system

Abstract

The system according to this embodiment aims to analyze purchase data and security camera footage to identify purchase patterns and optimize display methods. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a identification unit, a video analysis unit, and an optimization unit. The collection unit collects purchase records. The analysis unit analyzes the purchase records collected by the collection unit. The identification unit identifies purchase patterns based on the purchase records analyzed by the analysis unit. The video analysis unit analyzes security camera footage. The optimization unit optimizes the display method based on the data obtained by the identification unit and the video analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, purchase data and security camera images have not been fully utilized, and there is room for improvement in identifying purchase patterns and optimizing display methods.

[0005] The system according to the embodiment aims to analyze purchase data and security camera images, identify purchase patterns, and optimize display methods.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a identification unit, a video analysis unit, and an optimization unit. The collection unit collects purchase records. The analysis unit analyzes the purchase records collected by the collection unit. The identification unit identifies purchase patterns based on the purchase records analyzed by the analysis unit. The video analysis unit analyzes security camera footage. The optimization unit optimizes the display method based on the data obtained by the identification unit and the video analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze purchase data and security camera footage to identify purchase patterns and optimize display methods. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The purchase analysis AI agent system according to an embodiment of the present invention is a system that identifies purchase patterns and optimizes display methods by utilizing purchase data and security camera footage for supermarkets. This purchase analysis AI agent system collects and analyzes purchase records to identify tendencies for impulse and add-on purchases. It also analyzes security camera footage to track customers' movement within the store and their consideration time to infer purchase patterns. For example, the purchase analysis AI agent system analyzes purchase history data in detail to identify products that are often purchased together with specific products. Furthermore, the purchase analysis AI agent system analyzes security camera footage to understand which routes customers took to select products and how long they spent considering products on each shelf. Next, the purchase analysis AI agent system optimizes display methods based on this data. For example, it increases purchasing intent by placing products that are frequently purchased as add-ons or on impulse purchases close together. It also increases sales by placing products that are frequently purchased as promotions in prominent locations. In addition, the purchase analysis AI agent system evaluates layout changes and reports on the effects of fabric replacements. For example, it evaluates how sales changed when the placement of specific products was changed and suggests further improvements. This enables the AI-powered purchasing analysis agent system to implement display strategies that reflect purchasing patterns, thereby increasing sales and profits. It can also improve the customer purchasing experience and promote satisfaction. Furthermore, it allows for enhanced competitiveness through data-driven promotional activities. By identifying purchasing patterns and optimizing display methods, the AI-powered purchasing analysis agent system can increase sales and profits.

[0029] The purchase analysis AI agent system according to this embodiment comprises a collection unit, an analysis unit, a identification unit, a video analysis unit, and an optimization unit. The collection unit collects purchase records. The collection unit collects, for example, purchase history data. The collection unit states that purchase history data includes, but is not limited to, purchase date and time, purchased items, and purchase amount. The collection unit can obtain purchase history data from, for example, a POS system. The collection unit can also collect online shopping data. The collection unit can obtain data using, for example, an API of an online shopping site. The analysis unit analyzes the purchase records collected by the collection unit. The analysis unit analyzes the purchase records using, for example, data mining techniques. The analysis unit statistically analyzes the purchase history data to identify purchase patterns. The analysis unit can also analyze purchase records using, for example, a machine learning algorithm. The identification unit identifies purchase patterns based on the purchase records analyzed by the analysis unit. The identification unit performs, for example, frequency analysis to identify products that are often purchased together with a particular product. The identification unit, for example, performs time-series analysis to identify products that are frequently purchased during specific time periods. The identification unit can also identify purchasing patterns using clustering techniques. The video analysis unit analyzes security camera footage. The video analysis unit performs analysis considering factors such as video resolution and frame rate. The video analysis unit tracks customer movement within the store using video analysis algorithms. The video analysis unit analyzes, for example, which routes customers take to select products. The video analysis unit can also analyze, for example, how long customers spend examining products on each shelf. The optimization unit optimizes display methods based on data obtained by the identification unit and the video analysis unit. The optimization unit increases purchasing intent by, for example, placing products that are frequently purchased on impulse or as add-ons nearby. The optimization unit increases sales by, for example, placing products that are frequently purchased as part of promotions in prominent locations. The optimization unit evaluates how sales change when product placement is altered and suggests further improvements.As a result, the purchase analysis AI agent system according to this embodiment can increase sales and profits by analyzing purchase records and security camera footage, identifying purchase patterns, and optimizing display methods.

[0030] The data collection unit collects purchase records. For example, the data collection unit collects purchase history data. The data collection unit states that purchase history data includes, but is not limited to, purchase date and time, purchased items, and purchase amount. For example, the data collection unit can obtain purchase history data from a POS system. The data collection unit can also collect online shopping data. For example, the data collection unit can obtain data using the API of an online shopping site. The data collection unit centrally manages this data and stores it in a database. The data collection unit can adjust the frequency of data collection and can also update data in real time. For example, data from POS systems is collected in batch processing after the end of business each day, while online shopping data is obtained in real time via API. To ensure data quality, the data collection unit performs data integrity checks and error checks. For example, if the collected data contains missing or outlier values, the data collection unit detects this and performs appropriate imputation processing. The data collection unit also takes data privacy into consideration and performs processing to anonymize personal information. For example, personal information such as customer names and addresses is anonymized by hashing or tokenization. This allows the collection unit to efficiently and securely collect purchase records, making them available for use by the analysis unit and the identification unit.

[0031] The analysis unit analyzes the purchase records collected by the collection unit. The analysis unit may, for example, use data mining techniques to analyze the purchase records. The analysis unit may, for example, statistically analyze the purchase history data to identify purchase patterns. The analysis unit may also analyze purchase records using, for example, machine learning algorithms. Specifically, the analysis unit cleanses and preprocesses the purchase history data. For example, it imputes missing values ​​and removes outliers. Next, the analysis unit extracts data features and builds models to identify purchase patterns. For example, it performs frequency analysis to identify products that are often purchased together with specific products. The analysis unit performs time series analysis to identify products that are often purchased during specific time periods. The analysis unit can also group customers based on their purchase patterns using clustering techniques. For example, it segments customers based on purchase frequency and purchase amount, and develops different marketing strategies for each segment. The analysis unit visualizes these analysis results and makes them available to the identification and optimization units. For example, it uses dashboards to visually display the distribution of purchase patterns and customer segments. This allows the analysis unit to effectively analyze the collected data and contribute to identifying purchasing patterns and formulating marketing strategies.

[0032] The identification unit identifies purchasing patterns based on purchase records analyzed by the analysis unit. For example, the identification unit performs frequency analysis to identify products that are often purchased together with a particular product. For example, the identification unit performs time series analysis to identify products that are often purchased during a specific time period. The identification unit can also identify purchasing patterns using clustering techniques. Specifically, when performing frequency analysis, the identification unit uses association rule mining to identify product relationships. For example, it calculates the probability that product B is purchased together with product A, and identifies highly related product pairs. When performing time series analysis, the identification unit considers seasonality and trends to identify products that are often purchased during specific time periods or seasons. For example, it identifies a tendency for ice cream purchases to increase in the summer and develops a promotional strategy. The identification unit uses clustering techniques to group customers based on their purchasing patterns. For example, it uses K-means clustering to segment customers based on purchase frequency and purchase amount, and develops different marketing strategies for each segment. This allows a specific unit to pinpoint purchasing patterns in detail, contributing to the development of marketing strategies and the optimization of product placement.

[0033] The video analysis unit analyzes security camera footage. For example, the video analysis unit considers factors such as video resolution and frame rate during the analysis. The video analysis unit tracks customer movement within the store using video analysis algorithms. For example, it analyzes the routes customers take to select products. The video analysis unit can also analyze, for example, how long customers spend examining products on each shelf. Specifically, the video analysis unit preprocesses the video data, performing noise reduction and resolution adjustments. Next, it uses video analysis algorithms to perform customer face recognition and motion detection, tracking customer movement within the store. For example, it analyzes the customer's movement path from entering to leaving the store, identifying the route they took to select products. The video analysis unit measures dwell time to analyze how long customers spend examining products on each shelf. For example, if a customer stays in front of a particular shelf for a long time, it determines that they have a high level of interest in the products on that shelf. The video analysis unit visualizes these analysis results, making them available for use by the identification and optimization units. For example, heatmaps can be used to visually display customer dwell time and movement paths within a store. This allows the video analysis unit to analyze customer behavior in detail, contributing to the optimization of product placement and the development of marketing strategies.

[0034] The Optimization Unit optimizes display methods based on data obtained by the Identification Unit and the Video Analysis Unit. For example, the Optimization Unit increases purchasing intent by placing products that are frequently purchased on impulse or as a side purchase nearby. For example, the Optimization Unit increases sales by placing products that are frequently purchased as promotional items in prominent locations. For example, the Optimization Unit evaluates how sales have changed after product placement is altered and proposes further improvements. Specifically, the Optimization Unit optimizes product placement based on purchasing patterns identified by the Identification Unit and customer behavior data analyzed by the Video Analysis Unit. For example, it promotes impulse purchases by placing highly relevant products identified through frequency analysis nearby. The Optimization Unit redesigns the store layout to place products that are frequently purchased as promotional items in prominent locations. For example, it places promotional items near the entrance and around the cash registers to make them more visible to customers. The Optimization Unit monitors sales data and measures the effect to evaluate how sales have changed after product placement is altered. For example, it compares sales data before and after product placement changes to evaluate the increase or decrease in sales. Based on these evaluation results, the Optimization Unit proposes further improvements and continuously optimizes product placement. This allows the optimization unit to provide an effective display method that increases purchasing intent and boosts sales and profits.

[0035] The optimization unit can optimize product displays based on factors such as "impulse purchases," "buyback purchases," and "promotional purchases." For example, the optimization unit can increase purchasing intent by placing products that are frequently bought as impulses close together. For example, the optimization unit can increase sales by placing products that are frequently bought as impulses in prominent locations. For example, the optimization unit can increase sales by placing products that are frequently bought as promotions in specific locations. In this way, sales can be increased by optimizing product displays based on purchasing patterns.

[0036] The optimization unit can evaluate the effects of layout changes and report on the effects of fabric replacement. For example, the optimization unit can evaluate how sales changed when the placement of a specific product was changed. For example, the optimization unit can evaluate how customer purchasing behavior changed when the placement of products was changed. For example, the optimization unit can report on the effects of fabric replacement and suggest further improvement plans. In this way, by evaluating the effects of layout changes and reporting on the effects of fabric replacement, further improvement plans can be suggested.

[0037] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit collects purchase records based on products the user has frequently purchased in the past. For example, the data collection unit collects data from the user's purchase history at specific time periods. For example, the data collection unit analyzes the user's purchase patterns and proposes the optimal data collection method. This allows the optimal data collection method to be selected by analyzing the user's past purchase history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter purchase records based on the user's current purchasing intent and areas of interest. For example, if a user has shown interest in a particular product, the data collection unit will prioritize collecting purchase records for that product. For example, the data collection unit will collect data during times when the user's purchasing intent is high. For example, the data collection unit will filter relevant purchase records based on the user's areas of interest. This allows for the collection of highly relevant data by filtering based on the user's purchasing intent and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0039] The collection unit can prioritize the collection of highly relevant purchase records by considering the user's geographical location information when collecting purchase records. For example, if the user is in a specific region, the collection unit will prioritize the collection of purchase records from that region. For example, the collection unit will filter relevant purchase records based on the user's location information. For example, if the user is on the move, the collection unit will collect purchase records based on their current location. This allows for the priority collection of highly relevant purchase records by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant records.

[0040] The data collection unit can analyze a user's social media activity and collect relevant records when collecting purchase records. For example, if a user mentions a specific product on social media, the data collection unit will prioritize collecting purchase records for that product. For example, the data collection unit can identify products of interest from a user's social media activity and collect purchase records. For example, the data collection unit can filter relevant purchase records based on the content of a user's social media posts. This allows for the collection of relevant purchase records by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's social media data into a generating AI and have the generating AI collect relevant records.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the purchase records during the analysis. For example, the analysis unit performs a detailed analysis on purchase records with high importance. For example, the analysis unit performs a simplified analysis on purchase records with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the purchase records. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the purchase records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input purchase record importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the purchase record during analysis. For example, the analysis unit can apply a specific analysis algorithm to purchase records in the food category. For example, the analysis unit can apply a different analysis algorithm to purchase records in the clothing category. For example, the analysis unit can apply a dedicated analysis algorithm to purchase records in the electronics category. By applying different analysis algorithms depending on the category of the purchase record, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input purchase record category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the submission date of purchase records during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted purchase records. For example, the analysis unit may postpone the analysis of older purchase records. The analysis unit may adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of purchase records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input purchase record submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of purchase records during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant purchase records. For example, the analysis unit may postpone the analysis of less relevant purchase records. The analysis unit adjusts the order of analysis based on the relevance of purchase records. This allows for efficient analysis by adjusting the order of analysis based on the relevance of purchase records. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data of purchase records into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The identification unit can improve the accuracy of identification by considering the interrelationships of purchase records at the time of identification. For example, the identification unit analyzes the interrelationships of purchase records to improve the accuracy of identification. For example, the identification unit improves the accuracy of identification by considering the relationships of purchase records. For example, the identification unit improves the accuracy of identification based on the interrelationships of purchase records. As a result, the accuracy of identification is improved by considering the interrelationships of purchase records. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input interrelationship data of purchase records into a generating AI and have the generating AI perform the accuracy improvement of identification.

[0046] The identification unit can perform identification by considering the attribute information of the person submitting the purchase record. For example, the identification unit may consider the age and gender of the person submitting the purchase record. For example, the identification unit may consider the purchase history of the person submitting the purchase record. For example, the identification unit may perform identification based on the attribute information of the person submitting the purchase record. This improves the accuracy of identification by considering the attribute information of the person submitting the purchase record. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the submitter's attribute information into a generating AI and have the generating AI perform the identification.

[0047] The identification unit can perform identification while considering the geographical distribution of purchase records. For example, the identification unit analyzes the geographical distribution of purchase records and performs identification. For example, the identification unit performs identification based on the geographical distribution of purchase records. For example, the identification unit performs identification while considering the geographical distribution of purchase records. This improves the accuracy of identification by considering the geographical distribution of purchase records. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data into a generating AI and have the generating AI perform the identification.

[0048] The identification unit can improve the accuracy of identification by referring to relevant literature in the purchase record at the time of identification. The identification unit improves the accuracy of identification by referring to relevant literature in the purchase record, for example. The identification unit improves the accuracy of identification based on relevant literature in the purchase record, for example. The identification unit improves the accuracy of identification by considering relevant literature in the purchase record, for example. As a result, the accuracy of identification is improved by referring to relevant literature in the purchase record. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input relevant literature data into a generating AI and have the generating AI perform the accuracy improvement of identification.

[0049] The video analysis unit can improve the accuracy of its analysis by considering customer behavior patterns during video analysis. For example, the video analysis unit can analyze how often customers take a particular route and identify behavior patterns. For example, the video analysis unit can analyze how long customers spend on a particular product shelf and identify behavior patterns. For example, the video analysis unit can improve the accuracy of its video analysis based on customer behavior patterns. This improves the accuracy of video analysis by considering customer behavior patterns. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without AI. For example, the video analysis unit can input customer behavior pattern data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0050] The video analysis unit can perform video analysis while considering customer attribute information. For example, the video analysis unit can perform video analysis while considering the customer's age and gender. For example, the video analysis unit can perform video analysis while considering the customer's purchase history. For example, the video analysis unit can perform video analysis based on customer attribute information. By considering customer attribute information, the accuracy of the video analysis is improved. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without using AI. For example, the video analysis unit can input customer attribute information data into a generating AI and have the generating AI perform the analysis.

[0051] The video analysis unit can perform video analysis while considering the geographical distribution of customers. For example, the video analysis unit can analyze the geographical distribution of customers and perform video analysis. For example, the video analysis unit can perform video analysis based on the geographical distribution of customers. For example, the video analysis unit can perform video analysis while considering the geographical distribution of customers. This improves the accuracy of video analysis by considering the geographical distribution of customers. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without AI. For example, the video analysis unit can input customer geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0052] The video analysis unit can improve the accuracy of its analysis by referring to relevant customer behavior data during video analysis. For example, the video analysis unit can improve the accuracy of its video analysis by referring to relevant customer behavior data. For example, the video analysis unit can improve the accuracy of its video analysis based on relevant customer behavior data. For example, the video analysis unit can improve the accuracy of its video analysis by considering relevant customer behavior data. As a result, the accuracy of the video analysis is improved by referring to relevant customer behavior data. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without using AI. For example, the video analysis unit can input relevant customer behavior data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0053] The optimization unit can improve the accuracy of optimization by considering the interrelationships of purchasing patterns during optimization. For example, the optimization unit analyzes the interrelationships of purchasing patterns to improve the accuracy of optimization. For example, the optimization unit improves the accuracy of optimization by considering the relationships between purchasing patterns. For example, the optimization unit improves the accuracy of optimization based on the interrelationships of purchasing patterns. As a result, the accuracy of optimization is improved by considering the interrelationships of purchasing patterns. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input data on the interrelationships of purchasing patterns into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0054] The optimization unit can perform optimization while considering the attribute information of the person submitting the purchase record. For example, the optimization unit can perform optimization while considering the age and gender of the person submitting the purchase record. For example, the optimization unit can perform optimization while considering the purchase history of the person submitting the purchase record. For example, the optimization unit can perform optimization based on the attribute information of the person submitting the purchase record. This improves the accuracy of optimization by considering the attribute information of the person submitting the purchase record. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the submitter's attribute information into a generating AI and have the generating AI perform the optimization.

[0055] The optimization unit can perform optimization while considering the geographical distribution of purchase records. For example, the optimization unit analyzes the geographical distribution of purchase records and performs optimization. For example, the optimization unit performs optimization based on the geographical distribution of purchase records. For example, the optimization unit performs optimization while considering the geographical distribution of purchase records. This improves the accuracy of optimization by considering the geographical distribution of purchase records. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input geographical distribution data into a generating AI and have the generating AI perform the optimization.

[0056] The optimization unit can improve the accuracy of optimization by referring to relevant literature in the purchase record during optimization. For example, the optimization unit improves the accuracy of optimization by referring to relevant literature in the purchase record. For example, the optimization unit improves the accuracy of optimization based on relevant literature in the purchase record. For example, the optimization unit improves the accuracy of optimization by considering relevant literature in the purchase record. As a result, the accuracy of optimization is improved by referring to relevant literature in the purchase record. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input relevant literature data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can predict the purchase frequency of specific products based on a user's purchase history. For example, the data collection unit can analyze data on products a user has purchased in the past and predict products they are likely to purchase next. The data collection unit can also predict purchase trends for products related to specific seasons or events based on a user's purchase history. For example, the data collection unit can predict the purchase frequency of specific products based on a user's purchase history and use this information for inventory management. By predicting purchase frequency based on a user's purchase history, it can contribute to more efficient inventory management and sales promotion.

[0059] The analytics unit can analyze a user's purchase history and identify the motivation for purchasing a specific product. For example, the analytics unit can analyze the relationship between a user's purchase of a specific product and other products, thereby identifying the motivation for purchase. The analytics unit can also analyze the timing and frequency of purchases of a specific product from the user's purchase history, thereby identifying the motivation for purchase. For example, the analytics unit can identify the motivation for purchasing a specific product based on the user's purchase history and use this information to inform marketing strategies. This allows for an improvement in the accuracy of marketing strategies by identifying the motivation for purchase based on the user's purchase history.

[0060] The identification unit can identify purchase patterns for specific products based on a user's purchase history. For example, the identification unit can analyze the relationship between a user's purchase of a specific product and other products to identify purchase patterns. The identification unit can also analyze the timing and frequency of purchases of specific products from a user's purchase history to identify purchase patterns. The identification unit can identify purchase patterns for specific products based on a user's purchase history and use this information to improve sales strategies. This allows for improved accuracy of sales strategies by identifying purchase patterns based on user purchase history.

[0061] The video analysis unit can analyze users' in-store behavior and estimate their purchase intent for specific products. For example, the video analysis unit can analyze the time users spend on a particular product shelf and estimate their purchase intent. The video analysis unit can also analyze, for example, the frequency with which users travel a particular route and estimate their purchase intent. The video analysis unit can estimate the purchase intent for specific products based on users' in-store behavior and use this information to improve sales promotion. This allows for improved accuracy in sales promotion by estimating purchase intent based on users' in-store behavior.

[0062] The optimization unit can optimize the display method of specific products based on the user's purchase history. For example, the optimization unit analyzes data on products the user has purchased in the past and places products that the user is likely to purchase next in a prominent location. The optimization unit can also optimize the display method of products related to specific seasons or events based on the user's purchase history. The optimization unit can optimize the display method of specific products based on the user's purchase history and use this to promote sales. This improves the accuracy of sales promotions by optimizing the display method based on the user's purchase history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects purchase records. The collection unit can, for example, obtain purchase history data from a POS system. It can also collect online shopping data. The collection unit can, for example, obtain data using the API of an online shopping site. Step 2: The analysis unit analyzes the purchase records collected by the collection unit. The analysis unit analyzes the purchase records using, for example, data mining techniques or machine learning algorithms. Step 3: The identification unit identifies purchasing patterns based on the purchasing records analyzed by the analysis unit. The identification unit identifies purchasing patterns using, for example, frequency analysis, time series analysis, or clustering techniques. Step 4: The video analysis unit analyzes the security camera footage. For example, the video analysis unit uses video analysis algorithms to track customers' movements within the store and analyze which routes customers took to select products and how long they spent examining products on each shelf. Step 5: The optimization unit optimizes the display method based on the data obtained by the identification unit and the video analysis unit. For example, the optimization unit increases purchasing intent by placing products that are frequently bought on impulse or as a side purchase nearby, or increases sales by placing products that are frequently bought as part of a promotion in a prominent location.

[0065] (Example of form 2) The purchase analysis AI agent system according to an embodiment of the present invention is a system that identifies purchase patterns and optimizes display methods by utilizing purchase data and security camera footage for supermarkets. This purchase analysis AI agent system collects and analyzes purchase records to identify tendencies for impulse and add-on purchases. It also analyzes security camera footage to track customers' movement within the store and their consideration time to infer purchase patterns. For example, the purchase analysis AI agent system analyzes purchase history data in detail to identify products that are often purchased together with specific products. Furthermore, the purchase analysis AI agent system analyzes security camera footage to understand which routes customers took to select products and how long they spent considering products on each shelf. Next, the purchase analysis AI agent system optimizes display methods based on this data. For example, it increases purchasing intent by placing products that are frequently purchased as add-ons or on impulse purchases close together. It also increases sales by placing products that are frequently purchased as promotions in prominent locations. In addition, the purchase analysis AI agent system evaluates layout changes and reports on the effects of fabric replacements. For example, it evaluates how sales changed when the placement of specific products was changed and suggests further improvements. This enables the AI-powered purchasing analysis agent system to implement display strategies that reflect purchasing patterns, thereby increasing sales and profits. It can also improve the customer purchasing experience and promote satisfaction. Furthermore, it allows for enhanced competitiveness through data-driven promotional activities. By identifying purchasing patterns and optimizing display methods, the AI-powered purchasing analysis agent system can increase sales and profits.

[0066] The purchase analysis AI agent system according to this embodiment comprises a collection unit, an analysis unit, a identification unit, a video analysis unit, and an optimization unit. The collection unit collects purchase records. The collection unit collects, for example, purchase history data. The collection unit states that purchase history data includes, but is not limited to, purchase date and time, purchased items, and purchase amount. The collection unit can obtain purchase history data from, for example, a POS system. The collection unit can also collect online shopping data. The collection unit can obtain data using, for example, an API of an online shopping site. The analysis unit analyzes the purchase records collected by the collection unit. The analysis unit analyzes the purchase records using, for example, data mining techniques. The analysis unit statistically analyzes the purchase history data to identify purchase patterns. The analysis unit can also analyze purchase records using, for example, a machine learning algorithm. The identification unit identifies purchase patterns based on the purchase records analyzed by the analysis unit. The identification unit performs, for example, frequency analysis to identify products that are often purchased together with a particular product. The identification unit, for example, performs time-series analysis to identify products that are frequently purchased during specific time periods. The identification unit can also identify purchasing patterns using clustering techniques. The video analysis unit analyzes security camera footage. The video analysis unit performs analysis considering factors such as video resolution and frame rate. The video analysis unit tracks customer movement within the store using video analysis algorithms. The video analysis unit analyzes, for example, which routes customers take to select products. The video analysis unit can also analyze, for example, how long customers spend examining products on each shelf. The optimization unit optimizes display methods based on data obtained by the identification unit and the video analysis unit. The optimization unit increases purchasing intent by, for example, placing products that are frequently purchased on impulse or as add-ons nearby. The optimization unit increases sales by, for example, placing products that are frequently purchased as part of promotions in prominent locations. The optimization unit evaluates how sales change when product placement is altered and suggests further improvements.As a result, the purchase analysis AI agent system according to this embodiment can increase sales and profits by analyzing purchase records and security camera footage, identifying purchase patterns, and optimizing display methods.

[0067] The data collection unit collects purchase records. For example, the data collection unit collects purchase history data. The data collection unit states that purchase history data includes, but is not limited to, purchase date and time, purchased items, and purchase amount. For example, the data collection unit can obtain purchase history data from a POS system. The data collection unit can also collect online shopping data. For example, the data collection unit can obtain data using the API of an online shopping site. The data collection unit centrally manages this data and stores it in a database. The data collection unit can adjust the frequency of data collection and can also update data in real time. For example, data from POS systems is collected in batch processing after the end of business each day, while online shopping data is obtained in real time via API. To ensure data quality, the data collection unit performs data integrity checks and error checks. For example, if the collected data contains missing or outlier values, the data collection unit detects this and performs appropriate imputation processing. The data collection unit also takes data privacy into consideration and performs processing to anonymize personal information. For example, personal information such as customer names and addresses is anonymized by hashing or tokenization. This allows the collection unit to efficiently and securely collect purchase records, making them available for use by the analysis unit and the identification unit.

[0068] The analysis unit analyzes the purchase records collected by the collection unit. The analysis unit may, for example, use data mining techniques to analyze the purchase records. The analysis unit may, for example, statistically analyze the purchase history data to identify purchase patterns. The analysis unit may also analyze purchase records using, for example, machine learning algorithms. Specifically, the analysis unit cleanses and preprocesses the purchase history data. For example, it imputes missing values ​​and removes outliers. Next, the analysis unit extracts data features and builds models to identify purchase patterns. For example, it performs frequency analysis to identify products that are often purchased together with specific products. The analysis unit performs time series analysis to identify products that are often purchased during specific time periods. The analysis unit can also group customers based on their purchase patterns using clustering techniques. For example, it segments customers based on purchase frequency and purchase amount, and develops different marketing strategies for each segment. The analysis unit visualizes these analysis results and makes them available to the identification and optimization units. For example, it uses dashboards to visually display the distribution of purchase patterns and customer segments. This allows the analysis unit to effectively analyze the collected data and contribute to identifying purchasing patterns and formulating marketing strategies.

[0069] The identification unit identifies purchasing patterns based on purchase records analyzed by the analysis unit. For example, the identification unit performs frequency analysis to identify products that are often purchased together with a particular product. For example, the identification unit performs time series analysis to identify products that are often purchased during a specific time period. The identification unit can also identify purchasing patterns using clustering techniques. Specifically, when performing frequency analysis, the identification unit uses association rule mining to identify product relationships. For example, it calculates the probability that product B is purchased together with product A, and identifies highly related product pairs. When performing time series analysis, the identification unit considers seasonality and trends to identify products that are often purchased during specific time periods or seasons. For example, it identifies a tendency for ice cream purchases to increase in the summer and develops a promotional strategy. The identification unit uses clustering techniques to group customers based on their purchasing patterns. For example, it uses K-means clustering to segment customers based on purchase frequency and purchase amount, and develops different marketing strategies for each segment. This allows a specific unit to pinpoint purchasing patterns in detail, contributing to the development of marketing strategies and the optimization of product placement.

[0070] The video analysis unit analyzes security camera footage. For example, the video analysis unit considers factors such as video resolution and frame rate during the analysis. The video analysis unit tracks customer movement within the store using video analysis algorithms. For example, it analyzes the routes customers take to select products. The video analysis unit can also analyze, for example, how long customers spend examining products on each shelf. Specifically, the video analysis unit preprocesses the video data, performing noise reduction and resolution adjustments. Next, it uses video analysis algorithms to perform customer face recognition and motion detection, tracking customer movement within the store. For example, it analyzes the customer's movement path from entering to leaving the store, identifying the route they took to select products. The video analysis unit measures dwell time to analyze how long customers spend examining products on each shelf. For example, if a customer stays in front of a particular shelf for a long time, it determines that they have a high level of interest in the products on that shelf. The video analysis unit visualizes these analysis results, making them available for use by the identification and optimization units. For example, heatmaps can be used to visually display customer dwell time and movement paths within a store. This allows the video analysis unit to analyze customer behavior in detail, contributing to the optimization of product placement and the development of marketing strategies.

[0071] The Optimization Unit optimizes display methods based on data obtained by the Identification Unit and the Video Analysis Unit. For example, the Optimization Unit increases purchasing intent by placing products that are frequently purchased on impulse or as a side purchase nearby. For example, the Optimization Unit increases sales by placing products that are frequently purchased as promotional items in prominent locations. For example, the Optimization Unit evaluates how sales have changed after product placement is altered and proposes further improvements. Specifically, the Optimization Unit optimizes product placement based on purchasing patterns identified by the Identification Unit and customer behavior data analyzed by the Video Analysis Unit. For example, it promotes impulse purchases by placing highly relevant products identified through frequency analysis nearby. The Optimization Unit redesigns the store layout to place products that are frequently purchased as promotional items in prominent locations. For example, it places promotional items near the entrance and around the cash registers to make them more visible to customers. The Optimization Unit monitors sales data and measures the effect to evaluate how sales have changed after product placement is altered. For example, it compares sales data before and after product placement changes to evaluate the increase or decrease in sales. Based on these evaluation results, the Optimization Unit proposes further improvements and continuously optimizes product placement. This allows the optimization unit to provide an effective display method that increases purchasing intent and boosts sales and profits.

[0072] The optimization unit can optimize product displays based on factors such as "impulse purchases," "buyback purchases," and "promotional purchases." For example, the optimization unit can increase purchasing intent by placing products that are frequently bought as impulses close together. For example, the optimization unit can increase sales by placing products that are frequently bought as impulses in prominent locations. For example, the optimization unit can increase sales by placing products that are frequently bought as promotions in specific locations. In this way, sales can be increased by optimizing product displays based on purchasing patterns.

[0073] The optimization unit can evaluate the effects of layout changes and report on the effects of fabric replacement. For example, the optimization unit can evaluate how sales changed when the placement of a specific product was changed. For example, the optimization unit can evaluate how customer purchasing behavior changed when the placement of products was changed. For example, the optimization unit can report on the effects of fabric replacement and suggest further improvement plans. In this way, by evaluating the effects of layout changes and reporting on the effects of fabric replacement, further improvement plans can be suggested.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of purchase record collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will refrain from collecting purchase records and collect them when the user is relaxed. For example, if the user is excited, the data collection unit will collect purchase records quickly and obtain data in real time. For example, if the user is tired, the data collection unit will delay collecting purchase records and collect them after the user has rested. By adjusting the timing of purchase record collection based on the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit collects purchase records based on products the user has frequently purchased in the past. For example, the data collection unit collects data from the user's purchase history at specific time periods. For example, the data collection unit analyzes the user's purchase patterns and proposes the optimal data collection method. This allows the optimal data collection method to be selected by analyzing the user's past purchase history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter purchase records based on the user's current purchasing intent and areas of interest. For example, if a user has shown interest in a particular product, the data collection unit will prioritize collecting purchase records for that product. For example, the data collection unit will collect data during times when the user's purchasing intent is high. For example, the data collection unit will filter relevant purchase records based on the user's areas of interest. This allows for the collection of highly relevant data by filtering based on the user's purchasing intent and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of purchase records to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will collect purchase records quickly and obtain data in real time. For example, if the user is relaxed, the data collection unit will collect purchase records at normal intervals. For example, if the user is stressed, the data collection unit will refrain from collecting purchase records and collect them when the user is relaxed. This allows for the priority collection of important data by determining the priority of purchase records based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The collection unit can prioritize the collection of highly relevant purchase records by considering the user's geographical location information when collecting purchase records. For example, if the user is in a specific region, the collection unit will prioritize the collection of purchase records from that region. For example, the collection unit will filter relevant purchase records based on the user's location information. For example, if the user is on the move, the collection unit will collect purchase records based on their current location. This allows for the priority collection of highly relevant purchase records by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant records.

[0079] The data collection unit can analyze a user's social media activity and collect relevant records when collecting purchase records. For example, if a user mentions a specific product on social media, the data collection unit will prioritize collecting purchase records for that product. For example, the data collection unit can identify products of interest from a user's social media activity and collect purchase records. For example, the data collection unit can filter relevant purchase records based on the content of a user's social media posts. This allows for the collection of relevant purchase records by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a user's social media data into a generating AI and have the generating AI collect relevant records.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit provides visually stimulating analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the purchase records during the analysis. For example, the analysis unit performs a detailed analysis on purchase records with high importance. For example, the analysis unit performs a simplified analysis on purchase records with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the purchase records. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the purchase records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input purchase record importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the purchase record during analysis. For example, the analysis unit can apply a specific analysis algorithm to purchase records in the food category. For example, the analysis unit can apply a different analysis algorithm to purchase records in the clothing category. For example, the analysis unit can apply a dedicated analysis algorithm to purchase records in the electronics category. By applying different analysis algorithms depending on the category of the purchase record, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input purchase record category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit will provide a detailed analysis result. For example, if the user is excited, the analysis unit will provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can determine the priority of analysis based on the submission date of purchase records during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted purchase records. For example, the analysis unit may postpone the analysis of older purchase records. The analysis unit may adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of purchase records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input purchase record submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of purchase records during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant purchase records. For example, the analysis unit may postpone the analysis of less relevant purchase records. The analysis unit adjusts the order of analysis based on the relevance of purchase records. This allows for efficient analysis by adjusting the order of analysis based on the relevance of purchase records. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data of purchase records into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The identification unit can estimate the user's emotions and adjust the method of identifying purchase patterns based on the estimated user emotions. For example, if the user is relaxed, the identification unit will identify a detailed purchase pattern. For example, if the user is in a hurry, the identification unit will identify a concise purchase pattern that gets straight to the point. For example, if the user is excited, the identification unit will identify a visually stimulating purchase pattern. By adjusting the method of identifying purchase patterns based on the user's emotions, more appropriate identification results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The identification unit can improve the accuracy of identification by considering the interrelationships of purchase records at the time of identification. For example, the identification unit analyzes the interrelationships of purchase records to improve the accuracy of identification. For example, the identification unit improves the accuracy of identification by considering the relationships of purchase records. For example, the identification unit improves the accuracy of identification based on the interrelationships of purchase records. As a result, the accuracy of identification is improved by considering the interrelationships of purchase records. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input interrelationship data of purchase records into a generating AI and have the generating AI perform the accuracy improvement of identification.

[0088] The identification unit can perform identification by considering the attribute information of the person submitting the purchase record. For example, the identification unit may consider the age and gender of the person submitting the purchase record. For example, the identification unit may consider the purchase history of the person submitting the purchase record. For example, the identification unit may perform identification based on the attribute information of the person submitting the purchase record. This improves the accuracy of identification by considering the attribute information of the person submitting the purchase record. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the submitter's attribute information into a generating AI and have the generating AI perform the identification.

[0089] The identification unit can estimate the user's emotions and adjust the order in which it displays specific results based on the estimated emotions. For example, if the user is relaxed, the identification unit will display detailed results. If the user is in a hurry, the identification unit will display concise results that get straight to the point. If the user is excited, the identification unit will display visually stimulating results. This allows for the provision of more appropriate results by adjusting the order in which specific results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The identification unit can perform identification while considering the geographical distribution of purchase records. For example, the identification unit analyzes the geographical distribution of purchase records and performs identification. For example, the identification unit performs identification based on the geographical distribution of purchase records. For example, the identification unit performs identification while considering the geographical distribution of purchase records. This improves the accuracy of identification by considering the geographical distribution of purchase records. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data into a generating AI and have the generating AI perform the identification.

[0091] The identification unit can improve the accuracy of identification by referring to relevant literature in the purchase record at the time of identification. The identification unit improves the accuracy of identification by referring to relevant literature in the purchase record, for example. The identification unit improves the accuracy of identification based on relevant literature in the purchase record, for example. The identification unit improves the accuracy of identification by considering relevant literature in the purchase record, for example. As a result, the accuracy of identification is improved by referring to relevant literature in the purchase record. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input relevant literature data into a generating AI and have the generating AI perform the accuracy improvement of identification.

[0092] The video analysis unit can estimate the user's emotions and adjust the video analysis criteria based on the estimated emotions. For example, if the user is relaxed, the video analysis unit will perform a detailed video analysis. If the user is in a hurry, the video analysis unit will perform a concise video analysis focusing on the key points. If the user is excited, the video analysis unit will perform a visually stimulating video analysis. By adjusting the video analysis criteria based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The video analysis unit can improve the accuracy of its analysis by considering customer behavior patterns during video analysis. For example, the video analysis unit can analyze how often customers take a particular route and identify behavior patterns. For example, the video analysis unit can analyze how long customers spend on a particular product shelf and identify behavior patterns. For example, the video analysis unit can improve the accuracy of its video analysis based on customer behavior patterns. This improves the accuracy of video analysis by considering customer behavior patterns. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without AI. For example, the video analysis unit can input customer behavior pattern data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0094] The video analysis unit can perform video analysis while considering customer attribute information. For example, the video analysis unit can perform video analysis while considering the customer's age and gender. For example, the video analysis unit can perform video analysis while considering the customer's purchase history. For example, the video analysis unit can perform video analysis based on customer attribute information. By considering customer attribute information, the accuracy of the video analysis is improved. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without using AI. For example, the video analysis unit can input customer attribute information data into a generating AI and have the generating AI perform the analysis.

[0095] The video analysis unit can estimate the user's emotions and adjust the order in which the video analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the video analysis unit will display detailed video analysis results. For example, if the user is in a hurry, the video analysis unit will display concise video analysis results that get straight to the point. For example, if the user is excited, the video analysis unit will display visually stimulating video analysis results. By adjusting the order in which the video analysis results are displayed based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The video analysis unit can perform video analysis while considering the geographical distribution of customers. For example, the video analysis unit can analyze the geographical distribution of customers and perform video analysis. For example, the video analysis unit can perform video analysis based on the geographical distribution of customers. For example, the video analysis unit can perform video analysis while considering the geographical distribution of customers. This improves the accuracy of video analysis by considering the geographical distribution of customers. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without AI. For example, the video analysis unit can input customer geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0097] The video analysis unit can improve the accuracy of its analysis by referring to relevant customer behavior data during video analysis. For example, the video analysis unit can improve the accuracy of its video analysis by referring to relevant customer behavior data. For example, the video analysis unit can improve the accuracy of its video analysis based on relevant customer behavior data. For example, the video analysis unit can improve the accuracy of its video analysis by considering relevant customer behavior data. As a result, the accuracy of the video analysis is improved by referring to relevant customer behavior data. Some or all of the above processing in the video analysis unit may be performed using AI, for example, or without using AI. For example, the video analysis unit can input relevant customer behavior data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0098] The optimization unit can estimate the user's emotions and optimize the display method based on the estimated emotions. For example, if the user is relaxed, the optimization unit will suggest a detailed display method. For example, if the user is in a hurry, the optimization unit will suggest a concise display method that gets straight to the point. For example, if the user is excited, the optimization unit will suggest a visually stimulating display method. In this way, by optimizing the display method based on the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The optimization unit can improve the accuracy of optimization by considering the interrelationships of purchasing patterns during optimization. For example, the optimization unit analyzes the interrelationships of purchasing patterns to improve the accuracy of optimization. For example, the optimization unit improves the accuracy of optimization by considering the relationships between purchasing patterns. For example, the optimization unit improves the accuracy of optimization based on the interrelationships of purchasing patterns. As a result, the accuracy of optimization is improved by considering the interrelationships of purchasing patterns. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input data on the interrelationships of purchasing patterns into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0100] The optimization unit can perform optimization while considering the attribute information of the person submitting the purchase record. For example, the optimization unit can perform optimization while considering the age and gender of the person submitting the purchase record. For example, the optimization unit can perform optimization while considering the purchase history of the person submitting the purchase record. For example, the optimization unit can perform optimization based on the attribute information of the person submitting the purchase record. This improves the accuracy of optimization by considering the attribute information of the person submitting the purchase record. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the submitter's attribute information into a generating AI and have the generating AI perform the optimization.

[0101] The optimization unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, the optimization unit will display a detailed display method. If the user is in a hurry, the optimization unit will display a concise display method that gets straight to the point. If the user is excited, the optimization unit will display a visually stimulating display method. In this way, by adjusting the display method based on the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The optimization unit can perform optimization while considering the geographical distribution of purchase records. For example, the optimization unit analyzes the geographical distribution of purchase records and performs optimization. For example, the optimization unit performs optimization based on the geographical distribution of purchase records. For example, the optimization unit performs optimization while considering the geographical distribution of purchase records. This improves the accuracy of optimization by considering the geographical distribution of purchase records. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input geographical distribution data into a generating AI and have the generating AI perform the optimization.

[0103] The optimization unit can improve the accuracy of optimization by referring to relevant literature in the purchase record during optimization. For example, the optimization unit improves the accuracy of optimization by referring to relevant literature in the purchase record. For example, the optimization unit improves the accuracy of optimization based on relevant literature in the purchase record. For example, the optimization unit improves the accuracy of optimization by considering relevant literature in the purchase record. As a result, the accuracy of optimization is improved by referring to relevant literature in the purchase record. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input relevant literature data into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The data collection unit can predict the purchase frequency of specific products based on a user's purchase history. For example, the data collection unit can analyze data on products a user has purchased in the past and predict products they are likely to purchase next. The data collection unit can also predict purchase trends for products related to specific seasons or events based on a user's purchase history. For example, the data collection unit can predict the purchase frequency of specific products based on a user's purchase history and use this information for inventory management. By predicting purchase frequency based on a user's purchase history, it can contribute to more efficient inventory management and sales promotion.

[0106] The analytics unit can analyze a user's purchase history and identify the motivation for purchasing a specific product. For example, the analytics unit can analyze the relationship between a user's purchase of a specific product and other products, thereby identifying the motivation for purchase. The analytics unit can also analyze the timing and frequency of purchases of a specific product from the user's purchase history, thereby identifying the motivation for purchase. For example, the analytics unit can identify the motivation for purchasing a specific product based on the user's purchase history and use this information to inform marketing strategies. This allows for an improvement in the accuracy of marketing strategies by identifying the motivation for purchase based on the user's purchase history.

[0107] The identification unit can identify purchase patterns for specific products based on a user's purchase history. For example, the identification unit can analyze the relationship between a user's purchase of a specific product and other products to identify purchase patterns. The identification unit can also analyze the timing and frequency of purchases of specific products from a user's purchase history to identify purchase patterns. The identification unit can identify purchase patterns for specific products based on a user's purchase history and use this information to improve sales strategies. This allows for improved accuracy of sales strategies by identifying purchase patterns based on user purchase history.

[0108] The video analysis unit can analyze users' in-store behavior and estimate their purchase intent for specific products. For example, the video analysis unit can analyze the time users spend on a particular product shelf and estimate their purchase intent. The video analysis unit can also analyze, for example, the frequency with which users travel a particular route and estimate their purchase intent. The video analysis unit can estimate the purchase intent for specific products based on users' in-store behavior and use this information to improve sales promotion. This allows for improved accuracy in sales promotion by estimating purchase intent based on users' in-store behavior.

[0109] The optimization unit can optimize the display method of specific products based on the user's purchase history. For example, the optimization unit analyzes data on products the user has purchased in the past and places products that the user is likely to purchase next in a prominent location. The optimization unit can also optimize the display method of products related to specific seasons or events based on the user's purchase history. The optimization unit can optimize the display method of specific products based on the user's purchase history and use this to promote sales. This improves the accuracy of sales promotions by optimizing the display method based on the user's purchase history.

[0110] The data collection unit can estimate the user's emotions and adjust the method of collecting purchase records based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect detailed purchase records. If the user is in a hurry, for example, the data collection unit may collect concise purchase records that get straight to the point. The data collection unit can improve the accuracy of the data by adjusting the method of collecting purchase records based on the user's emotions. This means that the accuracy of the data can be improved by adjusting the method of collecting purchase records based on the user's emotions.

[0111] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit will display detailed analysis results. If the user is in a hurry, for example, the analysis unit can also display concise analysis results that get straight to the point. The analysis unit can adjust how the analysis results are displayed based on the user's emotions to facilitate user understanding. In this way, by adjusting how the analysis results are displayed based on the user's emotions, user understanding can be improved.

[0112] The identification unit can estimate the user's emotions and adjust how the identification results are displayed based on the estimated emotions. For example, if the user is relaxed, the identification unit will display detailed identification results. If the user is in a hurry, for example, the identification unit may also display concise identification results that get straight to the point. The identification unit can adjust how the identification results are displayed based on the user's emotions to facilitate user understanding. In this way, by adjusting how the identification results are displayed based on the user's emotions, user understanding can be improved.

[0113] The video analysis unit can estimate the user's emotions and adjust the video analysis method based on the estimated emotions. For example, if the user is relaxed, the video analysis unit will perform a detailed video analysis. If the user is in a hurry, for example, the video analysis unit can perform a concise video analysis that focuses on the key points. The video analysis unit can improve the accuracy of the analysis by adjusting the video analysis method based on the user's emotions. In this way, the accuracy of the analysis can be improved by adjusting the video analysis method based on the user's emotions.

[0114] The optimization unit can estimate the user's emotions and suggest display methods based on those emotions. For example, if the user is relaxed, the optimization unit will suggest a detailed display method. If the user is in a hurry, for example, the optimization unit can suggest a concise display method that gets straight to the point. The optimization unit can use the user's emotions to suggest display methods and help promote sales. By suggesting display methods based on the user's emotions, the accuracy of sales promotion can be improved.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The collection unit collects purchase records. The collection unit can, for example, obtain purchase history data from a POS system. It can also collect online shopping data. The collection unit can, for example, obtain data using the API of an online shopping site. Step 2: The analysis unit analyzes the purchase records collected by the collection unit. The analysis unit analyzes the purchase records using, for example, data mining techniques or machine learning algorithms. Step 3: The identification unit identifies purchasing patterns based on the purchasing records analyzed by the analysis unit. The identification unit identifies purchasing patterns using, for example, frequency analysis, time series analysis, or clustering techniques. Step 4: The video analysis unit analyzes the security camera footage. For example, the video analysis unit uses video analysis algorithms to track customers' movements within the store and analyze which routes customers took to select products and how long they spent examining products on each shelf. Step 5: The optimization unit optimizes the display method based on the data obtained by the identification unit and the video analysis unit. For example, the optimization unit increases purchasing intent by placing products that are frequently bought on impulse or as a side purchase nearby, or increases sales by placing products that are frequently bought as part of a promotion in a prominent location.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, video analysis unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects purchase history data using the control unit 46A of the smart device 14 and analyzes it using the identification unit 290 of the data processing unit 12. The analysis unit analyzes purchase records using the identification unit 290 of the data processing unit 12. The identification unit identifies purchase patterns using the identification unit 290 of the data processing unit 12. The video analysis unit tracks customer movement within the store using the camera 42 of the smart device 14 and analyzes it using the identification unit 290 of the data processing unit 12. The optimization unit optimizes the display method using the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, video analysis unit, and optimization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects purchase history data using the control unit 46A of the smart glasses 214 and analyzes it using the identification unit 290 of the data processing unit 12. The analysis unit analyzes purchase records using the identification unit 290 of the data processing unit 12, for example. The identification unit identifies purchase patterns using the identification unit 290 of the data processing unit 12, for example. The video analysis unit tracks customer movement within the store using the camera 42 of the smart glasses 214 and analyzes it using the identification unit 290 of the data processing unit 12. The optimization unit optimizes the display method using the identification unit 290 of the data processing unit 12, for example. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, video analysis unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects purchase history data using the control unit 46A of the headset terminal 314 and analyzes it using the identification unit 290 of the data processing unit 12. The analysis unit analyzes purchase records using the identification unit 290 of the data processing unit 12. The identification unit identifies purchase patterns using the identification unit 290 of the data processing unit 12. The video analysis unit tracks customer movement within the store using the camera 42 of the headset terminal 314 and analyzes it using the identification unit 290 of the data processing unit 12. The optimization unit optimizes the display method using the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, video analysis unit, and optimization unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects purchase history data using the control unit 46A of the robot 414 and analyzes it using the identification unit 290 of the data processing unit 12. The analysis unit analyzes purchase records using, for example, the identification unit 290 of the data processing unit 12. The identification unit identifies purchase patterns using, for example, the identification unit 290 of the data processing unit 12. The video analysis unit tracks customer movement within the store using, for example, the camera 42 of the robot 414 and analyzes it using the identification unit 290 of the data processing unit 12. The optimization unit optimizes the display method using, for example, the identification unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) The collection department collects purchase records, An analysis unit analyzes the purchase records collected by the collection unit, A unit that identifies a purchase pattern based on the purchase records analyzed by the aforementioned analysis unit, The video analysis unit analyzes security camera footage, The system includes an optimization unit that optimizes the display method based on data obtained by the identification unit and the video analysis unit. A system characterized by the following features. (Note 2) The optimization unit, Optimize product displays based on factors such as "impulse purchases," "buybacks," and "promotional purchases." The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, Evaluate the effects of the layout changes and report on the effects of replacing the fabric. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of purchase record collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting purchase records, filtering is performed based on the user's current purchasing intent and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase records to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting purchase records, the system prioritizes collecting records that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting purchase records, analyze users' social media activity and collect relevant records. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the purchase records. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the purchase record. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the purchase records were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of purchase records. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, We estimate user emotions and adjust the method of identifying purchasing patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, At specific times, improve accuracy by considering the interrelationships of purchase records. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, When identifying a person, the attribute information of the person who submitted the purchase record is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, It estimates the user's emotions and adjusts the order in which specific results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, When identifying a target, the geographical distribution of purchase records is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, At specific times, referencing relevant literature in purchase records improves the accuracy of specific tasks. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned video analysis unit, The system estimates the user's emotions and adjusts the video analysis criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned video analysis unit, When analyzing video footage, consider customer behavior patterns to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned video analysis unit, When analyzing video footage, the analysis is performed while taking into account customer attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned video analysis unit, It estimates the user's emotions and adjusts the order in which the video analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned video analysis unit, When analyzing video footage, the analysis should take into account the geographical distribution of customers. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned video analysis unit, When analyzing video footage, we improve the accuracy of the analysis by referencing relevant customer behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, The system estimates user emotions and optimizes the display method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, During optimization, consider the interrelationships of purchasing patterns to improve the accuracy of the optimization. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, During optimization, the attribute information of the person who submitted the purchase record is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, The system estimates the user's emotions and adjusts the display method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, During optimization, the geographical distribution of purchase records is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, During optimization, we improve the accuracy of the optimization by referring to relevant literature related to purchase records. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects purchase records, An analysis unit analyzes the purchase records collected by the collection unit, A unit that identifies a purchase pattern based on the purchase records analyzed by the aforementioned analysis unit, The video analysis unit analyzes security camera footage, The system includes an optimization unit that optimizes the display method based on data obtained by the identification unit and the video analysis unit. A system characterized by the following features.

2. The optimization unit, Evaluate the effects of the layout changes and report on the effects of replacing the fabric. The system according to feature 1.

3. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of purchase record collection based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system according to feature 1.

5. The aforementioned collection unit is When collecting purchase records, filtering is performed based on the user's current purchasing intent and areas of interest. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase records to collect based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting purchase records, the system prioritizes collecting records that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is When collecting purchase records, analyze users' social media activity and collect relevant records. The system according to feature 1.

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