system

Abstract

The system according to the embodiment aims to effectively prevent the fraudulent resale of tickets and the resale of tickets at high prices. [Solution] A system according to an embodiment includes a face authentication unit, a collection unit, an analysis unit, and an information collection unit. The face authentication unit recognizes the faces of attendees and performs log management. The collection unit collects access histories or purchase histories from ticket sales sites. The analysis unit analyzes the data collected by the collection unit and determines the risk of resale. The information collection unit collects information from resale sites and determines the risk of resale.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to effectively prevent fraudulent ticket resales and resales at high prices.

[0005] The system according to the embodiment aims to effectively prevent the fraudulent resale of tickets and the resale of tickets at high prices. [Means for solving the problem]

[0006] The system according to the embodiment includes a face authentication unit, a collection unit, an analysis unit, and an information collection unit. The face authentication unit recognizes the faces of visitors and performs log management. The collection unit collects access histories or purchase histories from ticket sales sites. The analysis unit analyzes the data collected by the collection unit and determines the risk of resale. The information collection unit collects information from resale sites and determines the risk of resale. [Effects of the Invention]

[0007] The system according to the embodiment can effectively prevent the fraudulent resale of tickets and the resale of tickets at high prices. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The ticket resale prevention system according to an embodiment of the present invention utilizes AI to prevent the fraudulent resale and high-price resale of tickets for live concerts and sporting events that have resumed after the COVID-19 pandemic. This system first employs a facial recognition system, and AI accurately logs who entered and when. Next, AI collects and analyzes ticket sales site access histories, purchase histories, and resale site information in real time to determine the risk of resale. Immediately taking necessary steps prevents fraudulent resale. For example, a facial recognition camera is installed at the entrance to an event venue to capture the faces of attendees. This facial recognition camera uses AI to accurately log who entered and when. This prevents the fraudulent use of tickets. Next, AI collects and analyzes ticket sales site access histories, purchase histories, and resale site information in real time. For example, AI analyzes the IP addresses and purchase histories of users who access ticket sales sites to determine the risk of resale. It also collects information from resale sites to identify which tickets are being resold. This allows users at high risk of resale to be identified and the necessary steps to be taken immediately. This mechanism can prevent the fraudulent resale of tickets and the resale of tickets at high prices. For example, it can restrict ticket purchases for users who pose a high risk of reselling. It can also invalidate tickets that are being sold fraudulently on resale sites. This allows event organizers to hold events with peace of mind, and fans to purchase tickets with peace of mind. In this way, the fraudulent ticket resale prevention system can effectively prevent the fraudulent resale and the resale of tickets at high prices for live concerts and sporting events.

[0029] The system for preventing unauthorized ticket resale according to the embodiment includes a facial recognition unit, a collection unit, an analysis unit, and an information collection unit. The facial recognition unit recognizes the faces of attendees and manages logs. For example, the facial recognition unit photographs attendees' faces using a facial recognition camera installed at the entrance to the event venue, and AI accurately manages logs of who entered and when. The facial recognition unit can also incorporate a facial recognition algorithm using deep learning. For example, using deep learning improves the accuracy of facial recognition. The collection unit collects access history or purchase history of ticket sales sites. For example, the collection unit can collect user behavior history using cookies or session data. The collection unit can also collect IP addresses and purchase history of users who access the ticket sales site. The analysis unit analyzes the data collected by the collection unit and determines the risk of resale. For example, the analysis unit analyzes the collected data and determines the risk of resale. The information collection unit collects information about resale sites and determines the risk of resale. For example, the information collection unit can collect information about resale sites using web scraping technology. As a result, the system for preventing fraudulent ticket resale in accordance with the embodiment can assess the risk of resale and prevent fraudulent resale through facial recognition and log management of attendees, collection of access history and purchase history of ticket sales sites, analysis of data, and collection of information on resale sites.

[0030] The face authentication unit can incorporate a face recognition algorithm using deep learning. Deep learning is realized using algorithms such as a convolutional neural network (CNN) or a recurrent neural network (RNN). For example, the face authentication unit can use a CNN to extract facial features and improve recognition accuracy. The face authentication unit can also use an RNN to analyze facial movements and changes in facial expressions and improve recognition accuracy. This improves the accuracy of face recognition by using deep learning. Some or all of the above-described processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can perform face recognition using a deep learning model and output the recognition result.

[0031] The collection unit can collect user behavior history using cookies or session data. Cookies include data such as a session ID and user settings. The collection unit can collect user behavior history using cookies. For example, the collection unit can set a cookie when a user accesses a ticket sales site and track the user behavior history. The collection unit can also collect user behavior history using session data. Session data includes data such as a session ID and access time. The collection unit can collect user behavior history using session data. For example, the collection unit can record session data when a user accesses a ticket sales site and track the user behavior history. In this way, by using cookies or session data, the user behavior history can be efficiently collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input cookies or session data into AI and have the AI ​​analyze the user behavior history.

[0032] The information collection unit can collect information about resale sites using web scraping technology. Web scraping technology is, for example, a technology for automatically extracting data from specific web pages. The information collection unit can collect information about resale sites using web scraping technology. For example, the information collection unit can access a specific resale site and collect ticket resale information. The information collection unit can also periodically collect information about resale sites using web scraping technology. For example, the information collection unit can access the resale site at a fixed time every day and collect the latest resale information. In this way, using web scraping technology, information about resale sites can be efficiently collected. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can have AI execute the web scraping technology and analyze the collected data.

[0033] The analysis unit can analyze the collected data and determine the risk of resale. The data analysis is performed using, for example, statistical analysis or a machine learning algorithm. The analysis unit can analyze the collected data and determine the risk of resale. For example, the analysis unit can perform statistical analysis of the collected access history and purchase history to evaluate the risk of resale. The analysis unit can also analyze the collected data and determine the risk of resale using a machine learning algorithm. For example, the analysis unit can predict the risk of resale based on past resale history and abnormal purchase patterns. This allows the risk of resale to be accurately determined by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform a resale risk analysis.

[0034] The collection unit can collect IP addresses or purchase histories of users who access the ticket sales site. IP addresses are extracted, for example, from log files. The collection unit can collect IP addresses of users who access the ticket sales site. For example, the collection unit can record IP addresses when users access the ticket sales site and use the records to evaluate the risk of resale. The collection unit can also collect purchase histories. The purchase histories include data such as the purchase date and time and the purchased items. The collection unit can collect purchase histories of users who access the ticket sales site and use the records to evaluate the risk of resale. By collecting users' IP addresses and purchase histories, the risk of resale can be more accurately determined. Some or all of the above-described processing by the collection unit can be performed, for example, using AI, or can be performed without AI. For example, the collection unit can input IP addresses and purchase histories into AI and have the AI ​​perform a resale risk analysis.

[0035] The facial authentication unit can improve the accuracy of facial authentication by referring to the visitor's past admission history during facial authentication. For example, if the visitor has a history of attending the same event in the past, the facial authentication unit can improve the accuracy of facial authentication by referring to that history. For example, if the visitor has a history of being successfully authenticated in the past, the speed of authentication can be improved based on that data. Also, if the visitor has a history of failing authentication in the past, the cause can be analyzed to improve the accuracy when authenticating again. In this way, by referring to the past admission history, the accuracy of facial authentication is improved. Some or all of the above-mentioned processing in the facial authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial authentication unit can input past admission history data into AI and have the AI ​​improve the accuracy of authentication.

[0036] The facial recognition unit can optimize the authentication algorithm based on the visitor's age and gender during facial recognition. For example, if the visitor is older, the facial recognition unit can optimize the authentication algorithm taking into account facial wrinkles and features. For example, the facial features can be analyzed in more detail based on the visitor's gender, improving authentication accuracy. The balance between facial recognition speed and accuracy can also be adjusted based on the visitor's age and gender. This improves authentication accuracy by optimizing the authentication algorithm based on the visitor's age and gender. Some or all of the above-mentioned processing in the facial recognition unit may be performed using, or without, AI. For example, the facial recognition unit can input the visitor's age and gender data into AI and have the AI ​​optimize the authentication algorithm.

[0037] The facial recognition unit can improve the accuracy of facial recognition by taking into account the geographic location information of the visitor. For example, if the visitor comes from a distant location, the facial recognition unit can use AI to analyze facial features in more detail to improve the accuracy of facial recognition. For example, if the visitor comes from a nearby area, authentication speed can be prioritized and authentication can be performed with normal accuracy. Authentication accuracy can also be improved by comparing the visitor's geographic location information with past data. This improves the accuracy of facial recognition by taking the visitor's geographic location information into account. Some or all of the above-described processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input the visitor's geographic location information into AI and have the AI ​​improve the accuracy of authentication.

[0038] The facial recognition unit can analyze the attendee's social media activity and acquire related information during facial recognition. For example, the facial recognition unit can analyze posts related to the event from the attendee's social media activity to improve authentication accuracy. For example, the attendee's past event participation history can be acquired from the attendee's social media activity and used for authentication. A facial photo can also be acquired from the attendee's social media activity and reflected in the authentication algorithm. In this way, the accuracy of facial recognition can be improved by analyzing the attendee's social media activity. Some or all of the above-mentioned processing in the facial recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial recognition unit can input the attendee's social media data into AI and have the AI ​​acquire related information.

[0039] The collection unit can analyze the user's past access history and select the optimal collection method. For example, the collection unit can prioritize collection of sites that the user frequently accessed in the past. For example, the collection unit can analyze the user's past access history to determine access trends during specific time periods and perform collection during those time periods. The collection unit can also select the most efficient collection method based on the user's past access history. In this way, the optimal collection method can be selected by analyzing the past access history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past access history data into AI and have the AI ​​select the optimal collection method.

[0040] When collecting access histories, the collection unit can filter them based on the user's current interests. For example, the collection unit can prioritize collection of access histories related to topics in which the user is currently interested. For example, unnecessary access histories can be filtered based on the user's current interests. The collection unit can also analyze the user's current interests and collect the most relevant access histories. This improves the accuracy of collection by filtering based on the user's current interests. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's interest data into AI and have the AI ​​perform the filtering.

[0041] When collecting access histories, the collection unit can prioritize collecting highly relevant histories by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting access histories related to that area. For example, the collection unit can collect highly relevant histories by comparing them with past data based on the user's geographical location information. Furthermore, if the user is moving, the most relevant access histories can be collected based on the user's current location. In this way, highly relevant histories can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant histories.

[0042] When collecting the access history, the collection unit can analyze the user's social media activity and collect related history. For example, the collection unit can analyze the user's current interests and concerns from the user's social media activity and collect related access history. For example, the collection unit can obtain the user's past event participation history from the user's social media activity and collect related access history. It can also preferentially collect access history related to a specific topic from the user's social media activity. This allows for efficient collection of related history by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect related history.

[0043] During analysis, the analysis unit can predict current risk by referring to past resale risk data. The analysis unit can predict current risk based on, for example, past resale risk data. For example, it can extract specific patterns from past data and evaluate current risk. It can also analyze past resale risk data and build a model for predicting current risk. This allows accurate prediction of current risk by referring to past resale risk data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past resale risk data into AI and have the AI ​​perform current risk prediction.

[0044] During analysis, the analysis unit can apply different analysis algorithms based on the user's attribute information. The analysis unit can apply different analysis algorithms based on the user's age or gender, for example. For example, the analysis unit can select an optimal analysis algorithm based on the user's past behavioral history. A customized analysis algorithm for assessing resale risk can also be applied based on the user's attribute information. This improves the accuracy of risk assessment by applying the optimal analysis algorithm based on the user's attribute information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's attribute information into AI and have the AI ​​apply different analysis algorithms.

[0045] During analysis, the analysis unit can evaluate risk based on the geographic distribution of users. For example, if users are concentrated in a specific area, the analysis unit can prioritize the risk assessment of that area. For example, the analysis unit can evaluate risk based on the geographic distribution of users by comparing it with past data. The analysis unit can also analyze the geographic distribution of users to identify the area with the highest risk. This improves the accuracy of risk assessment by taking the geographic distribution of users into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographic distribution data of users into AI and have the AI ​​perform risk assessment.

[0046] The analysis unit can evaluate risk by referring to related market data during analysis. The analysis unit can, for example, evaluate resale risk based on market data. For example, it can analyze market trends and predict current risks. It can also build a model for evaluating resale risk by referring to market data. This improves the accuracy of risk assessment by referring to related market data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input market data into AI and have the AI ​​perform risk assessment.

[0047] When collecting information, the information collection unit can optimize the collection algorithm by referring to past resale site data. The information collection unit can, for example, select an optimal collection algorithm based on past resale site data. For example, the information collection unit can extract specific patterns from past data and optimize the collection algorithm. It can also analyze past resale site data and build a model for improving the collection algorithm. This allows the collection algorithm to be optimized by referring to past resale site data. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input past resale site data into AI and have the AI ​​optimize the collection algorithm.

[0048] When collecting information, the information collection unit can apply different collection methods depending on the category of the resale site. The information collection unit can, for example, select the optimal collection method depending on the category of the resale site. For example, a different collection algorithm can be applied to each category of the resale site. The category of the resale site can also be analyzed and the most efficient collection method can be applied. This improves the accuracy of collection by applying the optimal collection method depending on the category of the resale site. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input category data of the resale site into AI and have the AI ​​apply the collection method.

[0049] The information collection unit can collect information while taking into account the geographic distribution of resale sites. The information collection unit can, for example, select the optimal collection method based on the geographic distribution of resale sites. For example, the information collection unit can analyze the geographic distribution of resale sites and collect the most relevant information. The collection algorithm can also be optimized by taking into account the geographic distribution of resale sites. This improves the accuracy of collection by taking into account the geographic distribution of resale sites. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input geographic distribution data of resale sites into AI and have the AI ​​perform the collection.

[0050] The information collection unit can improve the accuracy of collection by referring to related literature when collecting information. The information collection unit can, for example, improve the collection algorithm based on the related literature. For example, the optimal collection method can be extracted and applied from the related literature. A model for improving the accuracy of collection can also be constructed by referring to the related literature. As a result, the accuracy of collection is improved by referring to the related literature. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input related literature data into AI and have the AI ​​improve the collection algorithm.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When authenticating an attendee's face, the facial authentication unit can improve the accuracy of authentication by referring to the attendee's past purchase history. For example, if the attendee has a history of purchasing tickets to the same event in the past, the accuracy of facial authentication can be improved by referring to that history. Also, if the attendee has a history of being authenticated without any problems in the past, the authentication speed can be improved based on that data. Furthermore, if the attendee has a history of failing authentication in the past, the cause can be analyzed to improve the accuracy when authenticating again. In this way, the accuracy of facial authentication is improved by referring to the past purchase history.

[0053] The analysis unit can evaluate the risk of resale based on the geographic distribution of users. For example, if users are concentrated in a particular area, the risk of that area can be evaluated preferentially. The analysis unit can also evaluate the risk based on the geographic distribution of users by comparing it with past data. Furthermore, the analysis unit can analyze the geographic distribution of users and identify the areas with the highest risk. This improves the accuracy of risk assessment by taking the geographic distribution of users into consideration.

[0054] When collecting information from resale sites, the information collection unit can adjust the collection timing based on the update frequency of the site. For example, information can be collected in real time from frequently updated sites, and periodically from less frequently updated sites. The information collection unit can also analyze the update history of the site and determine the optimal collection timing. This improves the efficiency of information collection by adjusting the collection timing based on the update frequency of the resale site.

[0055] The collection unit can analyze the user's social media activities and collect related data. For example, it can analyze the user's current interests and collect related data from the user's social media activities. It can also obtain the user's past event participation history from the user's social media activities and collect related data. It can also preferentially collect data related to specific topics from the user's social media activities. This allows for efficient collection of related data by analyzing the user's social media activities.

[0056] The information collection unit can apply different collection methods depending on the category of the resale site. For example, it can select the optimal collection method depending on the category of the resale site. It can also apply different collection algorithms to each category of the resale site. It can also analyze the category of the resale site and apply the most efficient collection method. This improves the accuracy of collection by applying the optimal collection method depending on the category of the resale site.

[0057] The analysis unit can predict current risks by referencing past resale risk data. For example, current risks can be predicted based on past resale risk data. It can also extract specific patterns from past data and evaluate current risks. Furthermore, it can analyze past resale risk data and build a model for predicting current risks. This allows current risks to be accurately predicted by referring to past resale risk data.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The facial recognition unit recognizes the faces of attendees and manages logs. For example, the facial recognition unit uses a facial recognition camera installed at the entrance to the event venue to photograph attendees' faces, and AI accurately manages logs of "who entered and when." The accuracy of facial recognition is improved by introducing a facial recognition algorithm that uses deep learning. Step 2: The collection unit collects the access history or purchase history of the ticket sales site. For example, the collection unit can collect user behavior history using cookies or session data. The collection unit can also collect the IP addresses and purchase histories of users who access the ticket sales site. Step 3: The analysis unit analyzes the data collected by the collection unit and determines the risk of resale. For example, the analysis unit analyzes the collected data and determines the risk of resale. Step 4: The information collection unit collects information on the resale site to determine the risk of resale. For example, the information collection unit can use web scraping technology to collect information on the resale site.

[0060] (Example 2) The ticket resale prevention system according to an embodiment of the present invention utilizes AI to prevent the fraudulent resale and high-price resale of tickets for live concerts and sporting events that have resumed after the COVID-19 pandemic. This system first employs a facial recognition system, and AI accurately logs who entered and when. Next, AI collects and analyzes ticket sales site access histories, purchase histories, and resale site information in real time to determine the risk of resale. Immediately taking necessary steps prevents fraudulent resale. For example, a facial recognition camera is installed at the entrance to an event venue to capture the faces of attendees. This facial recognition camera uses AI to accurately log who entered and when. This prevents the fraudulent use of tickets. Next, AI collects and analyzes ticket sales site access histories, purchase histories, and resale site information in real time. For example, AI analyzes the IP addresses and purchase histories of users who access ticket sales sites to determine the risk of resale. It also collects information from resale sites to identify which tickets are being resold. This allows users at high risk of resale to be identified and the necessary steps to be taken immediately. This mechanism can prevent the fraudulent resale of tickets and the resale of tickets at high prices. For example, it can restrict ticket purchases for users who pose a high risk of reselling. It can also invalidate tickets that are being sold fraudulently on resale sites. This allows event organizers to hold events with peace of mind, and fans to purchase tickets with peace of mind. In this way, the fraudulent ticket resale prevention system can effectively prevent the fraudulent resale and the resale of tickets at high prices for live concerts and sporting events.

[0061] The system for preventing unauthorized ticket resale according to the embodiment includes a facial recognition unit, a collection unit, an analysis unit, and an information collection unit. The facial recognition unit recognizes the faces of attendees and manages logs. For example, the facial recognition unit photographs attendees' faces using a facial recognition camera installed at the entrance to the event venue, and AI accurately manages logs of who entered and when. The facial recognition unit can also incorporate a facial recognition algorithm using deep learning. For example, using deep learning improves the accuracy of facial recognition. The collection unit collects access history or purchase history of ticket sales sites. For example, the collection unit can collect user behavior history using cookies or session data. The collection unit can also collect IP addresses and purchase history of users who access the ticket sales site. The analysis unit analyzes the data collected by the collection unit and determines the risk of resale. For example, the analysis unit analyzes the collected data and determines the risk of resale. The information collection unit collects information about resale sites and determines the risk of resale. For example, the information collection unit can collect information about resale sites using web scraping technology. As a result, the system for preventing fraudulent ticket resale in accordance with the embodiment can assess the risk of resale and prevent fraudulent resale through facial recognition and log management of attendees, collection of access history and purchase history of ticket sales sites, analysis of data, and collection of information on resale sites.

[0062] The face authentication unit can incorporate a face recognition algorithm using deep learning. Deep learning is realized using algorithms such as a convolutional neural network (CNN) or a recurrent neural network (RNN). For example, the face authentication unit can use a CNN to extract facial features and improve recognition accuracy. The face authentication unit can also use an RNN to analyze facial movements and changes in facial expressions and improve recognition accuracy. This improves the accuracy of face recognition by using deep learning. Some or all of the above-described processing in the face authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the face authentication unit can perform face recognition using a deep learning model and output the recognition result.

[0063] The collection unit can collect user behavior history using cookies or session data. Cookies include data such as a session ID and user settings. The collection unit can collect user behavior history using cookies. For example, the collection unit can set a cookie when a user accesses a ticket sales site and track the user behavior history. The collection unit can also collect user behavior history using session data. Session data includes data such as a session ID and access time. The collection unit can collect user behavior history using session data. For example, the collection unit can record session data when a user accesses a ticket sales site and track the user behavior history. In this way, by using cookies or session data, the user behavior history can be efficiently collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input cookies or session data into AI and have the AI ​​analyze the user behavior history.

[0064] The information collection unit can collect information about resale sites using web scraping technology. Web scraping technology is, for example, a technology for automatically extracting data from specific web pages. The information collection unit can collect information about resale sites using web scraping technology. For example, the information collection unit can access a specific resale site and collect ticket resale information. The information collection unit can also periodically collect information about resale sites using web scraping technology. For example, the information collection unit can access the resale site at a fixed time every day and collect the latest resale information. In this way, using web scraping technology, information about resale sites can be efficiently collected. Some or all of the above-mentioned processing in the information collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the information collection unit can have AI execute the web scraping technology and analyze the collected data.

[0065] The analysis unit can analyze the collected data and determine the risk of resale. The data analysis is performed using, for example, statistical analysis or a machine learning algorithm. The analysis unit can analyze the collected data and determine the risk of resale. For example, the analysis unit can perform statistical analysis of the collected access history and purchase history to evaluate the risk of resale. The analysis unit can also analyze the collected data and determine the risk of resale using a machine learning algorithm. For example, the analysis unit can predict the risk of resale based on past resale history and abnormal purchase patterns. This allows the risk of resale to be accurately determined by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform a resale risk analysis.

[0066] The collection unit can collect IP addresses or purchase histories of users who access the ticket sales site. IP addresses are extracted, for example, from log files. The collection unit can collect IP addresses of users who access the ticket sales site. For example, the collection unit can record IP addresses when users access the ticket sales site and use the records to evaluate the risk of resale. The collection unit can also collect purchase histories. The purchase histories include data such as the purchase date and time and the purchased items. The collection unit can collect purchase histories of users who access the ticket sales site and use the records to evaluate the risk of resale. By collecting users' IP addresses and purchase histories, the risk of resale can be more accurately determined. Some or all of the above-described processing by the collection unit can be performed, for example, using AI, or can be performed without AI. For example, the collection unit can input IP addresses and purchase histories into AI and have the AI ​​perform a resale risk analysis.

[0067] The facial recognition unit can estimate the visitor's emotions and adjust the accuracy of facial recognition based on the estimated emotions. For example, if a visitor is nervous, the AI ​​analyzes the visitor's subtle facial features in more detail to improve the accuracy of facial recognition. For example, if a visitor is relaxed, the facial recognition unit can return the accuracy of facial recognition to a normal setting and prioritize authentication speed. Furthermore, if a visitor is excited, the AI ​​can track facial movements and changes in facial expression in real time to improve the accuracy of facial recognition. This improves the accuracy of facial recognition by adjusting the accuracy of facial recognition according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the facial recognition unit may be performed using AI, or without AI. For example, the facial recognition unit can input the visitor's emotional data into AI and have the AI ​​adjust the accuracy of facial recognition.

[0068] The facial authentication unit can improve the accuracy of facial authentication by referring to the visitor's past admission history during facial authentication. For example, if the visitor has a history of attending the same event in the past, the facial authentication unit can improve the accuracy of facial authentication by referring to that history. For example, if the visitor has a history of being successfully authenticated in the past, the speed of authentication can be improved based on that data. Also, if the visitor has a history of failing authentication in the past, the cause can be analyzed to improve the accuracy when authenticating again. In this way, by referring to the past admission history, the accuracy of facial authentication is improved. Some or all of the above-mentioned processing in the facial authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial authentication unit can input past admission history data into AI and have the AI ​​improve the accuracy of authentication.

[0069] The facial recognition unit can optimize the authentication algorithm based on the visitor's age and gender during facial recognition. For example, if the visitor is older, the facial recognition unit can optimize the authentication algorithm taking into account facial wrinkles and features. For example, the facial features can be analyzed in more detail based on the visitor's gender, improving authentication accuracy. The balance between facial recognition speed and accuracy can also be adjusted based on the visitor's age and gender. This improves authentication accuracy by optimizing the authentication algorithm based on the visitor's age and gender. Some or all of the above-mentioned processing in the facial recognition unit may be performed using, or without, AI. For example, the facial recognition unit can input the visitor's age and gender data into AI and have the AI ​​optimize the authentication algorithm.

[0070] The facial recognition unit can estimate the emotions of visitors and determine entry priorities based on the estimated emotions. For example, if a visitor is nervous, the facial recognition unit can reduce stress by allowing them to enter first. For example, if a visitor is relaxed, the normal entry order can be maintained. Also, if a visitor is excited, the unit can allow them to enter quickly, avoiding confusion. This improves entry efficiency by determining entry priorities based on the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the facial recognition unit may be performed using AI, or may be performed without AI. For example, the facial recognition unit can input visitor emotion data into AI and have the AI ​​determine entry priorities.

[0071] The facial recognition unit can improve the accuracy of facial recognition by taking into account the geographic location information of the visitor. For example, if the visitor comes from a distant location, the facial recognition unit can use AI to analyze facial features in more detail to improve the accuracy of facial recognition. For example, if the visitor comes from a nearby area, authentication speed can be prioritized and authentication can be performed with normal accuracy. Authentication accuracy can also be improved by comparing the visitor's geographic location information with past data. This improves the accuracy of facial recognition by taking the visitor's geographic location information into account. Some or all of the above-described processing in the facial recognition unit may be performed using AI, for example, or without AI. For example, the facial recognition unit can input the visitor's geographic location information into AI and have the AI ​​improve the accuracy of authentication.

[0072] The facial recognition unit can analyze the attendee's social media activity and acquire related information during facial recognition. For example, the facial recognition unit can analyze posts related to the event from the attendee's social media activity to improve authentication accuracy. For example, the attendee's past event participation history can be acquired from the attendee's social media activity and used for authentication. A facial photo can also be acquired from the attendee's social media activity and reflected in the authentication algorithm. In this way, the accuracy of facial recognition can be improved by analyzing the attendee's social media activity. Some or all of the above-mentioned processing in the facial recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the facial recognition unit can input the attendee's social media data into AI and have the AI ​​acquire related information.

[0073] The collection unit can estimate the user's emotions and adjust the timing of collecting the access history based on the estimated user emotions. For example, if the user is relaxed, the collection unit can collect the access history at a normal timing. For example, if the user is stressed, the collection unit can refrain from collecting the access history and collect it later. Also, if the user is excited, the access history can be collected in real time and analyzed immediately. This improves collection efficiency by adjusting the timing of collecting the access history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the collection timing.

[0074] The collection unit can analyze the user's past access history and select the optimal collection method. For example, the collection unit can prioritize collection of sites that the user frequently accessed in the past. For example, the collection unit can analyze the user's past access history to determine access trends during specific time periods and perform collection during those time periods. The collection unit can also select the most efficient collection method based on the user's past access history. In this way, the optimal collection method can be selected by analyzing the past access history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past access history data into AI and have the AI ​​select the optimal collection method.

[0075] When collecting access histories, the collection unit can filter them based on the user's current interests. For example, the collection unit can prioritize collection of access histories related to topics in which the user is currently interested. For example, unnecessary access histories can be filtered based on the user's current interests. The collection unit can also analyze the user's current interests and collect the most relevant access histories. This improves the accuracy of collection by filtering based on the user's current interests. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's interest data into AI and have the AI ​​perform the filtering.

[0076] The collection unit can estimate the user's emotions and determine the priority of the access history to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit can collect access history with normal priority. For example, when the user is stressed, the collection unit can prioritize important access history. Furthermore, when the user is excited, the collection unit can collect access history in real time and analyze it immediately. This improves collection efficiency by determining the priority of the access history according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority.

[0077] When collecting access histories, the collection unit can prioritize collecting highly relevant histories by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting access histories related to that area. For example, the collection unit can collect highly relevant histories by comparing them with past data based on the user's geographical location information. Furthermore, if the user is moving, the most relevant access histories can be collected based on the user's current location. In this way, highly relevant histories can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant histories.

[0078] When collecting the access history, the collection unit can analyze the user's social media activity and collect related history. For example, the collection unit can analyze the user's current interests and concerns from the user's social media activity and collect related access history. For example, the collection unit can obtain the user's past event participation history from the user's social media activity and collect related access history. It can also preferentially collect access history related to a specific topic from the user's social media activity. This allows for efficient collection of related history by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect related history.

[0079] The analysis unit can estimate the user's emotions and adjust the resale risk analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can evaluate the resale risk using a normal analysis method. For example, if the user is stressed, the analysis unit can perform a detailed analysis and carefully evaluate the resale risk. Furthermore, if the user is excited, the analysis unit can evaluate the resale risk in real time and respond immediately. This improves the accuracy of risk assessment by adjusting the resale risk analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the resale risk analysis method.

[0080] During analysis, the analysis unit can predict current risk by referring to past resale risk data. The analysis unit can predict current risk based on, for example, past resale risk data. For example, it can extract specific patterns from past data and evaluate current risk. It can also analyze past resale risk data and build a model for predicting current risk. This allows accurate prediction of current risk by referring to past resale risk data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past resale risk data into AI and have the AI ​​perform current risk prediction.

[0081] During analysis, the analysis unit can apply different analysis algorithms based on the user's attribute information. The analysis unit can apply different analysis algorithms based on the user's age or gender, for example. For example, the analysis unit can select an optimal analysis algorithm based on the user's past behavioral history. A customized analysis algorithm for assessing resale risk can also be applied based on the user's attribute information. This improves the accuracy of risk assessment by applying the optimal analysis algorithm based on the user's attribute information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's attribute information into AI and have the AI ​​apply different analysis algorithms.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. Also, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the display method.

[0083] During analysis, the analysis unit can evaluate risk based on the geographic distribution of users. For example, if users are concentrated in a specific area, the analysis unit can prioritize the risk assessment of that area. For example, the analysis unit can evaluate risk based on the geographic distribution of users by comparing it with past data. The analysis unit can also analyze the geographic distribution of users to identify the area with the highest risk. This improves the accuracy of risk assessment by taking the geographic distribution of users into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographic distribution data of users into AI and have the AI ​​perform risk assessment.

[0084] The analysis unit can evaluate risk by referring to related market data during analysis. The analysis unit can, for example, evaluate resale risk based on market data. For example, it can analyze market trends and predict current risks. It can also build a model for evaluating resale risk by referring to market data. This improves the accuracy of risk assessment by referring to related market data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input market data into AI and have the AI ​​perform risk assessment.

[0085] The information collection unit can estimate the user's emotions and adjust the method of collecting resale site information based on the estimated user emotions. For example, if the user is relaxed, the information collection unit can collect resale site information using a normal collection method. For example, if the user is stressed, the information collection unit can collect detailed information and carefully evaluate the resale risk. Furthermore, if the user is excited, the information collection unit can collect resale site information in real time and analyze it immediately. This improves the accuracy of collection by adjusting the method of collecting resale site information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, an AI, or without an AI. For example, the information collection unit can input the user's emotion data into an AI and have the AI ​​adjust the collection method.

[0086] When collecting information, the information collection unit can optimize the collection algorithm by referring to past resale site data. The information collection unit can, for example, select an optimal collection algorithm based on past resale site data. For example, the information collection unit can extract specific patterns from past data and optimize the collection algorithm. It can also analyze past resale site data and build a model for improving the collection algorithm. This allows the collection algorithm to be optimized by referring to past resale site data. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input past resale site data into AI and have the AI ​​optimize the collection algorithm.

[0087] When collecting information, the information collection unit can apply different collection methods depending on the category of the resale site. The information collection unit can, for example, select the optimal collection method depending on the category of the resale site. For example, a different collection algorithm can be applied to each category of the resale site. The category of the resale site can also be analyzed and the most efficient collection method can be applied. This improves the accuracy of collection by applying the optimal collection method depending on the category of the resale site. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input category data of the resale site into AI and have the AI ​​apply the collection method.

[0088] The information collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is relaxed, the information collection unit can collect information with normal priority. For example, when the user is stressed, the information collection unit can prioritize collecting important information. Furthermore, when the user is excited, information can be collected in real time and analyzed immediately. This improves the efficiency of information collection by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the information collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of information.

[0089] The information collection unit can collect information while taking into account the geographic distribution of resale sites. The information collection unit can, for example, select the optimal collection method based on the geographic distribution of resale sites. For example, the information collection unit can analyze the geographic distribution of resale sites and collect the most relevant information. The collection algorithm can also be optimized by taking into account the geographic distribution of resale sites. This improves the accuracy of collection by taking into account the geographic distribution of resale sites. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input geographic distribution data of resale sites into AI and have the AI ​​perform the collection.

[0090] The information collection unit can improve the accuracy of collection by referring to related literature when collecting information. The information collection unit can, for example, improve the collection algorithm based on the related literature. For example, the optimal collection method can be extracted and applied from the related literature. A model for improving the accuracy of collection can also be constructed by referring to the related literature. As a result, the accuracy of collection is improved by referring to the related literature. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input related literature data into AI and have the AI ​​improve the collection algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the face authentication unit, collection unit, analysis unit, and information collection unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the face authentication unit photographs the faces of attendees using the camera 42 of the smart device 14, performs face recognition using the control unit 46A, and performs log management using the specific processing unit 290 of the data processing device 12. The collection unit collects access histories and purchase histories of ticket sales sites via the communication I / F 44 of the smart device 14, which are analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the risk of resale. The information collection unit collects information on resale sites using the communication I / F 44 of the smart device 14, and determines the risk of resale using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the face authentication unit, collection unit, analysis unit, and information collection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the face authentication unit photographs the face of an attendee using the camera 42 of the smart glasses 214, performs face recognition using the control unit 46A, and performs log management using the specific processing unit 290 of the data processing device 12. The collection unit collects access history and purchase history of ticket sales sites via the communication I / F 44 of the smart glasses 214, which are analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the risk of resale. The information collection unit collects information on resale sites using the communication I / F 44 of the smart glasses 214, and determines the risk of resale using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the face authentication unit, collection unit, analysis unit, and information collection unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the face authentication unit photographs the faces of attendees using the camera 42 of the headset terminal 314, performs face recognition using the control unit 46A, and performs log management using the specific processing unit 290 of the data processing device 12. The collection unit collects access histories and purchase histories of ticket sales sites via the communication I / F 44 of the headset terminal 314, which are analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the risk of resale. The information collection unit collects information on resale sites using the communication I / F 44 of the headset terminal 314, and determines the risk of resale using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the face authentication unit, collection unit, analysis unit, and information collection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the face authentication unit photographs the faces of visitors using the camera 42 of the robot 414, performs face recognition using the control unit 46A, and performs log management using the specific processing unit 290 of the data processing device 12. The collection unit collects access histories and purchase histories of ticket sales sites via the communication I / F 44 of the robot 414, which are analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and determines the risk of resale. The information collection unit collects information on resale sites using the communication I / F 44 of the robot 414, and determines the risk of resale using the specific processing unit 290 of the data processing device 12.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When authenticating an attendee's face, the facial authentication unit can improve the accuracy of authentication by referring to the attendee's past purchase history. For example, if the attendee has a history of purchasing tickets to the same event in the past, the accuracy of facial authentication can be improved by referring to that history. Also, if the attendee has a history of being authenticated without any problems in the past, the authentication speed can be improved based on that data. Furthermore, if the attendee has a history of failing authentication in the past, the cause can be analyzed to improve the accuracy when authenticating again. In this way, the accuracy of facial authentication is improved by referring to the past purchase history.

[0093] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is relaxed, normal data collection can be performed. If the user is stressed, detailed data can be collected and resale risk can be carefully evaluated. Also, if the user is excited, data can be collected in real time and analyzed immediately. This improves collection efficiency by adjusting the type of data collection according to the user's emotions.

[0094] The analysis unit can evaluate the risk of resale based on the geographic distribution of users. For example, if users are concentrated in a particular area, the risk of that area can be evaluated preferentially. The analysis unit can also evaluate the risk based on the geographic distribution of users by comparing it with past data. Furthermore, the analysis unit can analyze the geographic distribution of users and identify the areas with the highest risk. This improves the accuracy of risk assessment by taking the geographic distribution of users into consideration.

[0095] When collecting information from resale sites, the information collection unit can adjust the collection timing based on the update frequency of the site. For example, information can be collected in real time from frequently updated sites, and periodically from less frequently updated sites. The information collection unit can also analyze the update history of the site and determine the optimal collection timing. This improves the efficiency of information collection by adjusting the collection timing based on the update frequency of the resale site.

[0096] The facial recognition unit can estimate the emotions of the visitor and display a message to the visitor based on the estimated emotions. For example, if the visitor is nervous, a message encouraging them to relax can be displayed. If the visitor is relaxed, a regular information message can be displayed. Also, if the visitor is excited, a message urging caution can be displayed. In this way, the visitor's experience can be improved by displaying a message according to the visitor's emotions.

[0097] The collection unit can analyze the user's social media activities and collect related data. For example, it can analyze the user's current interests and collect related data from the user's social media activities. It can also obtain the user's past event participation history from the user's social media activities and collect related data. It can also preferentially collect data related to specific topics from the user's social media activities. This allows for efficient collection of related data by analyzing the user's social media activities.

[0098] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, visibility can be improved by adjusting the display method of the analysis results according to the user's emotions.

[0099] The information collection unit can apply different collection methods depending on the category of the resale site. For example, it can select the optimal collection method depending on the category of the resale site. It can also apply different collection algorithms to each category of the resale site. It can also analyze the category of the resale site and apply the most efficient collection method. This improves the accuracy of collection by applying the optimal collection method depending on the category of the resale site.

[0100] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated emotions. For example, if the user is relaxed, data can be collected with normal priority. If the user is stressed, important data can be collected with priority. Also, if the user is excited, data can be collected in real time and analyzed immediately. This improves collection efficiency by determining the priority of data according to the user's emotions.

[0101] The analysis unit can predict current risks by referencing past resale risk data. For example, current risks can be predicted based on past resale risk data. It can also extract specific patterns from past data and evaluate current risks. Furthermore, it can analyze past resale risk data and build a model for predicting current risks. This allows current risks to be accurately predicted by referring to past resale risk data.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The facial recognition unit recognizes the faces of attendees and manages logs. For example, the facial recognition unit uses a facial recognition camera installed at the entrance to the event venue to photograph attendees' faces, and AI accurately manages logs of "who entered and when." The accuracy of facial recognition is improved by introducing a facial recognition algorithm that uses deep learning. Step 2: The collection unit collects the access history or purchase history of the ticket sales site. For example, the collection unit can collect user behavior history using cookies or session data. The collection unit can also collect the IP addresses and purchase histories of users who access the ticket sales site. Step 3: The analysis unit analyzes the data collected by the collection unit and determines the risk of resale. For example, the analysis unit analyzes the collected data and determines the risk of resale. Step 4: The information collection unit collects information on the resale site to determine the risk of resale. For example, the information collection unit can use web scraping technology to collect information on the resale site.

[0104] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A face recognition unit that recognizes the faces of visitors and manages logs; a collection unit that collects access history or purchase history of the ticket sales site; an analysis unit that analyzes the data collected by the collection unit and determines the risk of resale; an information collection unit that collects information on resale sites and determines the risk of resale; A system characterized by:

2. The face authentication unit Introducing a deep learning facial recognition algorithm The system of claim 1 .

3. The collecting unit Collecting user behavior history using cookies or session data The system of claim 1 .

4. The information collecting unit Use web scraping technology to collect information from resale sites The system of claim 1 .

5. The analysis unit Analyze the collected data and determine the risk of resale The system of claim 1 .

6. The collecting unit Collecting IP addresses or purchase history of users who access ticket sales sites The system of claim 1 .

7. The face authentication unit Estimate the emotions of visitors and adjust the accuracy of facial recognition based on the estimated emotions of visitors. The system of claim 1 .

8. The face authentication unit When performing facial recognition, the accuracy of authentication is improved by referencing the visitor's past entry history. The system of claim 1 .

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