generative AI model to create a list of events and products tailored to the user. This list is personalized based on the user's past behavioral data and preferences.

[0474] Step 5:

[0475] The device retrieves a list of recommendations sent from the server and launches an application that displays an avatar on the screen. The avatar guides the user through the recommendations using text and voice.

[0476] Step 6:

[0477] The device captures the user's voice and facial expressions using its camera and microphone, and performs real-time emotion analysis. This information is used to dynamically change the avatar's responses and suggestions.

[0478] Step 7:

[0479] Users provide feedback on the suggestions. For example, they can request additional information if they are interested, or skip it if they are not. This feedback is recorded digitally.

[0480] Step 8:

[0481] The server analyzes user feedback and updates the generated AI model based on it, improving the accuracy of future suggestions. The feedback is stored in a database and used in the continuous learning process.

[0482] (Example 1)

[0483] 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."

[0484] Traditional methods of providing more personalized information to visitors make it difficult to improve the accuracy of suggestions and to grasp visitors' reactions in real time. In addition, it is difficult to adjust the information presented according to the visitor's emotional state, resulting in inefficient information transmission. This led to the problem of a low degree of match between the information visitors seek and the information they receive.

[0485] 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.

[0486] In this invention, the server includes means for acquiring visitor location information and creating visitor behavior information, means for analyzing the visitor's past behavior information and generating personalized suggestions, and means for presenting the generated suggestions to the visitor through a visual character. This makes it possible to provide visitors with optimized information in real time and improve visitor satisfaction and the operational efficiency of the facility.

[0487] A "visitor" is an individual who goes to a facility or a specific place.

[0488] "Location information" refers to information that indicates the geographical location of a specific place.

[0489] "Behavioral information" refers to a collection of data that records the activities and movements of visitors.

[0490] "Personalized suggestions" refer to providing visitors with customized information based on their specific interests and preferences.

[0491] A "visual character" is a person or character that is brought to life through animation or images displayed on a digital screen.

[0492] "Responses" refer to data that includes feedback and reactions from visitors.

[0493] "Methods for continuous learning" refer to the process of updating algorithms using past data to improve performance.

[0494] "Emotions" are expressions that describe the visitor's inner psychology or feelings.

[0495] "Voice and facial analysis" is a technique that evaluates a visitor's tone of voice and facial movements, and infers their emotions and intentions based on that.

[0496] An "artificial intelligence model" is an algorithm or program designed to solve complex problems by incorporating the learning process of computer systems.

[0497] This invention describes a method for specifically implementing a system for providing personalized information to visitors.

[0498] The server stores visitor location and behavior information in a database. This uses location data collection via Bluetooth beacons and Wi-Fi. By installing a dedicated app on the visitor's device, past purchase history and event participation history are also collected.

[0499] The terminal sends the collected data to the server for real-time processing. The server uses programming languages ​​such as Python and R to perform data analysis. This identifies visitors' interests and preferences and generates personalized suggestions.

[0500] The server generates suggestions using a generative AI model. These suggestions are input to the AI ​​model with prompts such as: "The visitor's past purchase history includes products A and B, and their event history includes events X and Y. What products or events would you suggest for the visitor's next visit?"

[0501] The terminal displays an avatar on digital signage based on suggestions sent from the server. Using a visual character, it provides information to visitors in an interactive format via voice and text. It utilizes natural language processing technologies such as the Google Cloud Speech-to-Text API.

[0502] After receiving a suggestion, the user provides feedback to the system through their response. The terminal sends this feedback to the server, which continuously trains the AI ​​model to improve the accuracy of the suggestions. A concrete example of this operation is when a visitor enters a new product section, and the terminal suggests, "Here are our latest recommended products," through an avatar.

[0503] This invention allows visitors to receive information of interest on the spot and have a unique experience, while facility operators can deepen their understanding of visitors and achieve efficient resource management.

[0504] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0505] Step 1:

[0506] The device acquires the visitor's location information using Bluetooth beacons and Wi-Fi. This allows it to identify which area the visitor is in. The input is signal strength from beacons and Wi-Fi connection information, and the output is the visitor's location coordinates. Specifically, the device analyzes the signal strength and determines the visitor's location as coordinates on a map of the facility.

[0507] Step 2:

[0508] The terminal retrieves visitors' past purchase and event participation history through a dedicated app. Input is historical information from the visitor's device, and output is a behavioral history database constructed based on that information. The terminal compiles this historical information into information packets and sends them to the server.

[0509] Step 3:

[0510] The server analyzes the received visitor location information and behavioral history data. Here, it processes the data using Python or similar tools to identify visitors' interests and preferences. The input is location information and behavioral history, and the output is estimated visitor preference data. The server uses statistical analysis techniques to model the data and reveal preference patterns.

[0511] Step 4:

[0512] The server generates suggestions using a generative AI model. The input consists of analyzed preference data and a prompt statement like this: "The visitor's past purchase history includes items A and B, and their event history includes events X and Y. What products or events would you suggest for the visitor's next visit?" The output is the generated personalized suggestions. The AI ​​model processes the input and outputs the suggestions in text format.

[0513] Step 5:

[0514] The terminal displays the suggested content received from the server on digital signage. It uses a visual character to convey the suggested content to visitors via voice. The input is the suggested content from the server, and the output is the presentation of visual and audio information through the avatar. The terminal uses the Google Cloud Speech-to-Text API to convert text to speech.

[0515] Step 6:

[0516] The user provides feedback on the information presented by the device. The server uses this feedback to train an AI model to improve the accuracy of its suggestions. The input is the user's feedback, and the output is an update to the AI ​​model based on the training. The server adds the feedback to a database and performs training to improve the model's performance.

[0517] (Application Example 1)

[0518] 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."

[0519] In today's retail industry, accurately understanding consumer needs and interests and providing personalized recommendations to each individual is becoming increasingly important. However, traditional systems have struggled to provide real-time, personalized recommendations to customers visiting stores, often resulting in missed sales opportunities. Therefore, there is a need for a way to provide individually personalized shopping experiences to customers within the store.

[0520] 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.

[0521] In this invention, the server includes means for collecting visitor location data and generating visitor behavior information, means for analyzing the visitor's past behavior history and generating personalized suggestions, and means for presenting the generated suggestions to the visitor via a virtual avatar. This makes it possible to provide each customer with optimal product information and event announcements in real time.

[0522] "Visitor location data" refers to information indicating the location of visitors within a facility or store, and is collected using communication technologies such as Bluetooth and Wi-Fi.

[0523] "Visitor behavior information" refers to data that includes visitors' past activity history and movement patterns within the facility, and is used to analyze visitors' interests and preferences.

[0524] "Personalized suggestions" refer to information that is individually optimized and presented to visitors based on their behavioral history and preferences, including product information and event announcements.

[0525] A "virtual avatar" is a digital character that is displayed on digital signage or communication terminals and provides information to visitors in a natural conversational format.

[0526] A "communication terminal" is an electronic device used to send and receive information, and specifically includes smartphones and tablets.

[0527] To implement this invention, it is essential to install communication terminals and servers within stores or facilities. The communication terminals mainly consist of visitors' smartphones and digital signage, and collect visitor location data using Bluetooth beacons and Wi-Fi. The server receives this location data and generates and stores behavioral information.

[0528] The server utilizes an AI model to analyze collected behavioral information and visitors' past history. This analysis generates optimal suggestions based on visitors' interests and preferences. These suggestions, which include information on selected products and event announcements, are provided to visitors through their smartphones or virtual avatars displayed on digital signage.

[0529] The virtual avatar is equipped with a generative AI model and uses natural language processing technology to interact with visitors. Based on feedback from visitors and emotional information obtained through voice and facial expression analysis, the avatar adjusts its suggestions in real time to capture visitors' attention.

[0530] For example, when a customer in an apparel shop moves around the store with their smartphone, Bluetooth beacons pinpoint the customer's location. Based on past purchase history and preferences, the server can then have an avatar on the smartphone suggest, "Here's a scarf that matches this coat."

[0531] This allows visitors to enjoy a shopping experience based on their own interests.

[0532] A concrete example of a prompt message would be a request sent to the server in the format of, "A customer has arrived at the apparel shop. Based on items related to recently purchased items, please suggest products that can be promoted." This prompt provides the basis for the generative AI model to make optimal suggestions.

[0533] This system allows stores to increase customers' willingness to buy and respond quickly to individual needs, thereby boosting sales.

[0534] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0535] Step 1:

[0536] The device uses Bluetooth beacons and Wi-Fi to detect the visitor's location data. Based on this, the device determines the visitor's current location and sends that data to the server. The input is location information, and the output generates location data that is sent to the server.

[0537] Step 2:

[0538] The server updates visitor behavior information based on the location data it receives. Furthermore, the server retrieves and analyzes past visitor behavior history and purchase data from a database. This identifies visitors' interests and preferences. The input is visitor location data and history data, and the output generates analysis results of interests and preferences.

[0539] Step 3:

[0540] The server uses the analysis results to activate a generative AI model, which generates product information and event guides best suited to the visitor. The generative AI model receives prompt text and analysis results as input and generates personalized suggestions as output.

[0541] Step 4:

[0542] The generated proposal is sent to the terminal, and a virtual avatar receives it. The avatar performs natural language processing and presents the proposal to the visitor visually and audibly. The input is the generated proposal, and the output is the generation of visual and audio data, which is then presented to the visitor.

[0543] Step 5:

[0544] The terminal uses its emotion analysis function to determine the visitor's reaction and sends it to the server as feedback. The server receives this feedback and uses it as training data for a generating AI model. The input is the visitor's reaction data, and the output is the generation of training data for the model.

[0545] Step 6:

[0546] The server continuously learns from feedback data and improves the accuracy of its suggestions. This allows it to provide more refined suggestions to future visitors. The input is training data, and the output is an improved generative AI model.

[0547] 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.

[0548] This invention is a system that combines an emotion engine to recognize user emotions, aiming to highly personalize the information provided to visitors. This system uses avatars displayed on digital signage to analyze the emotional state of visitors in real time and makes appropriate suggestions based on that information.

[0549] <Description of Embodiments>

[0550] 1. Utilizing the Emotional Engine

[0551] The terminal captures visitors' facial expressions with a camera and collects their voices with a microphone. This data is sent to an emotion engine, where emotion analysis is performed in real time. The emotion engine identifies states such as joy, surprise, and confusion, and sends this information to a server.

[0552] 2. Data collection and personalized suggestions

[0553] The server analyzes data from the emotion engine, combining it with past behavioral data. This analysis generates information and suggestions tailored to the visitor's current emotional state. For example, if a visitor expresses surprise, the server can suggest new products or services.

[0554] 3. Information presentation via avatars

[0555] The terminal displays suggestions sent from the server through an avatar. The avatar reflects the analysis results of the emotion engine and uses appropriate tone of voice and facial expressions to make suggestions to visitors. This enables more natural and approachable communication.

[0556] 4. Continuous feedback and system improvement

[0557] Users provide feedback on the presented content. This feedback is stored in a database on the server and used to improve future suggestions. The system continuously learns from this feedback, improving its coordination with the emotion engine and enabling it to provide more appropriate guidance to each visitor.

[0558] <Specific example>

[0559] For example, when a visitor to a tourist spot stops by an information center, the terminal uses an emotion engine to recognize that the visitor is relaxed based on their facial expression and voice. The server then suggests quiet parks or cafes so that the visitor can enjoy sightseeing while remaining relaxed. The avatar gently guides the visitor in a soft voice, saying, "Why not relax in a nearby park?"

[0560] This system can also help facilities understand the emotional state of visitors and provide more effective customer service. Visitors can maximize their experience within the facility by smoothly receiving information tailored to their individual needs.

[0561] The following describes the processing flow.

[0562] Step 1:

[0563] Users launch a dedicated app upon entering the facility and consent to the collection of location and emotional data. During the initial setup, the app requests permission to access the camera and microphone.

[0564] Step 2:

[0565] The device continuously captures the user's facial expressions and voice using cameras and microphones integrated into the facility's digital signage. This data is immediately transmitted to the emotion engine.

[0566] Step 3:

[0567] The emotion engine analyzes captured audio and facial expression data in real time to identify the user's emotional state (excitement, calmness, anxiety, etc.). The identified emotion data is immediately sent to the server.

[0568] Step 4:

[0569] The server receives data from the emotion engine and analyzes it in combination with previously accumulated behavioral history data. This process determines the most appropriate suggestion for the user's current emotional state.

[0570] Step 5:

[0571] The server uses a generative AI model to generate personalized suggestions based on the user's emotions and behavioral history. These suggestions include information on events of interest and recommended products.

[0572] Step 6:

[0573] The terminal presents information to the user through an avatar, based on suggestion data from the server. The avatar adjusts its facial expressions and tone of voice to match the user's emotional state, making suggestions in a friendly manner.

[0574] Step 7:

[0575] Users receive avatar suggestions and can act accordingly if they are interested. They can also skip suggestions if they are not interested.

[0576] Step 8:

[0577] The device records the user's responses and comments to the suggestions and sends them to the server as feedback. This feedback is used as training data to improve the accuracy of future suggestions.

[0578] Step 9:

[0579] The server analyzes the received feedback data and updates the AI ​​model. This enables more precise sentiment analysis and suggestion generation.

[0580] (Example 2)

[0581] 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."

[0582] Instead of providing visitors with uniform information, there is a need to provide personalized information at the appropriate time based on their individual emotional state and past behavioral history. Furthermore, effectively collecting visitor feedback and using it to improve the accuracy of suggestions is also a crucial challenge. Ultimately, this is necessary to optimize the visitor experience and deliver more engaging services.

[0583] 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.

[0584] In this invention, the server includes means for collecting visitor location information and generating visitor behavior data, means for analyzing the visitor's past behavior history and generating personalized suggestions, and means for presenting the generated suggestions to the visitor through a visual character. This enables the provision of timely and personalized information based on the visitor's emotional state. Furthermore, by collecting feedback from visitors and improving the accuracy of suggestions using a generative AI model, the visitor experience can be continuously enhanced.

[0585] "Location information" refers to data that represents the geographical location of an object or person within a specific place or space.

[0586] "Behavioral data" refers to information that records visitors' past movements and behavioral history, revealing their individual lifestyles and interests.

[0587] "Personalized suggestions" refer to suggestions that provide information and services tailored to individual needs based on the visitor's emotional state and past behavioral history.

[0588] A "visual character" is a virtual person or character displayed on digital signage or a terminal that serves to convey emotions or suggestions visually and audibly.

[0589] The "emotion analysis engine" is software that analyzes acquired voice and facial expression data and executes algorithms to identify the emotional state of visitors in real time.

[0590] A "generative AI model" is an artificial intelligence model used to dynamically optimize suggestions by learning from visitors' behavioral history and feedback data.

[0591] "Feedback" refers to evaluations and opinions provided by visitors regarding the information and services they received.

[0592] This invention relates to a system that recognizes the emotions of visitors and provides information tailored to their individual needs. The system includes a terminal equipped with a camera and microphone, software for performing emotion analysis, a server for managing and analyzing data, and a function for presenting information through a visual character.

[0593] The device captures visitors' facial expressions and voices using a camera and microphone. Specifically, it uses a high-resolution camera and a noise-canceling microphone to collect data in real time, effectively blocking out external noise. This data is sent to an emotion analysis engine for emotion analysis.

[0594] The server analyzes emotional data received from the emotion analysis engine, combining it with past behavioral history. This analysis uses a generative AI model based on deep learning algorithms. Based on the analysis results, it generates suggestions that are appropriate to the visitor's current emotional state.

[0595] The generated proposals are presented to visitors via a terminal using a visual character. This visual character uses an animation engine and a speech synthesis engine to express appropriate facial expressions and voice tones according to the emotional state, communicating with the user in a friendly manner.

[0596] Furthermore, users provide feedback on the presented information through input devices. This feedback information is stored in a server database and analyzed by a generating AI model. This makes it possible to improve the accuracy of suggestions and maximize the visitor experience.

[0597] As a concrete example, when a visitor to a tourist destination stops by an information center, the terminal uses emotion analysis to recognize that the visitor is relaxed based on their facial expressions and voice. Based on this information, the server suggests parks or cafes where the visitor can spend time quietly. A visual character, in a gentle voice, guides the visitor by saying, "Why not relax in a nearby park?" In this way, visitors can receive services tailored to their individual needs.

[0598] An example of a prompt to input into a generative AI model would be, "Based on visitor sentiment data, please suggest some activities that would allow visitors to spend their time quietly."

[0599] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0600] Step 1:

[0601] The device uses a camera and microphone to capture visitors' facial expressions and voices. The input is the visitor's face and voice, and the output is their digital data. In this process, a high-resolution camera captures facial expressions, and a microphone with noise cancellation digitizes the voice. This data is then prepared for transmission to an emotion analysis engine.

[0602] Step 2:

[0603] The device transmits the acquired facial and audio data to the emotion analysis engine. The input is digitized facial and audio data, and the output is an analysis result indicating the emotional state. The emotion analysis engine uses a deep learning algorithm to quantify specific emotions. For example, it identifies emotions such as joy, surprise, or confusion.

[0604] Step 3:

[0605] The server analyzes emotional data received from the emotion analysis engine, combining it with past behavioral history data of visitors. The input consists of emotional data and behavioral history, while the output is personalized suggestions. This analysis is performed using a generative AI model to generate optimal suggestions tailored to specific emotional states.

[0606] Step 4:

[0607] The server sends the generated suggestions to the terminal. The input is the generated suggestion data, and the output is the suggestion information for the terminal. The server processes this in real time and prepares to transmit relevant information to the visual character.

[0608] Step 5:

[0609] The terminal presents suggestions received from the server to visitors via a visual character. The input is the suggested information, and the output is the information presented by the visual character. Using an animation engine and a speech synthesis engine, the character guides visitors with the most appropriate facial expressions and voice.

[0610] Step 6:

[0611] Users provide feedback on information presented by animated characters. Input is the presented content and the customer response, while output is feedback data. Users can easily submit their opinions via touch panel or voice input.

[0612] Step 7:

[0613] The server collects user feedback and stores it in a database. The input is feedback data, and the output is training data for improvement. The server utilizes a generative AI model to analyze the feedback, thereby improving the accuracy of suggestions and optimizing the system.

[0614] (Application Example 2)

[0615] 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."

[0616] In modern brick-and-mortar stores, visitors are often offered a uniform service, making it difficult to provide personalized experiences tailored to each visitor's emotions and circumstances. Therefore, there is a need for technology that can provide optimal suggestions and guidance in real time, based on the visitor's emotional state and individual needs.

[0617] 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.

[0618] This invention includes a server that uses a facial expression analysis engine to recognize the emotional state of visitors and transmits emotional data to a large-scale distributed platform; a server that analyzes the information using a cloud service and provides visitors with the most suitable products and information; and a server that displays the generated suggestions to visitors via a digital character. This makes it possible to provide personalized suggestions tailored to the visitor's emotions and situation in real time, thereby improving the visitor's experience.

[0619] "Visitors" refers to individual customers or users who visit a facility or store.

[0620] "Location information" refers to data that indicates a visitor is in a specific location within a facility, and is obtained using GPS or beacon technology.

[0621] A "behavioral information infrastructure" refers to a database or platform for creating a dataset that includes visitor location information and past behavioral history.

[0622] A "facial expression analysis engine" refers to a software or hardware system that detects a visitor's facial expressions and analyzes their emotional state in real time.

[0623] A "large-scale distributed infrastructure" refers to a computing infrastructure that utilizes multiple servers and cloud services to efficiently process and analyze vast amounts of data.

[0624] "Cloud services" refer to services that allow users to remotely access and manage data and applications via the internet.

[0625] A "digital character" is a computer-generated avatar or virtual character used for interaction with visitors and for presenting information.

[0626] The system that implements this application uses multiple hardware and software components to analyze visitors' emotional states and provide personalized information. The server is built on a cloud service infrastructure and utilizes a facial expression analysis engine for emotion analysis. Specifically, services such as Microsoft Azure's Face API and Amazon Recognition are available.

[0627] The server receives image and audio data of visitors transmitted from terminals. Terminals consist of smartphones, fixed cameras in stores, microphones, etc., and capture visitors' facial expressions and voices in real time. The received data is processed by an emotion analysis engine in the cloud, and the visitor's emotional state is identified. The resulting emotion data is processed using a large-scale distributed infrastructure and analyzed in combination with the visitor's past behavior data.

[0628] The server generates optimal product suggestions and information for visitors based on the analysis results. This generation utilizes a generative AI model using Python and TensorFlow. The generated suggestions are presented to visitors through a digital character. The digital character operates on an application implemented using React Native and provides information by changing its voice tone and facial expressions based on emotional data.

[0629] Users can provide feedback on the information presented by the digital character. This feedback is stored in a database on the server side and used to improve the accuracy of subsequent suggestions. The system inputs this feedback into a generation AI model via prompts, continuously generating optimal suggestions.

[0630] For example, if a visitor who has stopped by a cafe in a shopping mall is seen smiling happily in front of a terminal, the system will pick up on that emotion and suggest that the visitor enjoy a new menu item. A prompt such as "Would you like to try a new dessert?" will be used to stimulate the visitor's desire to purchase.

[0631] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0632] Step 1:

[0633] The device acquires image and audio data when a visitor stands in front of the camera. It captures data in real time using the camera and microphone and sends that data to the server. The input is a facial image captured by the camera and audio captured by the microphone, and the output is a data packet containing this data.

[0634] Step 2:

[0635] The server receives image and audio data transmitted from the terminal and uses a facial expression analysis engine to identify emotional states. Data processing for emotion analysis involves processing the image data and analyzing facial expressions to output emotion labels such as joy or surprise. This processing is carried out on a large-scale distributed infrastructure.

[0636] Step 3:

[0637] The server compares the sentiment analysis results with the visitor's past behavior records stored in a cloud-based database to generate personalized suggestions. A generative AI model is used to take the analysis results as prompts and output the generated suggestions. In this process, the inputs are the sentiment analysis results and past behavior data, and the output data includes the most suitable suggestions based on those results.

[0638] Step 4:

[0639] The server passes the generated suggestions to a digital character, which then displays them to visitors on its screen. The digital character is built using a React Native application and provides information using emotionally responsive voice tones and facial expressions. The input here is the generated suggestions, and the output is the actions and sounds generated by the digital character.

[0640] Step 5:

[0641] Users provide feedback on suggestions offered by digital characters. This feedback is sent to a server and stored in a database. The input is user feedback information, and based on this, data analysis is performed to generate output for improving the suggestions.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] [Fourth Embodiment]

[0646] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0647] 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.

[0648] 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).

[0649] 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.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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".

[0659] This invention provides a system that uses an avatar agent displayed on digital signage to provide personalized information to visitors. Specific embodiments are shown below.

[0660] <Description of Embodiments>

[0661] 1. Data Collection and Analysis

[0662] The device collects location information using Bluetooth beacons and Wi-Fi when visitors enter the facility, and sends this data to a server to obtain visitor behavior data. With the visitor's permission, past purchase history and event participation history are also collected and stored on the server through devices with a dedicated app installed.

[0663] 2. Personalized suggestion generation

[0664] The server analyzes accumulated behavioral and historical data to identify visitors' interests and preferences. Using a generative AI model, it selects the most suitable events and products for each visitor. Specifically, it selects information that is likely to interest them on their next visit, based on the genres of events they have attended in the past and their purchase history.

[0665] 3. Information presentation via avatars

[0666] The terminal displays an avatar on the screen based on the suggestions sent from the server, and conveys information to visitors in a natural conversational format. The avatar uses voice to explain the details of the suggested events and products in a one-on-one dialogue-like manner.

[0667] 4. Utilization of Sentiment Analysis

[0668] The server analyzes visitors' voice data and facial expressions to evaluate their emotions. This allows the server to understand their reactions to suggestions in real time and determine if the suggestions are appropriate. For example, if a visitor smiles, the server can determine that the suggestion was successful and proceed with the guidance further.

[0669] 5. Feedback and Learning

[0670] The terminal sends feedback from visitors to the server. The server uses this feedback data to continuously train its AI model, improving the accuracy of its suggestions. This allows the system to take into account visitors' reactions and interests, enabling it to provide even more accurate information on subsequent visits.

[0671] <Specific example>

[0672] For example, when a visitor enters a specific area of ​​a tourist destination, an avatar placed on the device will guide them to nearby attractions and recommended routes, saying something like, "Here are some convenient spots to visit next." Furthermore, if the visitor's expression shows surprise, the avatar will continue to offer suggestions, providing information as long as their interest remains.

[0673] This system allows visitors to receive personalized information more efficiently and enjoy a special experience. Furthermore, it enables facilities to gain a deeper understanding of visitor behavior and optimize resources for operations and guidance.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The user installs a dedicated app on their smartphone and consents to the collection of location information and behavioral data. At this stage, the user's permission is explicitly obtained.

[0677] Step 2:

[0678] The device uses Bluetooth beacons and Wi-Fi access points placed within the facility to collect location information about the user's movement path. The collected data is used to instantly identify nearby shops and areas.

[0679] Step 3:

[0680] The server receives location information and activity history transmitted from the terminal and stores it in a database. Furthermore, it analyzes this data to extract user behavior patterns and interests.

[0681] Step 4:

[0682] The server uses a generative AI model to create a list of events and products tailored to the user. This list is personalized based on the user's past behavioral data and preferences.

[0683] Step 5:

[0684] The device retrieves a list of recommendations sent from the server and launches an application that displays an avatar on the screen. The avatar guides the user through the recommendations using text and voice.

[0685] Step 6:

[0686] The device captures the user's voice and facial expressions using its camera and microphone, and performs real-time emotion analysis. This information is used to dynamically change the avatar's responses and suggestions.

[0687] Step 7:

[0688] Users provide feedback on the suggestions. For example, they can request additional information if they are interested, or skip it if they are not. This feedback is recorded digitally.

[0689] Step 8:

[0690] The server analyzes user feedback and updates the generated AI model based on it, improving the accuracy of future suggestions. The feedback is stored in a database and used in the continuous learning process.

[0691] (Example 1)

[0692] 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".

[0693] Traditional methods of providing more personalized information to visitors make it difficult to improve the accuracy of suggestions and to grasp visitors' reactions in real time. In addition, it is difficult to adjust the information presented according to the visitor's emotional state, resulting in inefficient information transmission. This led to the problem of a low degree of match between the information visitors seek and the information they receive.

[0694] 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.

[0695] In this invention, the server includes means for acquiring visitor location information and creating visitor behavior information, means for analyzing the visitor's past behavior information and generating personalized suggestions, and means for presenting the generated suggestions to the visitor through a visual character. This makes it possible to provide visitors with optimized information in real time and improve visitor satisfaction and the operational efficiency of the facility.

[0696] A "visitor" is an individual who goes to a facility or a specific place.

[0697] "Location information" refers to information that indicates the geographical location of a specific place.

[0698] "Behavioral information" refers to a collection of data that records the activities and movements of visitors.

[0699] "Personalized suggestions" refer to providing visitors with customized information based on their specific interests and preferences.

[0700] A "visual character" is a person or character that is brought to life through animation or images displayed on a digital screen.

[0701] "Responses" refer to data that includes feedback and reactions from visitors.

[0702] "Methods for continuous learning" refer to the process of updating algorithms using past data to improve performance.

[0703] "Emotions" are expressions that describe the visitor's inner psychology or feelings.

[0704] "Voice and facial analysis" is a technique that evaluates a visitor's tone of voice and facial movements, and infers their emotions and intentions based on that.

[0705] An "artificial intelligence model" is an algorithm or program designed to solve complex problems by incorporating the learning process of computer systems.

[0706] This invention describes a method for specifically implementing a system for providing personalized information to visitors.

[0707] The server stores visitor location and behavior information in a database. This uses location data collection via Bluetooth beacons and Wi-Fi. By installing a dedicated app on the visitor's device, past purchase history and event participation history are also collected.

[0708] The terminal sends the collected data to the server for real-time processing. The server uses programming languages ​​such as Python and R to perform data analysis. This identifies visitors' interests and preferences and generates personalized suggestions.

[0709] The server generates suggestions using a generative AI model. These suggestions are input to the AI ​​model with prompts such as: "The visitor's past purchase history includes products A and B, and their event history includes events X and Y. What products or events would you suggest for the visitor's next visit?"

[0710] The terminal displays an avatar on digital signage based on suggestions sent from the server. Using a visual character, it provides information to visitors in an interactive format via voice and text. It utilizes natural language processing technologies such as the Google Cloud Speech-to-Text API.

[0711] After receiving a suggestion, the user provides feedback to the system through their response. The terminal sends this feedback to the server, which continuously trains the AI ​​model to improve the accuracy of the suggestions. A concrete example of this operation is when a visitor enters a new product section, and the terminal suggests, "Here are our latest recommended products," through an avatar.

[0712] This invention allows visitors to receive information of interest on the spot and have a unique experience, while facility operators can deepen their understanding of visitors and achieve efficient resource management.

[0713] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0714] Step 1:

[0715] The device acquires the visitor's location information using Bluetooth beacons and Wi-Fi. This allows it to identify which area the visitor is in. The input is signal strength from beacons and Wi-Fi connection information, and the output is the visitor's location coordinates. Specifically, the device analyzes the signal strength and determines the visitor's location as coordinates on a map of the facility.

[0716] Step 2:

[0717] The terminal retrieves visitors' past purchase and event participation history through a dedicated app. Input is historical information from the visitor's device, and output is a behavioral history database constructed based on that information. The terminal compiles this historical information into information packets and sends them to the server.

[0718] Step 3:

[0719] The server analyzes the received visitor location information and behavioral history data. Here, it processes the data using Python or similar tools to identify visitors' interests and preferences. The input is location information and behavioral history, and the output is estimated visitor preference data. The server uses statistical analysis techniques to model the data and reveal preference patterns.

[0720] Step 4:

[0721] The server generates suggestions using a generative AI model. The input consists of analyzed preference data and a prompt statement like this: "The visitor's past purchase history includes items A and B, and their event history includes events X and Y. What products or events would you suggest for the visitor's next visit?" The output is the generated personalized suggestions. The AI ​​model processes the input and outputs the suggestions in text format.

[0722] Step 5:

[0723] The terminal displays the suggested content received from the server on digital signage. It uses a visual character to convey the suggested content to visitors via voice. The input is the suggested content from the server, and the output is the presentation of visual and audio information through the avatar. The terminal uses the Google Cloud Speech-to-Text API to convert text to speech.

[0724] Step 6:

[0725] The user provides feedback on the information presented by the device. The server uses this feedback to train an AI model to improve the accuracy of its suggestions. The input is the user's feedback, and the output is an update to the AI ​​model based on the training. The server adds the feedback to a database and performs training to improve the model's performance.

[0726] (Application Example 1)

[0727] 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".

[0728] In today's retail industry, accurately understanding consumer needs and interests and providing personalized recommendations to each individual is becoming increasingly important. However, traditional systems have struggled to provide real-time, personalized recommendations to customers visiting stores, often resulting in missed sales opportunities. Therefore, there is a need for a way to provide individually personalized shopping experiences to customers within the store.

[0729] 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.

[0730] In this invention, the server includes means for collecting visitor location data and generating visitor behavior information, means for analyzing the visitor's past behavior history and generating personalized suggestions, and means for presenting the generated suggestions to the visitor via a virtual avatar. This makes it possible to provide each customer with optimal product information and event announcements in real time.

[0731] "Visitor location data" refers to information indicating the location of visitors within a facility or store, and is collected using communication technologies such as Bluetooth and Wi-Fi.

[0732] "Visitor behavior information" refers to data that includes visitors' past activity history and movement patterns within the facility, and is used to analyze visitors' interests and preferences.

[0733] "Personalized suggestions" refer to information that is individually optimized and presented to visitors based on their behavioral history and preferences, including product information and event announcements.

[0734] A "virtual avatar" is a digital character that is displayed on digital signage or communication terminals and provides information to visitors in a natural conversational format.

[0735] A "communication terminal" is an electronic device used to send and receive information, and specifically includes smartphones and tablets.

[0736] To implement this invention, it is essential to install communication terminals and servers within stores or facilities. The communication terminals mainly consist of visitors' smartphones and digital signage, and collect visitor location data using Bluetooth beacons and Wi-Fi. The server receives this location data and generates and stores behavioral information.

[0737] The server utilizes an AI model to analyze collected behavioral information and visitors' past history. This analysis generates optimal suggestions based on visitors' interests and preferences. These suggestions, which include information on selected products and event announcements, are provided to visitors through their smartphones or virtual avatars displayed on digital signage.

[0738] The virtual avatar is equipped with a generative AI model and uses natural language processing technology to interact with visitors. Based on feedback from visitors and emotional information obtained through voice and facial expression analysis, the avatar adjusts its suggestions in real time to capture visitors' attention.

[0739] For example, when a customer in an apparel shop moves around the store with their smartphone, Bluetooth beacons pinpoint the customer's location. Based on past purchase history and preferences, the server can then have an avatar on the smartphone suggest, "Here's a scarf that matches this coat."

[0740] This allows visitors to enjoy a shopping experience based on their own interests.

[0741] A concrete example of a prompt message would be a request sent to the server in the format of, "A customer has arrived at the apparel shop. Based on items related to recently purchased items, please suggest products that can be promoted." This prompt provides the basis for the generative AI model to make optimal suggestions.

[0742] This system allows stores to increase customers' willingness to buy and respond quickly to individual needs, thereby boosting sales.

[0743] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0744] Step 1:

[0745] The device uses Bluetooth beacons and Wi-Fi to detect the visitor's location data. Based on this, the device determines the visitor's current location and sends that data to the server. The input is location information, and the output generates location data that is sent to the server.

[0746] Step 2:

[0747] The server updates visitor behavior information based on the location data it receives. Furthermore, the server retrieves and analyzes past visitor behavior history and purchase data from a database. This identifies visitors' interests and preferences. The input is visitor location data and history data, and the output generates analysis results of interests and preferences.

[0748] Step 3:

[0749] The server uses the analysis results to activate a generative AI model, which generates product information and event guides best suited to the visitor. The generative AI model receives prompt text and analysis results as input and generates personalized suggestions as output.

[0750] Step 4:

[0751] The generated proposal is sent to the terminal, and a virtual avatar receives it. The avatar performs natural language processing and presents the proposal to the visitor visually and audibly. The input is the generated proposal, and the output is the generation of visual and audio data, which is then presented to the visitor.

[0752] Step 5:

[0753] The terminal uses its emotion analysis function to determine the visitor's reaction and sends it to the server as feedback. The server receives this feedback and uses it as training data for a generating AI model. The input is the visitor's reaction data, and the output is the generation of training data for the model.

[0754] Step 6:

[0755] The server continuously learns from feedback data and improves the accuracy of its suggestions. This allows it to provide more refined suggestions to future visitors. The input is training data, and the output is an improved generative AI model.

[0756] 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.

[0757] This invention is a system that combines an emotion engine to recognize user emotions, aiming to highly personalize the information provided to visitors. This system uses avatars displayed on digital signage to analyze the emotional state of visitors in real time and makes appropriate suggestions based on that information.

[0758] <Description of Embodiments>

[0759] 1. Utilizing the Emotional Engine

[0760] The terminal captures visitors' facial expressions with a camera and collects their voices with a microphone. This data is sent to an emotion engine, where emotion analysis is performed in real time. The emotion engine identifies states such as joy, surprise, and confusion, and sends this information to a server.

[0761] 2. Data collection and personalized suggestions

[0762] The server analyzes data from the emotion engine, combining it with past behavioral data. This analysis generates information and suggestions tailored to the visitor's current emotional state. For example, if a visitor expresses surprise, the server can suggest new products or services.

[0763] 3. Information presentation via avatars

[0764] The terminal displays suggestions sent from the server through an avatar. The avatar reflects the analysis results of the emotion engine and uses appropriate tone of voice and facial expressions to make suggestions to visitors. This enables more natural and approachable communication.

[0765] 4. Continuous feedback and system improvement

[0766] Users provide feedback on the presented content. This feedback is stored in a database on the server and used to improve future suggestions. The system continuously learns from this feedback, improving its coordination with the emotion engine and enabling it to provide more appropriate guidance to each visitor.

[0767] <Specific example>

[0768] For example, when a visitor to a tourist spot stops by an information center, the terminal uses an emotion engine to recognize that the visitor is relaxed based on their facial expression and voice. The server then suggests quiet parks or cafes so that the visitor can enjoy sightseeing while remaining relaxed. The avatar gently guides the visitor in a soft voice, saying, "Why not relax in a nearby park?"

[0769] This system can also help facilities understand the emotional state of visitors and provide more effective customer service. Visitors can maximize their experience within the facility by smoothly receiving information tailored to their individual needs.

[0770] The following describes the processing flow.

[0771] Step 1:

[0772] Users launch a dedicated app upon entering the facility and consent to the collection of location and emotional data. During the initial setup, the app requests permission to access the camera and microphone.

[0773] Step 2:

[0774] The device continuously captures the user's facial expressions and voice using cameras and microphones integrated into the facility's digital signage. This data is immediately transmitted to the emotion engine.

[0775] Step 3:

[0776] The emotion engine analyzes captured audio and facial expression data in real time to identify the user's emotional state (excitement, calmness, anxiety, etc.). The identified emotion data is immediately sent to the server.

[0777] Step 4:

[0778] The server receives data from the emotion engine and analyzes it in combination with previously accumulated behavioral history data. This process determines the most appropriate suggestion for the user's current emotional state.

[0779] Step 5:

[0780] The server uses a generative AI model to generate personalized suggestions based on the user's emotions and behavioral history. These suggestions include information on events of interest and recommended products.

[0781] Step 6:

[0782] The terminal presents information to the user through an avatar, based on suggestion data from the server. The avatar adjusts its facial expressions and tone of voice to match the user's emotional state, making suggestions in a friendly manner.

[0783] Step 7:

[0784] Users receive avatar suggestions and can act accordingly if they are interested. They can also skip suggestions if they are not interested.

[0785] Step 8:

[0786] The device records the user's responses and comments to the suggestions and sends them to the server as feedback. This feedback is used as training data to improve the accuracy of future suggestions.

[0787] Step 9:

[0788] The server analyzes the received feedback data and updates the AI ​​model. This enables more precise sentiment analysis and suggestion generation.

[0789] (Example 2)

[0790] 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".

[0791] Instead of providing visitors with uniform information, there is a need to provide personalized information at the appropriate time based on their individual emotional state and past behavioral history. Furthermore, effectively collecting visitor feedback and using it to improve the accuracy of suggestions is also a crucial challenge. Ultimately, this is necessary to optimize the visitor experience and deliver more engaging services.

[0792] 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.

[0793] In this invention, the server includes means for collecting visitor location information and generating visitor behavior data, means for analyzing the visitor's past behavior history and generating personalized suggestions, and means for presenting the generated suggestions to the visitor through a visual character. This enables the provision of timely and personalized information based on the visitor's emotional state. Furthermore, by collecting feedback from visitors and improving the accuracy of suggestions using a generative AI model, the visitor experience can be continuously enhanced.

[0794] "Location information" refers to data that represents the geographical location of an object or person within a specific place or space.

[0795] "Behavioral data" refers to information that records visitors' past movements and behavioral history, revealing their individual lifestyles and interests.

[0796] "Personalized suggestions" refer to suggestions that provide information and services tailored to individual needs based on the visitor's emotional state and past behavioral history.

[0797] A "visual character" is a virtual person or character displayed on digital signage or a terminal that serves to convey emotions or suggestions visually and audibly.

[0798] The "emotion analysis engine" is software that analyzes acquired voice and facial expression data and executes algorithms to identify the emotional state of visitors in real time.

[0799] A "generative AI model" is an artificial intelligence model used to dynamically optimize suggestions by learning from visitors' behavioral history and feedback data.

[0800] "Feedback" refers to evaluations and opinions provided by visitors regarding the information and services they received.

[0801] This invention relates to a system that recognizes the emotions of visitors and provides information tailored to their individual needs. The system includes a terminal equipped with a camera and microphone, software for performing emotion analysis, a server for managing and analyzing data, and a function for presenting information through a visual character.

[0802] The device captures visitors' facial expressions and voices using a camera and microphone. Specifically, it uses a high-resolution camera and a noise-canceling microphone to collect data in real time, effectively blocking out external noise. This data is sent to an emotion analysis engine for emotion analysis.

[0803] The server analyzes emotional data received from the emotion analysis engine, combining it with past behavioral history. This analysis uses a generative AI model based on deep learning algorithms. Based on the analysis results, it generates suggestions that are appropriate to the visitor's current emotional state.

[0804] The generated proposals are presented to visitors via a terminal using a visual character. This visual character uses an animation engine and a speech synthesis engine to express appropriate facial expressions and voice tones according to the emotional state, communicating with the user in a friendly manner.

[0805] Furthermore, users provide feedback on the presented information through input devices. This feedback information is stored in a server database and analyzed by a generating AI model. This makes it possible to improve the accuracy of suggestions and maximize the visitor experience.

[0806] As a concrete example, when a visitor to a tourist destination stops by an information center, the terminal uses emotion analysis to recognize that the visitor is relaxed based on their facial expressions and voice. Based on this information, the server suggests parks or cafes where the visitor can spend time quietly. A visual character, in a gentle voice, guides the visitor by saying, "Why not relax in a nearby park?" In this way, visitors can receive services tailored to their individual needs.

[0807] An example of a prompt to input into a generative AI model would be, "Based on visitor sentiment data, please suggest some activities that would allow visitors to spend their time quietly."

[0808] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0809] Step 1:

[0810] The device uses a camera and microphone to capture visitors' facial expressions and voices. The input is the visitor's face and voice, and the output is their digital data. In this process, a high-resolution camera captures facial expressions, and a microphone with noise cancellation digitizes the voice. This data is then prepared for transmission to an emotion analysis engine.

[0811] Step 2:

[0812] The device transmits the acquired facial and audio data to the emotion analysis engine. The input is digitized facial and audio data, and the output is an analysis result indicating the emotional state. The emotion analysis engine uses a deep learning algorithm to quantify specific emotions. For example, it identifies emotions such as joy, surprise, or confusion.

[0813] Step 3:

[0814] The server analyzes emotional data received from the emotion analysis engine, combining it with past behavioral history data of visitors. The input consists of emotional data and behavioral history, while the output is personalized suggestions. This analysis is performed using a generative AI model to generate optimal suggestions tailored to specific emotional states.

[0815] Step 4:

[0816] The server sends the generated suggestions to the terminal. The input is the generated suggestion data, and the output is the suggestion information for the terminal. The server processes this in real time and prepares to transmit relevant information to the visual character.

[0817] Step 5:

[0818] The terminal presents suggestions received from the server to visitors via a visual character. The input is the suggested information, and the output is the information presented by the visual character. Using an animation engine and a speech synthesis engine, the character guides visitors with the most appropriate facial expressions and voice.

[0819] Step 6:

[0820] Users provide feedback on information presented by animated characters. Input is the presented content and the customer response, while output is feedback data. Users can easily submit their opinions via touch panel or voice input.

[0821] Step 7:

[0822] The server collects user feedback and stores it in a database. The input is feedback data, and the output is training data for improvement. The server utilizes a generative AI model to analyze the feedback, thereby improving the accuracy of suggestions and optimizing the system.

[0823] (Application Example 2)

[0824] 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".

[0825] In modern brick-and-mortar stores, visitors are often offered a uniform service, making it difficult to provide personalized experiences tailored to each visitor's emotions and circumstances. Therefore, there is a need for technology that can provide optimal suggestions and guidance in real time, based on the visitor's emotional state and individual needs.

[0826] 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.

[0827] This invention includes a server that uses a facial expression analysis engine to recognize the emotional state of visitors and transmits emotional data to a large-scale distributed platform; a server that analyzes the information using a cloud service and provides visitors with the most suitable products and information; and a server that displays the generated suggestions to visitors via a digital character. This makes it possible to provide personalized suggestions tailored to the visitor's emotions and situation in real time, thereby improving the visitor's experience.

[0828] "Visitors" refers to individual customers or users who visit a facility or store.

[0829] "Location information" refers to data that indicates a visitor is in a specific location within a facility, and is obtained using GPS or beacon technology.

[0830] A "behavioral information infrastructure" refers to a database or platform for creating a dataset that includes visitor location information and past behavioral history.

[0831] A "facial expression analysis engine" refers to a software or hardware system that detects a visitor's facial expressions and analyzes their emotional state in real time.

[0832] A "large-scale distributed infrastructure" refers to a computing infrastructure that utilizes multiple servers and cloud services to efficiently process and analyze vast amounts of data.

[0833] "Cloud services" refer to services that allow users to remotely access and manage data and applications via the internet.

[0834] A "digital character" is a computer-generated avatar or virtual character used for interaction with visitors and for presenting information.

[0835] The system that implements this application uses multiple hardware and software components to analyze visitors' emotional states and provide personalized information. The server is built on a cloud service infrastructure and utilizes a facial expression analysis engine for emotion analysis. Specifically, services such as Microsoft Azure's Face API and Amazon Recognition are available.

[0836] The server receives image and audio data of visitors transmitted from terminals. Terminals consist of smartphones, fixed cameras in stores, microphones, etc., and capture visitors' facial expressions and voices in real time. The received data is processed by an emotion analysis engine in the cloud, and the visitor's emotional state is identified. The resulting emotion data is processed using a large-scale distributed infrastructure and analyzed in combination with the visitor's past behavior data.

[0837] The server generates optimal product suggestions and information for visitors based on the analysis results. This generation utilizes a generative AI model using Python and TensorFlow. The generated suggestions are presented to visitors through a digital character. The digital character operates on an application implemented using React Native and provides information by changing its voice tone and facial expressions based on emotional data.

[0838] Users can provide feedback on the information presented by the digital character. This feedback is stored in a database on the server side and used to improve the accuracy of subsequent suggestions. The system inputs this feedback into a generation AI model via prompts, continuously generating optimal suggestions.

[0839] For example, if a visitor who has stopped by a cafe in a shopping mall is seen smiling happily in front of a terminal, the system will pick up on that emotion and suggest that the visitor enjoy a new menu item. A prompt such as "Would you like to try a new dessert?" will be used to stimulate the visitor's desire to purchase.

[0840] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0841] Step 1:

[0842] The device acquires image and audio data when a visitor stands in front of the camera. It captures data in real time using the camera and microphone and sends that data to the server. The input is a facial image captured by the camera and audio captured by the microphone, and the output is a data packet containing this data.

[0843] Step 2:

[0844] The server receives image and audio data transmitted from the terminal and uses a facial expression analysis engine to identify emotional states. Data processing for emotion analysis involves processing the image data and analyzing facial expressions to output emotion labels such as joy or surprise. This processing is carried out on a large-scale distributed infrastructure.

[0845] Step 3:

[0846] The server compares the sentiment analysis results with the visitor's past behavior records stored in a cloud-based database to generate personalized suggestions. A generative AI model is used to take the analysis results as prompts and output the generated suggestions. In this process, the inputs are the sentiment analysis results and past behavior data, and the output data includes the most suitable suggestions based on those results.

[0847] Step 4:

[0848] The server passes the generated suggestions to a digital character, which then displays them to visitors on its screen. The digital character is built using a React Native application and provides information using emotionally responsive voice tones and facial expressions. The input here is the generated suggestions, and the output is the actions and sounds generated by the digital character.

[0849] Step 5:

[0850] Users provide feedback on suggestions offered by digital characters. This feedback is sent to a server and stored in a database. The input is user feedback information, and based on this, data analysis is performed to generate output for improving the suggestions.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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."

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0872] The following is further disclosed regarding the embodiments described above.

[0873] (Claim 1)

[0874] A means for collecting visitor location information and generating a visitor behavior database,

[0875] A means of analyzing visitors' past behavioral history and generating personalized suggestions,

[0876] A means of presenting the generated proposals to visitors via avatars,

[0877] A means of collecting feedback from visitors and learning to improve the accuracy of proposals,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, further comprising means for identifying the emotional state of visitors through voice and facial expression analysis, and adjusting the suggested content based on the identification results.

[0881] (Claim 3)

[0882] The system according to claim 1, further comprising means for using a generative AI model that optimizes the content of suggestions by utilizing the behavior history of visitors and past feedback data.

[0883] "Example 1"

[0884] (Claim 1)

[0885] A means of acquiring visitor location information and creating visitor behavior information,

[0886] A means of analyzing visitors' past behavioral information and generating personalized suggestions,

[0887] A means of presenting the generated proposals to visitors through visual characters,

[0888] A means of continuously learning by collecting responses from visitors and improving the accuracy of suggestions,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, further comprising means for identifying the visitor's emotions through voice and facial expression analysis and adjusting the proposed content based on the analysis results.

[0892] (Claim 3)

[0893] The system according to claim 1, further comprising means for using an artificial intelligence model that optimizes the content of suggestions using visitor behavior information and past response information.

[0894] "Application Example 1"

[0895] (Claim 1)

[0896] A function to collect visitor location data and generate visitor behavior information,

[0897] A function that analyzes visitors' past behavioral history and generates personalized suggestions,

[0898] A function to present the generated proposals to visitors via a virtual avatar,

[0899] It has a function to collect feedback from visitors and learn in order to improve the accuracy of the proposals,

[0900] The system acquires location information using the visitor's communication device and displays product information corresponding to that information.

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, further comprising a function to identify the emotional state of visitors through voice and facial expression analysis, and to adjust the suggested content based on the identification results.

[0904] (Claim 3)

[0905] The system according to claim 1, further comprising a function that uses a generative AI model to optimize the content of suggestions by utilizing the visitor's behavior history and past feedback data.

[0906] "Example 2 of combining an emotion engine"

[0907] (Claim 1)

[0908] A means for collecting visitor location information and generating visitor behavior data,

[0909] A means of generating personalized suggestions by analyzing the past behavioral history of visitors,

[0910] A means of presenting the generated proposals to visitors through visual characters,

[0911] A means for acquiring visitors' facial expressions and voices and identifying their emotional state using an emotion analysis engine,

[0912] Means for adjusting and optimizing proposals based on identified emotional states and past behavioral data,

[0913] A means of collecting feedback from visitors and learning to improve the accuracy of proposals,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, further comprising means for identifying the emotional state using the voice and facial expression data of visitors, and adjusting the proposed content using a generative AI model based on the identification results.

[0917] (Claim 3)

[0918] The system according to claim 1, further comprising means for optimizing suggested content based on a generating AI model, utilizing visitor behavior history and feedback data.

[0919] "Application example 2 when combining with an emotional engine"

[0920] (Claim 1)

[0921] A means of acquiring visitor location information and forming a visitor behavior information base,

[0922] A means to analyze visitors' past behavioral history and generate personalized suggestions,

[0923] A means of displaying the generated proposals to visitors through digital characters,

[0924] A means of collecting feedback from visitors and learning to improve the accuracy of proposals,

[0925] A means of using a facial expression analysis engine to recognize the emotional state of visitors and transmitting emotional data to a large-scale distributed platform,

[0926] A means of analyzing information using cloud services to provide visitors with the most suitable products and information,

[0927] A system that includes this.

[0928] (Claim 2)

[0929] The system according to claim 1, further comprising means for a digital character displayed to a visitor to change its voice tone and facial expression based on emotional data and make suggestions.

[0930] (Claim 3)

[0931] The system according to claim 1, further comprising means for using an AI model that optimizes the content of suggestions by utilizing visitor behavior records and past feedback information, and executing the process within a cloud service. [Explanation of symbols]

[0932] 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 collecting visitor location information and generating a visitor behavior database, A means of analyzing visitors' past behavioral history and generating personalized suggestions, A means of presenting the generated proposals to visitors via avatars, A means of collecting feedback from visitors and learning to improve the accuracy of proposals, A system that includes this.

2. The system according to claim 1, further comprising means for identifying the emotional state of visitors through voice and facial expression analysis, and adjusting the suggested content based on the identification results.

3. The system according to claim 1, further comprising means for using a generative AI model that optimizes the content of suggestions by utilizing the behavior history of visitors and past feedback data.

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