System

Abstract

An object of a system according to an embodiment is to analyze user behavior data and optimize salesperson support and digital signage display.SOLUTION: A system includes an analysis unit, a notification unit, and a display unit. The analysis unit analyzes an action of a user. The notification unit transmits a notification to the salesperson based on the information analyzed by the analysis unit. The display unit displays the appeal article on the digital signage based on the information obtained by the analysis unit. The analysis unit analyzes at least one of a stay time, a position, and a guide line of the user.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 technologies have not yet effectively utilized user behavior data to optimize sales staff support and digital signage displays, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze user behavior data and optimize sales staff support and digital signage display. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a notification unit, and a display unit. The analysis unit analyzes user behavior. The notification unit sends a notification to a salesperson based on the information analyzed by the analysis unit. The display unit displays an appeal on a digital signage based on the information obtained by the analysis unit. The analysis unit analyzes at least one of the user's stay time, location, and path. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user behavior data and optimize sales staff support and digital signage display. [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) A system according to an embodiment of the present invention provides smart support by using AI to identify users considering a purchase through in-store cameras and notifying sales staff if support is needed. This system can automatically display promotional materials tailored to each customer through digital signage. Furthermore, AI analyzes users' time spent in stores, their locations, and their shopping paths, enabling promotional materials, displays, and layouts tailored to the characteristics of each store. For example, the system analyzes users' behavioral patterns and facial expressions through in-store cameras to identify users with a high desire to purchase. If the AI ​​determines that support is needed, it notifies a sales staff member. Furthermore, digital signage can automatically display promotional materials tailored to each customer. For example, the system can promote the latest fashion items to younger customers and health-related products to senior customers. The system can also analyze users' time spent in stores, their locations, and their shopping paths, and design promotional materials, displays, and layouts tailored to the characteristics of each store. This allows the system to improve store operations efficiency and customer satisfaction. This allows users to quickly find the products they want, increasing their purchasing motivation. In addition, the provision of appropriate support from sales staff will improve the user's purchasing experience, and effective marketing using digital signage can be expected to increase sales.

[0029] A smart support system according to an embodiment includes an analysis unit, a notification unit, a display unit, and the analysis unit. The analysis unit analyzes user behavior. User behavior includes, but is not limited to, purchasing behavior, browsing behavior, and traveling behavior. For example, the analysis unit analyzes whether the user is staring at a product for a long time or reaching for the product. The analysis unit can also analyze the user's facial expression to determine whether the user is highly motivated to purchase. For example, the analysis unit can use facial recognition technology to analyze the user's facial expression and determine whether the user is interested. The notification unit sends a notification to a salesperson based on the information analyzed by the analysis unit. Examples of notifications include, but are not limited to, text messages, alert sounds, and push notifications. The notification unit sends a notification to, for example, the salesperson's smartphone or tablet. The notification unit can also adjust the timing of the notification so that the salesperson can approach the user at an appropriate time. For example, the notification unit sends a notification the moment the user picks up a product. The display unit displays an appeal on a digital signage based on the information obtained by the analysis unit. Examples of appeals include, but are not limited to, advertisements, promotional videos, and product information. For example, the display unit appeals to younger customers by promoting the latest fashion items and to senior customers by promoting health-related products. The display unit also analyzes at least one of the user's stay time, location, and path. For example, the display unit analyzes which area the user spends the most time in and which route the user takes to move around the store. As a result, the smart support system according to the embodiment analyzes user behavior, sends notifications to sales staff, and displays appeals on digital signage, enabling smart support and effective marketing.

[0030] The smart support system includes a face recognition unit that uses face recognition technology or motion analysis technology. The face recognition unit analyzes a user's behavior using face recognition technology or motion analysis technology. Examples of face recognition technology include, but are not limited to, deep learning and pattern matching. For example, the face recognition unit recognizes a user's face and analyzes their behavior using deep learning. The face recognition unit can also recognize a user's face and analyze their behavior using pattern matching. Examples of motion analysis technology include, but are not limited to, motion capture and gesture recognition. For example, the face recognition unit analyzes a user's behavior using motion capture. The face recognition unit can also analyze a user's behavior using gesture recognition. This allows for more accurate analysis of a user's behavior by using face recognition technology or motion analysis technology.

[0031] The smart support system includes a notification sending unit that sends notifications to the salesperson's smartphone or tablet. The notification sending unit sends notifications to the salesperson's smartphone or tablet. Notification sending includes, but is not limited to, real-time sending and batch sending, for example. The notification sending unit sends notifications to the salesperson's smartphone in real time, for example. The notification sending unit can also send notifications at regular intervals using batch processing. This allows for a quick response by sending notifications to the salesperson's smartphone or tablet.

[0032] The smart support system includes an age / gender estimation unit that estimates the age or gender of a customer. The age / gender estimation unit estimates the age or gender of a customer. Examples of age estimation include, but are not limited to, estimation using facial recognition or behavioral data. The age / gender estimation unit estimates the age of a customer using, for example, facial recognition technology. The age / gender estimation unit can also estimate the age of a customer by analyzing behavioral data. Examples of gender estimation include, but are not limited to, estimation using facial recognition or behavioral data. The age / gender estimation unit estimates the gender of a customer using, for example, facial recognition technology. The age / gender estimation unit can also estimate the gender of a customer by analyzing behavioral data. This enables more effective marketing by estimating the age and gender of a customer.

[0033] The smart support system includes a motion tracking unit that tracks a user's motion using two or more cameras. The motion tracking unit tracks a user's motion using two or more cameras. Motion tracking includes, but is not limited to, camera placement, tracking algorithms, and the like. For example, the motion tracking unit places multiple cameras in a store and tracks the user's motion. The motion tracking unit can also analyze the user's motion using a tracking algorithm. As a result, the use of multiple cameras allows for more accurate tracking of the user's motion.

[0034] The analysis unit can optimize the analysis algorithm based on the user's past behavior data. For example, the analysis unit refers to which areas the user spent the most time in in the past and analyzes the user's current behavior. The analysis unit can also refer to what products the user has picked up in the past and analyze the user's current behavior. The analysis unit can also refer to what routes the user has taken in the store in the past and analyze the user's current behavior. In this way, the analysis algorithm can be optimized by referring to the user's past behavior data.

[0035] During analysis, the analysis unit can identify behavioral patterns based on the user's purchasing history. The analysis unit can identify current behavioral patterns based on, for example, the history of products purchased by the user in the past. The analysis unit can also identify current behavioral patterns based on the categories of products purchased by the user in the past. The analysis unit can also identify current behavioral patterns based on the price ranges of products purchased by the user in the past. This makes it possible to more accurately identify behavioral patterns by taking the user's purchasing history into consideration.

[0036] During the analysis, the analysis unit can incorporate the user's movement speed or gaze direction into the analysis. For example, the analysis unit can analyze the user's movement speed to help identify products in which the user is interested. The analysis unit can also analyze the user's gaze direction to help identify products in which the user is paying attention. The analysis unit can also combine the user's movement speed and gaze direction to identify behavioral patterns with higher accuracy. In this way, by incorporating the user's movement speed and gaze direction into the analysis, behavioral patterns can be identified more accurately.

[0037] During analysis, the analysis unit can identify behavioral patterns based on the user's geographical location information. For example, the analysis unit can identify behavioral patterns based on the area the user is in. The analysis unit can also identify behavioral patterns based on the store the user is in. The analysis unit can also identify behavioral patterns based on the floor the user is on. This allows for more accurate identification of behavioral patterns by taking the user's geographical location information into consideration.

[0038] During the analysis, the analysis unit can analyze the user's social media activity and identify behavioral patterns. For example, the analysis unit can identify behavioral patterns based on the locations where the user checks in on social media. The analysis unit can also identify behavioral patterns based on the content of the user's posts on social media. The analysis unit can also identify behavioral patterns based on the activities of the user's friends on social media. This makes it possible to more accurately identify behavioral patterns by analyzing the user's social media activity.

[0039] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. The analysis unit customizes the analysis algorithm, for example, based on feedback provided by the user in the past. The analysis unit can also customize the analysis algorithm based on the content of feedback provided by the user in the past. The analysis unit can also customize the analysis algorithm based on the frequency of feedback provided by the user in the past. In this way, the analysis algorithm can be customized by reflecting the user's past feedback.

[0040] At the time of notification, the notification unit can determine the optimal notification content based on the salesperson's past response history. The notification unit determines the optimal notification content based on, for example, the history of users with whom the salesperson has previously responded. The notification unit can also determine the optimal notification content based on support content that the salesperson has previously provided. The notification unit can also determine the optimal notification content based on feedback that the salesperson has previously received. In this way, the optimal notification content can be determined by referring to the salesperson's past response history.

[0041] When sending a notification, the notification unit can adjust the timing of the notification based on the user's current behavioral status. For example, the notification unit sends the notification the moment the user picks up the product. The notification unit can also send the notification while the user is looking at the product. The notification unit can also send the notification immediately after the user puts the product back. In this way, by adjusting the timing of the notification based on the user's current behavioral status, the notification can be sent at a more appropriate time.

[0042] The notification unit can determine the priority of notifications based on the salesperson's current work status when sending notifications. For example, if the salesperson is busy, the notification unit will prioritize sending only important notifications. Also, if the salesperson is available, the notification unit can send all notifications. Also, if the salesperson is performing a specific task, the notification unit can prioritize sending notifications related to that task. This allows important notifications to be sent with priority, taking into account the salesperson's current work status.

[0043] The notification unit can select the optimal notification method based on the geographical location information of the salesperson when sending a notification. For example, the notification unit can send a voice notification if the salesperson is nearby. The notification unit can also send a text notification if the salesperson is far away. The notification unit can also send a notification related to a specific area if the salesperson is in that area. This allows the optimal notification method to be selected by taking the geographical location information of the salesperson into consideration.

[0044] The notification unit can analyze the social media activity of the salesperson and provide relevant notification content at the time of notification. For example, the notification unit can provide a notification regarding a location where the salesperson has checked in on social media. The notification unit can also provide relevant notification content based on the content posted by the salesperson on social media. The notification unit can also provide relevant notification content based on the activities of the salesperson's friends on social media. In this way, relevant notification content can be provided by analyzing the social media activity of the salesperson.

[0045] The notification unit can customize the notification content based on the salesperson's past feedback when sending a notification. The notification unit customizes the notification content based on, for example, feedback provided by the salesperson in the past. The notification unit can also customize the notification content based on feedback received by the salesperson in the past. The notification unit can also customize the notification content based on support provided by the salesperson in the past. In this way, the notification content can be customized by reflecting the salesperson's past feedback.

[0046] The display unit can determine optimal display content based on the user's past browsing history when displaying. The display unit can determine optimal display content based on, for example, the history of products previously browsed by the user. The display unit can also determine optimal display content based on the category of products previously browsed by the user. The display unit can also determine optimal display content based on the price range of products previously browsed by the user. This makes it possible to determine optimal display content by referring to the user's past browsing history.

[0047] The display unit can adjust the timing of display based on the user's current behavioral status when displaying information. For example, the display unit changes the display content the moment the user picks up a product. The display unit can also change the display content while the user is looking at the product. The display unit can also change the display content immediately after the user puts the product back. In this way, by adjusting the display timing based on the user's current behavioral status, information can be provided at a more appropriate time.

[0048] The display unit can optimize the display method based on the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking the user's device information into consideration.

[0049] The display unit can select optimal display content based on the user's geographical location information when displaying. For example, when the user is in a specific area, the display unit displays information related to that area. Furthermore, when the user is in a specific store, the display unit can also display information related to that store. Furthermore, when the user is on a specific floor, the display unit can also display information related to that floor. In this way, optimal display content can be selected by taking the user's geographical location information into consideration.

[0050] The display unit can analyze the user's social media activity and provide related display content when displaying the information. For example, the display unit displays information about places where the user has checked in on social media. The display unit can also display related information based on the content posted by the user on social media. The display unit can also display related information based on the activities of the user's friends on social media. In this way, related display content can be provided by analyzing the user's social media activity.

[0051] The display unit can customize the display content based on the user's past feedback when displaying the content. The display unit customizes the display content based on, for example, feedback provided by the user in the past. The display unit can also customize the display content based on the content of feedback provided by the user in the past. The display unit can also customize the display content based on the frequency of feedback provided by the user in the past. In this way, the display content can be customized by reflecting the user's past feedback.

[0052] The face recognition unit can optimize the recognition algorithm based on the user's past face data during face recognition. For example, the face recognition unit optimizes the current face recognition algorithm based on the user's past face data. The face recognition unit can also optimize the current face recognition algorithm based on the user's past facial expression data. The face recognition unit can also optimize the current face recognition algorithm based on the user's past facial features. This allows the recognition algorithm to be optimized by referring to the user's past face data.

[0053] The face recognition unit can incorporate changes in a user's facial expression into the analysis when recognizing a face. For example, the face recognition unit analyzes changes in a user's facial expression to identify changes in emotions. The face recognition unit can also analyze changes in a user's facial expression to help identify products in which the user is interested. The face recognition unit can also analyze changes in a user's facial expression to identify increased purchasing intent. In this way, by incorporating changes in a user's facial expression into the analysis, more accurate face recognition becomes possible.

[0054] The face recognition unit can incorporate the user's movement speed or gaze direction into the analysis when recognizing a face. For example, the face recognition unit can analyze the user's movement speed to help identify products in which the user is interested. The face recognition unit can also analyze the user's gaze direction to help identify products in which the user is paying attention. The face recognition unit can also combine the user's movement speed and gaze direction to perform more accurate face recognition. In this way, by incorporating the user's movement speed and gaze direction into the analysis, more accurate face recognition is possible.

[0055] The face recognition unit can improve the recognition accuracy based on the user's geographical location information during face recognition. For example, when the user is in a specific area, the face recognition unit uses face recognition data related to that area. Furthermore, when the user is in a specific store, the face recognition unit can also use face recognition data related to that store. Furthermore, when the user is on a specific floor, the face recognition unit can also use face recognition data related to that floor. In this way, recognition accuracy can be improved by taking the user's geographical location information into consideration.

[0056] The face recognition unit can analyze the user's social media activity during face recognition to improve recognition accuracy. For example, the face recognition unit can improve recognition accuracy by using data related to the locations where the user has checked in on social media. The face recognition unit can also improve recognition accuracy based on the content of the user's social media posts. The face recognition unit can also improve recognition accuracy based on the activities of the user's friends on social media. In this way, recognition accuracy can be improved by analyzing the user's social media activity.

[0057] The face recognition unit can customize the recognition algorithm by reflecting the user's past feedback during face recognition. The face recognition unit customizes the recognition algorithm based on, for example, feedback provided by the user in the past. The face recognition unit can also customize the recognition algorithm based on the content of feedback provided by the user in the past. The face recognition unit can also customize the recognition algorithm based on the frequency of feedback provided by the user in the past. In this way, the recognition algorithm can be customized by reflecting the user's past feedback.

[0058] When sending a notification, the notification sending unit can determine the optimal sending method based on the salesperson's past response history. The notification sending unit determines the optimal sending method, for example, based on the history of users with whom the salesperson has previously responded. The notification sending unit can also determine the optimal sending method based on the support content that the salesperson has previously provided. The notification sending unit can also determine the optimal sending method based on feedback that the salesperson has previously received. In this way, the optimal sending method can be determined by referring to the salesperson's past response history.

[0059] When sending notifications, the notification sending unit can determine the priority of sending notifications by taking into account the current work status of the salesperson. For example, if the salesperson is busy, the notification sending unit can prioritize sending only important notifications. Also, if the salesperson is available, the notification sending unit can send all notifications. Also, if the salesperson is performing a specific task, the notification sending unit can prioritize sending notifications related to that task. In this way, important notifications can be sent with priority by taking into account the current work status of the salesperson.

[0060] When sending a notification, the notification sending unit can select the optimal sending method based on the geographical location information of the salesperson. For example, if the salesperson is nearby, the notification sending unit can send a voice notification. If the salesperson is far away, the notification sending unit can also send a text notification. If the salesperson is in a specific area, the notification sending unit can also send a notification related to that area. In this way, the optimal sending method can be selected by taking the geographical location information of the salesperson into consideration.

[0061] The notification sending unit can analyze the social media activity of the salesperson and provide relevant notification content when sending a notification. For example, the notification sending unit can provide a notification regarding a location where the salesperson has checked in on social media. The notification sending unit can also provide relevant notification content based on the content posted by the salesperson on social media. The notification sending unit can also provide relevant notification content based on the activities of the salesperson's friends on social media. In this way, relevant notification content can be provided by analyzing the social media activity of the salesperson.

[0062] The age and gender estimation unit can optimize the estimation algorithm based on the user's past data when estimating the age and gender. The age and gender estimation unit optimizes the current estimation algorithm based on the user's past age data, for example. The age and gender estimation unit can also optimize the current estimation algorithm based on the user's past gender data. The age and gender estimation unit can also optimize the current estimation algorithm by combining the user's past age and gender data. This allows the estimation algorithm to be optimized by referring to the user's past data.

[0063] The age and gender estimation unit can incorporate changes in the user's facial expression into the analysis when estimating the age and gender. For example, the age and gender estimation unit analyzes changes in the user's facial expression to help estimate the age and gender. The age and gender estimation unit can also analyze changes in the user's facial expression to identify changes in emotion and use these to help estimate the age and gender. The age and gender estimation unit can also analyze changes in the user's facial expression to identify an increase in purchasing intent and use these to help estimate the age and gender. In this way, by incorporating changes in the user's facial expression into the analysis, more accurate estimation is possible.

[0064] The age / gender estimation unit can improve estimation accuracy by taking into account the user's geographical location information when estimating the user's age and gender. For example, when the user is in a specific area, the age / gender estimation unit can improve estimation accuracy by using age / gender data related to the area. Furthermore, when the user is in a specific store, the age / gender estimation unit can improve estimation accuracy by using age / gender data related to the store. Furthermore, when the user is on a specific floor, the age / gender estimation unit can improve estimation accuracy by using age / gender data related to the floor. In this way, estimation accuracy can be improved by taking into account the user's geographical location information.

[0065] The age / gender estimation unit can improve the accuracy of the age / gender estimation by analyzing the user's social media activity. For example, the age / gender estimation unit can improve the accuracy of the estimation by using data on the location where the user checked in on social media. The age / gender estimation unit can also improve the accuracy of the estimation based on the content of the user's posts on social media. The age / gender estimation unit can also improve the accuracy of the estimation based on the activities of the user's friends on social media. In this way, the estimation accuracy can be improved by analyzing the user's social media activity.

[0066] The motion tracking unit can optimize the tracking algorithm based on the user's past motion data when tracking the motion. The motion tracking unit optimizes the current tracking algorithm based on the user's past motion data, for example. The motion tracking unit can also optimize the current tracking algorithm based on the user's past motion patterns. The motion tracking unit can also optimize the current tracking algorithm based on the user's past motion speeds. In this way, the tracking algorithm can be optimized by referring to the user's past motion data.

[0067] The motion tracking unit can incorporate the user's motion speed and gaze direction into the analysis when tracking the motion. For example, the motion tracking unit analyzes the user's motion speed to help identify products in which the user is interested. The motion tracking unit can also analyze the user's gaze direction to help identify products in which the user is paying attention. The motion tracking unit can also combine the user's motion speed and gaze direction to perform more accurate motion tracking. In this way, by incorporating the user's motion speed and gaze direction into the analysis, more accurate motion tracking is possible.

[0068] The motion tracking unit can improve tracking accuracy by taking into account the user's geographical location information when tracking motion. For example, when the user is in a specific area, the motion tracking unit can improve tracking accuracy by using motion data related to the area. Also, when the user is in a specific store, the motion tracking unit can improve tracking accuracy by using motion data related to the store. Also, when the user is on a specific floor, the motion tracking unit can improve tracking accuracy by using motion data related to the floor. In this way, tracking accuracy can be improved by taking into account the user's geographical location information.

[0069] The motion tracking unit can analyze the user's social media activities during motion tracking to improve tracking accuracy. For example, the motion tracking unit can improve tracking accuracy by using data on locations where the user has checked in on social media. The motion tracking unit can also improve tracking accuracy based on the content of the user's social media posts. The motion tracking unit can also improve tracking accuracy based on the activities of the user's friends on social media. In this way, tracking accuracy can be improved by analyzing the user's social media activities.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The smart support system may include a recommendation unit that recommends specific products based on the user's purchasing history. For example, the recommendation unit may recommend related products based on the category of products the user has previously purchased. The recommendation unit may also recommend products that fit the user's budget based on the price range of products the user has previously purchased. Furthermore, the recommendation unit may also recommend new products of the same brand based on the brand of products the user has previously purchased. This makes it possible to utilize the user's purchasing history to provide more personalized product recommendations.

[0072] The smart support system may include a social analysis unit that analyzes a user's social media activity and recommends related products. For example, the social analysis unit may recommend related products based on products that the user has "liked" on social media. The social analysis unit may also recommend similar products based on products purchased by the user's friends. Furthermore, the social analysis unit may also recommend products from brands that the user follows on social media. This makes it possible to utilize the user's social media activity to make more relevant product recommendations.

[0073] The smart support system may include a feedback reflecting unit that customizes a product recommendation algorithm by reflecting a user's past feedback. The feedback reflecting unit customizes the recommendation algorithm based on, for example, feedback provided by the user in the past. The feedback reflecting unit can also customize the recommendation algorithm based on the content of feedback provided by the user in the past. Furthermore, the feedback reflecting unit can also customize the recommendation algorithm based on the frequency of feedback provided by the user in the past. This enables more accurate product recommendations by reflecting a user's past feedback.

[0074] The smart support system can include a location information recommendation unit that recommends products related to a specific store or area based on the user's geographic location information. For example, if the user is in a specific store, the location information recommendation unit can recommend products that are popular in that store. Also, if the user is in a specific area, the location information recommendation unit can recommend products that are particularly in demand in that area. Furthermore, if the user is on a specific floor, the location information recommendation unit can recommend products that are particularly popular on that floor. This makes it possible to utilize the user's geographic location information to make more appropriate product recommendations.

[0075] The smart support system may include a time slot recommendation unit that recommends specific products for specific time slots based on the user's purchasing history and behavioral patterns. The time slot recommendation unit, for example, recommends products related to the same time slot based on the history of products the user previously purchased during that time slot. The time slot recommendation unit may also recommend products that are popular in a specific area based on the user's tendency to stay in that area during a specific time slot. Furthermore, the time slot recommendation unit may also recommend products related to a user's tendency to engage in specific behavior during a specific time slot. This enables more appropriate product recommendations to be made based on the user's behavioral patterns.

[0076] The smart support system may include an event recommendation unit that recommends specific events or campaigns based on the user's past behavioral data. The event recommendation unit, for example, recommends related events based on the user's history of events that he or she has previously attended. The event recommendation unit may also recommend campaigns related to a product category based on a product category in which the user has previously shown interest. Furthermore, the event recommendation unit may also recommend events held at a store based on the user's history of visiting that store. This makes it possible to recommend more appropriate events and campaigns by utilizing the user's past behavioral data.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The analysis unit analyzes the user's behavior. User behavior includes purchasing behavior, browsing behavior, and movement behavior. The analysis unit analyzes whether the user is staring at a product for a long time or reaching out for the product. The analysis unit also analyzes the user's facial expressions to determine whether they are interested in purchasing. For example, facial recognition technology can be used to analyze the user's facial expressions and determine whether they are interested. Step 2: The notification unit sends a notification to the salesperson based on the information analyzed by the analysis unit. Notifications include text messages, alert sounds, and push notifications. The notification unit sends notifications to the salesperson's smartphone or tablet and adjusts the timing of the notifications so that the salesperson can approach the user at the appropriate time. For example, the notification is sent the moment the user picks up a product. Step 3: The display unit displays appealing materials on the digital signage based on the information obtained by the analysis unit. Appealing materials include advertisements, promotional videos, product information, etc. The display unit appeals to younger customers by promoting the latest fashion items, and to senior customers by promoting health-related products. The display unit also analyzes at least one of the user's length of stay, location, and path of movement. For example, it analyzes which areas the user spends the most time in and which route they take to move around the store.

[0079] (Example 2) A system according to an embodiment of the present invention provides smart support by using AI to identify users considering a purchase through in-store cameras and notifying sales staff if support is needed. This system can automatically display promotional materials tailored to each customer through digital signage. Furthermore, AI analyzes users' time spent in stores, their locations, and their shopping paths, enabling promotional materials, displays, and layouts tailored to the characteristics of each store. For example, the system analyzes users' behavioral patterns and facial expressions through in-store cameras to identify users with a high desire to purchase. If the AI ​​determines that support is needed, it notifies a sales staff member. Furthermore, digital signage can automatically display promotional materials tailored to each customer. For example, the system can promote the latest fashion items to younger customers and health-related products to senior customers. The system can also analyze users' time spent in stores, their locations, and their shopping paths, and design promotional materials, displays, and layouts tailored to the characteristics of each store. This allows the system to improve store operations efficiency and customer satisfaction. This allows users to quickly find the products they want, increasing their purchasing motivation. In addition, the provision of appropriate support from sales staff will improve the user's purchasing experience, and effective marketing using digital signage can be expected to increase sales.

[0080] A smart support system according to an embodiment includes an analysis unit, a notification unit, a display unit, and the analysis unit. The analysis unit analyzes user behavior. User behavior includes, but is not limited to, purchasing behavior, browsing behavior, and traveling behavior. For example, the analysis unit analyzes whether the user is staring at a product for a long time or reaching for the product. The analysis unit can also analyze the user's facial expression to determine whether the user is highly motivated to purchase. For example, the analysis unit can use facial recognition technology to analyze the user's facial expression and determine whether the user is interested. The notification unit sends a notification to a salesperson based on the information analyzed by the analysis unit. Examples of notifications include, but are not limited to, text messages, alert sounds, and push notifications. The notification unit sends a notification to, for example, the salesperson's smartphone or tablet. The notification unit can also adjust the timing of the notification so that the salesperson can approach the user at an appropriate time. For example, the notification unit sends a notification the moment the user picks up a product. The display unit displays an appeal on a digital signage based on the information obtained by the analysis unit. Examples of appeals include, but are not limited to, advertisements, promotional videos, and product information. For example, the display unit appeals to younger customers by promoting the latest fashion items and to senior customers by promoting health-related products. The display unit also analyzes at least one of the user's stay time, location, and path. For example, the display unit analyzes which area the user spends the most time in and which route the user takes to move around the store. As a result, the smart support system according to the embodiment analyzes user behavior, sends notifications to sales staff, and displays appeals on digital signage, enabling smart support and effective marketing.

[0081] The smart support system includes a face recognition unit that uses face recognition technology or motion analysis technology. The face recognition unit analyzes a user's behavior using face recognition technology or motion analysis technology. Examples of face recognition technology include, but are not limited to, deep learning and pattern matching. For example, the face recognition unit recognizes a user's face and analyzes their behavior using deep learning. The face recognition unit can also recognize a user's face and analyze their behavior using pattern matching. Examples of motion analysis technology include, but are not limited to, motion capture and gesture recognition. For example, the face recognition unit analyzes a user's behavior using motion capture. The face recognition unit can also analyze a user's behavior using gesture recognition. This allows for more accurate analysis of a user's behavior by using face recognition technology or motion analysis technology.

[0082] The smart support system includes a notification sending unit that sends notifications to the salesperson's smartphone or tablet. The notification sending unit sends notifications to the salesperson's smartphone or tablet. Notification sending includes, but is not limited to, real-time sending and batch sending, for example. The notification sending unit sends notifications to the salesperson's smartphone in real time, for example. The notification sending unit can also send notifications at regular intervals using batch processing. This allows for a quick response by sending notifications to the salesperson's smartphone or tablet.

[0083] The smart support system includes an age / gender estimation unit that estimates the age or gender of a customer. The age / gender estimation unit estimates the age or gender of a customer. Examples of age estimation include, but are not limited to, estimation using facial recognition or behavioral data. The age / gender estimation unit estimates the age of a customer using, for example, facial recognition technology. The age / gender estimation unit can also estimate the age of a customer by analyzing behavioral data. Examples of gender estimation include, but are not limited to, estimation using facial recognition or behavioral data. The age / gender estimation unit estimates the gender of a customer using, for example, facial recognition technology. The age / gender estimation unit can also estimate the gender of a customer by analyzing behavioral data. This enables more effective marketing by estimating the age and gender of a customer.

[0084] The smart support system includes a motion tracking unit that tracks a user's motion using two or more cameras. The motion tracking unit tracks a user's motion using two or more cameras. Motion tracking includes, but is not limited to, camera placement, tracking algorithms, and the like. For example, the motion tracking unit places multiple cameras in a store and tracks the user's motion. The motion tracking unit can also analyze the user's motion using a tracking algorithm. As a result, the use of multiple cameras allows for more accurate tracking of the user's motion.

[0085] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, when the user is excited, the analysis unit increases the accuracy of the analysis to identify a detailed behavioral pattern. Furthermore, when the user is relaxed, the analysis unit can adjust the accuracy of the analysis to identify a general behavioral pattern. Furthermore, when the user is stressed, the analysis unit can decrease the accuracy of the analysis to identify a simple behavioral pattern. This allows for more accurate analysis by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] The analysis unit can optimize the analysis algorithm based on the user's past behavior data. For example, the analysis unit refers to which areas the user spent the most time in in the past and analyzes the user's current behavior. The analysis unit can also refer to what products the user has picked up in the past and analyze the user's current behavior. The analysis unit can also refer to what routes the user has taken in the store in the past and analyze the user's current behavior. In this way, the analysis algorithm can be optimized by referring to the user's past behavior data.

[0087] During analysis, the analysis unit can identify behavioral patterns based on the user's purchasing history. The analysis unit can identify current behavioral patterns based on, for example, the history of products purchased by the user in the past. The analysis unit can also identify current behavioral patterns based on the categories of products purchased by the user in the past. The analysis unit can also identify current behavioral patterns based on the price ranges of products purchased by the user in the past. This makes it possible to more accurately identify behavioral patterns by taking the user's purchasing history into consideration.

[0088] During the analysis, the analysis unit can incorporate the user's movement speed or gaze direction into the analysis. For example, the analysis unit can analyze the user's movement speed to help identify products in which the user is interested. The analysis unit can also analyze the user's gaze direction to help identify products in which the user is paying attention. The analysis unit can also combine the user's movement speed and gaze direction to identify behavioral patterns with higher accuracy. In this way, by incorporating the user's movement speed and gaze direction into the analysis, behavioral patterns can be identified more accurately.

[0089] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize displaying important analysis results. Furthermore, if the user is relaxed, the analysis unit can also prioritize displaying general analysis results. Furthermore, if the user is stressed, the analysis unit can also prioritize displaying simple analysis results. In this way, by determining the priority of analysis results based on the user's emotions, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] During analysis, the analysis unit can identify behavioral patterns based on the user's geographical location information. For example, the analysis unit can identify behavioral patterns based on the area the user is in. The analysis unit can also identify behavioral patterns based on the store the user is in. The analysis unit can also identify behavioral patterns based on the floor the user is on. This allows for more accurate identification of behavioral patterns by taking the user's geographical location information into consideration.

[0091] During the analysis, the analysis unit can analyze the user's social media activity and identify behavioral patterns. For example, the analysis unit can identify behavioral patterns based on the locations where the user checks in on social media. The analysis unit can also identify behavioral patterns based on the content of the user's posts on social media. The analysis unit can also identify behavioral patterns based on the activities of the user's friends on social media. This makes it possible to more accurately identify behavioral patterns by analyzing the user's social media activity.

[0092] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. The analysis unit customizes the analysis algorithm, for example, based on feedback provided by the user in the past. The analysis unit can also customize the analysis algorithm based on the content of feedback provided by the user in the past. The analysis unit can also customize the analysis algorithm based on the frequency of feedback provided by the user in the past. In this way, the analysis algorithm can be customized by reflecting the user's past feedback.

[0093] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. For example, if the user is excited, the notification unit can notify the user of detailed support content. If the user is relaxed, the notification unit can also notify the user of general support content. If the user is stressed, the notification unit can also notify the user of simple support content. By adjusting the content of the notification based on the user's emotions, more appropriate support can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] At the time of notification, the notification unit can determine the optimal notification content based on the salesperson's past response history. The notification unit determines the optimal notification content based on, for example, the history of users with whom the salesperson has previously responded. The notification unit can also determine the optimal notification content based on support content that the salesperson has previously provided. The notification unit can also determine the optimal notification content based on feedback that the salesperson has previously received. In this way, the optimal notification content can be determined by referring to the salesperson's past response history.

[0095] When sending a notification, the notification unit can adjust the timing of the notification based on the user's current behavioral status. For example, the notification unit sends the notification the moment the user picks up the product. The notification unit can also send the notification while the user is looking at the product. The notification unit can also send the notification immediately after the user puts the product back. In this way, by adjusting the timing of the notification based on the user's current behavioral status, the notification can be sent at a more appropriate time.

[0096] The notification unit can determine the priority of notifications based on the salesperson's current work status when sending notifications. For example, if the salesperson is busy, the notification unit will prioritize sending only important notifications. Also, if the salesperson is available, the notification unit can send all notifications. Also, if the salesperson is performing a specific task, the notification unit can prioritize sending notifications related to that task. This allows important notifications to be sent with priority, taking into account the salesperson's current work status.

[0097] The notification unit can estimate the user's emotion and adjust the urgency of the notification based on the estimated user's emotion. For example, if the user is excited, the notification unit can send a notification with a high level of urgency. If the user is relaxed, the notification unit can also send a notification with a low level of urgency. If the user is stressed, the notification unit can also send a notification with a medium level of urgency. This allows for a more appropriate response by adjusting the urgency of the notification based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] The notification unit can select the optimal notification method based on the geographical location information of the salesperson when sending a notification. For example, the notification unit can send a voice notification if the salesperson is nearby. The notification unit can also send a text notification if the salesperson is far away. The notification unit can also send a notification related to a specific area if the salesperson is in that area. This allows the optimal notification method to be selected by taking the geographical location information of the salesperson into consideration.

[0099] The notification unit can analyze the social media activity of the salesperson and provide relevant notification content at the time of notification. For example, the notification unit can provide a notification regarding a location where the salesperson has checked in on social media. The notification unit can also provide relevant notification content based on the content posted by the salesperson on social media. The notification unit can also provide relevant notification content based on the activities of the salesperson's friends on social media. In this way, relevant notification content can be provided by analyzing the social media activity of the salesperson.

[0100] The notification unit can customize the notification content based on the salesperson's past feedback when sending a notification. The notification unit customizes the notification content based on, for example, feedback provided by the salesperson in the past. The notification unit can also customize the notification content based on feedback received by the salesperson in the past. The notification unit can also customize the notification content based on support provided by the salesperson in the past. In this way, the notification content can be customized by reflecting the salesperson's past feedback.

[0101] The display unit can estimate the user's emotions and adjust the display content based on the estimated user's emotions. For example, if the user is excited, the display unit can provide visually stimulating display content. Furthermore, if the user is relaxed, the display unit can provide calming display content. Furthermore, if the user is stressed, the display unit can provide simple display content. This allows for more effective appeal by adjusting the display content based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] The display unit can determine optimal display content based on the user's past browsing history when displaying. The display unit can determine optimal display content based on, for example, the history of products previously browsed by the user. The display unit can also determine optimal display content based on the category of products previously browsed by the user. The display unit can also determine optimal display content based on the price range of products previously browsed by the user. This makes it possible to determine optimal display content by referring to the user's past browsing history.

[0103] The display unit can adjust the timing of display based on the user's current behavioral status when displaying information. For example, the display unit changes the display content the moment the user picks up a product. The display unit can also change the display content while the user is looking at the product. The display unit can also change the display content immediately after the user puts the product back. In this way, by adjusting the display timing based on the user's current behavioral status, information can be provided at a more appropriate time.

[0104] The display unit can optimize the display method based on the user's device information when displaying. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking the user's device information into consideration.

[0105] The display unit can estimate the user's emotions and determine display priorities based on the estimated user emotions. For example, when the user is excited, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can prioritize displaying general information. Furthermore, when the user is stressed, the display unit can prioritize displaying simple information. In this way, by determining the display priorities based on the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0106] The display unit can select optimal display content based on the user's geographical location information when displaying. For example, when the user is in a specific area, the display unit displays information related to that area. Furthermore, when the user is in a specific store, the display unit can also display information related to that store. Furthermore, when the user is on a specific floor, the display unit can also display information related to that floor. In this way, optimal display content can be selected by taking the user's geographical location information into consideration.

[0107] The display unit can analyze the user's social media activity and provide related display content when displaying the information. For example, the display unit displays information about places where the user has checked in on social media. The display unit can also display related information based on the content posted by the user on social media. The display unit can also display related information based on the activities of the user's friends on social media. In this way, related display content can be provided by analyzing the user's social media activity.

[0108] The display unit can customize the display content based on the user's past feedback when displaying the content. The display unit customizes the display content based on, for example, feedback provided by the user in the past. The display unit can also customize the display content based on the content of feedback provided by the user in the past. The display unit can also customize the display content based on the frequency of feedback provided by the user in the past. In this way, the display content can be customized by reflecting the user's past feedback.

[0109] The face recognition unit can estimate the user's emotions and adjust the accuracy of face recognition based on the estimated user emotions. For example, when the user is excited, the face recognition unit increases the accuracy of face recognition to identify a more detailed facial expression. Furthermore, when the user is relaxed, the face recognition unit can adjust the accuracy of face recognition to identify a more general facial expression. Furthermore, when the user is stressed, the face recognition unit can decrease the accuracy of face recognition to identify a more simplified facial expression. This allows for more accurate face recognition by adjusting the accuracy of face recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The face recognition unit can optimize the recognition algorithm based on the user's past face data during face recognition. For example, the face recognition unit optimizes the current face recognition algorithm based on the user's past face data. The face recognition unit can also optimize the current face recognition algorithm based on the user's past facial expression data. The face recognition unit can also optimize the current face recognition algorithm based on the user's past facial features. This allows the recognition algorithm to be optimized by referring to the user's past face data.

[0111] The face recognition unit can incorporate changes in a user's facial expression into the analysis when recognizing a face. For example, the face recognition unit analyzes changes in a user's facial expression to identify changes in emotions. The face recognition unit can also analyze changes in a user's facial expression to help identify products in which the user is interested. The face recognition unit can also analyze changes in a user's facial expression to identify increased purchasing intent. In this way, by incorporating changes in a user's facial expression into the analysis, more accurate face recognition becomes possible.

[0112] The face recognition unit can incorporate the user's movement speed or gaze direction into the analysis when recognizing a face. For example, the face recognition unit can analyze the user's movement speed to help identify products in which the user is interested. The face recognition unit can also analyze the user's gaze direction to help identify products in which the user is paying attention. The face recognition unit can also combine the user's movement speed and gaze direction to perform more accurate face recognition. In this way, by incorporating the user's movement speed and gaze direction into the analysis, more accurate face recognition is possible.

[0113] The face recognition unit can estimate the user's emotions and prioritize the face recognition results based on the estimated user emotions. For example, when the user is excited, the face recognition unit can prioritize displaying important face recognition results. Furthermore, when the user is relaxed, the face recognition unit can prioritize displaying general face recognition results. Furthermore, when the user is stressed, the face recognition unit can prioritize displaying simple face recognition results. In this way, by prioritizing the face recognition results based on the user's emotions, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The face recognition unit can improve the recognition accuracy based on the user's geographical location information during face recognition. For example, when the user is in a specific area, the face recognition unit uses face recognition data related to that area. Furthermore, when the user is in a specific store, the face recognition unit can also use face recognition data related to that store. Furthermore, when the user is on a specific floor, the face recognition unit can also use face recognition data related to that floor. In this way, recognition accuracy can be improved by taking the user's geographical location information into consideration.

[0115] The face recognition unit can analyze the user's social media activity during face recognition to improve recognition accuracy. For example, the face recognition unit can improve recognition accuracy by using data related to the locations where the user has checked in on social media. The face recognition unit can also improve recognition accuracy based on the content of the user's social media posts. The face recognition unit can also improve recognition accuracy based on the activities of the user's friends on social media. In this way, recognition accuracy can be improved by analyzing the user's social media activity.

[0116] The face recognition unit can customize the recognition algorithm by reflecting the user's past feedback during face recognition. The face recognition unit customizes the recognition algorithm based on, for example, feedback provided by the user in the past. The face recognition unit can also customize the recognition algorithm based on the content of feedback provided by the user in the past. The face recognition unit can also customize the recognition algorithm based on the frequency of feedback provided by the user in the past. In this way, the recognition algorithm can be customized by reflecting the user's past feedback.

[0117] The notification sending unit can estimate the user's emotion and adjust the timing of notification transmission based on the estimated user's emotion. For example, if the user is excited, the notification sending unit can immediately send a notification. Furthermore, if the user is relaxed, the notification sending unit can also send a notification at an appropriate timing. Furthermore, if the user is feeling stressed, the notification sending unit can delay notification transmission. In this way, by adjusting the timing of notification transmission based on the user's emotion, the notification can be sent at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0118] When sending a notification, the notification sending unit can determine the optimal sending method based on the salesperson's past response history. The notification sending unit determines the optimal sending method, for example, based on the history of users with whom the salesperson has previously responded. The notification sending unit can also determine the optimal sending method based on the support content that the salesperson has previously provided. The notification sending unit can also determine the optimal sending method based on feedback that the salesperson has previously received. In this way, the optimal sending method can be determined by referring to the salesperson's past response history.

[0119] When sending notifications, the notification sending unit can determine the priority of sending notifications by taking into account the current work status of the salesperson. For example, if the salesperson is busy, the notification sending unit can prioritize sending only important notifications. Also, if the salesperson is available, the notification sending unit can send all notifications. Also, if the salesperson is performing a specific task, the notification sending unit can prioritize sending notifications related to that task. In this way, important notifications can be sent with priority by taking into account the current work status of the salesperson.

[0120] The notification sending unit can estimate the user's emotion and adjust the urgency of sending a notification based on the estimated user's emotion. For example, if the user is excited, the notification sending unit can send a notification with a high level of urgency. Furthermore, if the user is relaxed, the notification sending unit can also send a notification with a low level of urgency. Furthermore, if the user is stressed, the notification sending unit can also send a notification with a medium level of urgency. This allows for adjusting the urgency of sending a notification based on the user's emotion, enabling a more appropriate response. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0121] When sending a notification, the notification sending unit can select the optimal sending method based on the geographical location information of the salesperson. For example, if the salesperson is nearby, the notification sending unit can send a voice notification. If the salesperson is far away, the notification sending unit can also send a text notification. If the salesperson is in a specific area, the notification sending unit can also send a notification related to that area. In this way, the optimal sending method can be selected by taking the geographical location information of the salesperson into consideration.

[0122] The notification sending unit can analyze the social media activity of the salesperson and provide relevant notification content when sending a notification. For example, the notification sending unit can provide a notification regarding a location where the salesperson has checked in on social media. The notification sending unit can also provide relevant notification content based on the content posted by the salesperson on social media. The notification sending unit can also provide relevant notification content based on the activities of the salesperson's friends on social media. In this way, relevant notification content can be provided by analyzing the social media activity of the salesperson.

[0123] The age / gender estimation unit can estimate the user's emotions and adjust the accuracy of the age / gender estimation based on the estimated user's emotions. For example, when the user is excited, the age / gender estimation unit increases the accuracy of the age / gender estimation to identify more detailed information. Furthermore, when the user is relaxed, the age / gender estimation unit can adjust the accuracy of the age / gender estimation to identify more general information. Furthermore, when the user is stressed, the age / gender estimation unit can decrease the accuracy of the age / gender estimation to identify more simplified information. This allows for more accurate estimation by adjusting the accuracy of the age / gender estimation based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0124] The age and gender estimation unit can optimize the estimation algorithm based on the user's past data when estimating the age and gender. The age and gender estimation unit optimizes the current estimation algorithm based on the user's past age data, for example. The age and gender estimation unit can also optimize the current estimation algorithm based on the user's past gender data. The age and gender estimation unit can also optimize the current estimation algorithm by combining the user's past age and gender data. This allows the estimation algorithm to be optimized by referring to the user's past data.

[0125] The age and gender estimation unit can incorporate changes in the user's facial expression into the analysis when estimating the age and gender. For example, the age and gender estimation unit analyzes changes in the user's facial expression to help estimate the age and gender. The age and gender estimation unit can also analyze changes in the user's facial expression to identify changes in emotion and use these to help estimate the age and gender. The age and gender estimation unit can also analyze changes in the user's facial expression to identify an increase in purchasing intent and use these to help estimate the age and gender. In this way, by incorporating changes in the user's facial expression into the analysis, more accurate estimation is possible.

[0126] The age / gender estimation unit can estimate the user's emotions and determine the priority of the age / gender estimation results based on the estimated user's emotions. For example, when the user is excited, the age / gender estimation unit can prioritize displaying important estimation results. Furthermore, when the user is relaxed, the age / gender estimation unit can also prioritize displaying general estimation results. Furthermore, when the user is stressed, the age / gender estimation unit can also prioritize displaying simple estimation results. In this way, by determining the priority of the age / gender estimation results based on the user's emotions, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0127] The age / gender estimation unit can improve estimation accuracy by taking into account the user's geographical location information when estimating the user's age and gender. For example, when the user is in a specific area, the age / gender estimation unit can improve estimation accuracy by using age / gender data related to the area. Furthermore, when the user is in a specific store, the age / gender estimation unit can improve estimation accuracy by using age / gender data related to the store. Furthermore, when the user is on a specific floor, the age / gender estimation unit can improve estimation accuracy by using age / gender data related to the floor. In this way, estimation accuracy can be improved by taking into account the user's geographical location information.

[0128] The age / gender estimation unit can improve the accuracy of the age / gender estimation by analyzing the user's social media activity. For example, the age / gender estimation unit can improve the accuracy of the estimation by using data on the location where the user checked in on social media. The age / gender estimation unit can also improve the accuracy of the estimation based on the content of the user's posts on social media. The age / gender estimation unit can also improve the accuracy of the estimation based on the activities of the user's friends on social media. In this way, the estimation accuracy can be improved by analyzing the user's social media activity.

[0129] The motion tracking unit can estimate the user's emotion and adjust the accuracy of motion tracking based on the estimated user's emotion. For example, when the user is excited, the motion tracking unit increases the accuracy of motion tracking to identify detailed movements. Furthermore, when the user is relaxed, the motion tracking unit can adjust the accuracy of motion tracking to identify general movements. Furthermore, when the user is stressed, the motion tracking unit can decrease the accuracy of motion tracking to identify simple movements. In this way, adjusting the accuracy of motion tracking based on the user's emotion enables more accurate motion tracking. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0130] The motion tracking unit can optimize the tracking algorithm based on the user's past motion data when tracking the motion. The motion tracking unit optimizes the current tracking algorithm based on the user's past motion data, for example. The motion tracking unit can also optimize the current tracking algorithm based on the user's past motion patterns. The motion tracking unit can also optimize the current tracking algorithm based on the user's past motion speeds. In this way, the tracking algorithm can be optimized by referring to the user's past motion data.

[0131] The motion tracking unit can incorporate the user's motion speed and gaze direction into the analysis when tracking the motion. For example, the motion tracking unit analyzes the user's motion speed to help identify products in which the user is interested. The motion tracking unit can also analyze the user's gaze direction to help identify products in which the user is paying attention. The motion tracking unit can also combine the user's motion speed and gaze direction to perform more accurate motion tracking. In this way, by incorporating the user's motion speed and gaze direction into the analysis, more accurate motion tracking is possible.

[0132] The motion tracking unit can estimate the user's emotion and determine the priority of the motion tracking results based on the estimated user's emotion. For example, when the user is excited, the motion tracking unit can prioritize displaying important motion tracking results. Furthermore, when the user is relaxed, the motion tracking unit can prioritize displaying general motion tracking results. Furthermore, when the user is stressed, the motion tracking unit can prioritize displaying simple motion tracking results. In this way, by prioritizing the motion tracking results based on the user's emotion, important information can be displayed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0133] The motion tracking unit can improve tracking accuracy by taking into account the user's geographical location information when tracking motion. For example, when the user is in a specific area, the motion tracking unit can improve tracking accuracy by using motion data related to the area. Also, when the user is in a specific store, the motion tracking unit can improve tracking accuracy by using motion data related to the store. Also, when the user is on a specific floor, the motion tracking unit can improve tracking accuracy by using motion data related to the floor. In this way, tracking accuracy can be improved by taking into account the user's geographical location information.

[0134] The motion tracking unit can analyze the user's social media activities during motion tracking to improve tracking accuracy. For example, the motion tracking unit can improve tracking accuracy by using data on locations where the user has checked in on social media. The motion tracking unit can also improve tracking accuracy based on the content of the user's social media posts. The motion tracking unit can also improve tracking accuracy based on the activities of the user's friends on social media. In this way, tracking accuracy can be improved by analyzing the user's social media activities. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, notification unit, display unit, face recognition unit, notification transmission unit, age / gender estimation unit, and motion tracking unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the control unit 46A of the smart device 14. For example, the display unit is realized by the control unit 46A of the smart device 14. For example, the face recognition unit is realized by the camera 42 and control unit 46A of the smart device 14. For example, the notification transmission unit is realized by the control unit 46A of the smart device 14. For example, the age / gender estimation unit is realized by the camera 42 and control unit 46A of the smart device 14. For example, the motion tracking unit is realized by the camera 42 and control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, notification unit, display unit, face recognition unit, notification transmission unit, age / gender estimation unit, and motion tracking unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the control unit 46A of the smart glasses 214. For example, the display unit is realized by the control unit 46A of the smart glasses 214. For example, the face recognition unit is realized by the camera 42 and control unit 46A of the smart glasses 214. For example, the notification transmission unit is realized by the control unit 46A of the smart glasses 214. For example, the age / gender estimation unit is realized by the camera 42 and control unit 46A of the smart glasses 214. For example, the motion tracking unit is realized by the camera 42 and control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, notification unit, display unit, face recognition unit, notification transmission unit, age / gender estimation unit, and action tracking unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the control unit 46A of the headset type terminal 314. For example, the display unit is realized by the control unit 46A of the headset type terminal 314. For example, the face recognition unit is realized by the camera 42 and control unit 46A of the headset type terminal 314. For example, the notification transmission unit is realized by the control unit 46A of the headset type terminal 314. For example, the age / gender estimation unit is realized by the camera 42 and control unit 46A of the headset type terminal 314. For example, the action tracking unit is realized by the camera 42 and control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, notification unit, display unit, face recognition unit, notification transmission unit, age / gender estimation unit, and motion tracking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the control unit 46A of the robot 414. For example, the display unit is realized by the control unit 46A of the robot 414. For example, the face recognition unit is realized by the camera 42 and control unit 46A of the robot 414. For example, the notification transmission unit is realized by the control unit 46A of the robot 414. For example, the age / gender estimation unit is realized by the camera 42 and control unit 46A of the robot 414. For example, the motion tracking unit is realized by the camera 42 and control unit 46A of the robot 414.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] The smart support system may include a recommendation unit that recommends specific products based on the user's purchasing history. For example, the recommendation unit may recommend related products based on the category of products the user has previously purchased. The recommendation unit may also recommend products that fit the user's budget based on the price range of products the user has previously purchased. Furthermore, the recommendation unit may also recommend new products of the same brand based on the brand of products the user has previously purchased. This makes it possible to utilize the user's purchasing history to provide more personalized product recommendations.

[0137] The smart support system may include a social analysis unit that analyzes a user's social media activity and recommends related products. For example, the social analysis unit may recommend related products based on products that the user has "liked" on social media. The social analysis unit may also recommend similar products based on products purchased by the user's friends. Furthermore, the social analysis unit may also recommend products from brands that the user follows on social media. This makes it possible to utilize the user's social media activity to make more relevant product recommendations.

[0138] The smart support system may include a recommendation adjustment unit that estimates the user's emotions and adjusts the content of product recommendations based on the estimated emotions. For example, if the user is excited, the recommendation adjustment unit may recommend the latest trending products. If the user is relaxed, the recommendation adjustment unit may also recommend products that have a relaxing effect. Furthermore, if the user is stressed, the recommendation adjustment unit may also recommend products that are useful for relieving stress. This enables more appropriate product recommendations based on the user's emotions.

[0139] The smart support system may include a feedback reflecting unit that customizes a product recommendation algorithm by reflecting a user's past feedback. The feedback reflecting unit customizes the recommendation algorithm based on, for example, feedback provided by the user in the past. The feedback reflecting unit can also customize the recommendation algorithm based on the content of feedback provided by the user in the past. Furthermore, the feedback reflecting unit can also customize the recommendation algorithm based on the frequency of feedback provided by the user in the past. This enables more accurate product recommendations by reflecting a user's past feedback.

[0140] The smart support system can include a location information recommendation unit that recommends products related to a specific store or area based on the user's geographic location information. For example, if the user is in a specific store, the location information recommendation unit can recommend products that are popular in that store. Also, if the user is in a specific area, the location information recommendation unit can recommend products that are particularly in demand in that area. Furthermore, if the user is on a specific floor, the location information recommendation unit can recommend products that are particularly popular on that floor. This makes it possible to utilize the user's geographic location information to make more appropriate product recommendations.

[0141] The smart support system may include an environment adjustment unit that estimates the user's emotions and adjusts the music and lighting in the store based on the estimated emotions. For example, if the user is excited, the environment adjustment unit may provide bright and lively music and lighting. If the user is relaxed, the environment adjustment unit may also provide calming music and lighting. Furthermore, if the user is stressed, the environment adjustment unit may also provide music and lighting that has a relaxing effect. This allows the environment in the store to be optimized based on the user's emotions.

[0142] The smart support system may include a time slot recommendation unit that recommends specific products for specific time slots based on the user's purchasing history and behavioral patterns. The time slot recommendation unit, for example, recommends products related to the same time slot based on the history of products the user previously purchased during that time slot. The time slot recommendation unit may also recommend products that are popular in a specific area based on the user's tendency to stay in that area during a specific time slot. Furthermore, the time slot recommendation unit may also recommend products related to a user's tendency to engage in specific behavior during a specific time slot. This enables more appropriate product recommendations to be made based on the user's behavioral patterns.

[0143] The smart support system may include a response adjustment unit that estimates the user's emotions and adjusts the salesperson's response method based on the estimated emotions. For example, the response adjustment unit may recommend an aggressive approach if the user is excited. The response adjustment unit may also recommend a gentle approach if the user is relaxed. Furthermore, the response adjustment unit may also recommend a gentle approach if the user is feeling stressed. This allows the salesperson to respond more appropriately based on the user's emotions.

[0144] The smart support system may include an event recommendation unit that recommends specific events or campaigns based on the user's past behavioral data. The event recommendation unit, for example, recommends related events based on the user's history of events that he or she has previously attended. The event recommendation unit may also recommend campaigns related to a product category based on a product category in which the user has previously shown interest. Furthermore, the event recommendation unit may also recommend events held at a store based on the user's history of visiting that store. This makes it possible to recommend more appropriate events and campaigns by utilizing the user's past behavioral data.

[0145] The smart support system may include a display adjustment unit that estimates a user's emotions and adjusts the display content of the digital signage based on the estimated emotions. For example, if the user is excited, the display adjustment unit may display a visually stimulating advertisement. If the user is relaxed, the display adjustment unit may also display a calming advertisement. Furthermore, if the user is feeling stressed, the display adjustment unit may display a simple advertisement with a relaxing effect. This enables more effective advertisement display based on the user's emotions.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The analysis unit analyzes the user's behavior. User behavior includes purchasing behavior, browsing behavior, and movement behavior. The analysis unit analyzes whether the user is staring at a product for a long time or reaching out for the product. The analysis unit also analyzes the user's facial expressions to determine whether they are interested in purchasing. For example, facial recognition technology can be used to analyze the user's facial expressions and determine whether they are interested. Step 2: The notification unit sends a notification to the salesperson based on the information analyzed by the analysis unit. Notifications include text messages, alert sounds, and push notifications. The notification unit sends notifications to the salesperson's smartphone or tablet and adjusts the timing of the notifications so that the salesperson can approach the user at the appropriate time. For example, the notification is sent the moment the user picks up a product. Step 3: The display unit displays appealing materials on the digital signage based on the information obtained by the analysis unit. Appealing materials include advertisements, promotional videos, product information, etc. The display unit appeals to younger customers by promoting the latest fashion items, and to senior customers by promoting health-related products. The display unit also analyzes at least one of the user's length of stay, location, and path of movement. For example, it analyzes which areas the user spends the most time in and which route they take to move around the store.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 AI 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0176] 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.

[0177] 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.

[0178] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] 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 AI 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 7, a 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0196] 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.

[0197] 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.

[0198] 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 AI 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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, in order to avoid confusion and to 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.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 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. an analysis unit that analyzes user behavior; a notification unit that sends a notification to a salesperson based on the information analyzed by the analysis unit; a display unit that displays an appeal on a digital signage based on the information obtained by the analysis unit; and an analysis unit that analyzes at least one of the user's stay time, location, and path. A system characterized by:

2. Equipped with a face recognition unit that uses face recognition technology or motion analysis technology 2. The system of claim 1.

3. A notification sending unit is provided to send notifications to the salesperson's smartphone or tablet.

2. The system of claim 1.

4. Equipped with an age and gender estimation unit that estimates the age or gender of customers 2. The system of claim 1.

5. It has a motion tracking unit that tracks the user's movements using two or more cameras.

2. The system of claim 1.

6. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit Optimize analysis algorithms based on users' past behavior data 2. The system of claim 1.

8. The analysis unit During analysis, behavioral patterns are identified based on the user's purchasing history.

2. The system of claim 1.

9. The analysis unit During analysis, incorporate the user's movement speed or gaze direction into the analysis.

2. The system of claim 1.

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