System

Abstract

An object of a system according to an embodiment is to analyze user behavior in a store and notify a salesperson at an appropriate timing.SOLUTION: A system according to an embodiment includes a camera, a AI analyzer, a notifier, and an analyzer. The camera acquires the behavior of the user through a camera in the store. The AI analysis unit analyzes the behavior of the user acquired by the camera. The notifying unit notifies the salesperson when the AI analyzing unit determines that the user is the purchase contemplation user. The analysis unit analyzes a staying time, a position, and a guiding line of the user acquired by the camera.SELECTED DRAWING: Figure 1

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 the problem that it is difficult to analyze user behavior in a store in real time and notify sales staff at the appropriate time.

[0005] The system according to the embodiment aims to analyze user behavior in a store and notify sales staff at an appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, an AI analysis unit, a notification unit, and an analysis unit. The camera captures user behavior through an in-store camera. The AI ​​analysis unit analyzes the user behavior captured by the camera. The notification unit notifies a salesperson when the AI ​​analysis unit determines that the user is considering a purchase. The analysis unit analyzes the user's stay time, location, and path captured by the camera. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user behavior in a store and notify sales staff at an appropriate time. [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 purchase consideration user decision system according to an embodiment of the present invention captures user behavior via in-store cameras, analyzes it using a generation AI, notifies sales staff as needed, and analyzes the user's time spent, location, and flow. This allows the purchase consideration user decision system to analyze user behavior in real time and notify sales staff, enabling prompt support. Furthermore, by analyzing the user's time spent, location, and flow, it is possible to optimize appeals, displays, and layouts tailored to the characteristics of each store.

[0029] A system for determining a potential purchase user according to an embodiment includes a camera, an AI analysis unit, a notification unit, and an analysis unit. The camera captures user behavior in a store. For example, the camera is installed in a specific area in the store and monitors user movements in real time. The camera can record user behavior in high resolution. The AI ​​analysis unit analyzes the user behavior captured by the camera. For example, the AI ​​analysis unit analyzes user behavior patterns using deep learning. The AI ​​analysis unit can analyze user behavior in detail using image recognition technology. The AI ​​analysis unit can determine a potential purchase user based on the user behavior data. The notification unit notifies a salesperson when the AI ​​analysis unit determines that the user is a potential purchase user. For example, the notification unit can send a text message to the salesperson's smartphone. The notification unit can sound an alert on the salesperson's tablet. The notification unit can send a notification to the salesperson's wearable device. The analysis unit analyzes the user's stay time, location, and path captured by the camera. For example, the analysis unit analyzes which areas users stay in for the longest time. The analysis unit can also analyze which route users take as they move around the store. The analysis unit can also optimize the store layout based on user flow data. As a result, the purchase consideration user determination system according to the embodiment analyzes user behavior in real time and notifies sales staff, enabling prompt support. Furthermore, by analyzing the user's stay time, location, and flow, it is possible to optimize appeals, displays, and layouts to suit the characteristics of each store.

[0030] The AI ​​analysis unit can refer to a user's past purchase history and membership information to make individually customized judgments about users considering a purchase. For example, the AI ​​analysis unit retrieves a user's past purchase history from a database and compares it with their current behavior to determine their willingness to purchase. For example, if a user who previously purchased a product from the same category is in the same area again, it will determine that they have a high willingness to purchase. The AI ​​analysis unit also refers to membership information to identify users considering a purchase based on their preferences and interests. For example, it will prioritize analysis of users who are interested in specific products based on their membership information. The AI ​​analysis unit also combines a user's past purchase history with their current behavior to make individually customized judgments about their willingness to purchase. For example, if a user picks up a product related to a product they previously purchased, it will determine that they have a high willingness to purchase. This makes it possible to make individually customized judgments based on a user's past purchase history and membership information.

[0031] The AI ​​analysis unit learns user behavior patterns over the long term and can accurately predict whether specific behavior indicates purchase intent. For example, the AI ​​analysis unit accumulates user behavior patterns over the long term in a database, and the AI ​​predicts purchase intent based on that data. For example, if a specific behavior pattern is repeated, it may determine that the intent to purchase is high. The AI ​​analysis unit also analyzes the user's behavior history and accurately predicts whether specific behavior indicates purchase intent. For example, it analyzes the amount of time spent in front of a specific product shelf and how often the user picks up the product. The AI ​​analysis unit also develops an algorithm that uses long-term behavioral data to predict the user's purchase intent. For example, it compares past behavior patterns with current behavior and accurately predicts purchase intent. In this way, by learning user behavior patterns over the long term, purchase intent can be predicted with high accuracy.

[0032] The AI ​​analysis unit analyzes in-store voice data and can determine that a user is considering purchasing a specific product when the user is talking about that product. For example, the AI ​​analysis unit analyzes voice data collected through in-store microphones and determines that a user is highly interested in purchasing a specific product when the user is talking about that product. For example, it detects product names and related keywords. The AI ​​analysis unit also uses voice recognition technology to analyze the content of users' conversations in real time and detects statements that indicate a desire to purchase. For example, it makes a determination based on statements such as "Shall I buy this?" The AI ​​analysis unit also analyzes in-store voice data and determines that a user is highly interested in purchasing a specific product when the user expresses a positive opinion about that product. For example, it detects statements such as "This product looks good." In this way, by analyzing voice data, it is possible to determine a user's desire to purchase a specific product when the user is talking about that product.

[0033] The AI ​​analysis unit analyzes user behavior in response to changes in in-store temperature and lighting, and can consider the impact of environmental factors on purchase willingness. The AI ​​analysis unit monitors changes in environmental factors in real time using, for example, in-store temperature and lighting sensors, and analyzes user behavior. For example, it analyzes changes in user behavior when the temperature rises. The AI ​​analysis unit also analyzes user behavior in response to changes in lighting brightness and color temperature, and considers the impact on purchase willingness. For example, it analyzes purchase willingness under specific lighting conditions. The AI ​​analysis unit also analyzes user behavior patterns based on data on environmental factors, and considers the impact on purchase willingness. For example, it analyzes the impact of changes in temperature and lighting on purchase willingness. This makes it possible to consider the impact of changes in temperature and lighting on user purchase willingness.

[0034] The notification unit can send notifications at the optimal timing, taking into account the salesperson's schedule and current work status. The notification unit, for example, manages the salesperson's schedule in real time and sends notifications when there is less work. For example, it sends a notification immediately after the salesperson has finished other work. The notification unit also monitors the salesperson's current work status and sends notifications at the optimal timing. For example, it sends a notification immediately after the salesperson has finished dealing with another customer. The notification unit also manages the salesperson's schedule and work status in an integrated manner, building a system that sends notifications at the optimal timing. For example, it selects a time when the salesperson is not on a break and sends the notification. In this way, it is possible to send notifications at the optimal timing by taking into account the salesperson's schedule and work status.

[0035] The notification unit can include the user's past purchase history and product information of interest in the notification content, allowing the salesperson to respond quickly. The notification unit, for example, includes the user's past purchase history in the notification content, allowing the salesperson to respond quickly. For example, information about products purchased in the past is included in the notification. The notification unit also includes information about products the user is interested in in the notification content, allowing the salesperson to respond quickly. For example, information about products the user is currently holding in the notification. The notification unit also integrates the user's past purchase history and product information of interest in the user, allowing the salesperson to respond quickly. For example, the notification content is customized based on the user's past purchase history and current behavior. In this way, the salesperson can respond quickly by including the user's past purchase history and product information of interest in the notification content.

[0036] The notification unit can utilize in-store digital signage to notify not only sales staff but also users that support is needed. The notification unit, for example, utilizes in-store digital signage to notify users that support is needed. For example, it displays a message such as, "If you need support, press this button." The notification unit also uses digital signage to display information about users who need support to sales staff. For example, it displays a message such as, "A user in front of shelf A needs support." The notification unit also utilizes digital signage to build a system that notifies both users and sales staff that support is needed. For example, when a user presses a button requesting support, a notification is sent to the sales staff. In this way, by utilizing digital signage, it is possible to notify not only sales staff but also users that support is needed.

[0037] The notification unit can send notifications to the wearable devices worn by the salesperson, enabling them to respond immediately. The notification unit can, for example, send notifications to a smartwatch worn by the salesperson, enabling them to respond immediately. For example, it can display a message such as, "A user in front of shelf A needs support." The notification unit can also build a system that sends notifications to the wearable devices worn by the salesperson, enabling them to respond immediately. For example, it can display notifications on smart glasses. The notification unit can also develop a system that uses wearable devices to send notifications to salespersons in real time. For example, it can add a function that notifies them by vibration or sound. This allows the salesperson to respond immediately by sending a notification to the wearable device.

[0038] The analysis unit analyzes the user's movement speed and walking pattern, and can distinguish between users who are in a hurry and users who are looking around slowly. The analysis unit, for example, analyzes the user's movement speed in real time and distinguishes between users who are in a hurry and users who are looking around slowly. For example, it determines that a user who moves quickly is in a hurry. The analysis unit also analyzes the user's walking pattern and distinguishes between users who are in a hurry and users who are looking around slowly. For example, it determines that a user whose walking pattern is not constant is in a hurry. The analysis unit also analyzes the user's movement speed and walking pattern in an integrated manner and distinguishes between users who are in a hurry and users who are looking around slowly. For example, it makes this determination based on changes in the movement speed and walking pattern. In this way, by analyzing the user's movement speed and walking pattern, it is possible to distinguish between users who are in a hurry and users who are looking around slowly.

[0039] The analysis unit can accumulate user behavior data over the long term and analyze changes in behavior patterns due to seasons and time periods. The analysis unit, for example, accumulates user behavior data over the long term and analyzes changes in behavior patterns due to seasons and time periods. For example, it analyzes behavior patterns during specific seasons and time periods. The analysis unit also builds a system that analyzes changes in behavior patterns due to seasons and time periods based on long-term behavior data. For example, it compares behavior patterns by season. The analysis unit also accumulates user behavior data over the long term and analyzes changes in behavior patterns due to seasons and time periods in real time. For example, it analyzes behavior patterns during specific time periods. This allows the analysis unit to accumulate user behavior data over the long term and analyze changes in behavior patterns due to seasons and time periods.

[0040] The analysis unit analyzes acoustic data within the store and can determine in which area the user is standing. The analysis unit, for example, analyzes acoustic data within the store and determines in which area the user is standing. For example, the analysis unit identifies the area where the user is standing based on the reverberation of sound and changes in volume. The analysis unit also analyzes the acoustic data in real time to build a system that determines the area where the user is standing. For example, it analyzes the intensity and direction of sound. The analysis unit also determines in real time in which area the user is standing based on the acoustic data within the store. For example, it analyzes sound patterns to identify the area where the user is standing. In this way, by analyzing the acoustic data, it can be determined in which area the user is standing.

[0041] The analysis unit can dynamically change the lighting and display layout in the store to guide the user's path. The analysis unit, for example, builds a system that dynamically changes the lighting in the store to guide the user's path. For example, it can guide the user by brightening specific areas. The analysis unit also dynamically changes the layout of displays to guide the user's path. For example, it can guide the user by placing specific products in prominent locations. The analysis unit also develops a system that manages the layout of lighting and displays in an integrated manner and dynamically guides the user's path. For example, it can guide the user by changing the color and brightness of the lighting. In this way, it is possible to guide the user's path by dynamically changing the layout of lighting and displays.

[0042] The analysis unit can analyze store sales data and inventory data to propose the most effective promotion and display methods. The analysis unit, for example, analyzes store sales data and builds a system that proposes the most effective promotion and display methods. For example, products with high sales are placed in prominent locations. The analysis unit also proposes the most effective promotion and display methods based on inventory data. For example, products with high inventory are placed in prominent locations. The analysis unit also develops a system that analyzes sales data and inventory data in an integrated manner to propose the most effective promotion and display methods. For example, products with high sales and high inventory are placed in prominent locations. In this way, the most effective promotion and display methods can be proposed by analyzing sales data and inventory data.

[0043] The analysis unit is able to propose layouts that suit regional characteristics, taking into account the store's location and surrounding environment. For example, the analysis unit analyzes store location data and builds a system that proposes layouts that suit regional characteristics. For example, different layouts are proposed for urban and suburban areas. The analysis unit also proposes layouts that suit regional characteristics based on surrounding environment data. For example, it uses the layouts of nearby competing stores as reference. The analysis unit also develops a system that comprehensively analyzes location data and surrounding environment data and proposes layouts that suit regional characteristics. For example, it proposes layouts that suit regional consumer needs. This makes it possible to propose layouts that suit regional characteristics by taking into account the store's location and surrounding environment.

[0044] The analysis unit can propose individually customized layouts based on the user's purchasing history and preferences. The analysis unit, for example, builds a system that proposes individually customized layouts based on the user's purchasing history. For example, it proposes a layout based on products purchased in the past. The analysis unit also proposes individually customized layouts based on the user's preference data. For example, it places products that match the user's preferences in prominent locations. The analysis unit also develops a system that comprehensively analyzes the purchasing history and preference data and proposes individually customized layouts. For example, it proposes a layout that matches the user's preferences. This makes it possible to propose individually customized layouts based on the user's purchasing history and preferences.

[0045] The analysis unit can change the design of the store's exterior or entrance to attract users' interest. The analysis unit, for example, builds a system that changes the exterior of a store to attract users' interest. For example, changing the exterior design attracts users' interest. The analysis unit also changes the design of the store's entrance to attract users' interest. For example, changing the entrance design attracts users' interest. The analysis unit also develops a system that manages the exterior and entrance designs in an integrated manner to attract users' interest. For example, combining the exterior and entrance designs to attract users' interest. In this way, changing the design of the store's exterior or entrance can attract users' interest.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The acquisition unit can provide individually customized recommendations based on the user's purchase history and membership information. For example, it can suggest products related to products purchased in the past. The acquisition unit can also preferentially display specific products based on the user's preferences and interests. For example, it can recommend products in a specific category based on membership information. The acquisition unit can also provide individually customized recommendations by combining the user's past purchase history and current behavior. For example, it can suggest new products related to products purchased in the past. This makes it possible to provide individually customized recommendations based on the user's purchase history and membership information.

[0048] The provision unit learns user behavior patterns over the long term and can predict with high accuracy whether specific behavior indicates purchase intent. For example, the user's behavior patterns are accumulated in a database over the long term, and the AI ​​predicts purchase intent based on that data. For example, if a specific behavior pattern is repeated, it is determined that the purchase intent is high. The provision unit also analyzes the user's behavior history and predicts with high accuracy whether specific behavior indicates purchase intent. For example, it analyzes the amount of time spent in front of a specific product shelf and how often the user picks up the product. The provision unit also develops an algorithm that uses the AI ​​to predict the user's purchase intent based on long-term behavioral data. For example, it compares past behavior patterns with current behavior and predicts purchase intent with high accuracy. In this way, by learning user behavior patterns over the long term, purchase intent can be predicted with high accuracy.

[0049] The acquisition unit analyzes in-store voice data and can determine that a user is considering purchasing a specific product when the user is talking about that product. For example, the acquisition unit analyzes voice data collected through a microphone in the store and determines that a user is highly motivated to purchase a specific product when the user is talking about that product. For example, it detects the product name and related keywords. The acquisition unit also uses voice recognition technology to analyze the content of the user's conversation in real time and detects statements that indicate a desire to purchase. For example, it makes a determination based on statements such as "Shall I buy this?" The acquisition unit also analyzes in-store voice data and determines that a user is highly motivated to purchase a specific product when the user expresses a positive opinion about that product. For example, it detects statements such as "This product looks good." In this way, by analyzing the voice data, it is possible to determine a user's desire to purchase a specific product when the user is talking about that product.

[0050] The providing unit can analyze user behavior in response to changes in temperature and lighting in the store, and consider the impact of environmental factors on purchase willingness. For example, the providing unit can use temperature sensors and lighting sensors in the store to monitor changes in environmental factors in real time and analyze user behavior. For example, it can analyze changes in user behavior when the temperature rises. The providing unit can also analyze user behavior in response to changes in lighting brightness and color temperature, and consider the impact on purchase willingness. For example, it can analyze purchase willingness under specific lighting conditions. The providing unit can also analyze user behavior patterns based on data on environmental factors, and consider the impact on purchase willingness. For example, it can analyze the impact of changes in temperature and lighting on purchase willingness. This makes it possible to consider the impact of changes in temperature and lighting on user purchase willingness.

[0051] The provision unit can send notifications at the optimal timing, taking into account the salesperson's schedule and current work status. For example, it can manage the salesperson's schedule in real time and send notifications when there is less work. For example, it can send notifications immediately after the salesperson has finished other work. The provision unit can also monitor the salesperson's current work status and send notifications at the optimal timing. For example, it can send notifications immediately after the salesperson has finished dealing with another customer. The provision unit can also build a system that manages the salesperson's schedule and work status in an integrated manner and sends notifications at the optimal timing. For example, it can send notifications when the salesperson is not on a break. This makes it possible to send notifications at the optimal timing by taking into account the salesperson's schedule and work status.

[0052] The providing unit can include the user's past purchasing history and product information of interest in the notification content, allowing the salesperson to respond quickly. For example, the notification content can include the user's past purchasing history, allowing the salesperson to respond quickly. For example, information about products purchased in the past can be included in the notification. The providing unit can also include information about products the user is interested in in the notification content, allowing the salesperson to respond quickly. For example, information about the product the user is currently holding in their hand can be included in the notification. The providing unit can also integrate the user's past purchasing history and product information of interest in the user, allowing the salesperson to respond quickly. For example, the notification content can be customized based on the user's past purchasing history and current behavior. In this way, the notification content can include the user's past purchasing history and product information of interest, allowing the salesperson to respond quickly.

[0053] The provision unit can use in-store digital signage to notify not only sales staff but also users that they need support. For example, in-store digital signage can be used to notify users that they need support. For example, it can display a message such as, "If you need support, press this button." The provision unit can also use digital signage to display information about users who need support to sales staff. For example, it can display a message such as, "The user in front of shelf A needs support." The provision unit can also use digital signage to build a system that notifies both users and sales staff that they need support. For example, when a user presses a button requesting support, a notification is sent to the sales staff. In this way, by utilizing digital signage, it is possible to notify not only sales staff but also users that they need support.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: Cameras capture user behavior in the store. For example, cameras can be installed in specific areas of the store to monitor user movements in real time and record them in high resolution. Step 2: The AI ​​analysis unit analyzes the user behavior captured by the camera. For example, it can use deep learning and image recognition technology to analyze user behavior patterns in detail and determine users who are considering purchasing. Step 3: The notification unit notifies the salesperson if the AI ​​analysis unit determines that the user is considering purchasing. For example, it can send a text message to the salesperson's smartphone, sound an alert on their tablet, or send a notification to their wearable device. Step 4: The analysis unit analyzes the user's time spent, location, and path captured by the camera. For example, it can analyze which areas users spend the most time in and which routes they take to move around the store, allowing it to optimize the store layout.

[0056] (Example 2) The purchase consideration user decision system according to an embodiment of the present invention captures user behavior via in-store cameras, analyzes it using a generation AI, notifies sales staff as needed, and analyzes the user's time spent, location, and flow. This allows the purchase consideration user decision system to analyze user behavior in real time and notify sales staff, enabling prompt support. Furthermore, by analyzing the user's time spent, location, and flow, it is possible to optimize appeals, displays, and layouts tailored to the characteristics of each store.

[0057] A system for determining a potential purchase user according to an embodiment includes a camera, an AI analysis unit, a notification unit, and an analysis unit. The camera captures user behavior in a store. For example, the camera is installed in a specific area in the store and monitors user movements in real time. The camera can record user behavior in high resolution. The AI ​​analysis unit analyzes the user behavior captured by the camera. For example, the AI ​​analysis unit analyzes user behavior patterns using deep learning. The AI ​​analysis unit can analyze user behavior in detail using image recognition technology. The AI ​​analysis unit can determine a potential purchase user based on the user behavior data. The notification unit notifies a salesperson when the AI ​​analysis unit determines that the user is a potential purchase user. For example, the notification unit can send a text message to the salesperson's smartphone. The notification unit can sound an alert on the salesperson's tablet. The notification unit can send a notification to the salesperson's wearable device. The analysis unit analyzes the user's stay time, location, and path captured by the camera. For example, the analysis unit analyzes which areas users stay in for the longest time. The analysis unit can also analyze which route users take as they move around the store. The analysis unit can also optimize the store layout based on user flow data. As a result, the purchase consideration user determination system according to the embodiment analyzes user behavior in real time and notifies sales staff, enabling prompt support. Furthermore, by analyzing the user's stay time, location, and flow, it is possible to optimize appeals, displays, and layouts to suit the characteristics of each store.

[0058] The AI ​​analysis unit can infer emotions from users' facial expressions and body movements, and identify users with a high desire to purchase. For example, the AI ​​analysis unit analyzes users' facial expressions in real time using in-store cameras and detects positive emotions such as smiling or surprise. This allows it to identify users with a high desire to purchase. The AI ​​analysis unit also analyzes users' body movements and detects behavior that indicates interest in a product (for example, picking up a product or looking at it closely). This allows it to identify users with a high desire to purchase. The AI ​​analysis unit also uses user gaze tracking technology to analyze the degree of gaze concentration on a specific product, and identify users with a high desire to purchase. In this way, it is possible to identify users with a high desire to purchase by analyzing users' emotions.

[0059] The AI ​​analysis unit can refer to a user's past purchase history and membership information to make individually customized judgments about users considering a purchase. For example, the AI ​​analysis unit retrieves a user's past purchase history from a database and compares it with their current behavior to determine their willingness to purchase. For example, if a user who previously purchased a product from the same category is in the same area again, it will determine that they have a high willingness to purchase. The AI ​​analysis unit also refers to membership information to identify users considering a purchase based on their preferences and interests. For example, it will prioritize analysis of users who are interested in specific products based on their membership information. The AI ​​analysis unit also combines a user's past purchase history with their current behavior to make individually customized judgments about their willingness to purchase. For example, if a user picks up a product related to a product they previously purchased, it will determine that they have a high willingness to purchase. This makes it possible to make individually customized judgments based on a user's past purchase history and membership information.

[0060] The AI ​​analysis unit learns user behavior patterns over the long term and can accurately predict whether specific behavior indicates purchase intent. For example, the AI ​​analysis unit accumulates user behavior patterns over the long term in a database, and the AI ​​predicts purchase intent based on that data. For example, if a specific behavior pattern is repeated, it may determine that the intent to purchase is high. The AI ​​analysis unit also analyzes the user's behavior history and accurately predicts whether specific behavior indicates purchase intent. For example, it analyzes the amount of time spent in front of a specific product shelf and how often the user picks up the product. The AI ​​analysis unit also develops an algorithm that uses long-term behavioral data to predict the user's purchase intent. For example, it compares past behavior patterns with current behavior and accurately predicts purchase intent. In this way, by learning user behavior patterns over the long term, purchase intent can be predicted with high accuracy.

[0061] The AI ​​analysis unit analyzes in-store voice data and can determine that a user is considering purchasing a specific product when the user is talking about that product. For example, the AI ​​analysis unit analyzes voice data collected through in-store microphones and determines that a user is highly interested in purchasing a specific product when the user is talking about that product. For example, it detects product names and related keywords. The AI ​​analysis unit also uses voice recognition technology to analyze the content of users' conversations in real time and detects statements that indicate a desire to purchase. For example, it makes a determination based on statements such as "Shall I buy this?" The AI ​​analysis unit also analyzes in-store voice data and determines that a user is highly interested in purchasing a specific product when the user expresses a positive opinion about that product. For example, it detects statements such as "This product looks good." In this way, by analyzing voice data, it is possible to determine a user's desire to purchase a specific product when the user is talking about that product.

[0062] The AI ​​analysis unit analyzes user behavior in response to changes in in-store temperature and lighting, and can consider the impact of environmental factors on purchase willingness. The AI ​​analysis unit monitors changes in environmental factors in real time using, for example, in-store temperature and lighting sensors, and analyzes user behavior. For example, it analyzes changes in user behavior when the temperature rises. The AI ​​analysis unit also analyzes user behavior in response to changes in lighting brightness and color temperature, and considers the impact on purchase willingness. For example, it analyzes purchase willingness under specific lighting conditions. The AI ​​analysis unit also analyzes user behavior patterns based on data on environmental factors, and considers the impact on purchase willingness. For example, it analyzes the impact of changes in temperature and lighting on purchase willingness. This makes it possible to consider the impact of changes in temperature and lighting on user purchase willingness.

[0063] The AI ​​analysis unit can use the emotion estimation function to determine that a user is considering purchasing a particular product if they show positive emotions toward that product. For example, the AI ​​analysis unit uses the emotion estimation function to determine that a user has a high desire to purchase if they show positive emotions toward that product. For example, it detects expressions of smiles or surprise. The AI ​​analysis unit also analyzes the user's facial expressions and body movements to determine that a user has a high desire to purchase if they show positive emotions toward that product. For example, it detects the behavior of picking up a product and looking at it carefully. The AI ​​analysis unit also uses the emotion estimation function to determine that a user has a high desire to purchase if they show positive emotions toward that product. For example, it uses eye-tracking technology to analyze the degree of gaze concentration. In this way, the emotion estimation function can determine that a user has a high desire to purchase if they show positive emotions toward that product.

[0064] The notification unit can send notifications at the optimal timing, taking into account the salesperson's schedule and current work status. The notification unit, for example, manages the salesperson's schedule in real time and sends notifications when there is less work. For example, it sends a notification immediately after the salesperson has finished other work. The notification unit also monitors the salesperson's current work status and sends notifications at the optimal timing. For example, it sends a notification immediately after the salesperson has finished dealing with another customer. The notification unit also manages the salesperson's schedule and work status in an integrated manner, building a system that sends notifications at the optimal timing. For example, it selects a time when the salesperson is not on a break and sends the notification. In this way, it is possible to send notifications at the optimal timing by taking into account the salesperson's schedule and work status.

[0065] The notification unit can include the user's past purchase history and product information of interest in the notification content, allowing the salesperson to respond quickly. The notification unit, for example, includes the user's past purchase history in the notification content, allowing the salesperson to respond quickly. For example, information about products purchased in the past is included in the notification. The notification unit also includes information about products the user is interested in in the notification content, allowing the salesperson to respond quickly. For example, information about products the user is currently holding in the notification. The notification unit also integrates the user's past purchase history and product information of interest in the user, allowing the salesperson to respond quickly. For example, the notification content is customized based on the user's past purchase history and current behavior. In this way, the salesperson can respond quickly by including the user's past purchase history and product information of interest in the notification content.

[0066] The notification unit can utilize in-store digital signage to notify not only sales staff but also users that support is needed. The notification unit, for example, utilizes in-store digital signage to notify users that support is needed. For example, it displays a message such as, "If you need support, press this button." The notification unit also uses digital signage to display information about users who need support to sales staff. For example, it displays a message such as, "A user in front of shelf A needs support." The notification unit also utilizes digital signage to build a system that notifies both users and sales staff that support is needed. For example, when a user presses a button requesting support, a notification is sent to the sales staff. In this way, by utilizing digital signage, it is possible to notify not only sales staff but also users that support is needed.

[0067] The notification unit can send notifications to the wearable devices worn by the salesperson, enabling them to respond immediately. The notification unit can, for example, send notifications to a smartwatch worn by the salesperson, enabling them to respond immediately. For example, it can display a message such as, "A user in front of shelf A needs support." The notification unit can also build a system that sends notifications to the wearable devices worn by the salesperson, enabling them to respond immediately. For example, it can display notifications on smart glasses. The notification unit can also develop a system that uses wearable devices to send notifications to salespersons in real time. For example, it can add a function that notifies them by vibration or sound. This allows the salesperson to respond immediately by sending a notification to the wearable device.

[0068] The notification unit can use the emotion estimation function to send a notification when the user feels that they need support. The notification unit, for example, uses the emotion estimation function to build a system that sends a notification when the user feels that they need support. For example, the notification unit analyzes the user's facial expressions and body movements. The notification unit also sends a notification to a salesperson when support is needed based on the user's emotion data. For example, the notification unit sends a notification when a confused expression is detected. The notification unit also uses the emotion estimation function to send a notification in real time when the user feels that support is needed. For example, the notification unit analyzes the user's behavioral patterns and identifies the timing when support is needed. In this way, by using the emotion estimation function, a notification can be sent when the user feels that support is needed.

[0069] The analysis unit analyzes the user's movement speed and walking pattern, and can distinguish between users who are in a hurry and users who are looking around slowly. The analysis unit, for example, analyzes the user's movement speed in real time and distinguishes between users who are in a hurry and users who are looking around slowly. For example, it determines that a user who moves quickly is in a hurry. The analysis unit also analyzes the user's walking pattern and distinguishes between users who are in a hurry and users who are looking around slowly. For example, it determines that a user whose walking pattern is not constant is in a hurry. The analysis unit also analyzes the user's movement speed and walking pattern in an integrated manner and distinguishes between users who are in a hurry and users who are looking around slowly. For example, it makes this determination based on changes in the movement speed and walking pattern. In this way, by analyzing the user's movement speed and walking pattern, it is possible to distinguish between users who are in a hurry and users who are looking around slowly.

[0070] The analysis unit can accumulate user behavior data over the long term and analyze changes in behavior patterns due to seasons and time periods. The analysis unit, for example, accumulates user behavior data over the long term and analyzes changes in behavior patterns due to seasons and time periods. For example, it analyzes behavior patterns during specific seasons and time periods. The analysis unit also builds a system that analyzes changes in behavior patterns due to seasons and time periods based on long-term behavior data. For example, it compares behavior patterns by season. The analysis unit also accumulates user behavior data over the long term and analyzes changes in behavior patterns due to seasons and time periods in real time. For example, it analyzes behavior patterns during specific time periods. This allows the analysis unit to accumulate user behavior data over the long term and analyze changes in behavior patterns due to seasons and time periods.

[0071] The analysis unit analyzes acoustic data within the store and can determine in which area the user is standing. The analysis unit, for example, analyzes acoustic data within the store and determines in which area the user is standing. For example, the analysis unit identifies the area where the user is standing based on the reverberation of sound and changes in volume. The analysis unit also analyzes the acoustic data in real time to build a system that determines the area where the user is standing. For example, it analyzes the intensity and direction of sound. The analysis unit also determines in real time in which area the user is standing based on the acoustic data within the store. For example, it analyzes sound patterns to identify the area where the user is standing. In this way, by analyzing the acoustic data, it can be determined in which area the user is standing.

[0072] The analysis unit can dynamically change the lighting and display layout in the store to guide the user's path. The analysis unit, for example, builds a system that dynamically changes the lighting in the store to guide the user's path. For example, it can guide the user by brightening specific areas. The analysis unit also dynamically changes the layout of displays to guide the user's path. For example, it can guide the user by placing specific products in prominent locations. The analysis unit also develops a system that manages the layout of lighting and displays in an integrated manner and dynamically guides the user's path. For example, it can guide the user by changing the color and brightness of the lighting. In this way, it is possible to guide the user's path by dynamically changing the layout of lighting and displays.

[0073] The analysis unit can use the emotion estimation function to enhance the appeal of a specific area when a user expresses positive emotion in that area. The analysis unit, for example, uses the emotion estimation function to build a system that enhances the appeal of that area when a user expresses positive emotion in that area. For example, by placing specific products in the area where the user expressed positive emotion. The analysis unit also enhances the appeal of a specific area when the user expresses positive emotion in that area based on the user's emotion data. For example, by brightening the lights in the area where the user expressed positive emotion. The analysis unit also uses the emotion estimation function to enhance the appeal of the area in real time when the user expresses positive emotion in that area. For example, by placing a specific display in the area where the user expressed positive emotion. In this way, by using the emotion estimation function, the appeal of the area can be enhanced when the user expresses positive emotion in that area.

[0074] The analysis unit can analyze store sales data and inventory data to propose the most effective promotion and display methods. The analysis unit, for example, analyzes store sales data and builds a system that proposes the most effective promotion and display methods. For example, products with high sales are placed in prominent locations. The analysis unit also proposes the most effective promotion and display methods based on inventory data. For example, products with high inventory are placed in prominent locations. The analysis unit also develops a system that analyzes sales data and inventory data in an integrated manner to propose the most effective promotion and display methods. For example, products with high sales and high inventory are placed in prominent locations. In this way, the most effective promotion and display methods can be proposed by analyzing sales data and inventory data.

[0075] The analysis unit is able to propose layouts that suit regional characteristics, taking into account the store's location and surrounding environment. For example, the analysis unit analyzes store location data and builds a system that proposes layouts that suit regional characteristics. For example, different layouts are proposed for urban and suburban areas. The analysis unit also proposes layouts that suit regional characteristics based on surrounding environment data. For example, it uses the layouts of nearby competing stores as reference. The analysis unit also develops a system that comprehensively analyzes location data and surrounding environment data and proposes layouts that suit regional characteristics. For example, it proposes layouts that suit regional consumer needs. This makes it possible to propose layouts that suit regional characteristics by taking into account the store's location and surrounding environment.

[0076] The analysis unit can propose individually customized layouts based on the user's purchasing history and preferences. The analysis unit, for example, builds a system that proposes individually customized layouts based on the user's purchasing history. For example, it proposes a layout based on products purchased in the past. The analysis unit also proposes individually customized layouts based on the user's preference data. For example, it places products that match the user's preferences in prominent locations. The analysis unit also develops a system that comprehensively analyzes the purchasing history and preference data and proposes individually customized layouts. For example, it proposes a layout that matches the user's preferences. This makes it possible to propose individually customized layouts based on the user's purchasing history and preferences.

[0077] The analysis unit can change the design of the store's exterior or entrance to attract users' interest. The analysis unit, for example, builds a system that changes the exterior of a store to attract users' interest. For example, changing the exterior design attracts users' interest. The analysis unit also changes the design of the store's entrance to attract users' interest. For example, changing the entrance design attracts users' interest. The analysis unit also develops a system that manages the exterior and entrance designs in an integrated manner to attract users' interest. For example, combining the exterior and entrance designs to attract users' interest. In this way, changing the design of the store's exterior or entrance can attract users' interest.

[0078] The analysis unit can use the emotion estimation function to enhance a specific layout when a user expresses positive emotion toward the layout. For example, the analysis unit uses the emotion estimation function to build a system that enhances a specific layout when a user expresses positive emotion toward the layout. For example, the analysis unit maintains a layout that indicates positive emotion. Furthermore, the analysis unit enhances a specific layout when a user expresses positive emotion toward the specific layout based on the user's emotion data. For example, the analysis unit enlarges a layout that indicates positive emotion. Furthermore, the analysis unit uses the emotion estimation function to enhance a specific layout in real time when a user expresses positive emotion toward the specific layout. For example, the analysis unit adds a specific product to a layout that indicates positive emotion. In this way, by using the emotion estimation function, a layout can be enhanced when a user expresses positive emotion toward the specific layout.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The acquisition unit can provide individually customized recommendations based on the user's purchase history and membership information. For example, it can suggest products related to products purchased in the past. The acquisition unit can also preferentially display specific products based on the user's preferences and interests. For example, it can recommend products in a specific category based on membership information. The acquisition unit can also provide individually customized recommendations by combining the user's past purchase history and current behavior. For example, it can suggest new products related to products purchased in the past. This makes it possible to provide individually customized recommendations based on the user's purchase history and membership information.

[0081] The determination unit can estimate emotions from the user's facial expressions and body movements, and identify users who are highly motivated to purchase. For example, in-store cameras can analyze the user's facial expressions in real time to detect positive emotions such as smiling or surprise. This allows users with a high desire to purchase to be identified. The determination unit can also analyze the user's body movements to detect behavior that indicates interest in a product (for example, picking up a product or looking at it carefully). This allows users with a high desire to purchase to be identified. The determination unit can also use user gaze tracking technology to analyze the degree of gaze concentration on a specific product, and identify users with a high desire to purchase. This allows users with a high desire to purchase to be identified by analyzing the user's emotions.

[0082] The provision unit learns user behavior patterns over the long term and can predict with high accuracy whether specific behavior indicates purchase intent. For example, the user's behavior patterns are accumulated in a database over the long term, and the AI ​​predicts purchase intent based on that data. For example, if a specific behavior pattern is repeated, it is determined that the purchase intent is high. The provision unit also analyzes the user's behavior history and predicts with high accuracy whether specific behavior indicates purchase intent. For example, it analyzes the amount of time spent in front of a specific product shelf and how often the user picks up the product. The provision unit also develops an algorithm that uses the AI ​​to predict the user's purchase intent based on long-term behavioral data. For example, it compares past behavior patterns with current behavior and predicts purchase intent with high accuracy. In this way, by learning user behavior patterns over the long term, purchase intent can be predicted with high accuracy.

[0083] The acquisition unit analyzes in-store voice data and can determine that a user is considering purchasing a specific product when the user is talking about that product. For example, the acquisition unit analyzes voice data collected through a microphone in the store and determines that a user is highly motivated to purchase a specific product when the user is talking about that product. For example, it detects the product name and related keywords. The acquisition unit also uses voice recognition technology to analyze the content of the user's conversation in real time and detects statements that indicate a desire to purchase. For example, it makes a determination based on statements such as "Shall I buy this?" The acquisition unit also analyzes in-store voice data and determines that a user is highly motivated to purchase a specific product when the user expresses a positive opinion about that product. For example, it detects statements such as "This product looks good." In this way, by analyzing the voice data, it is possible to determine a user's desire to purchase a specific product when the user is talking about that product.

[0084] The providing unit can analyze user behavior in response to changes in temperature and lighting in the store, and consider the impact of environmental factors on purchase willingness. For example, the providing unit can use temperature sensors and lighting sensors in the store to monitor changes in environmental factors in real time and analyze user behavior. For example, it can analyze changes in user behavior when the temperature rises. The providing unit can also analyze user behavior in response to changes in lighting brightness and color temperature, and consider the impact on purchase willingness. For example, it can analyze purchase willingness under specific lighting conditions. The providing unit can also analyze user behavior patterns based on data on environmental factors, and consider the impact on purchase willingness. For example, it can analyze the impact of changes in temperature and lighting on purchase willingness. This makes it possible to consider the impact of changes in temperature and lighting on user purchase willingness.

[0085] The determination unit can use the emotion estimation function to determine that a user is a user considering purchasing if they show positive emotions toward a specific product. For example, the emotion estimation function can be used to determine that a user has a high willingness to purchase if they show positive emotions toward a specific product. For example, it can detect expressions of smiles or surprise. The determination unit can also analyze the user's facial expressions and body movements to determine that a user has a high willingness to purchase if they show positive emotions toward a specific product. For example, it can detect the action of picking up a product and looking at it carefully. The determination unit can also use the emotion estimation function to determine that a user has a high willingness to purchase if they show positive emotions toward a specific product. For example, it can use eye-tracking technology to analyze the degree of gaze concentration. In this way, the emotion estimation function can be used to determine that a user has a high willingness to purchase if they show positive emotions toward a specific product.

[0086] The provision unit can send notifications at the optimal timing, taking into account the salesperson's schedule and current work status. For example, it can manage the salesperson's schedule in real time and send notifications when there is less work. For example, it can send notifications immediately after the salesperson has finished other work. The provision unit can also monitor the salesperson's current work status and send notifications at the optimal timing. For example, it can send notifications immediately after the salesperson has finished dealing with another customer. The provision unit can also build a system that manages the salesperson's schedule and work status in an integrated manner and sends notifications at the optimal timing. For example, it can send notifications when the salesperson is not on a break. This makes it possible to send notifications at the optimal timing by taking into account the salesperson's schedule and work status.

[0087] The providing unit can include the user's past purchasing history and product information of interest in the notification content, allowing the salesperson to respond quickly. For example, the notification content can include the user's past purchasing history, allowing the salesperson to respond quickly. For example, information about products purchased in the past can be included in the notification. The providing unit can also include information about products the user is interested in in the notification content, allowing the salesperson to respond quickly. For example, information about the product the user is currently holding in their hand can be included in the notification. The providing unit can also integrate the user's past purchasing history and product information of interest in the user, allowing the salesperson to respond quickly. For example, the notification content can be customized based on the user's past purchasing history and current behavior. In this way, the notification content can include the user's past purchasing history and product information of interest, allowing the salesperson to respond quickly.

[0088] The provision unit can use in-store digital signage to notify not only sales staff but also users that they need support. For example, in-store digital signage can be used to notify users that they need support. For example, it can display a message such as, "If you need support, press this button." The provision unit can also use digital signage to display information about users who need support to sales staff. For example, it can display a message such as, "The user in front of shelf A needs support." The provision unit can also use digital signage to build a system that notifies both users and sales staff that they need support. For example, when a user presses a button requesting support, a notification is sent to the sales staff. In this way, by utilizing digital signage, it is possible to notify not only sales staff but also users that they need support.

[0089] The providing unit can use the emotion estimation function to send a notification when the user feels that they need support. For example, a system is constructed that uses the emotion estimation function to send a notification when the user feels that they need support. For example, the user's facial expressions and body movements are analyzed. The providing unit also sends a notification to a salesperson when support is needed based on the user's emotion data. For example, a notification is sent when a confused expression is detected. The providing unit also uses the emotion estimation function to send a notification in real time when the user feels that they need support. For example, the user's behavioral patterns are analyzed to identify the timing when support is needed. In this way, the emotion estimation function can be used to send a notification when the user feels that support is needed.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: Cameras capture user behavior in the store. For example, cameras can be installed in specific areas of the store to monitor user movements in real time and record them in high resolution. Step 2: The AI ​​analysis unit analyzes the user behavior captured by the camera. For example, it can use deep learning and image recognition technology to analyze user behavior patterns in detail and determine users who are considering purchasing. Step 3: The notification unit notifies the salesperson if the AI ​​analysis unit determines that the user is considering purchasing. For example, it can send a text message to the salesperson's smartphone, sound an alert on their tablet, or send a notification to their wearable device. Step 4: The analysis unit analyzes the user's time spent, location, and path captured by the camera. For example, it can analyze which areas users spend the most time in and which routes they take to move around the store, allowing it to optimize the store layout.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] In the robot 414, 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 robot 414 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.

[0137] 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.

[0138] 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.

[0139] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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. [Explanation of symbols]

[0159] 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 camera that captures user behavior through cameras inside the store; an AI analysis unit that analyzes the user's behavior acquired by the camera; a notification unit that notifies a salesperson when the AI ​​analysis unit determines that the user is a user considering purchasing; and an analysis unit that analyzes the user's stay time, location, and path acquired by the camera. A system characterized by:

2. The AI ​​analysis unit Estimate the user's emotions from their facial expressions and body movements to identify users who are highly motivated to purchase.

2. The system of claim 1.

3. The AI ​​analysis unit Refer to the user's past purchase history and membership information to make an individually customized judgment on the user considering a purchase 2. The system of claim 1.

4. The AI ​​analysis unit The behavioral patterns of the user are learned over the long term, and a highly accurate prediction is made as to whether a particular behavior indicates a purchase intent.

2. The system of claim 1.

5. The AI ​​analysis unit Analyzes in-store voice data, and if the user is talking about a specific product, determines that the user is considering purchasing.

2. The system of claim 1.

Images