system

Abstract

We provide the system. [Solution] A means of collecting visitor movement information and transaction history information, A means of analyzing collected data to generate visitor attribute information, A means of proposing marketing measures to facility managers based on generated attribute information, A means of suggesting facilities based on the visitor's current location information and attributes, A means of providing real-time congestion information, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since the facility manager does not have sufficient knowledge of the detailed attribute information of visitors, it is difficult to carry out effective targeted marketing. On the other hand, visitors lack a means to obtain real-time information on the congestion situation around the destination and facility information that suits their preferences, resulting in a problem of reduced satisfaction with out-of-purpose activities. There is a need to solve such problems and enable optimal information provision according to the needs of visitors and facility managers.

Means for Solving the Problems

[0005] This invention provides means for collecting visitor movement information and transaction history information to create detailed visitor profiles. Furthermore, it provides means for analyzing the collected data to generate visitor attribute information and propose marketing measures to facility managers. In addition, it provides means for making real-time facility recommendations to visitors based on their current location information and attributes, and also provides means for providing congestion level information, thereby improving visitor satisfaction.

[0006] "Visitors" refer to individuals who visit a facility or a specific location, and are the subjects of analysis for their movement information and attribute information.

[0007] "Movement information" refers to data that shows where visitors moved and at what time, and is obtained through location information services and other means.

[0008] "Transaction history information" refers to records of visitors' purchasing behavior and service usage, and is data that shows which products or services were purchased at which facilities.

[0009] A "facility manager" refers to an individual or legal entity responsible for the operation and management of a specific facility, and is the entity that conducts marketing activities based on visitor attribute information.

[0010] "Marketing measures" refer to specific action plans and strategies aimed at promoting and selling products and services, and are formulated based on customer attributes.

[0011] "Attribute information" refers to information that indicates the individual characteristics of a visitor, such as their age, gender, and preferences, and is data used to identify marketing targets.

[0012] "Location information" refers to data that indicates a visitor's current geographical location and is used to provide facility information and navigation.

[0013] "Crowding information" refers to data that shows the level of crowding in a specific location or facility, and is presented to visitors in real time. [Brief explanation of the drawing]

[0014] [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 a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] 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, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that collects visitor movement information and transaction history information, proposes marketing strategies to facility managers based on this information, and simultaneously provides visitors with real-time information on congestion levels and appropriate facility information. This system consists mainly of a server and terminals, which work together to meet the needs of visitors and facility managers.

[0036] Server functions and processing:

[0037] The server first collects visitor movement and transaction history information via an API. This data is processed sequentially and stored in a database. To maintain data integrity, cleansing and anonymization processes are performed. Next, a machine learning model is installed on the server, which analyzes the cleansed data and generates visitor attribute profiles. Based on the attribute information generated, the server proposes marketing strategies to facility managers to optimize targeting. For example, if there are many young visitors during a particular time slot, the server will advise on promotions tailored to that demographic.

[0038] Terminal functions and processing:

[0039] The visitor's device (such as a smartphone) constantly communicates with the server to receive real-time data. Based on the current location, facility information tailored to the visitor's attributes is generated and displayed on the device. When the user launches the application, the device suggests the most suitable facilities to visit and their congestion levels. At this time, multiple options are presented, and the system is designed to provide choices that match the user's preferences. For example, a cafe with low congestion levels near the current location that matches the visitor's preferences might be suggested.

[0040] Collecting user feedback:

[0041] The system also includes a feature that allows users to input feedback on the results of their actions based on information provided via their device. This feedback is sent to the server and used for future data analysis and system improvements. Examples of feedback include evaluations such as "The information on facility congestion was accurate" and "The store information provided was helpful."

[0042] Thus, this system aims to improve visitor satisfaction and facility managers' marketing effectiveness throughout the entire process of collecting, processing, analyzing, and providing digital data.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server uses an API to collect visitor movement and transaction history information. This includes accessing external databases that provide location and purchase information. The collected data is stored in a control database within the server.

[0046] Step 2:

[0047] The server performs a data cleansing process. To maintain data integrity, it removes redundant entries and missing data, and standardizes the format. It also anonymizes the data to protect privacy.

[0048] Step 3:

[0049] The server applies machine learning algorithms to generate visitor attribute information from cleansed data. This analysis forms cluster information based on age group and purchasing patterns, creating visitor profiles.

[0050] Step 4:

[0051] The server utilizes AI generation to create marketing strategies for facility managers based on visitor attributes. An interface is provided that proposes promotional strategies tailored to facility usage trends in a voice and text-based dialogue format.

[0052] Step 5:

[0053] The user's device acquires their current location information, and based on visitor attribute information sent from the server, it displays real-time information about facilities that are suitable for the visitor. Using notifications on the device and interactive elements within the app, it lists facilities that meet the visitor's preferences.

[0054] Step 6:

[0055] Users utilize information provided through their devices to make actual visits and purchases. They contribute to system improvement by inputting feedback on congestion levels at visited locations and service satisfaction into their devices.

[0056] Step 7:

[0057] The server collects user feedback and incorporates it back into the data analysis process. This improves the accuracy of subsequent analyses and AD strategies, and continuously enhances system performance.

[0058] (Example 1)

[0059] 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."

[0060] Traditional facility management and marketing strategies struggle to provide flexible responses based on the individual behaviors and characteristics of visitors, making it difficult to increase visitor satisfaction and operate facilities efficiently. Furthermore, the information visitors receive is often static and lacks real-time relevance and individuality, highlighting the need for more personalized information.

[0061] 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.

[0062] In this invention, the server includes means for collecting visitor location information and transaction history, means for analyzing the collected data using a machine learning model to generate visitor characteristic information, and means for proposing marketing measures to facility managers based on the generated characteristic information. This enables the provision of real-time information tailored to the characteristics of visitors and the proposal of effective marketing measures to facility managers.

[0063] "Visitor location information" refers to data that shows the current location and travel history of people visiting a facility or area.

[0064] "Transaction history" refers to data that records a visitor's purchasing activities, whether at a store or online.

[0065] A "machine learning model" is a program that includes algorithms for analyzing collected data and deriving patterns and regularities.

[0066] "Visitor characteristic information" refers to profile data generated based on the visitor's attributes and behavioral patterns.

[0067] A "facility manager" is an individual or organization involved in the operation and management of commercial facilities, public facilities, etc.

[0068] "Marketing strategies" refer to plans for promotional and advertising activities that target a specific customer segment.

[0069] "Providing information in real time" refers to the process of immediately disseminating the latest information to visitors on the spot.

[0070] "Feedback" refers to collecting user experiences and opinions provided by visitors.

[0071] A "prompt" is an input sentence used to form a specific instruction or question.

[0072] To implement this invention, the system operates with a server and terminals as its main components. The server collects visitor location information and transaction history in real time through an advanced API. This information is then stored in a database and subjected to data cleansing and anonymization to maintain integrity. A machine learning model is installed on the server, which analyzes the cleansed data and generates visitor characteristic information.

[0073] The terminal functions as a visitor's smartphone or tablet device. It constantly communicates with a server to receive the latest information, generating and displaying facility information tailored to the visitor's attributes. When a user launches the application, the terminal presents optimal facility options and their congestion levels, offering multiple choices. This allows visitors to make choices that suit their preferences. For example, it can guide users to a less crowded cafe near their current location that matches their tastes.

[0074] Furthermore, by receiving feedback from users on their actions based on the information provided via their devices, the server can use this feedback for future analysis. This feedback process improves the overall accuracy and reliability of the system and increases visitor satisfaction. An example of a specific prompt might be, "Based on visitor profiles and visit frequency data, what strategies would be particularly effective for our next promotional campaign?"

[0075] In this way, this system provides value to both visitors and facility managers, enabling the provision of highly personalized information in real time and the proposal of effective marketing strategies.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The server receives visitor location information and transaction history as input via an API. Specifically, this includes GPS data obtained when visitors use a smartphone app, and purchase history within the facility. This input data is saved to a database in real time. After saving, data cleansing processes, including noise reduction and format normalization, are performed to output consistent and clean data.

[0079] Step 2:

[0080] The server uses the cleansed data as input to a machine learning model. This model analyzes visitor behavior patterns and attribute information, generating visitor characteristic information as a result. This characteristic information is stored in a database as profile data and used to propose marketing strategies to facility managers.

[0081] Step 3:

[0082] The server retrieves visitor characteristic information from a database and optimizes marketing strategies for facility managers based on this information. The server proposes promotional and advertising strategies for specific visitor groups and outputs them as concrete action plans. Input includes visitor attributes and visit frequency.

[0083] Step 4:

[0084] The terminal combines the latest facility information received from the server with visitor characteristic information to generate and output real-time information tailored to the visitor. Specific examples include recommendations for less crowded facilities based on the visitor's current location, and service recommendations that match the visitor's preferences. The information users receive through the app is output at this stage.

[0085] Step 5:

[0086] Users act based on information provided via their devices and input the results as feedback. This feedback is sent from the device to the server and used for future data analysis and system improvements. Specific examples of feedback include "The congestion level was accurate and helpful" and "The recommended facilities were of high quality." The output of this step is the aggregated opinions and evaluation data collected as feedback.

[0087] (Application Example 1)

[0088] 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."

[0089] In modern commercial facilities, with increasing visitor numbers, facility managers need to accurately understand visitor data to implement effective marketing strategies. Furthermore, visitors need information to avoid crowds and ensure a comfortable shopping experience. However, current systems struggle to achieve this in real time. An innovative approach is needed that enables personalized information delivery to visitors based on facility congestion levels and the optimization of marketing strategies based on that information.

[0090] 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.

[0091] In this invention, the server includes means for collecting visitor movement information and transaction history information, means for analyzing the collected data to generate visitor attribute information, and means for proposing marketing measures to facility managers based on the generated attribute information. This enables the provision of real-time personalized information to visitors and the efficient execution of marketing measures for facility managers.

[0092] "Visitor movement information" refers to data about the routes and visit times of individuals visiting a specific location.

[0093] "Transaction history information" refers to data that shows the history of purchases and service usage made by an individual.

[0094] "Attribute information" refers to information that indicates the characteristics of a visitor, such as their age, gender, hobbies, and preferences.

[0095] A "facility manager" refers to a person who is responsible for operating and managing commercial facilities and providing services to visitors.

[0096] "Marketing strategies" refer to plans and activities aimed at effectively promoting the sale of products and services.

[0097] "Providing real-time congestion information" means immediately informing users of the current congestion status of a facility.

[0098] "Personalizing and providing promotional information" refers to providing sales promotion information tailored to the preferences and needs of individual visitors.

[0099] The system that implements this application example consists primarily of a server and a visitor's terminal. The server collects location information and transaction history information from the visitor's smartphone or terminal via an API. After storing this data in a database, the server performs cleansing and anonymization processes to maintain data integrity.

[0100] Data analysis is performed using a machine learning model with Python, and visitor attribute information is generated using libraries such as Scikit-learn. Based on the generated attribute information, the server can propose marketing strategies for target optimization to facility managers. In particular, it maximizes the effectiveness of marketing by suggesting promotional actions tailored to the specific attributes of visitors.

[0101] Meanwhile, the terminal receives data transmitted from the server in real time. Smartphone applications built using Swift or Kotlin display congestion information and promotional information tailored to the visitor's current location and preferences. Visitors can operate the terminal to obtain information on the most suitable facilities to visit.

[0102] As a concrete example, when a visitor is in a shopping mall, the terminal will suggest cafes in the vicinity that are less crowded and match the visitor's preferences in real time. Examples of prompts from the generated AI model include, "Based on data from peak visitor times, which customer segment should we target for a specific promotion?" and "Please create a list of recommended products for user segments that are likely to visit at this time."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects location and transaction history information from visitors' devices. The input consists of records of the visitor's travel route and services used, received digitally via an API. This allows travel data, such as time and location, to be stored in a database.

[0106] Step 2:

[0107] The server performs cleansing and anonymization processes to maintain the integrity of the collected data. Cleansing corrects data inconsistencies and missing data, while anonymization transforms the data so that individuals cannot be identified. The output is a clean dataset that is ready for analysis.

[0108] Step 3:

[0109] The server uses Python's Scikit-learn to analyze a clean dataset with a machine learning model and generate visitor attribute information. This process identifies attribute patterns from the data and infers the visitor's age group and preferences. The output is an individual attribute information profile.

[0110] Step 4:

[0111] The server proposes marketing strategies to facility managers based on the generated attribute information. In this step, a generative AI model is used to identify attribute groups that are highly effective for specific promotions and reports this to the managers. The output is advice on targeting strategies.

[0112] Step 5:

[0113] The terminal receives data from the server in real time and presents the user with congestion information and personalized promotional information regarding the facilities they plan to visit. Input consists of facility information sent from the server, which is processed in combination with the user's location information. This allows the user to understand congestion levels and make the best facility selection in real time.

[0114] Step 6:

[0115] Users perform actions based on information provided via their devices and provide feedback on the results. The input consists of evaluations and opinions after the user's actions, which are sent from the device to the server. This feedback information is collected in a database to improve the accuracy of future suggestions.

[0116] 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.

[0117] This invention is a system that aggregates visitor movement information, transaction history information, and emotional information to enable the proposal of advanced marketing measures to facility managers and personalized facility recommendations to visitors. By incorporating an emotional engine, it is possible to provide information that takes into account the user's emotions.

[0118] Server functions and processing:

[0119] The server first uses an API to collect visitor movement information and transaction history information, and stores it in a database. Based on the collected information, it performs data cleansing to create consistent analytical data. In addition, the device is equipped with an emotion engine, which also acquires user emotion data obtained from the user's device. This generates integrated attribute data that includes the generated emotion information.

[0120] The server analyzes this data and uses machine learning algorithms to generate profiles optimized for visitors' preferences and emotions. Based on these profiles, the AI ​​generates proposals to facility managers that match visitor attributes and emotional tendencies. When managers receive these proposals, they are provided with detailed explanations in a conversational format to support the implementation of specific measures.

[0121] Terminal functions and processing:

[0122] The user terminal synchronizes with the server and has the function of sending the visitor's current location information and emotional state in a timely manner. When the user uses the terminal, their emotional state is measured through in-application operations and external sensors, and the emotion engine processes this data and sends it to the server.

[0123] The terminal application uses the received visitor profile to suggest facilities tailored to the visitor's current emotions and preferences. For example, if anxiety is detected, it might suggest a quiet cafe or a relaxing facility. The application also provides information including an estimate of crowd levels, allowing for more appropriate choices.

[0124] Collecting user feedback:

[0125] After using a facility or service, users can input feedback on their satisfaction level and experience via a terminal. This feedback data, along with the emotional information provided by the user, is sent to the server and used for future analysis and improvement of suggested algorithms.

[0126] The introduction of this system will enable facility managers to implement marketing strategies based on visitors' emotional behavior, and visitors will be provided with more accurate and personalized services. As a result, it will contribute to improving the experience value for both parties.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server collects visitor movement and transaction history information via an interface. This includes the process of collecting location data and purchase history data via APIs provided by the application.

[0130] Step 2:

[0131] The device acquires user emotion information. This is done by an emotion engine analyzing data from sensors built into the device and user interactions within the app to measure the emotional state.

[0132] Step 3:

[0133] The device sends the emotional information it acquires to the server. Here, the device encrypts the user's emotional data while respecting privacy before transmitting it to the server.

[0134] Step 4:

[0135] The server cleanses movement information, including sentiment data, and transaction history information, and stores it in an integrated database. Here, data integrity is ensured, and duplicate information and noise are removed.

[0136] Step 5:

[0137] The server analyzes the integrated dataset to generate visitor attribute profiles. This profile includes a process that uses machine learning algorithms to understand visitors' preferences and emotional tendencies based on attribute information and sentiment data.

[0138] Step 6:

[0139] The server proposes marketing strategies to facility managers based on the generated attribute profiles. These proposals are presented in an interactive format, offering optimal strategies that reflect the emotional tendencies obtained from the emotion engine.

[0140] Step 7:

[0141] The device displays personalized facility suggestions to visitors. It references information based on the user's current location and emotional state to present appropriate facility options in real time, taking into account congestion levels.

[0142] Step 8:

[0143] Users visit facilities based on suggestions and input feedback on their experiences, including their feelings and satisfaction levels, via a terminal.

[0144] Step 9:

[0145] The terminal sends user feedback to the server, which uses it to improve the next data analysis and suggestion system. The server updates the algorithms and processes information to improve the quality of the information it provides.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] To accurately understand the diverse needs of visitors to a facility and to provide individually optimized services and proposals, it is necessary to integrate and process multifaceted information such as visitors' movement patterns, transaction history, and emotional state. However, there is a challenge in the lack of efficient systems to effectively collect and analyze this information and link it to appropriate marketing measures and facility proposals.

[0149] 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.

[0150] In this invention, the server includes means for collecting visitor movement information, transaction history information, and sentiment information; means for cleansing the collected data and creating consistent analytical data; and means for generating visitor preference profiles based on the analyzed data and sentiment information. This makes it possible to provide appropriate services and marketing measures in real time that meet the diverse needs of visitors.

[0151] "Visitor movement information" refers to data about visitors' current location and movement history both inside and outside the facility.

[0152] "Transaction history information" refers to data that includes information about purchases and service usage made by visitors within the facility.

[0153] "Emotional information" refers to data that indicates a visitor's current emotional state, and is obtained from sensors and user interfaces.

[0154] "Means of collection" refers to the technical means and processes used to gather various types of information about visitors.

[0155] "Methods of cleansing" refer to processing methods used to remove inappropriate information from collected data and ensure consistency.

[0156] "Means of analysis" refers to the process of analyzing information based on collected data and deriving specific patterns or trends.

[0157] "Means for generating preference profiles" refers to technical means for analyzing visitor data to construct profiles that show individual preferences and tendencies.

[0158] A "generative AI model" refers to artificial intelligence technology that learns patterns based on large amounts of data and generates new information.

[0159] In an embodiment of this invention, the server first uses an API to collect visitor movement information, transaction history information, and sentiment information. This information is obtained through a GPS sensor, a purchase history management system, and a sentiment sensor, respectively.

[0160] Next, a data processing module within the server performs data cleansing using the collected information to generate consistent data for analysis. This process utilizes filtering algorithms to ensure data consistency. Furthermore, an emotion engine is used to analyze the user's emotional state and generate an integrated preference profile that includes this analysis. This preference profile is estimated from the visitor's past behavior using machine learning algorithms.

[0161] Subsequently, the server uses a generative AI model to design marketing strategies based on preference profiles and proposes them to facility managers. At this time, it prompts the generative AI with a question in the form of, "It has been detected that users currently in the shopping mall are tired. Based on this information, what kind of relaxing facilities would you suggest?"

[0162] The device constantly sends the visitor's current location and emotional state to the server while syncing with it. The device's application then makes appropriate facility recommendations based on this updated profile. For example, if an anxious mood is detected, it can suggest a quiet cafe that takes crowd levels into consideration.

[0163] Furthermore, users can input feedback from their terminal after using the facilities or services. This feedback is sent to the server to improve the accuracy of future suggestions and is used to improve the analysis algorithms.

[0164] In this way, by having servers, terminals, and users work together, a system is realized that provides highly personalized services to visitors and proposes effective marketing strategies to facility managers.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server collects visitor movement information and transaction history information via an API. It receives real-time location data and transaction data transmitted from terminals and sensors as input. This data is then stored in a database while maintaining consistency, making it ready for analysis.

[0168] Step 2:

[0169] The device acquires emotional information based on the user's operation history and data obtained from emotion sensors. It receives emotional state values ​​generated by the user's device sensors as input. This is sent to a server, where the emotion engine analyzes the information to generate data representing a specific emotional state.

[0170] Step 3:

[0171] The server performs data cleansing based on collected movement information, transaction history information, and sentiment information. The input for this step is a collection of raw data, which undergoes duplicate removal and missing data filling. The output is a clean dataset suitable for analysis.

[0172] Step 4:

[0173] The server analyzes the cleansed data and uses machine learning algorithms to create visitor preference profiles. It uses prepared analytical data and sentiment information as input to learn visitor patterns and preferences. The output is the generation of individual preference profiles.

[0174] Step 5:

[0175] The server uses a generative AI model to design marketing strategies based on preference profiles. This step includes the generated preference profiles as input, which are used to prompt the generative AI, generating strategy proposals. The output is a detailed strategy report for facility managers.

[0176] Step 6:

[0177] The terminal provides visitors with customized facility recommendations based on the received policies. It processes policy information sent from the server and real-time emotional states as input. As output, it displays information on facilities and services suitable for the user on the screen.

[0178] Step 7:

[0179] Users input feedback about the facilities and services they used via a terminal. The input is in text format and includes the user's satisfaction level and specific experiences. The terminal sends this to a server, which then uses the feedback data for analysis to improve the accuracy of future recommendations.

[0180] (Application Example 2)

[0181] 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".

[0182] In modern commercial facilities, improving customer satisfaction and increasing repeat customers are crucial challenges. Traditional methods make it difficult to understand visitors' movements and emotions in real time and provide immediate, personalized suggestions. Furthermore, flexible responses that take into account emotions and current circumstances are required, rather than simply offering suggestions based on transaction history.

[0183] 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. In this invention, the server includes means for collecting visitor movement information, transaction history, and emotional information; means for analyzing the collected data to generate visitor attribute information and emotional tendencies; and means for using AI to propose marketing measures to facility managers based on the generated attribute information and emotional tendencies. This makes it possible to provide visitors with optimal suggestions in real time that are tailored to their emotional state and preferences at any given time.

[0184] A "visitor" is an individual who visits a specific facility or store.

[0185] "Mobility information" refers to data that shows how visitors moved around within or near the facility.

[0186] "Transaction history" refers to records of purchases and transactions made by a visitor in the past.

[0187] "Emotional information" refers to data that indicates the psychological state and mood of visitors.

[0188] "Attribute information" refers to information such as the visitor's age, gender, hobbies, and preferences.

[0189] "Emotional tendencies" refer to the fluctuations and patterns in a visitor's psychological state.

[0190] "Generative AI" refers to a technology that uses artificial intelligence to generate insights and suggestions from data.

[0191] A "facility manager" is a person or organization responsible for the operation and provision of services at a specific facility.

[0192] "Congestion level" is an indicator that shows the degree of crowding or density of people within a facility.

[0193] "Personalized service" means providing suggestions and services that are optimized according to the individual characteristics and circumstances of each visitor.

[0194] A system for implementing this invention consists of a program that performs a series of processes to collect, analyze, and propose visitor movement information, transaction history, and sentiment information.

[0195] The server first uses an API to obtain visitor location information and transaction history, and stores this information in a database. The API used retrieves data in real time from various data sources and cleanses that data into a consistent format. Data processing software is used for this task. Next, an emotion engine is used to collect emotion data obtained from user devices, and all the data is integrated to generate visitor attributes and emotional tendencies. An emotion analysis API is used in this process.

[0196] The user terminal uses sensors and cameras to detect the visitor's current emotional state. The collected emotional data is sent to a server and used as part of the suggestion process. Suggestions are made by a generative AI model, which recommends the most suitable facilities and products based on the visitor's attributes and emotions. Personalized recommendations are then delivered to the visitor from the terminal in real time.

[0197] Furthermore, the server estimates congestion levels in real time and provides visitors with information at the most appropriate time. In addition, visitor satisfaction and opinions obtained through the feedback function are used to improve the accuracy of future suggestions.

[0198] For example, if the emotion engine determines that a visitor is restless, the system will suggest quiet bookstores or cafes to the visitor. Generative AI could be used to generate these suggestions, using prompts such as: "The visitor's emotions are unstable; please recommend relaxing facilities." In this way, the generative AI can provide appropriate suggestions based on the visitor's emotional state.

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

[0200] Step 1:

[0201] The server retrieves visitor movement information and transaction history via an API. It receives real-time movement information and transaction history from various data sources as input. Data cleansing is performed to ensure data consistency and accuracy before storing this information in the database. The output is cleansed and consistent data.

[0202] Step 2:

[0203] The user terminal uses sensors and cameras to collect visitor emotional information. It receives raw data from the sensors and cameras on the terminal as input. An emotion analysis API processes this data and quantifies the actual emotional state. The output is data indicating the visitor's specific emotional state.

[0204] Step 3:

[0205] The server integrates the data obtained in Step 1 and Step 2 to generate visitor attribute information and emotional tendencies. It receives movement information, transaction history, and emotional state as input. Based on this, it performs data analysis and builds a profile using machine learning techniques. The output is a profile containing visitor attribute information and emotional tendencies.

[0206] Step 4:

[0207] The server uses a generative AI model to propose marketing strategies to facility managers based on the generated visitor profiles. It receives visitor profiles as input and provides prompts to the generative AI model to create appropriate suggestions. The output consists of specific marketing strategy proposals for facility managers.

[0208] Step 5:

[0209] The terminal provides visitors with personalized facility recommendations based on suggestions received from the server. It receives suggestions from the server and the visitor's emotional state as input. Based on this, it displays suggestions on the terminal at the optimal time and in the most appropriate way, also including information on congestion levels. The output is real-time information about specific facilities and services presented to the visitor.

[0210] Step 6:

[0211] Users provide feedback via a terminal after using a facility or service. This feedback includes user satisfaction and experience information. This feedback is sent to a server and stored as data to improve the accuracy of future suggestions. The output is feedback data that serves as material for improving the suggestion algorithm.

[0212] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

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

[0217] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0219] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] This invention is a system that collects visitor movement information and transaction history information, proposes marketing strategies to facility managers based on this information, and simultaneously provides visitors with real-time information on congestion levels and appropriate facility information. This system consists mainly of a server and terminals, which work together to meet the needs of visitors and facility managers.

[0229] Server functions and processing:

[0230] The server first collects visitor movement and transaction history information via an API. This data is processed sequentially and stored in a database. To maintain data integrity, cleansing and anonymization processes are performed. Next, a machine learning model is installed on the server, which analyzes the cleansed data and generates visitor attribute profiles. Based on the attribute information generated, the server proposes marketing strategies to facility managers to optimize targeting. For example, if there are many young visitors during a particular time slot, the server will advise on promotions tailored to that demographic.

[0231] Terminal functions and processing:

[0232] The visitor's device (such as a smartphone) constantly communicates with the server to receive real-time data. Based on the current location, facility information tailored to the visitor's attributes is generated and displayed on the device. When the user launches the application, the device suggests the most suitable facilities to visit and their congestion levels. At this time, multiple options are presented, and the system is designed to provide choices that match the user's preferences. For example, a cafe with low congestion levels near the current location that matches the visitor's preferences might be suggested.

[0233] Collecting user feedback:

[0234] The system also includes a feature that allows users to input feedback on the results of their actions based on information provided via their device. This feedback is sent to the server and used for future data analysis and system improvements. Examples of feedback include evaluations such as "The information on facility congestion was accurate" and "The store information provided was helpful."

[0235] Thus, this system aims to improve visitor satisfaction and facility managers' marketing effectiveness throughout the entire process of collecting, processing, analyzing, and providing digital data.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The server uses an API to collect visitor movement and transaction history information. This includes accessing external databases that provide location and purchase information. The collected data is stored in a control database within the server.

[0239] Step 2:

[0240] The server performs a data cleansing process. To maintain data integrity, it removes redundant entries and missing data, and standardizes the format. It also anonymizes the data to protect privacy.

[0241] Step 3:

[0242] The server applies machine learning algorithms to generate visitor attribute information from cleansed data. This analysis forms cluster information based on age group and purchasing patterns, creating visitor profiles.

[0243] Step 4:

[0244] The server utilizes AI generation to create marketing strategies for facility managers based on visitor attributes. An interface is provided that proposes promotional strategies tailored to facility usage trends in a voice and text-based dialogue format.

[0245] Step 5:

[0246] The user's device acquires their current location information, and based on visitor attribute information sent from the server, it displays real-time information about facilities that are suitable for the visitor. Using notifications on the device and interactive elements within the app, it lists facilities that meet the visitor's preferences.

[0247] Step 6:

[0248] Users utilize information provided through their devices to make actual visits and purchases. They contribute to system improvement by inputting feedback on congestion levels at visited locations and service satisfaction into their devices.

[0249] Step 7:

[0250] The server collects user feedback and incorporates it back into the data analysis process. This improves the accuracy of subsequent analyses and AD strategies, and continuously enhances system performance.

[0251] (Example 1)

[0252] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0253] Traditional facility management and marketing strategies struggle to provide flexible responses based on the individual behaviors and characteristics of visitors, making it difficult to increase visitor satisfaction and operate facilities efficiently. Furthermore, the information visitors receive is often static and lacks real-time relevance and individuality, highlighting the need for more personalized information.

[0254] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0255] In this invention, the server includes means for collecting visitor location information and transaction history, means for analyzing the collected data using a machine learning model to generate visitor characteristic information, and means for proposing marketing measures to facility managers based on the generated characteristic information. This enables the provision of real-time information tailored to the characteristics of visitors and the proposal of effective marketing measures to facility managers.

[0256] "Visitor location information" refers to data that shows the current location and travel history of people visiting a facility or area.

[0257] "Transaction history" refers to data that records a visitor's purchasing activities, whether at a store or online.

[0258] A "machine learning model" is a program that includes algorithms for analyzing collected data and deriving patterns and regularities.

[0259] "Visitor characteristic information" refers to profile data generated based on the visitor's attributes and behavioral patterns.

[0260] A "facility manager" is an individual or organization involved in the operation and management of commercial facilities, public facilities, etc.

[0261] "Marketing strategies" refer to plans for promotional and advertising activities that target a specific customer segment.

[0262] "Providing information in real time" refers to the process of immediately disseminating the latest information to visitors on the spot.

[0263] "Feedback" refers to collecting user experiences and opinions provided by visitors.

[0264] A "prompt" is an input sentence used to form a specific instruction or question.

[0265] To implement this invention, the system operates with a server and terminals as its main components. The server collects visitor location information and transaction history in real time through an advanced API. This information is then stored in a database and subjected to data cleansing and anonymization to maintain integrity. A machine learning model is installed on the server, which analyzes the cleansed data and generates visitor characteristic information.

[0266] The terminal functions as a visitor's smartphone or tablet device. It constantly communicates with a server to receive the latest information, generating and displaying facility information tailored to the visitor's attributes. When a user launches the application, the terminal presents optimal facility options and their congestion levels, offering multiple choices. This allows visitors to make choices that suit their preferences. For example, it can guide users to a less crowded cafe near their current location that matches their tastes.

[0267] Furthermore, by receiving feedback from users on their actions based on the information provided via their devices, the server can use this feedback for future analysis. This feedback process improves the overall accuracy and reliability of the system and increases visitor satisfaction. An example of a specific prompt might be, "Based on visitor profiles and visit frequency data, what strategies would be particularly effective for our next promotional campaign?"

[0268] In this way, this system provides value to both visitors and facility managers, enabling the provision of highly personalized information in real time and the proposal of effective marketing strategies.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The server receives visitor location information and transaction history as input via an API. Specifically, this includes GPS data obtained when visitors use a smartphone app, and purchase history within the facility. This input data is saved to a database in real time. After saving, data cleansing processes, including noise reduction and format normalization, are performed to output consistent and clean data.

[0272] Step 2:

[0273] The server uses the cleansed data as input to a machine learning model. This model analyzes visitor behavior patterns and attribute information, generating visitor characteristic information as a result. This characteristic information is stored in a database as profile data and used to propose marketing strategies to facility managers.

[0274] Step 3:

[0275] The server retrieves visitor characteristic information from a database and optimizes marketing strategies for facility managers based on this information. The server proposes promotional and advertising strategies for specific visitor groups and outputs them as concrete action plans. Input includes visitor attributes and visit frequency.

[0276] Step 4:

[0277] The terminal combines the latest facility information received from the server with visitor characteristic information to generate and output real-time information tailored to the visitor. Specific examples include recommendations for less crowded facilities based on the visitor's current location, and service recommendations that match the visitor's preferences. The information users receive through the app is output at this stage.

[0278] Step 5:

[0279] Users act based on information provided via their devices and input the results as feedback. This feedback is sent from the device to the server and used for future data analysis and system improvements. Specific examples of feedback include "The congestion level was accurate and helpful" and "The recommended facilities were of high quality." The output of this step is the aggregated opinions and evaluation data collected as feedback.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0282] In modern commercial facilities, as the number of visitors increases, facility managers need to accurately grasp visitor information for implementing effective marketing measures. Also, there is a demand for providing information to visitors to avoid congestion and enable comfortable shopping. However, with the current systems, it is difficult to achieve these in real time. An innovative means is required that enables the provision of personalized information to visitors based on the degree of congestion in the facility and the optimization of marketing measures based on that information.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for collecting movement information and transaction history information of visitors, means for analyzing the collected data to generate attribute information of visitors, and means for proposing marketing measures to facility managers based on the generated attribute information. Thereby, it becomes possible to provide real-time personalized information to visitors and execute efficient marketing measures for facility managers.

[0285] "Movement information of visitors" is data regarding the route and visit time of an individual who visits a specific location.

[0286] "Transaction history information" is data indicating the history of purchases and service usage made by an individual.

[0287] "Attribute information" is information indicating characteristics such as the age, gender, hobbies, etc. of visitors.

[0288] "Facility manager" refers to a person who operates and manages a commercial facility, etc., and has the responsibility of providing services to visitors.

[0289] "Marketing strategies" refer to plans and activities aimed at effectively promoting the sale of products and services.

[0290] "Providing real-time congestion information" means immediately informing users of the current congestion status of a facility.

[0291] "Personalizing and providing promotional information" refers to providing sales promotion information tailored to the preferences and needs of individual visitors.

[0292] The system that implements this application example consists primarily of a server and a visitor's terminal. The server collects location information and transaction history information from the visitor's smartphone or terminal via an API. After storing this data in a database, the server performs cleansing and anonymization processes to maintain data integrity.

[0293] Data analysis is performed using a machine learning model with Python, and visitor attribute information is generated using libraries such as Scikit-learn. Based on the generated attribute information, the server can propose marketing strategies for target optimization to facility managers. In particular, it maximizes the effectiveness of marketing by suggesting promotional actions tailored to the specific attributes of visitors.

[0294] Meanwhile, the terminal receives data transmitted from the server in real time. Smartphone applications built using Swift or Kotlin display congestion information and promotional information tailored to the visitor's current location and preferences. Visitors can operate the terminal to obtain information on the most suitable facilities to visit.

[0295] As a concrete example, when a visitor is in a shopping mall, the terminal will suggest cafes in the vicinity that are less crowded and match the visitor's preferences in real time. Examples of prompts from the generated AI model include, "Based on data from peak visitor times, which customer segment should we target for a specific promotion?" and "Please create a list of recommended products for user segments that are likely to visit at this time."

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The server collects location and transaction history information from visitors' devices. The input consists of records of the visitor's travel route and services used, received digitally via an API. This allows travel data, such as time and location, to be stored in a database.

[0299] Step 2:

[0300] The server performs cleansing and anonymization processes to maintain the integrity of the collected data. Cleansing corrects data inconsistencies and missing data, while anonymization transforms the data so that individuals cannot be identified. The output is a clean dataset that is ready for analysis.

[0301] Step 3:

[0302] The server uses Python's Scikit-learn to analyze a clean dataset with a machine learning model and generate visitor attribute information. This process identifies attribute patterns from the data and infers the visitor's age group and preferences. The output is an individual attribute information profile.

[0303] Step 4:

[0304] The server proposes marketing measures to the facility manager based on the generated attribute information. In this step, the generation AI model is used to identify attribute groups with high specific promotion effects and report them to the manager. The output is advice on the targeting strategy.

[0305] Step 5:

[0306] The terminal receives data from the server in real time and presents congestion information and personalized promotion information about the facility the user plans to visit to the user. The input is the facility information sent from the server, which is processed in combination with the user's location information. This enables the user to grasp the congestion level and optimal facility selection in real time.

[0307] Step 6:

[0308] The user performs an action based on the information provided via the terminal and feeds back the result. The input is the evaluation and opinion after the user's action, which is sent from the terminal to the server. This feedback information is collected in the database for improving the accuracy of the next proposal.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0310] The present invention is a system that aggregates the movement information, transaction history information, and emotion information of visitors, enables the proposal of advanced marketing measures to the facility manager, and the proposal of personalized facilities to visitors. By incorporating an emotion engine, information provision considering the user's emotion can be realized.

[0311] Functions and processing of the server:

[0312] The server first uses an API to collect visitor movement information and transaction history information, and stores it in a database. Based on the collected information, it performs data cleansing to create consistent analytical data. In addition, the device is equipped with an emotion engine, which also acquires user emotion data obtained from the user's device. This generates integrated attribute data that includes the generated emotion information.

[0313] The server analyzes this data and uses machine learning algorithms to generate profiles optimized for visitors' preferences and emotions. Based on these profiles, the AI ​​generates proposals to facility managers that match visitor attributes and emotional tendencies. When managers receive these proposals, they are provided with detailed explanations in a conversational format to support the implementation of specific measures.

[0314] Terminal functions and processing:

[0315] The user terminal synchronizes with the server and has the function of sending the visitor's current location information and emotional state in a timely manner. When the user uses the terminal, their emotional state is measured through in-application operations and external sensors, and the emotion engine processes this data and sends it to the server.

[0316] The terminal application uses the received visitor profile to suggest facilities tailored to the visitor's current emotions and preferences. For example, if anxiety is detected, it might suggest a quiet cafe or a relaxing facility. The application also provides information including an estimate of crowd levels, allowing for more appropriate choices.

[0317] Collecting user feedback:

[0318] After using a facility or service, users can input feedback on their satisfaction level and experience via a terminal. This feedback data, along with the emotional information provided by the user, is sent to the server and used for future analysis and improvement of suggested algorithms.

[0319] The introduction of this system will enable facility managers to implement marketing strategies based on visitors' emotional behavior, and visitors will be provided with more accurate and personalized services. As a result, it will contribute to improving the experience value for both parties.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The server collects visitor movement and transaction history information via an interface. This includes the process of collecting location data and purchase history data via APIs provided by the application.

[0323] Step 2:

[0324] The device acquires user emotion information. This is done by an emotion engine analyzing data from sensors built into the device and user interactions within the app to measure the emotional state.

[0325] Step 3:

[0326] The device sends the emotional information it acquires to the server. Here, the device encrypts the user's emotional data while respecting privacy before transmitting it to the server.

[0327] Step 4:

[0328] The server cleanses movement information, including sentiment data, and transaction history information, and stores it in an integrated database. Here, data integrity is ensured, and duplicate information and noise are removed.

[0329] Step 5:

[0330] The server analyzes the integrated dataset to generate visitor attribute profiles. This profile includes a process that uses machine learning algorithms to understand visitors' preferences and emotional tendencies based on attribute information and sentiment data.

[0331] Step 6:

[0332] The server proposes marketing strategies to facility managers based on the generated attribute profiles. These proposals are presented in an interactive format, offering optimal strategies that reflect the emotional tendencies obtained from the emotion engine.

[0333] Step 7:

[0334] The device displays personalized facility suggestions to visitors. It references information based on the user's current location and emotional state to present appropriate facility options in real time, taking into account congestion levels.

[0335] Step 8:

[0336] Users visit facilities based on suggestions and input feedback on their experiences, including their feelings and satisfaction levels, via a terminal.

[0337] Step 9:

[0338] The terminal sends user feedback to the server, which uses it to improve the next data analysis and suggestion system. The server updates the algorithms and processes information to improve the quality of the information it provides.

[0339] (Example 2)

[0340] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0341] To accurately understand the diverse needs of visitors to a facility and to provide individually optimized services and proposals, it is necessary to integrate and process multifaceted information such as visitors' movement patterns, transaction history, and emotional state. However, there is a challenge in the lack of efficient systems to effectively collect and analyze this information and link it to appropriate marketing measures and facility proposals.

[0342] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0343] In this invention, the server includes means for collecting visitor movement information, transaction history information, and sentiment information; means for cleansing the collected data and creating consistent analytical data; and means for generating visitor preference profiles based on the analyzed data and sentiment information. This makes it possible to provide appropriate services and marketing measures in real time that meet the diverse needs of visitors.

[0344] "Visitor movement information" refers to data about visitors' current location and movement history both inside and outside the facility.

[0345] "Transaction history information" refers to data that includes information about purchases and service usage made by visitors within the facility.

[0346] "Emotional information" refers to data that indicates a visitor's current emotional state, and is obtained from sensors and user interfaces.

[0347] "Means of collection" refers to the technical means and processes used to gather various types of information about visitors.

[0348] "Methods of cleansing" refer to processing methods used to remove inappropriate information from collected data and ensure consistency.

[0349] "Means of analysis" refers to the process of analyzing information based on collected data and deriving specific patterns or trends.

[0350] "Means for generating preference profiles" refers to technical means for analyzing visitor data to construct profiles that show individual preferences and tendencies.

[0351] A "generative AI model" refers to artificial intelligence technology that learns patterns based on large amounts of data and generates new information.

[0352] In an embodiment of this invention, the server first uses an API to collect visitor movement information, transaction history information, and sentiment information. This information is obtained through a GPS sensor, a purchase history management system, and a sentiment sensor, respectively.

[0353] Next, a data processing module within the server performs data cleansing using the collected information to generate consistent data for analysis. This process utilizes filtering algorithms to ensure data consistency. Furthermore, an emotion engine is used to analyze the user's emotional state and generate an integrated preference profile that includes this analysis. This preference profile is estimated from the visitor's past behavior using machine learning algorithms.

[0354] Subsequently, the server uses a generative AI model to design marketing strategies based on preference profiles and proposes them to facility managers. At this time, it prompts the generative AI with a question in the form of, "It has been detected that users currently in the shopping mall are tired. Based on this information, what kind of relaxing facilities would you suggest?"

[0355] The device constantly sends the visitor's current location and emotional state to the server while syncing with it. The device's application then makes appropriate facility recommendations based on this updated profile. For example, if an anxious mood is detected, it can suggest a quiet cafe that takes crowd levels into consideration.

[0356] Furthermore, users can input feedback from their terminal after using the facilities or services. This feedback is sent to the server to improve the accuracy of future suggestions and is used to improve the analysis algorithms.

[0357] In this way, by having servers, terminals, and users work together, a system is realized that provides highly personalized services to visitors and proposes effective marketing strategies to facility managers.

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

[0359] Step 1:

[0360] The server collects visitor movement information and transaction history information via an API. It receives real-time location data and transaction data transmitted from terminals and sensors as input. This data is then stored in a database while maintaining consistency, making it ready for analysis.

[0361] Step 2:

[0362] The device acquires emotional information based on the user's operation history and data obtained from emotion sensors. It receives emotional state values ​​generated by the user's device sensors as input. This is sent to a server, where the emotion engine analyzes the information to generate data representing a specific emotional state.

[0363] Step 3:

[0364] The server performs data cleansing based on collected movement information, transaction history information, and sentiment information. The input for this step is a collection of raw data, which undergoes duplicate removal and missing data filling. The output is a clean dataset suitable for analysis.

[0365] Step 4:

[0366] The server analyzes the cleansed data and uses machine learning algorithms to create visitor preference profiles. It uses prepared analytical data and sentiment information as input to learn visitor patterns and preferences. The output is the generation of individual preference profiles.

[0367] Step 5:

[0368] The server uses a generative AI model to design marketing strategies based on preference profiles. This step includes the generated preference profiles as input, which are used to prompt the generative AI, generating strategy proposals. The output is a detailed strategy report for facility managers.

[0369] Step 6:

[0370] The terminal provides visitors with customized facility recommendations based on the received policies. It processes policy information sent from the server and real-time emotional states as input. As output, it displays information on facilities and services suitable for the user on the screen.

[0371] Step 7:

[0372] Users input feedback about the facilities and services they used via a terminal. The input is in text format and includes the user's satisfaction level and specific experiences. The terminal sends this to a server, which then uses the feedback data for analysis to improve the accuracy of future recommendations.

[0373] (Application Example 2)

[0374] 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."

[0375] In modern commercial facilities, improving customer satisfaction and increasing repeat customers are crucial challenges. Traditional methods make it difficult to understand visitors' movements and emotions in real time and provide immediate, personalized suggestions. Furthermore, flexible responses that take into account emotions and current circumstances are required, rather than simply offering suggestions based on transaction history.

[0376] 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. In this invention, the server includes means for collecting visitor movement information, transaction history, and emotional information; means for analyzing the collected data to generate visitor attribute information and emotional tendencies; and means for using AI to propose marketing measures to facility managers based on the generated attribute information and emotional tendencies. This makes it possible to provide visitors with optimal suggestions in real time that are tailored to their emotional state and preferences at any given time.

[0377] A "visitor" is an individual who visits a specific facility or store.

[0378] "Mobility information" refers to data that shows how visitors moved around within or near the facility.

[0379] "Transaction history" refers to records of purchases and transactions made by a visitor in the past.

[0380] "Emotional information" refers to data that indicates the psychological state and mood of visitors.

[0381] "Attribute information" refers to information such as the visitor's age, gender, hobbies, and preferences.

[0382] "Emotional tendencies" refer to the fluctuations and patterns in a visitor's psychological state.

[0383] "Generative AI" refers to a technology that uses artificial intelligence to generate insights and suggestions from data.

[0384] A "facility manager" is a person or organization responsible for the operation and provision of services at a specific facility.

[0385] "Congestion level" is an indicator that shows the degree of crowding or density of people within a facility.

[0386] "Personalized service" means providing suggestions and services that are optimized according to the individual characteristics and circumstances of each visitor.

[0387] A system for implementing this invention consists of a program that performs a series of processes to collect, analyze, and propose visitor movement information, transaction history, and sentiment information.

[0388] The server first uses an API to obtain visitor location information and transaction history, and stores this information in a database. The API used retrieves data in real time from various data sources and cleanses that data into a consistent format. Data processing software is used for this task. Next, an emotion engine is used to collect emotion data obtained from user devices, and all the data is integrated to generate visitor attributes and emotional tendencies. An emotion analysis API is used in this process.

[0389] The user terminal uses sensors and cameras to detect the visitor's current emotional state. The collected emotional data is sent to a server and used as part of the suggestion process. Suggestions are made by a generative AI model, which recommends the most suitable facilities and products based on the visitor's attributes and emotions. Personalized recommendations are then delivered to the visitor from the terminal in real time.

[0390] Furthermore, the server estimates congestion levels in real time and provides visitors with information at the most appropriate time. In addition, visitor satisfaction and opinions obtained through the feedback function are used to improve the accuracy of future suggestions.

[0391] For example, if the emotion engine determines that a visitor is restless, the system will suggest quiet bookstores or cafes to the visitor. Generative AI could be used to generate these suggestions, using prompts such as: "The visitor's emotions are unstable; please recommend relaxing facilities." In this way, the generative AI can provide appropriate suggestions based on the visitor's emotional state.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The server retrieves visitor movement information and transaction history via an API. It receives real-time movement information and transaction history from various data sources as input. Data cleansing is performed to ensure data consistency and accuracy before storing this information in the database. The output is cleansed and consistent data.

[0395] Step 2:

[0396] The user terminal uses sensors and cameras to collect visitor emotional information. It receives raw data from the sensors and cameras on the terminal as input. An emotion analysis API processes this data and quantifies the actual emotional state. The output is data indicating the visitor's specific emotional state.

[0397] Step 3:

[0398] The server integrates the data obtained in Step 1 and Step 2 to generate visitor attribute information and emotional tendencies. It receives movement information, transaction history, and emotional state as input. Based on this, it performs data analysis and builds a profile using machine learning techniques. The output is a profile containing visitor attribute information and emotional tendencies.

[0399] Step 4:

[0400] The server uses a generative AI model to propose marketing strategies to facility managers based on the generated visitor profiles. It receives visitor profiles as input and provides prompts to the generative AI model to create appropriate suggestions. The output consists of specific marketing strategy proposals for facility managers.

[0401] Step 5:

[0402] The terminal provides visitors with personalized facility recommendations based on suggestions received from the server. It receives suggestions from the server and the visitor's emotional state as input. Based on this, it displays suggestions on the terminal at the optimal time and in the most appropriate way, also including information on congestion levels. The output is real-time information about specific facilities and services presented to the visitor.

[0403] Step 6:

[0404] Users provide feedback via a terminal after using a facility or service. This feedback includes user satisfaction and experience information. This feedback is sent to a server and stored as data to improve the accuracy of future suggestions. The output is feedback data that serves as material for improving the suggestion algorithm.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] This invention is a system that collects visitor movement information and transaction history information, proposes marketing strategies to facility managers based on this information, and simultaneously provides visitors with real-time information on congestion levels and appropriate facility information. This system consists mainly of a server and terminals, which work together to meet the needs of visitors and facility managers.

[0422] Server functions and processing:

[0423] The server first collects visitor movement and transaction history information via an API. This data is processed sequentially and stored in a database. To maintain data integrity, cleansing and anonymization processes are performed. Next, a machine learning model is installed on the server, which analyzes the cleansed data and generates visitor attribute profiles. Based on the attribute information generated, the server proposes marketing strategies to facility managers to optimize targeting. For example, if there are many young visitors during a particular time slot, the server will advise on promotions tailored to that demographic.

[0424] Terminal functions and processing:

[0425] The visitor's device (such as a smartphone) constantly communicates with the server to receive real-time data. Based on the current location, facility information tailored to the visitor's attributes is generated and displayed on the device. When the user launches the application, the device suggests the most suitable facilities to visit and their congestion levels. At this time, multiple options are presented, and the system is designed to provide choices that match the user's preferences. For example, a cafe with low congestion levels near the current location that matches the visitor's preferences might be suggested.

[0426] Collecting user feedback:

[0427] The system also includes a feature that allows users to input feedback on the results of their actions based on information provided via their device. This feedback is sent to the server and used for future data analysis and system improvements. Examples of feedback include evaluations such as "The information on facility congestion was accurate" and "The store information provided was helpful."

[0428] Thus, this system aims to improve visitor satisfaction and facility managers' marketing effectiveness throughout the entire process of collecting, processing, analyzing, and providing digital data.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The server uses an API to collect visitor movement and transaction history information. This includes accessing external databases that provide location and purchase information. The collected data is stored in a control database within the server.

[0432] Step 2:

[0433] The server performs a data cleansing process. To maintain data integrity, it removes redundant entries and missing data, and standardizes the format. It also anonymizes the data to protect privacy.

[0434] Step 3:

[0435] The server applies machine learning algorithms to generate visitor attribute information from cleansed data. This analysis forms cluster information based on age group and purchasing patterns, creating visitor profiles.

[0436] Step 4:

[0437] The server utilizes AI generation to create marketing strategies for facility managers based on visitor attributes. An interface is provided that proposes promotional strategies tailored to facility usage trends in a voice and text-based dialogue format.

[0438] Step 5:

[0439] The user's device acquires their current location information, and based on visitor attribute information sent from the server, it displays real-time information about facilities that are suitable for the visitor. Using notifications on the device and interactive elements within the app, it lists facilities that meet the visitor's preferences.

[0440] Step 6:

[0441] Users utilize information provided through their devices to make actual visits and purchases. They contribute to system improvement by inputting feedback on congestion levels at visited locations and service satisfaction into their devices.

[0442] Step 7:

[0443] The server collects user feedback and incorporates it back into the data analysis process. This improves the accuracy of subsequent analyses and AD strategies, and continuously enhances system performance.

[0444] (Example 1)

[0445] 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."

[0446] Traditional facility management and marketing strategies struggle to provide flexible responses based on the individual behaviors and characteristics of visitors, making it difficult to increase visitor satisfaction and operate facilities efficiently. Furthermore, the information visitors receive is often static and lacks real-time relevance and individuality, highlighting the need for more personalized information.

[0447] 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.

[0448] In this invention, the server includes means for collecting visitor location information and transaction history, means for analyzing the collected data using a machine learning model to generate visitor characteristic information, and means for proposing marketing measures to facility managers based on the generated characteristic information. This enables the provision of real-time information tailored to the characteristics of visitors and the proposal of effective marketing measures to facility managers.

[0449] "Visitor location information" refers to data that shows the current location and travel history of people visiting a facility or area.

[0450] "Transaction history" refers to data that records a visitor's purchasing activities, whether at a store or online.

[0451] A "machine learning model" is a program that includes algorithms for analyzing collected data and deriving patterns and regularities.

[0452] "Visitor characteristic information" refers to profile data generated based on the visitor's attributes and behavioral patterns.

[0453] A "facility manager" is an individual or organization involved in the operation and management of commercial facilities, public facilities, etc.

[0454] "Marketing strategies" refer to plans for promotional and advertising activities that target a specific customer segment.

[0455] "Providing information in real time" refers to the process of immediately disseminating the latest information to visitors on the spot.

[0456] "Feedback" refers to collecting user experiences and opinions provided by visitors.

[0457] A "prompt" is an input sentence used to form a specific instruction or question.

[0458] To implement this invention, the system operates with a server and terminals as its main components. The server collects visitor location information and transaction history in real time through an advanced API. This information is then stored in a database and subjected to data cleansing and anonymization to maintain integrity. A machine learning model is installed on the server, which analyzes the cleansed data and generates visitor characteristic information.

[0459] The terminal functions as a visitor's smartphone or tablet device. It constantly communicates with a server to receive the latest information, generating and displaying facility information tailored to the visitor's attributes. When a user launches the application, the terminal presents optimal facility options and their congestion levels, offering multiple choices. This allows visitors to make choices that suit their preferences. For example, it can guide users to a less crowded cafe near their current location that matches their tastes.

[0460] Furthermore, by receiving feedback from users on their actions based on the information provided via their devices, the server can use this feedback for future analysis. This feedback process improves the overall accuracy and reliability of the system and increases visitor satisfaction. An example of a specific prompt might be, "Based on visitor profiles and visit frequency data, what strategies would be particularly effective for our next promotional campaign?"

[0461] In this way, this system provides value to both visitors and facility managers, enabling the provision of highly personalized information in real time and the proposal of effective marketing strategies.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] The server receives visitor location information and transaction history as input via an API. Specifically, this includes GPS data obtained when visitors use a smartphone app, and purchase history within the facility. This input data is saved to a database in real time. After saving, data cleansing processes, including noise reduction and format normalization, are performed to output consistent and clean data.

[0465] Step 2:

[0466] The server uses the cleansed data as input to a machine learning model. This model analyzes visitor behavior patterns and attribute information, generating visitor characteristic information as a result. This characteristic information is stored in a database as profile data and used to propose marketing strategies to facility managers.

[0467] Step 3:

[0468] The server retrieves visitor characteristic information from a database and optimizes marketing strategies for facility managers based on this information. The server proposes promotional and advertising strategies for specific visitor groups and outputs them as concrete action plans. Input includes visitor attributes and visit frequency.

[0469] Step 4:

[0470] The terminal combines the latest facility information received from the server with visitor characteristic information to generate and output real-time information tailored to the visitor. Specific examples include recommendations for less crowded facilities based on the visitor's current location, and service recommendations that match the visitor's preferences. The information users receive through the app is output at this stage.

[0471] Step 5:

[0472] Users act based on information provided via their devices and input the results as feedback. This feedback is sent from the device to the server and used for future data analysis and system improvements. Specific examples of feedback include "The congestion level was accurate and helpful" and "The recommended facilities were of high quality." The output of this step is the aggregated opinions and evaluation data collected as feedback.

[0473] (Application Example 1)

[0474] 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."

[0475] In modern commercial facilities, with increasing visitor numbers, facility managers need to accurately understand visitor data to implement effective marketing strategies. Furthermore, visitors need information to avoid crowds and ensure a comfortable shopping experience. However, current systems struggle to achieve this in real time. An innovative approach is needed that enables personalized information delivery to visitors based on facility congestion levels and the optimization of marketing strategies based on that information.

[0476] 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.

[0477] In this invention, the server includes means for collecting visitor movement information and transaction history information, means for analyzing the collected data to generate visitor attribute information, and means for proposing marketing measures to facility managers based on the generated attribute information. This enables the provision of real-time personalized information to visitors and the efficient execution of marketing measures for facility managers.

[0478] "Visitor movement information" refers to data about the routes and visit times of individuals visiting a specific location.

[0479] "Transaction history information" refers to data that shows the history of purchases and service usage made by an individual.

[0480] "Attribute information" refers to information that indicates the characteristics of a visitor, such as their age, gender, hobbies, and preferences.

[0481] A "facility manager" refers to a person who is responsible for operating and managing commercial facilities and providing services to visitors.

[0482] "Marketing strategies" refer to plans and activities aimed at effectively promoting the sale of products and services.

[0483] "Providing real-time congestion information" means immediately informing users of the current congestion status of a facility.

[0484] "Personalizing and providing promotional information" refers to providing sales promotion information tailored to the preferences and needs of individual visitors.

[0485] The system that implements this application example consists primarily of a server and a visitor's terminal. The server collects location information and transaction history information from the visitor's smartphone or terminal via an API. After storing this data in a database, the server performs cleansing and anonymization processes to maintain data integrity.

[0486] Data analysis is performed using a machine learning model with Python, and visitor attribute information is generated using libraries such as Scikit-learn. Based on the generated attribute information, the server can propose marketing strategies for target optimization to facility managers. In particular, it maximizes the effectiveness of marketing by suggesting promotional actions tailored to the specific attributes of visitors.

[0487] Meanwhile, the terminal receives data transmitted from the server in real time. Smartphone applications built using Swift or Kotlin display congestion information and promotional information tailored to the visitor's current location and preferences. Visitors can operate the terminal to obtain information on the most suitable facilities to visit.

[0488] As a concrete example, when a visitor is in a shopping mall, the terminal will suggest cafes in the vicinity that are less crowded and match the visitor's preferences in real time. Examples of prompts from the generated AI model include, "Based on data from peak visitor times, which customer segment should we target for a specific promotion?" and "Please create a list of recommended products for user segments that are likely to visit at this time."

[0489] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0490] Step 1:

[0491] The server collects location and transaction history information from visitors' devices. The input consists of records of the visitor's travel route and services used, received digitally via an API. This allows travel data, such as time and location, to be stored in a database.

[0492] Step 2:

[0493] The server performs cleansing and anonymization processes to maintain the integrity of the collected data. Cleansing corrects data inconsistencies and missing data, while anonymization transforms the data so that individuals cannot be identified. The output is a clean dataset that is ready for analysis.

[0494] Step 3:

[0495] The server uses Python's Scikit-learn to analyze a clean dataset with a machine learning model and generate visitor attribute information. This process identifies attribute patterns from the data and infers the visitor's age group and preferences. The output is an individual attribute information profile.

[0496] Step 4:

[0497] The server proposes marketing strategies to facility managers based on the generated attribute information. In this step, a generative AI model is used to identify attribute groups that are highly effective for specific promotions and reports this to the managers. The output is advice on targeting strategies.

[0498] Step 5:

[0499] The terminal receives data from the server in real time and presents the user with congestion information and personalized promotional information regarding the facilities they plan to visit. Input consists of facility information sent from the server, which is processed in combination with the user's location information. This allows the user to understand congestion levels and make the best facility selection in real time.

[0500] Step 6:

[0501] Users perform actions based on information provided via their devices and provide feedback on the results. The input consists of evaluations and opinions after the user's actions, which are sent from the device to the server. This feedback information is collected in a database to improve the accuracy of future suggestions.

[0502] 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.

[0503] This invention is a system that aggregates visitor movement information, transaction history information, and emotional information to enable the proposal of advanced marketing measures to facility managers and personalized facility recommendations to visitors. By incorporating an emotional engine, it is possible to provide information that takes into account the user's emotions.

[0504] Server functions and processing:

[0505] The server first uses an API to collect visitor movement information and transaction history information, and stores it in a database. Based on the collected information, it performs data cleansing to create consistent analytical data. In addition, the device is equipped with an emotion engine, which also acquires user emotion data obtained from the user's device. This generates integrated attribute data that includes the generated emotion information.

[0506] The server analyzes this data and uses machine learning algorithms to generate profiles optimized for visitors' preferences and emotions. Based on these profiles, the AI ​​generates proposals to facility managers that match visitor attributes and emotional tendencies. When managers receive these proposals, they are provided with detailed explanations in a conversational format to support the implementation of specific measures.

[0507] Terminal functions and processing:

[0508] The user terminal synchronizes with the server and has the function of sending the visitor's current location information and emotional state in a timely manner. When the user uses the terminal, their emotional state is measured through in-application operations and external sensors, and the emotion engine processes this data and sends it to the server.

[0509] The terminal application uses the received visitor profile to suggest facilities tailored to the visitor's current emotions and preferences. For example, if anxiety is detected, it might suggest a quiet cafe or a relaxing facility. The application also provides information including an estimate of crowd levels, allowing for more appropriate choices.

[0510] Collecting user feedback:

[0511] After using a facility or service, users can input feedback on their satisfaction level and experience via a terminal. This feedback data, along with the emotional information provided by the user, is sent to the server and used for future analysis and improvement of suggested algorithms.

[0512] The introduction of this system will enable facility managers to implement marketing strategies based on visitors' emotional behavior, and visitors will be provided with more accurate and personalized services. As a result, it will contribute to improving the experience value for both parties.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The server collects visitor movement and transaction history information via an interface. This includes the process of collecting location data and purchase history data via APIs provided by the application.

[0516] Step 2:

[0517] The device acquires user emotion information. This is done by an emotion engine analyzing data from sensors built into the device and user interactions within the app to measure the emotional state.

[0518] Step 3:

[0519] The device sends the emotional information it acquires to the server. Here, the device encrypts the user's emotional data while respecting privacy before transmitting it to the server.

[0520] Step 4:

[0521] The server cleanses movement information, including sentiment data, and transaction history information, and stores it in an integrated database. Here, data integrity is ensured, and duplicate information and noise are removed.

[0522] Step 5:

[0523] The server analyzes the integrated dataset to generate visitor attribute profiles. This profile includes a process that uses machine learning algorithms to understand visitors' preferences and emotional tendencies based on attribute information and sentiment data.

[0524] Step 6:

[0525] The server proposes marketing strategies to facility managers based on the generated attribute profiles. These proposals are presented in an interactive format, offering optimal strategies that reflect the emotional tendencies obtained from the emotion engine.

[0526] Step 7:

[0527] The device displays personalized facility suggestions to visitors. It references information based on the user's current location and emotional state to present appropriate facility options in real time, taking into account congestion levels.

[0528] Step 8:

[0529] Users visit facilities based on suggestions and input feedback on their experiences, including their feelings and satisfaction levels, via a terminal.

[0530] Step 9:

[0531] The terminal sends user feedback to the server, which uses it to improve the next data analysis and suggestion system. The server updates the algorithms and processes information to improve the quality of the information it provides.

[0532] (Example 2)

[0533] 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."

[0534] To accurately understand the diverse needs of visitors to a facility and to provide individually optimized services and proposals, it is necessary to integrate and process multifaceted information such as visitors' movement patterns, transaction history, and emotional state. However, there is a challenge in the lack of efficient systems to effectively collect and analyze this information and link it to appropriate marketing measures and facility proposals.

[0535] 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.

[0536] In this invention, the server includes means for collecting visitor movement information, transaction history information, and sentiment information; means for cleansing the collected data and creating consistent analytical data; and means for generating visitor preference profiles based on the analyzed data and sentiment information. This makes it possible to provide appropriate services and marketing measures in real time that meet the diverse needs of visitors.

[0537] "Visitor movement information" refers to data about visitors' current location and movement history both inside and outside the facility.

[0538] "Transaction history information" refers to data that includes information about purchases and service usage made by visitors within the facility.

[0539] "Emotional information" refers to data that indicates a visitor's current emotional state, and is obtained from sensors and user interfaces.

[0540] "Means of collection" refers to the technical means and processes used to gather various types of information about visitors.

[0541] "Methods of cleansing" refer to processing methods used to remove inappropriate information from collected data and ensure consistency.

[0542] "Means of analysis" refers to the process of analyzing information based on collected data and deriving specific patterns or trends.

[0543] "Means for generating preference profiles" refers to technical means for analyzing visitor data to construct profiles that show individual preferences and tendencies.

[0544] A "generative AI model" refers to artificial intelligence technology that learns patterns based on large amounts of data and generates new information.

[0545] In an embodiment of this invention, the server first uses an API to collect visitor movement information, transaction history information, and sentiment information. This information is obtained through a GPS sensor, a purchase history management system, and a sentiment sensor, respectively.

[0546] Next, a data processing module within the server performs data cleansing using the collected information to generate consistent data for analysis. This process utilizes filtering algorithms to ensure data consistency. Furthermore, an emotion engine is used to analyze the user's emotional state and generate an integrated preference profile that includes this analysis. This preference profile is estimated from the visitor's past behavior using machine learning algorithms.

[0547] Subsequently, the server uses a generative AI model to design marketing strategies based on preference profiles and proposes them to facility managers. At this time, it prompts the generative AI with a question in the form of, "It has been detected that users currently in the shopping mall are tired. Based on this information, what kind of relaxing facilities would you suggest?"

[0548] The device constantly sends the visitor's current location and emotional state to the server while syncing with it. The device's application then makes appropriate facility recommendations based on this updated profile. For example, if an anxious mood is detected, it can suggest a quiet cafe that takes crowd levels into consideration.

[0549] Furthermore, users can input feedback from their terminal after using the facilities or services. This feedback is sent to the server to improve the accuracy of future suggestions and is used to improve the analysis algorithms.

[0550] In this way, by having servers, terminals, and users work together, a system is realized that provides highly personalized services to visitors and proposes effective marketing strategies to facility managers.

[0551] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0552] Step 1:

[0553] The server collects visitor movement information and transaction history information via an API. It receives real-time location data and transaction data transmitted from terminals and sensors as input. This data is then stored in a database while maintaining consistency, making it ready for analysis.

[0554] Step 2:

[0555] The device acquires emotional information based on the user's operation history and data obtained from emotion sensors. It receives emotional state values ​​generated by the user's device sensors as input. This is sent to a server, where the emotion engine analyzes the information to generate data representing a specific emotional state.

[0556] Step 3:

[0557] The server performs data cleansing based on collected movement information, transaction history information, and sentiment information. The input for this step is a collection of raw data, which undergoes duplicate removal and missing data filling. The output is a clean dataset suitable for analysis.

[0558] Step 4:

[0559] The server analyzes the cleansed data and uses machine learning algorithms to create visitor preference profiles. It uses prepared analytical data and sentiment information as input to learn visitor patterns and preferences. The output is the generation of individual preference profiles.

[0560] Step 5:

[0561] The server uses a generative AI model to design marketing strategies based on preference profiles. This step includes the generated preference profiles as input, which are used to prompt the generative AI, generating strategy proposals. The output is a detailed strategy report for facility managers.

[0562] Step 6:

[0563] The terminal provides visitors with customized facility recommendations based on the received policies. It processes policy information sent from the server and real-time emotional states as input. As output, it displays information on facilities and services suitable for the user on the screen.

[0564] Step 7:

[0565] Users input feedback about the facilities and services they used via a terminal. The input is in text format and includes the user's satisfaction level and specific experiences. The terminal sends this to a server, which then uses the feedback data for analysis to improve the accuracy of future recommendations.

[0566] (Application Example 2)

[0567] 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."

[0568] In modern commercial facilities, improving customer satisfaction and increasing repeat customers are crucial challenges. Traditional methods make it difficult to understand visitors' movements and emotions in real time and provide immediate, personalized suggestions. Furthermore, flexible responses that take into account emotions and current circumstances are required, rather than simply offering suggestions based on transaction history.

[0569] 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. In this invention, the server includes means for collecting visitor movement information, transaction history, and emotional information; means for analyzing the collected data to generate visitor attribute information and emotional tendencies; and means for using AI to propose marketing measures to facility managers based on the generated attribute information and emotional tendencies. This makes it possible to provide visitors with optimal suggestions in real time that are tailored to their emotional state and preferences at any given time.

[0570] A "visitor" is an individual who visits a specific facility or store.

[0571] "Mobility information" refers to data that shows how visitors moved around within or near the facility.

[0572] "Transaction history" refers to records of purchases and transactions made by a visitor in the past.

[0573] "Emotional information" refers to data that indicates the psychological state and mood of visitors.

[0574] "Attribute information" refers to information such as the visitor's age, gender, hobbies, and preferences.

[0575] "Emotional tendencies" refer to the fluctuations and patterns in a visitor's psychological state.

[0576] "Generative AI" refers to a technology that uses artificial intelligence to generate insights and suggestions from data.

[0577] A "facility manager" is a person or organization responsible for the operation and provision of services at a specific facility.

[0578] "Congestion level" is an indicator that shows the degree of crowding or density of people within a facility.

[0579] "Personalized service" means providing suggestions and services that are optimized according to the individual characteristics and circumstances of each visitor.

[0580] A system for implementing this invention consists of a program that performs a series of processes to collect, analyze, and propose visitor movement information, transaction history, and sentiment information.

[0581] The server first uses an API to obtain visitor location information and transaction history, and stores this information in a database. The API used retrieves data in real time from various data sources and cleanses that data into a consistent format. Data processing software is used for this task. Next, an emotion engine is used to collect emotion data obtained from user devices, and all the data is integrated to generate visitor attributes and emotional tendencies. An emotion analysis API is used in this process.

[0582] The user terminal uses sensors and cameras to detect the visitor's current emotional state. The collected emotional data is sent to a server and used as part of the suggestion process. Suggestions are made by a generative AI model, which recommends the most suitable facilities and products based on the visitor's attributes and emotions. Personalized recommendations are then delivered to the visitor from the terminal in real time.

[0583] Furthermore, the server estimates congestion levels in real time and provides visitors with information at the most appropriate time. In addition, visitor satisfaction and opinions obtained through the feedback function are used to improve the accuracy of future suggestions.

[0584] For example, if the emotion engine determines that a visitor is restless, the system will suggest quiet bookstores or cafes to the visitor. Generative AI could be used to generate these suggestions, using prompts such as: "The visitor's emotions are unstable; please recommend relaxing facilities." In this way, the generative AI can provide appropriate suggestions based on the visitor's emotional state.

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The server retrieves visitor movement information and transaction history via an API. It receives real-time movement information and transaction history from various data sources as input. Data cleansing is performed to ensure data consistency and accuracy before storing this information in the database. The output is cleansed and consistent data.

[0588] Step 2:

[0589] The user terminal uses sensors and cameras to collect visitor emotional information. It receives raw data from the sensors and cameras on the terminal as input. An emotion analysis API processes this data and quantifies the actual emotional state. The output is data indicating the visitor's specific emotional state.

[0590] Step 3:

[0591] The server integrates the data obtained in Step 1 and Step 2 to generate visitor attribute information and emotional tendencies. It receives movement information, transaction history, and emotional state as input. Based on this, it performs data analysis and builds a profile using machine learning techniques. The output is a profile containing visitor attribute information and emotional tendencies.

[0592] Step 4:

[0593] The server uses a generative AI model to propose marketing strategies to facility managers based on the generated visitor profiles. It receives visitor profiles as input and provides prompts to the generative AI model to create appropriate suggestions. The output consists of specific marketing strategy proposals for facility managers.

[0594] Step 5:

[0595] The terminal provides visitors with personalized facility recommendations based on suggestions received from the server. It receives suggestions from the server and the visitor's emotional state as input. Based on this, it displays suggestions on the terminal at the optimal time and in the most appropriate way, also including information on congestion levels. The output is real-time information about specific facilities and services presented to the visitor.

[0596] Step 6:

[0597] Users provide feedback via a terminal after using a facility or service. This feedback includes user satisfaction and experience information. This feedback is sent to a server and stored as data to improve the accuracy of future suggestions. The output is feedback data that serves as material for improving the suggestion algorithm.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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".

[0615] This invention is a system that collects visitor movement information and transaction history information, proposes marketing strategies to facility managers based on this information, and simultaneously provides visitors with real-time information on congestion levels and appropriate facility information. This system consists mainly of a server and terminals, which work together to meet the needs of visitors and facility managers.

[0616] Server functions and processing:

[0617] The server first collects visitor movement and transaction history information via an API. This data is processed sequentially and stored in a database. To maintain data integrity, cleansing and anonymization processes are performed. Next, a machine learning model is installed on the server, which analyzes the cleansed data and generates visitor attribute profiles. Based on the attribute information generated, the server proposes marketing strategies to facility managers to optimize targeting. For example, if there are many young visitors during a particular time slot, the server will advise on promotions tailored to that demographic.

[0618] Terminal functions and processing:

[0619] The visitor's device (such as a smartphone) constantly communicates with the server to receive real-time data. Based on the current location, facility information tailored to the visitor's attributes is generated and displayed on the device. When the user launches the application, the device suggests the most suitable facilities to visit and their congestion levels. At this time, multiple options are presented, and the system is designed to provide choices that match the user's preferences. For example, a cafe with low congestion levels near the current location that matches the visitor's preferences might be suggested.

[0620] Collecting user feedback:

[0621] The system also includes a feature that allows users to input feedback on the results of their actions based on information provided via their device. This feedback is sent to the server and used for future data analysis and system improvements. Examples of feedback include evaluations such as "The information on facility congestion was accurate" and "The store information provided was helpful."

[0622] Thus, this system aims to improve visitor satisfaction and facility managers' marketing effectiveness throughout the entire process of collecting, processing, analyzing, and providing digital data.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The server uses an API to collect visitor movement and transaction history information. This includes accessing external databases that provide location and purchase information. The collected data is stored in a control database within the server.

[0626] Step 2:

[0627] The server performs a data cleansing process. To maintain data integrity, it removes redundant entries and missing data, and standardizes the format. It also anonymizes the data to protect privacy.

[0628] Step 3:

[0629] The server applies machine learning algorithms to generate visitor attribute information from cleansed data. This analysis forms cluster information based on age group and purchasing patterns, creating visitor profiles.

[0630] Step 4:

[0631] The server utilizes AI generation to create marketing strategies for facility managers based on visitor attributes. An interface is provided that proposes promotional strategies tailored to facility usage trends in a voice and text-based dialogue format.

[0632] Step 5:

[0633] The user's device acquires their current location information, and based on visitor attribute information sent from the server, it displays real-time information about facilities that are suitable for the visitor. Using notifications on the device and interactive elements within the app, it lists facilities that meet the visitor's preferences.

[0634] Step 6:

[0635] Users utilize information provided through their devices to make actual visits and purchases. They contribute to system improvement by inputting feedback on congestion levels at visited locations and service satisfaction into their devices.

[0636] Step 7:

[0637] The server collects user feedback and incorporates it back into the data analysis process. This improves the accuracy of subsequent analyses and AD strategies, and continuously enhances system performance.

[0638] (Example 1)

[0639] 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".

[0640] Traditional facility management and marketing strategies struggle to provide flexible responses based on the individual behaviors and characteristics of visitors, making it difficult to increase visitor satisfaction and operate facilities efficiently. Furthermore, the information visitors receive is often static and lacks real-time relevance and individuality, highlighting the need for more personalized information.

[0641] 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.

[0642] In this invention, the server includes means for collecting visitor location information and transaction history, means for analyzing the collected data using a machine learning model to generate visitor characteristic information, and means for proposing marketing measures to facility managers based on the generated characteristic information. This enables the provision of real-time information tailored to the characteristics of visitors and the proposal of effective marketing measures to facility managers.

[0643] "Visitor location information" refers to data that shows the current location and travel history of people visiting a facility or area.

[0644] "Transaction history" refers to data that records a visitor's purchasing activities, whether at a store or online.

[0645] A "machine learning model" is a program that includes algorithms for analyzing collected data and deriving patterns and regularities.

[0646] "Visitor characteristic information" refers to profile data generated based on the visitor's attributes and behavioral patterns.

[0647] A "facility manager" is an individual or organization involved in the operation and management of commercial facilities, public facilities, etc.

[0648] "Marketing strategies" refer to plans for promotional and advertising activities that target a specific customer segment.

[0649] "Providing information in real time" refers to the process of immediately disseminating the latest information to visitors on the spot.

[0650] "Feedback" refers to collecting user experiences and opinions provided by visitors.

[0651] A "prompt" is an input sentence used to form a specific instruction or question.

[0652] To implement this invention, the system operates with a server and terminals as its main components. The server collects visitor location information and transaction history in real time through an advanced API. This information is then stored in a database and subjected to data cleansing and anonymization to maintain integrity. A machine learning model is installed on the server, which analyzes the cleansed data and generates visitor characteristic information.

[0653] The terminal functions as a visitor's smartphone or tablet device. It constantly communicates with a server to receive the latest information, generating and displaying facility information tailored to the visitor's attributes. When a user launches the application, the terminal presents optimal facility options and their congestion levels, offering multiple choices. This allows visitors to make choices that suit their preferences. For example, it can guide users to a less crowded cafe near their current location that matches their tastes.

[0654] Furthermore, by receiving feedback from users on their actions based on the information provided via their devices, the server can use this feedback for future analysis. This feedback process improves the overall accuracy and reliability of the system and increases visitor satisfaction. An example of a specific prompt might be, "Based on visitor profiles and visit frequency data, what strategies would be particularly effective for our next promotional campaign?"

[0655] In this way, this system provides value to both visitors and facility managers, enabling the provision of highly personalized information in real time and the proposal of effective marketing strategies.

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

[0657] Step 1:

[0658] The server receives visitor location information and transaction history as input via an API. Specifically, this includes GPS data obtained when visitors use a smartphone app, and purchase history within the facility. This input data is saved to a database in real time. After saving, data cleansing processes, including noise reduction and format normalization, are performed to output consistent and clean data.

[0659] Step 2:

[0660] The server uses the cleansed data as input to a machine learning model. This model analyzes visitor behavior patterns and attribute information, generating visitor characteristic information as a result. This characteristic information is stored in a database as profile data and used to propose marketing strategies to facility managers.

[0661] Step 3:

[0662] The server retrieves visitor characteristic information from a database and optimizes marketing strategies for facility managers based on this information. The server proposes promotional and advertising strategies for specific visitor groups and outputs them as concrete action plans. Input includes visitor attributes and visit frequency.

[0663] Step 4:

[0664] The terminal combines the latest facility information received from the server with visitor characteristic information to generate and output real-time information tailored to the visitor. Specific examples include recommendations for less crowded facilities based on the visitor's current location, and service recommendations that match the visitor's preferences. The information users receive through the app is output at this stage.

[0665] Step 5:

[0666] Users act based on information provided via their devices and input the results as feedback. This feedback is sent from the device to the server and used for future data analysis and system improvements. Specific examples of feedback include "The congestion level was accurate and helpful" and "The recommended facilities were of high quality." The output of this step is the aggregated opinions and evaluation data collected as feedback.

[0667] (Application Example 1)

[0668] 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".

[0669] In modern commercial facilities, with increasing visitor numbers, facility managers need to accurately understand visitor data to implement effective marketing strategies. Furthermore, visitors need information to avoid crowds and ensure a comfortable shopping experience. However, current systems struggle to achieve this in real time. An innovative approach is needed that enables personalized information delivery to visitors based on facility congestion levels and the optimization of marketing strategies based on that information.

[0670] 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.

[0671] In this invention, the server includes means for collecting visitor movement information and transaction history information, means for analyzing the collected data to generate visitor attribute information, and means for proposing marketing measures to facility managers based on the generated attribute information. This enables the provision of real-time personalized information to visitors and the efficient execution of marketing measures for facility managers.

[0672] "Visitor movement information" refers to data about the routes and visit times of individuals visiting a specific location.

[0673] "Transaction history information" refers to data that shows the history of purchases and service usage made by an individual.

[0674] "Attribute information" refers to information that indicates the characteristics of a visitor, such as their age, gender, hobbies, and preferences.

[0675] A "facility manager" refers to a person who is responsible for operating and managing commercial facilities and providing services to visitors.

[0676] "Marketing strategies" refer to plans and activities aimed at effectively promoting the sale of products and services.

[0677] "Providing real-time congestion information" means immediately informing users of the current congestion status of a facility.

[0678] "Personalizing and providing promotional information" refers to providing sales promotion information tailored to the preferences and needs of individual visitors.

[0679] The system that implements this application example consists primarily of a server and a visitor's terminal. The server collects location information and transaction history information from the visitor's smartphone or terminal via an API. After storing this data in a database, the server performs cleansing and anonymization processes to maintain data integrity.

[0680] Data analysis is performed using a machine learning model with Python, and visitor attribute information is generated using libraries such as Scikit-learn. Based on the generated attribute information, the server can propose marketing strategies for target optimization to facility managers. In particular, it maximizes the effectiveness of marketing by suggesting promotional actions tailored to the specific attributes of visitors.

[0681] Meanwhile, the terminal receives data transmitted from the server in real time. Smartphone applications built using Swift or Kotlin display congestion information and promotional information tailored to the visitor's current location and preferences. Visitors can operate the terminal to obtain information on the most suitable facilities to visit.

[0682] As a concrete example, when a visitor is in a shopping mall, the terminal will suggest cafes in the vicinity that are less crowded and match the visitor's preferences in real time. Examples of prompts from the generated AI model include, "Based on data from peak visitor times, which customer segment should we target for a specific promotion?" and "Please create a list of recommended products for user segments that are likely to visit at this time."

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The server collects location and transaction history information from visitors' devices. The input consists of records of the visitor's travel route and services used, received digitally via an API. This allows travel data, such as time and location, to be stored in a database.

[0686] Step 2:

[0687] The server performs cleansing and anonymization processes to maintain the integrity of the collected data. Cleansing corrects data inconsistencies and missing data, while anonymization transforms the data so that individuals cannot be identified. The output is a clean dataset that is ready for analysis.

[0688] Step 3:

[0689] The server uses Python's Scikit-learn to analyze a clean dataset with a machine learning model and generate visitor attribute information. This process identifies attribute patterns from the data and infers the visitor's age group and preferences. The output is an individual attribute information profile.

[0690] Step 4:

[0691] The server proposes marketing strategies to facility managers based on the generated attribute information. In this step, a generative AI model is used to identify attribute groups that are highly effective for specific promotions and reports this to the managers. The output is advice on targeting strategies.

[0692] Step 5:

[0693] The terminal receives data from the server in real time and presents the user with congestion information and personalized promotional information regarding the facilities they plan to visit. Input consists of facility information sent from the server, which is processed in combination with the user's location information. This allows the user to understand congestion levels and make the best facility selection in real time.

[0694] Step 6:

[0695] Users perform actions based on information provided via their devices and provide feedback on the results. The input consists of evaluations and opinions after the user's actions, which are sent from the device to the server. This feedback information is collected in a database to improve the accuracy of future suggestions.

[0696] 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.

[0697] This invention is a system that aggregates visitor movement information, transaction history information, and emotional information to enable the proposal of advanced marketing measures to facility managers and personalized facility recommendations to visitors. By incorporating an emotional engine, it is possible to provide information that takes into account the user's emotions.

[0698] Server functions and processing:

[0699] The server first uses an API to collect visitor movement information and transaction history information, and stores it in a database. Based on the collected information, it performs data cleansing to create consistent analytical data. In addition, the device is equipped with an emotion engine, which also acquires user emotion data obtained from the user's device. This generates integrated attribute data that includes the generated emotion information.

[0700] The server analyzes this data and uses machine learning algorithms to generate profiles optimized for visitors' preferences and emotions. Based on these profiles, the AI ​​generates proposals to facility managers that match visitor attributes and emotional tendencies. When managers receive these proposals, they are provided with detailed explanations in a conversational format to support the implementation of specific measures.

[0701] Terminal functions and processing:

[0702] The user terminal synchronizes with the server and has the function of sending the visitor's current location information and emotional state in a timely manner. When the user uses the terminal, their emotional state is measured through in-application operations and external sensors, and the emotion engine processes this data and sends it to the server.

[0703] The terminal application uses the received visitor profile to suggest facilities tailored to the visitor's current emotions and preferences. For example, if anxiety is detected, it might suggest a quiet cafe or a relaxing facility. The application also provides information including an estimate of crowd levels, allowing for more appropriate choices.

[0704] Collecting user feedback:

[0705] After using a facility or service, users can input feedback on their satisfaction level and experience via a terminal. This feedback data, along with the emotional information provided by the user, is sent to the server and used for future analysis and improvement of suggested algorithms.

[0706] The introduction of this system will enable facility managers to implement marketing strategies based on visitors' emotional behavior, and visitors will be provided with more accurate and personalized services. As a result, it will contribute to improving the experience value for both parties.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The server collects visitor movement and transaction history information via an interface. This includes the process of collecting location data and purchase history data via APIs provided by the application.

[0710] Step 2:

[0711] The device acquires user emotion information. This is done by an emotion engine analyzing data from sensors built into the device and user interactions within the app to measure the emotional state.

[0712] Step 3:

[0713] The device sends the emotional information it acquires to the server. Here, the device encrypts the user's emotional data while respecting privacy before transmitting it to the server.

[0714] Step 4:

[0715] The server cleanses movement information, including sentiment data, and transaction history information, and stores it in an integrated database. Here, data integrity is ensured, and duplicate information and noise are removed.

[0716] Step 5:

[0717] The server analyzes the integrated dataset to generate visitor attribute profiles. This profile includes a process that uses machine learning algorithms to understand visitors' preferences and emotional tendencies based on attribute information and sentiment data.

[0718] Step 6:

[0719] The server proposes marketing strategies to facility managers based on the generated attribute profiles. These proposals are presented in an interactive format, offering optimal strategies that reflect the emotional tendencies obtained from the emotion engine.

[0720] Step 7:

[0721] The device displays personalized facility suggestions to visitors. It references information based on the user's current location and emotional state to present appropriate facility options in real time, taking into account congestion levels.

[0722] Step 8:

[0723] Users visit facilities based on suggestions and input feedback on their experiences, including their feelings and satisfaction levels, via a terminal.

[0724] Step 9:

[0725] The terminal sends user feedback to the server, which uses it to improve the next data analysis and suggestion system. The server updates the algorithms and processes information to improve the quality of the information it provides.

[0726] (Example 2)

[0727] 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".

[0728] To accurately understand the diverse needs of visitors to a facility and to provide individually optimized services and proposals, it is necessary to integrate and process multifaceted information such as visitors' movement patterns, transaction history, and emotional state. However, there is a challenge in the lack of efficient systems to effectively collect and analyze this information and link it to appropriate marketing measures and facility proposals.

[0729] 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.

[0730] In this invention, the server includes means for collecting visitor movement information, transaction history information, and sentiment information; means for cleansing the collected data and creating consistent analytical data; and means for generating visitor preference profiles based on the analyzed data and sentiment information. This makes it possible to provide appropriate services and marketing measures in real time that meet the diverse needs of visitors.

[0731] "Visitor movement information" refers to data about visitors' current location and movement history both inside and outside the facility.

[0732] "Transaction history information" refers to data that includes information about purchases and service usage made by visitors within the facility.

[0733] "Emotional information" refers to data that indicates a visitor's current emotional state, and is obtained from sensors and user interfaces.

[0734] "Means of collection" refers to the technical means and processes used to gather various types of information about visitors.

[0735] "Methods of cleansing" refer to processing methods used to remove inappropriate information from collected data and ensure consistency.

[0736] "Means of analysis" refers to the process of analyzing information based on collected data and deriving specific patterns or trends.

[0737] "Means for generating preference profiles" refers to technical means for analyzing visitor data to construct profiles that show individual preferences and tendencies.

[0738] A "generative AI model" refers to artificial intelligence technology that learns patterns based on large amounts of data and generates new information.

[0739] In an embodiment of this invention, the server first uses an API to collect visitor movement information, transaction history information, and sentiment information. This information is obtained through a GPS sensor, a purchase history management system, and a sentiment sensor, respectively.

[0740] Next, a data processing module within the server performs data cleansing using the collected information to generate consistent data for analysis. This process utilizes filtering algorithms to ensure data consistency. Furthermore, an emotion engine is used to analyze the user's emotional state and generate an integrated preference profile that includes this analysis. This preference profile is estimated from the visitor's past behavior using machine learning algorithms.

[0741] Subsequently, the server uses a generative AI model to design marketing strategies based on preference profiles and proposes them to facility managers. At this time, it prompts the generative AI with a question in the form of, "It has been detected that users currently in the shopping mall are tired. Based on this information, what kind of relaxing facilities would you suggest?"

[0742] The device constantly sends the visitor's current location and emotional state to the server while syncing with it. The device's application then makes appropriate facility recommendations based on this updated profile. For example, if an anxious mood is detected, it can suggest a quiet cafe that takes crowd levels into consideration.

[0743] Furthermore, users can input feedback from their terminal after using the facilities or services. This feedback is sent to the server to improve the accuracy of future suggestions and is used to improve the analysis algorithms.

[0744] In this way, by having servers, terminals, and users work together, a system is realized that provides highly personalized services to visitors and proposes effective marketing strategies to facility managers.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] The server collects visitor movement information and transaction history information via an API. It receives real-time location data and transaction data transmitted from terminals and sensors as input. This data is then stored in a database while maintaining consistency, making it ready for analysis.

[0748] Step 2:

[0749] The device acquires emotional information based on the user's operation history and data obtained from emotion sensors. It receives emotional state values ​​generated by the user's device sensors as input. This is sent to a server, where the emotion engine analyzes the information to generate data representing a specific emotional state.

[0750] Step 3:

[0751] The server performs data cleansing based on collected movement information, transaction history information, and sentiment information. The input for this step is a collection of raw data, which undergoes duplicate removal and missing data filling. The output is a clean dataset suitable for analysis.

[0752] Step 4:

[0753] The server analyzes the cleansed data and uses machine learning algorithms to create visitor preference profiles. It uses prepared analytical data and sentiment information as input to learn visitor patterns and preferences. The output is the generation of individual preference profiles.

[0754] Step 5:

[0755] The server uses a generative AI model to design marketing strategies based on preference profiles. This step includes the generated preference profiles as input, which are used to prompt the generative AI, generating strategy proposals. The output is a detailed strategy report for facility managers.

[0756] Step 6:

[0757] The terminal provides visitors with customized facility recommendations based on the received policies. It processes policy information sent from the server and real-time emotional states as input. As output, it displays information on facilities and services suitable for the user on the screen.

[0758] Step 7:

[0759] Users input feedback about the facilities and services they used via a terminal. The input is in text format and includes the user's satisfaction level and specific experiences. The terminal sends this to a server, which then uses the feedback data for analysis to improve the accuracy of future recommendations.

[0760] (Application Example 2)

[0761] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0762] In modern commercial facilities, improving customer satisfaction and increasing repeat customers are crucial challenges. Traditional methods make it difficult to understand visitors' movements and emotions in real time and provide immediate, personalized suggestions. Furthermore, flexible responses that take into account emotions and current circumstances are required, rather than simply offering suggestions based on transaction history.

[0763] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting visitor movement information, transaction history, and emotional information; means for analyzing the collected data to generate visitor attribute information and emotional tendencies; and means for using AI to propose marketing measures to facility managers based on the generated attribute information and emotional tendencies. This makes it possible to provide visitors with optimal suggestions in real time that are tailored to their emotional state and preferences at any given time.

[0764] A "visitor" is an individual who visits a specific facility or store.

[0765] "Mobility information" refers to data that shows how visitors moved around within or near the facility.

[0766] "Transaction history" refers to records of purchases and transactions made by a visitor in the past.

[0767] "Emotional information" refers to data that indicates the psychological state and mood of visitors.

[0768] "Attribute information" refers to information such as the visitor's age, gender, hobbies, and preferences.

[0769] "Emotional tendencies" refer to the fluctuations and patterns in a visitor's psychological state.

[0770] "Generative AI" refers to a technology that uses artificial intelligence to generate insights and suggestions from data.

[0771] A "facility manager" is a person or organization responsible for the operation and provision of services at a specific facility.

[0772] "Congestion level" is an indicator that shows the degree of crowding or density of people within a facility.

[0773] "Personalized service" means providing suggestions and services that are optimized according to the individual characteristics and circumstances of each visitor.

[0774] A system for implementing this invention consists of a program that performs a series of processes to collect, analyze, and propose visitor movement information, transaction history, and sentiment information.

[0775] The server first uses an API to obtain visitor location information and transaction history, and stores this information in a database. The API used retrieves data in real time from various data sources and cleanses that data into a consistent format. Data processing software is used for this task. Next, an emotion engine is used to collect emotion data obtained from user devices, and all the data is integrated to generate visitor attributes and emotional tendencies. An emotion analysis API is used in this process.

[0776] The user terminal uses sensors and cameras to detect the visitor's current emotional state. The collected emotional data is sent to a server and used as part of the suggestion process. Suggestions are made by a generative AI model, which recommends the most suitable facilities and products based on the visitor's attributes and emotions. Personalized recommendations are then delivered to the visitor from the terminal in real time.

[0777] Furthermore, the server estimates congestion levels in real time and provides visitors with information at the most appropriate time. In addition, visitor satisfaction and opinions obtained through the feedback function are used to improve the accuracy of future suggestions.

[0778] For example, if the emotion engine determines that a visitor is restless, the system will suggest quiet bookstores or cafes to the visitor. Generative AI could be used to generate these suggestions, using prompts such as: "The visitor's emotions are unstable; please recommend relaxing facilities." In this way, the generative AI can provide appropriate suggestions based on the visitor's emotional state.

[0779] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0780] Step 1:

[0781] The server retrieves visitor movement information and transaction history via an API. It receives real-time movement information and transaction history from various data sources as input. Data cleansing is performed to ensure data consistency and accuracy before storing this information in the database. The output is cleansed and consistent data.

[0782] Step 2:

[0783] The user terminal uses sensors and cameras to collect visitor emotional information. It receives raw data from the sensors and cameras on the terminal as input. An emotion analysis API processes this data and quantifies the actual emotional state. The output is data indicating the visitor's specific emotional state.

[0784] Step 3:

[0785] The server integrates the data obtained in Step 1 and Step 2 to generate visitor attribute information and emotional tendencies. It receives movement information, transaction history, and emotional state as input. Based on this, it performs data analysis and builds a profile using machine learning techniques. The output is a profile containing visitor attribute information and emotional tendencies.

[0786] Step 4:

[0787] The server uses a generative AI model to propose marketing strategies to facility managers based on the generated visitor profiles. It receives visitor profiles as input and provides prompts to the generative AI model to create appropriate suggestions. The output consists of specific marketing strategy proposals for facility managers.

[0788] Step 5:

[0789] The terminal provides visitors with personalized facility recommendations based on suggestions received from the server. It receives suggestions from the server and the visitor's emotional state as input. Based on this, it displays suggestions on the terminal at the optimal time and in the most appropriate way, also including information on congestion levels. The output is real-time information about specific facilities and services presented to the visitor.

[0790] Step 6:

[0791] Users provide feedback via a terminal after using a facility or service. This feedback includes user satisfaction and experience information. This feedback is sent to a server and stored as data to improve the accuracy of future suggestions. The output is feedback data that serves as material for improving the suggestion algorithm.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0813] The following is further disclosed regarding the embodiments described above.

[0814] (Claim 1)

[0815] A means of collecting visitor movement information and transaction history information,

[0816] A means of analyzing collected data to generate visitor attribute information,

[0817] A means of proposing marketing measures to facility managers based on generated attribute information,

[0818] A means of suggesting facilities based on the visitor's current location information and attributes,

[0819] A means of providing real-time congestion information,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, which provides facility managers with proposed marketing measures in an interactive format.

[0823] (Claim 3)

[0824] The system according to claim 1, which collects visitor feedback and uses it to improve the accuracy of future suggestions.

[0825] "Example 1"

[0826] (Claim 1)

[0827] A means of collecting visitor location information and transaction history,

[0828] A method for analyzing data collected using machine learning models and generating characteristic information about visitors,

[0829] A means of proposing marketing measures to facility managers based on the generated characteristic information,

[0830] A means of suggesting potential facilities based on the visitor's current location and characteristics,

[0831] A means of providing real-time information on congestion levels,

[0832] A means of collecting feedback from visitors and using it for future data analysis,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, which provides marketing strategies to facility managers in an interactive format and generates prompts.

[0836] (Claim 3)

[0837] The system according to claim 1, which improves the accuracy of suggestions using collected visitor feedback.

[0838] "Application Example 1"

[0839] (Claim 1)

[0840] A means of collecting visitor movement information and transaction history information,

[0841] A means of analyzing collected data to generate visitor attribute information,

[0842] A means of proposing marketing measures to facility managers based on generated attribute information,

[0843] A means of suggesting facilities based on the visitor's current location information and attributes,

[0844] A means of providing real-time congestion information,

[0845] A means of providing personalized promotional information to visitors' devices,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, which provides facility managers with proposed marketing measures in an interactive format.

[0849] (Claim 3)

[0850] The system according to claim 1, which collects visitor feedback and uses it to improve the accuracy of future suggestions.

[0851] "Example 2 of combining an emotion engine"

[0852] (Claim 1)

[0853] A means of collecting visitor movement information, transaction history information, and sentiment information,

[0854] A means of cleansing the collected data and creating consistent analytical data,

[0855] A means for generating a visitor preference profile based on analyzed data and emotional information,

[0856] A method for proposing marketing strategies to facility managers based on the generated profile using a generative AI model,

[0857] A means of suggesting facilities based on the visitor's current emotional state and preferences,

[0858] A means of providing real-time congestion information,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, which provides facility managers with proposed marketing measures in an interactive format.

[0862] (Claim 3)

[0863] The system according to claim 1, which collects visitor feedback and incorporates that feedback into analysis to improve the accuracy of future suggestions.

[0864] "Application example 2 when combining with an emotional engine"

[0865] (Claim 1)

[0866] Means for collecting visitor movement information, transaction history, and sentiment information,

[0867] A means for analyzing collected data to generate visitor attribute information and emotional tendencies,

[0868] A means of using a generation AI to propose marketing measures to facility managers based on generated attribute information and emotional tendencies,

[0869] A means for dynamically suggesting appropriate facilities based on the visitor's current location information, attributes, and emotional state,

[0870] A means of estimating congestion levels in real time and providing information,

[0871] A means of providing personalized services to visitors using generated AI,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which provides facility managers with proposed marketing measures using a conversational AI.

[0875] (Claim 3)

[0876] The system according to claim 1, which collects visitor feedback and sentiment information and uses it to improve the accuracy of future suggestions. [Explanation of Symbols]

[0877] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting visitor movement information and transaction history information, A means of analyzing collected data to generate visitor attribute information, A means of proposing marketing measures to facility managers based on generated attribute information, A means of suggesting facilities based on the visitor's current location information and attributes, A means of providing real-time congestion information, A system that includes this.

2. The system according to claim 1, which provides facility managers with proposed marketing measures in an interactive format.

3. The system according to claim 1, which collects visitor feedback and uses it to improve the accuracy of future suggestions.

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