System

Abstract

An object of a system according to an embodiment is to analyze flow line data and information on product display in a store and propose an optimal store design.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection part collects flow line data in a store and information on commodity display. The analysis unit analyzes the data collected by the collection unit and proposes a specific display or arrangement. The proposal unit reflects the display or arrangement proposed by the analysis unit on an actual store design.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not adequately utilized in-store traffic flow data and product display information to optimize store design, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze in-store traffic flow data and product display information to propose optimal store designs. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects in-store traffic flow data and product display information. The analysis unit analyzes the data collected by the collection unit and proposes specific displays and layouts. The proposal unit reflects the displays and layouts proposed by the analysis unit in the actual store design. [Effects of the Invention]

[0007] The system according to the embodiment can analyze in-store traffic flow data and product display information to propose optimal store designs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A store design proposal system according to an embodiment of the present invention proposes efficient and attractive store designs based on in-store traffic flow data and product display information. The store design proposal system collects in-store traffic flow data and product display information, analyzes them using AI, and proposes efficient and attractive displays and layouts to improve customer experience and sales efficiency. For example, the store design proposal system uses in-store cameras and sensors to record detailed customer traffic flow and product layouts. For example, data such as which aisles customers pass through and which products they are interested in is collected. The store design proposal system then analyzes the collected data using AI to propose efficient and attractive displays and layouts. For example, placing popular products in aisles frequently visited by customers can improve sales efficiency. Furthermore, the store design proposal system allows retailers to optimize their store designs based on the displays and layouts proposed by the AI. For example, widening aisles and arranging products to allow customers to easily find products can increase customer satisfaction. This allows the store design proposal system to realize optimal store designs backed by data and strengthen competitiveness. This allows the store design proposal system to change the store layout based on customer movement data, thereby extending customer stay time and increasing purchasing motivation. In addition, by arranging products in a creative way, it makes it easier for customers to pick up products, improving sales efficiency.

[0029] A store design proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects in-store traffic flow data and product display information. The collection unit, for example, uses in-store cameras and sensors to record detailed customer traffic flow and product placement. For example, the collection unit can collect data such as which aisles customers pass through and which products they are interested in. The collection unit can also record customers' movement paths and stay times. The analysis unit analyzes the data collected by the collection unit and proposes efficient and attractive display and placement. The analysis unit proposes efficient and attractive display and placement based on, for example, customer traffic flow data and product display information. For example, the analysis unit can propose placing popular products in aisles frequently visited by customers. The analysis unit can also propose product placement to increase customer purchasing motivation. The proposal unit reflects the display and placement proposed by the analysis unit in the actual store design. The proposal unit, for example, optimizes the store design based on the display and placement proposed by AI. For example, the suggestion unit can make suggestions to widen aisles and improve product layout so that customers can find products more easily. This allows the store design suggestion system according to the embodiment to improve customer experience and sales efficiency. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide specific procedures for optimizing store design based on the displays and layouts suggested by AI.

[0030] The collection unit can specifically record customer movement and product placement status using in-store cameras and sensors. The collection unit, for example, records customer movement using in-store cameras. For example, the collection unit can use cameras to record which aisles customers pass through and which products they are interested in. The collection unit can also record product placement status using in-store sensors. For example, the collection unit can use sensors to record product placement locations and display methods. This allows for more accurate data collection by recording customer movement and product placement status in detail. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by cameras and sensors into AI and have the AI ​​analyze the data.

[0031] The analysis unit can propose specific display and layout based on customer movement data and product display information. The analysis unit proposes efficient and attractive display and layout based on, for example, customer movement data. For example, the analysis unit can propose placing popular products in aisles that customers frequently pass through. The analysis unit can also make proposals to increase customer purchasing motivation by improving product layout. For example, the analysis unit can propose product placement and display methods that make products more accessible to customers. This can improve sales efficiency by proposing efficient and attractive display and layout based on customer movement data and product display information. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input customer movement data and product display information into AI and have the AI ​​execute proposals for efficient and attractive display and layout.

[0032] The suggestion unit can specifically adjust the store design based on the display and layout proposed by the AI. The suggestion unit, for example, optimizes the store design based on the display and layout proposed by the AI. For example, the suggestion unit can make suggestions to widen aisles and improve product layout so that customers can easily find products. The suggestion unit can also make suggestions to increase customers' purchasing motivation by specifically adjusting the product placement location and display method. For example, the suggestion unit can make suggestions to improve product placement so that customers can easily pick up products. This makes it possible to improve the customer experience by optimizing the store design based on the display and layout proposed by the AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can provide specific procedures for optimizing the store design based on the display and layout proposed by the AI.

[0033] The analysis unit can make a suggestion to place popular products in aisles frequently used by customers. For example, the analysis unit can identify aisles frequently used by customers based on customer movement data and make a suggestion to place popular products in those aisles. The analysis unit can also identify popular products based on customer purchase histories and make a suggestion to place those products in aisles frequently used by customers. This can improve sales efficiency by placing popular products in aisles frequently used by customers. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input customer movement data and purchase histories into AI and have the AI ​​execute a suggestion to place popular products.

[0034] The suggestion unit can make suggestions to widen aisles and improve product layout so that customers can find products more easily. The suggestion unit, for example, makes suggestions to widen aisles so that customers can find products more easily. For example, the suggestion unit can make suggestions to widen aisles based on customer movement line data. The suggestion unit can also make suggestions to improve product layout so that customers can easily pick up products. For example, the suggestion unit can make suggestions to improve product placement and display methods so that customers can find products more easily. This allows customers to find products more easily, thereby improving customer satisfaction. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input customer movement line data into AI and cause the AI ​​to execute suggestions to widen aisles and improve product layout.

[0035] The collection unit can monitor the congestion status in the store in real time and change the frequency of data collection depending on the level of congestion. For example, the collection unit can monitor the congestion status in the store in real time and change the frequency of data collection depending on the level of congestion. For example, when the store is crowded, the collection unit increases the frequency of data collection and acquires detailed movement line data. Furthermore, when the store is empty, the collection unit can set the frequency of data collection to a low level and acquire movement line data over a long period of time. Furthermore, when a specific area is crowded, the collection unit can increase the frequency of data collection in that area and decrease the frequency in other areas. This enables efficient data collection by changing the frequency of data collection depending on the congestion status in the store. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the congestion status data in the store into AI and have the AI ​​adjust the frequency of data collection.

[0036] The collection unit can combine a customer's purchase history and movement data to select a collection method that will increase interest in a specific product. For example, the collection unit combines a customer's purchase history and movement data to select a collection method that will increase interest in a specific product. For example, if the collection unit estimates that the customer is interested in a specific product based on the customer's purchase history, it can focus on collecting movement data around that product. The collection unit can also collect movement data for products related to products the customer has previously purchased to increase interest. The collection unit can also combine the customer's purchase history and movement data to identify areas where interest in a specific product is high and strengthen data collection in those areas. In this way, by combining the customer's purchase history and movement data, interest in a specific product can be increased. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the customer's purchase history and movement data into AI and have the AI ​​select a collection method that will increase interest in a specific product.

[0037] The collection unit can collect customer movement data in response to changes in the temperature and lighting in the store. The collection unit collects customer movement data in response to changes in the temperature and lighting in the store, for example. For example, when the temperature in the store rises, the collection unit collects customer movement data and records changes in behavior due to the temperature change. The collection unit can also collect customer movement data in response to changes in the lighting in the store and record changes in behavior due to the change in lighting. The collection unit can also focus on collecting movement data in a specific area in response to changes in the temperature and lighting in the store. In this way, by collecting movement data in response to changes in the temperature and lighting in the store, changes in customer behavior due to environmental changes can be understood. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on changes in the temperature and lighting in the store into AI and have the AI ​​collect movement data.

[0038] The collection unit can collect customer movement data taking into account the acoustic environment in the store. The collection unit collects customer movement data taking into account, for example, the acoustic environment in the store. For example, when the music in the store changes, the collection unit collects customer movement data and records changes in behavior due to the change in music. Furthermore, when the noise level in the store increases, the collection unit can collect customer movement data and record changes in behavior due to the change in noise. Furthermore, the collection unit can focus on collecting movement data in a specific area depending on the acoustic environment in the store. In this way, by collecting movement data depending on the acoustic environment in the store, changes in customer behavior due to changes in acoustics can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input in-store acoustic environment data into AI and have the AI ​​collect movement data.

[0039] The collection unit can analyze the social media activities of customers and collect related movement data. The collection unit, for example, analyzes the social media activities of customers and collects related movement data. For example, the collection unit collects movement data of places where customers check in on social media. The collection unit can also analyze the content of customers' social media posts and collect related movement data. The collection unit can also collect related movement data by referring to the activities of the customers' friends on social media. In this way, by analyzing the customers' social media activities, related movement data can be collected and customer behavior can be more accurately understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customers' social media data into AI and have the AI ​​collect related movement data.

[0040] The collection unit can enhance collection of movement line data during specific in-store events or promotional periods. The collection unit enhances collection of movement line data, for example, during specific in-store events or promotional periods. For example, when a specific event is being held in the store, the collection unit focuses on collecting movement line data from that area. The collection unit can also collect movement line data around specific products during the promotional period and analyze the effectiveness of the promotion. The collection unit can also collect customer movement line data in real time during the event or promotional period and analyze it immediately. In this way, by enhancing collection of movement line data during the specific event or promotional period, the effectiveness of the event or promotion can be analyzed. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input movement line data from the event or promotional period into AI and have the AI ​​enhance data collection.

[0041] The analysis unit can analyze optimal display layouts by combining the flow line data and product sales data. For example, the analysis unit can analyze optimal display layouts by combining the flow line data and product sales data. For example, the analysis unit can combine the flow line data and sales data to analyze optimal display layouts by placing popular products in aisles frequently visited by customers. The analysis unit can also analyze product layouts based on the flow line data and sales data to enhance customer purchasing motivation. The analysis unit can also combine the flow line data and sales data to identify areas where interest in specific products is high and analyze optimal layouts for those areas. By combining the flow line data and sales data, optimal display layouts can be analyzed and sales efficiency can be improved. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the flow line data and sales data into AI and have the AI ​​analyze optimal display layouts.

[0042] The analysis unit can analyze customer movement patterns in the store at different time periods and propose optimal layouts for each time period. The analysis unit, for example, analyzes customer movement patterns in the store at different time periods and proposes optimal layouts for each time period. For example, the analysis unit can analyze movement data for different time periods in the store and propose optimal layouts for each time period. The analysis unit can also propose optimal layouts for displaying popular products during specific time periods based on customer movement patterns. The analysis unit can also analyze movement data for each time period and propose layouts that will increase customer purchasing motivation. In this way, by analyzing customer movement patterns for different time periods, optimal layouts for each time period can be proposed. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input movement data for different time periods into AI and have the AI ​​propose optimal layouts for each time period.

[0043] The analysis unit can analyze the effectiveness of different display layouts based on the age group and gender of the customer. The analysis unit analyzes the effectiveness of different display layouts based on, for example, the age group and gender of the customer. For example, the analysis unit can analyze the effectiveness of different display layouts based on the age group of the customer and propose an optimal layout. The analysis unit can also analyze the effectiveness of different display layouts based on the gender of the customer and propose an optimal layout. The analysis unit can also analyze the effectiveness of different display layouts by combining the age group and gender of the customer and propose an optimal layout. In this way, by analyzing the effectiveness of display layouts based on the age group and gender of the customer, a more effective layout can be proposed. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer age group and gender data into AI and have the AI ​​analyze the effectiveness of different display layouts.

[0044] The analysis unit can analyze the display layout in response to changes in the lighting and acoustic environment in the store. The analysis unit analyzes the display layout in response to changes in the lighting and acoustic environment in the store, for example. For example, when the lighting in the store changes, the analysis unit analyzes the display layout and analyzes the effect of the lighting change. The analysis unit can also analyze the display layout in response to changes in the acoustic environment in the store and analyze the effect of the acoustic change. The analysis unit can also analyze the display layout in a specific area in response to changes in the lighting and acoustic environment in the store. In this way, by analyzing the display layout in response to changes in the lighting and acoustic environment in the store, it is possible to understand changes in customer behavior due to environmental changes. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the lighting and acoustic environment in the store into AI and have the AI ​​perform an analysis of the display layout.

[0045] The analysis unit can combine customer purchase history and movement line data to analyze a layout that will increase interest in a specific product. For example, the analysis unit combines customer purchase history and movement line data to analyze a layout that will increase interest in a specific product. For example, the analysis unit combines customer purchase history and movement line data to identify an area where interest in a specific product is high and analyze the optimal layout for that area. The analysis unit can also analyze the movement line data based on the customer purchase history and propose a layout that will increase interest in a specific product. The analysis unit can also combine customer purchase history and movement line data to analyze a layout that will increase interest in a specific product. In this way, by combining customer purchase history and movement line data, it is possible to analyze a layout that will increase interest in a specific product. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input customer purchase history and movement line data into AI and have the AI ​​analyze a layout that will increase interest in a specific product.

[0046] The analysis unit can analyze data from specific in-store events or promotional periods and propose optimal layouts for each event. For example, the analysis unit can analyze data from specific in-store events or promotional periods and propose optimal layouts for each event. For example, when a specific event is being held in the store, the analysis unit can analyze data from that area and propose optimal layouts. The analysis unit can also analyze data around specific products during promotional periods and propose layouts that enhance the effectiveness of the promotion. The analysis unit can also analyze data from events or promotional periods and propose layouts that increase customer purchasing motivation. In this way, by analyzing data from specific events or promotional periods, optimal layouts for each event can be proposed. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from events or promotional periods into AI and have the AI ​​propose optimal layouts.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on product sales data when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on product sales data when making a suggestion. For example, the suggestion unit makes a detailed suggestion for a product with high sales, specifically indicating how to arrange and display the product. Furthermore, the suggestion unit can make a simple suggestion for a product with low sales, indicating a basic arrangement and display method. Furthermore, the suggestion unit can adjust the arrangement and display method of the product based on the sales data and make an optimal suggestion. In this way, adjusting the level of detail of the suggestion based on the product sales data enables more effective suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product sales data into AI and cause the AI ​​to adjust the level of detail of the suggestion.

[0048] The proposal unit can apply different proposal algorithms according to different store layouts when making a proposal. For example, the proposal unit can apply different proposal algorithms according to different store layouts when making a proposal. For example, if the store layout is large, the proposal unit can apply a proposal algorithm suitable for large spaces. Furthermore, if the store layout is small, the proposal unit can apply a proposal algorithm suitable for small spaces. Furthermore, if the store layout is complex, the proposal unit can apply a proposal algorithm suitable for complex layouts. This enables more appropriate proposals by applying proposal algorithms according to different store layouts. Some or all of the above-described processing in the proposal unit can be performed using AI, for example, or without AI. For example, the proposal unit can input store layout data into AI and cause the AI ​​to apply different proposal algorithms.

[0049] The proposal unit can improve the accuracy of a proposal by referring to past proposal results when making a proposal. For example, the proposal unit can improve the accuracy of a proposal by referring to past proposal results when making a proposal. For example, the proposal unit applies an algorithm that improves the accuracy of a proposal based on past proposal results. The proposal unit can also analyze past proposal results and extract successful proposal patterns to improve the accuracy of the proposal. The proposal unit can also build a feedback loop for improving the accuracy of a proposal by referring to past proposal results. In this way, the accuracy of a proposal can be improved by referring to past proposal results. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal result data into AI and cause the AI ​​to improve the accuracy of the proposal.

[0050] The suggestion unit can adjust the suggestion content taking into account the lighting and acoustic environment in the store when making a suggestion. For example, the suggestion unit adjusts the suggestion content taking into account the lighting and acoustic environment in the store when making a suggestion. For example, if the lighting in the store is bright, the suggestion unit adjusts the suggestion content to be suitable for a bright environment. Furthermore, if the acoustic environment in the store is quiet, the suggestion unit can adjust the suggestion content to be suitable for a quiet environment. Furthermore, the suggestion unit can adjust the suggestion content for a specific area depending on the lighting and acoustic environment in the store. This enables more appropriate suggestions by taking into account the lighting and acoustic environment in the store. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the lighting and acoustic environment in the store into AI and have the AI ​​adjust the suggestion content.

[0051] The suggestion unit can combine the customer's purchase history and movement data to make a suggestion that will increase interest in a specific product. For example, the suggestion unit can combine the customer's purchase history and movement data to make a suggestion that will increase interest in a specific product. For example, the suggestion unit can combine the customer's purchase history and movement data to identify areas where interest in a specific product is high and make optimal suggestions for those areas. The suggestion unit can also analyze the movement data based on the customer's purchase history to make a suggestion that will increase interest in a specific product. The suggestion unit can also combine the customer's purchase history and movement data to make a suggestion that will increase interest in a specific product. By combining the customer's purchase history and movement data, it is possible to make a suggestion that will increase interest in a specific product. Some or all of the above-described processing by the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the customer's purchase history and movement data into AI and cause the AI ​​to execute a suggestion that will increase interest in a specific product.

[0052] The suggestion unit can make optimal suggestions for each event based on data from a specific event or promotion period when making a suggestion. For example, the suggestion unit can make optimal suggestions for each event based on data from a specific event or promotion period when making a suggestion. For example, when a specific event is being held in a store, the suggestion unit can make optimal suggestions based on data from that area. Furthermore, during a promotion period, the suggestion unit can make suggestions that enhance the effectiveness of the promotion based on data from around a specific product. Furthermore, the suggestion unit can make suggestions that increase customer purchasing motivation based on data from the event or promotion period. This makes it possible to make optimal suggestions for each event based on data from the specific event or promotion period. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data from the event or promotion period into AI and have the AI ​​execute optimal suggestions.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can select a collection method that combines a customer's purchase history with movement line data to increase the level of interest in a specific product. For example, if the collection unit estimates from the customer's purchase history that the customer is interested in a specific product, it will focus on collecting movement line data around that product. The collection unit can also collect movement line data for products related to products the customer has previously purchased to increase the level of interest. The collection unit can also combine the customer's purchase history with movement line data to identify areas where interest in a specific product is high and strengthen data collection in those areas. In this way, by combining the customer's purchase history with movement line data, it is possible to increase the level of interest in a specific product.

[0055] When changing the store layout, the suggestion unit can propose an optimal layout by combining customer movement line data and purchase history. For example, the suggestion unit can propose placing popular products in aisles that customers frequently pass through. The suggestion unit can also propose placing related products nearby based on the customer's purchase history. Furthermore, the suggestion unit can combine customer movement line data and purchase history to propose placing specific products in specific areas. In this way, by combining customer movement line data and purchase history, it is possible to propose an optimal store layout.

[0056] The collection unit can collect customer movement data in response to changes in the temperature and lighting in the store. For example, if the temperature in the store rises, the collection unit collects customer movement data and records changes in behavior due to the temperature change. The collection unit can also collect customer movement data in response to changes in the lighting in the store and record changes in behavior due to the change in lighting. The collection unit can also focus on collecting movement data in specific areas in response to changes in the temperature and lighting in the store. In this way, by collecting movement data in response to changes in the temperature and lighting in the store, it is possible to understand changes in customer behavior due to environmental changes.

[0057] The collection unit can monitor the congestion status of the store in real time and change the frequency of data collection depending on the level of congestion. For example, when the store is crowded, the collection unit increases the frequency of data collection and acquires detailed movement line data. In addition, when the store is empty, the collection unit can set the frequency of data collection to a low level and acquire movement line data over a long period of time. In addition, when a specific area is crowded, the collection unit can increase the frequency of data collection for that area and decrease the frequency for other areas. This allows for efficient data collection by changing the frequency of data collection depending on the congestion status of the store.

[0058] When making a proposal, the suggestion unit can combine the customer's purchase history with the movement line data to make a proposal that will increase the customer's interest in a specific product. For example, the suggestion unit can combine the customer's purchase history with the movement line data to identify areas where the customer has a high level of interest in a specific product and make the optimal proposal for that area. The suggestion unit can also analyze the movement line data based on the customer's purchase history to make a proposal that will increase the customer's interest in a specific product. The suggestion unit can also combine the customer's purchase history with the movement line data to make a proposal that will increase the customer's interest in a specific product. In this way, by combining the customer's purchase history with the movement line data, it is possible to make a proposal that will increase the customer's interest in a specific product.

[0059] When making a proposal, the proposal unit can make the optimal proposal for each event based on data from a specific event or promotion period. For example, if a specific event is being held in the store, the proposal unit can make the optimal proposal based on data from that area. Furthermore, during a promotion period, the proposal unit can make proposals that enhance the effectiveness of the promotion based on data from around a specific product. Furthermore, the proposal unit can make proposals that increase the customer's desire to purchase based on data from the event or promotion period. In this way, the optimal proposal for each event can be made based on data from the specific event or promotion period.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects in-store traffic flow data and product display information. For example, the collection unit uses cameras and sensors inside the store to record detailed data on customer traffic flow and product placement. Specifically, it collects data such as which aisles customers passed through, which products they showed interest in, and their movement routes and dwell times. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes efficient and attractive displays and layouts. Based on customer movement data and product display information, the analysis unit makes suggestions, such as placing popular products in aisles frequently visited by customers, or arranging products in a way that will increase customer purchasing motivation. Step 3: The proposal unit reflects the displays and layouts proposed by the analysis unit in the actual store design. For example, the proposal unit optimizes the store design based on the displays and layouts proposed by the AI, making suggestions such as widening aisles and arranging products so that customers can find products easily. This improves the customer experience and sales efficiency.

[0062] (Example 2) A store design proposal system according to an embodiment of the present invention proposes efficient and attractive store designs based on in-store traffic flow data and product display information. The store design proposal system collects in-store traffic flow data and product display information, analyzes them using AI, and proposes efficient and attractive displays and layouts to improve customer experience and sales efficiency. For example, the store design proposal system uses in-store cameras and sensors to record detailed customer traffic flow and product layouts. For example, data such as which aisles customers pass through and which products they are interested in is collected. The store design proposal system then analyzes the collected data using AI to propose efficient and attractive displays and layouts. For example, placing popular products in aisles frequently visited by customers can improve sales efficiency. Furthermore, the store design proposal system allows retailers to optimize their store designs based on the displays and layouts proposed by the AI. For example, widening aisles and arranging products to allow customers to easily find products can increase customer satisfaction. This allows the store design proposal system to realize optimal store designs backed by data and strengthen competitiveness. This allows the store design proposal system to change the store layout based on customer movement data, thereby extending customer stay time and increasing purchasing motivation. In addition, by arranging products in a creative way, it makes it easier for customers to pick up products, improving sales efficiency.

[0063] A store design proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects in-store traffic flow data and product display information. The collection unit, for example, uses in-store cameras and sensors to record detailed customer traffic flow and product placement. For example, the collection unit can collect data such as which aisles customers pass through and which products they are interested in. The collection unit can also record customers' movement paths and stay times. The analysis unit analyzes the data collected by the collection unit and proposes efficient and attractive display and placement. The analysis unit proposes efficient and attractive display and placement based on, for example, customer traffic flow data and product display information. For example, the analysis unit can propose placing popular products in aisles frequently visited by customers. The analysis unit can also propose product placement to increase customer purchasing motivation. The proposal unit reflects the display and placement proposed by the analysis unit in the actual store design. The proposal unit, for example, optimizes the store design based on the display and placement proposed by AI. For example, the suggestion unit can make suggestions to widen aisles and improve product layout so that customers can find products more easily. This allows the store design suggestion system according to the embodiment to improve customer experience and sales efficiency. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can provide specific procedures for optimizing store design based on the displays and layouts suggested by AI.

[0064] The collection unit can specifically record customer movement and product placement status using in-store cameras and sensors. The collection unit, for example, records customer movement using in-store cameras. For example, the collection unit can use cameras to record which aisles customers pass through and which products they are interested in. The collection unit can also record product placement status using in-store sensors. For example, the collection unit can use sensors to record product placement locations and display methods. This allows for more accurate data collection by recording customer movement and product placement status in detail. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired by cameras and sensors into AI and have the AI ​​analyze the data.

[0065] The analysis unit can propose specific display and layout based on customer movement data and product display information. The analysis unit proposes efficient and attractive display and layout based on, for example, customer movement data. For example, the analysis unit can propose placing popular products in aisles that customers frequently pass through. The analysis unit can also make proposals to increase customer purchasing motivation by improving product layout. For example, the analysis unit can propose product placement and display methods that make products more accessible to customers. This can improve sales efficiency by proposing efficient and attractive display and layout based on customer movement data and product display information. Some or all of the above-described processing by the analysis unit may be performed using, or without, AI. For example, the analysis unit can input customer movement data and product display information into AI and have the AI ​​execute proposals for efficient and attractive display and layout.

[0066] The suggestion unit can specifically adjust the store design based on the display and layout proposed by the AI. The suggestion unit, for example, optimizes the store design based on the display and layout proposed by the AI. For example, the suggestion unit can make suggestions to widen aisles and improve product layout so that customers can easily find products. The suggestion unit can also make suggestions to increase customers' purchasing motivation by specifically adjusting the product placement location and display method. For example, the suggestion unit can make suggestions to improve product placement so that customers can easily pick up products. This makes it possible to improve the customer experience by optimizing the store design based on the display and layout proposed by the AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can provide specific procedures for optimizing the store design based on the display and layout proposed by the AI.

[0067] The analysis unit can make a suggestion to place popular products in aisles frequently used by customers. For example, the analysis unit can identify aisles frequently used by customers based on customer movement data and make a suggestion to place popular products in those aisles. The analysis unit can also identify popular products based on customer purchase histories and make a suggestion to place those products in aisles frequently used by customers. This can improve sales efficiency by placing popular products in aisles frequently used by customers. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input customer movement data and purchase histories into AI and have the AI ​​execute a suggestion to place popular products.

[0068] The suggestion unit can make suggestions to widen aisles and improve product layout so that customers can find products more easily. The suggestion unit, for example, makes suggestions to widen aisles so that customers can find products more easily. For example, the suggestion unit can make suggestions to widen aisles based on customer movement line data. The suggestion unit can also make suggestions to improve product layout so that customers can easily pick up products. For example, the suggestion unit can make suggestions to improve product placement and display methods so that customers can find products more easily. This allows customers to find products more easily, thereby improving customer satisfaction. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input customer movement line data into AI and cause the AI ​​to execute suggestions to widen aisles and improve product layout.

[0069] The collection unit can estimate a customer's emotions and adjust the timing of collecting movement line data based on the estimated customer emotions. For example, the collection unit estimates a customer's emotions and adjusts the timing of collecting movement line data based on the estimated customer emotions. For example, if a customer is excited, the collection unit increases the frequency of collecting movement line data and records detailed behavioral patterns. Furthermore, if a customer is relaxed, the collection unit can set the frequency of collecting movement line data lower than usual and record the customer's behavior for a longer period of time. Furthermore, if a customer is feeling stressed, the collection unit can temporarily stop collecting movement line data and resume it after the customer's stress has been alleviated. This allows for more accurate data collection by adjusting the timing of collecting movement line data according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input customer emotion data into AI and have the AI ​​adjust the timing of collecting movement data.

[0070] The collection unit can monitor the congestion status in the store in real time and change the frequency of data collection depending on the level of congestion. For example, the collection unit can monitor the congestion status in the store in real time and change the frequency of data collection depending on the level of congestion. For example, when the store is crowded, the collection unit increases the frequency of data collection and acquires detailed movement line data. Furthermore, when the store is empty, the collection unit can set the frequency of data collection to a low level and acquire movement line data over a long period of time. Furthermore, when a specific area is crowded, the collection unit can increase the frequency of data collection in that area and decrease the frequency in other areas. This enables efficient data collection by changing the frequency of data collection depending on the congestion status in the store. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the congestion status data in the store into AI and have the AI ​​adjust the frequency of data collection.

[0071] The collection unit can combine a customer's purchase history and movement data to select a collection method that will increase interest in a specific product. For example, the collection unit combines a customer's purchase history and movement data to select a collection method that will increase interest in a specific product. For example, if the collection unit estimates that the customer is interested in a specific product based on the customer's purchase history, it can focus on collecting movement data around that product. The collection unit can also collect movement data for products related to products the customer has previously purchased to increase interest. The collection unit can also combine the customer's purchase history and movement data to identify areas where interest in a specific product is high and strengthen data collection in those areas. In this way, by combining the customer's purchase history and movement data, interest in a specific product can be increased. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the customer's purchase history and movement data into AI and have the AI ​​select a collection method that will increase interest in a specific product.

[0072] The collection unit can collect customer movement data in response to changes in the temperature and lighting in the store. The collection unit collects customer movement data in response to changes in the temperature and lighting in the store, for example. For example, when the temperature in the store rises, the collection unit collects customer movement data and records changes in behavior due to the temperature change. The collection unit can also collect customer movement data in response to changes in the lighting in the store and record changes in behavior due to the change in lighting. The collection unit can also focus on collecting movement data in a specific area in response to changes in the temperature and lighting in the store. In this way, by collecting movement data in response to changes in the temperature and lighting in the store, changes in customer behavior due to environmental changes can be understood. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on changes in the temperature and lighting in the store into AI and have the AI ​​collect movement data.

[0073] The collection unit can estimate a customer's emotions and prioritize the data to be collected based on the estimated customer emotions. For example, the collection unit estimates a customer's emotions and prioritizes the data to be collected based on the estimated customer emotions. For example, if a customer is excited, the collection unit prioritizes collecting the customer's movement data and recording detailed behavioral patterns. Furthermore, if a customer is relaxed, the collection unit can prioritize collecting purchase history data and recording long-term behavior. Furthermore, if a customer is stressed, the collection unit can prioritize collecting the customer's emotion data and identifying the cause of the stress. This enables efficient data collection by prioritizing the data to be collected based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input customer emotion data into an AI and have the AI ​​determine the priority of the data to be collected.

[0074] The collection unit can collect customer movement data taking into account the acoustic environment in the store. The collection unit collects customer movement data taking into account, for example, the acoustic environment in the store. For example, when the music in the store changes, the collection unit collects customer movement data and records changes in behavior due to the change in music. Furthermore, when the noise level in the store increases, the collection unit can collect customer movement data and record changes in behavior due to the change in noise. Furthermore, the collection unit can focus on collecting movement data in a specific area depending on the acoustic environment in the store. In this way, by collecting movement data depending on the acoustic environment in the store, changes in customer behavior due to changes in acoustics can be understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input in-store acoustic environment data into AI and have the AI ​​collect movement data.

[0075] The collection unit can analyze the social media activities of customers and collect related movement data. The collection unit, for example, analyzes the social media activities of customers and collects related movement data. For example, the collection unit collects movement data of places where customers check in on social media. The collection unit can also analyze the content of customers' social media posts and collect related movement data. The collection unit can also collect related movement data by referring to the activities of the customers' friends on social media. In this way, by analyzing the customers' social media activities, related movement data can be collected and customer behavior can be more accurately understood. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the customers' social media data into AI and have the AI ​​collect related movement data.

[0076] The collection unit can enhance collection of movement line data during specific in-store events or promotional periods. The collection unit enhances collection of movement line data, for example, during specific in-store events or promotional periods. For example, when a specific event is being held in the store, the collection unit focuses on collecting movement line data from that area. The collection unit can also collect movement line data around specific products during the promotional period and analyze the effectiveness of the promotion. The collection unit can also collect customer movement line data in real time during the event or promotional period and analyze it immediately. In this way, by enhancing collection of movement line data during the specific event or promotional period, the effectiveness of the event or promotion can be analyzed. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input movement line data from the event or promotional period into AI and have the AI ​​enhance data collection.

[0077] The analysis unit can estimate a customer's emotions and adjust the analysis algorithm based on the estimated customer emotions. For example, the analysis unit estimates a customer's emotions and adjusts the analysis algorithm based on the estimated customer emotions. For example, if a customer is excited, the analysis unit adjusts the analysis algorithm for the movement path data to extract detailed behavioral patterns. Furthermore, if a customer is relaxed, the analysis unit can adjust the analysis algorithm for the purchase history data to analyze long-term behavior. Furthermore, if a customer is stressed, the analysis unit can adjust the analysis algorithm for the emotion data to identify the cause of the stress. By adjusting the analysis algorithm according to the customer's emotions, more accurate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input customer emotion data into an AI and have the AI ​​adjust the analysis algorithm.

[0078] The analysis unit can analyze optimal display layouts by combining the flow line data and product sales data. For example, the analysis unit can analyze optimal display layouts by combining the flow line data and product sales data. For example, the analysis unit can combine the flow line data and sales data to analyze optimal display layouts by placing popular products in aisles frequently visited by customers. The analysis unit can also analyze product layouts based on the flow line data and sales data to enhance customer purchasing motivation. The analysis unit can also combine the flow line data and sales data to identify areas where interest in specific products is high and analyze optimal layouts for those areas. By combining the flow line data and sales data, optimal display layouts can be analyzed and sales efficiency can be improved. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the flow line data and sales data into AI and have the AI ​​analyze optimal display layouts.

[0079] The analysis unit can analyze customer movement patterns in the store at different time periods and propose optimal layouts for each time period. The analysis unit, for example, analyzes customer movement patterns in the store at different time periods and proposes optimal layouts for each time period. For example, the analysis unit can analyze movement data for different time periods in the store and propose optimal layouts for each time period. The analysis unit can also propose optimal layouts for displaying popular products during specific time periods based on customer movement patterns. The analysis unit can also analyze movement data for each time period and propose layouts that will increase customer purchasing motivation. In this way, by analyzing customer movement patterns for different time periods, optimal layouts for each time period can be proposed. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input movement data for different time periods into AI and have the AI ​​propose optimal layouts for each time period.

[0080] The analysis unit can analyze the effectiveness of different display layouts based on the age group and gender of the customer. The analysis unit analyzes the effectiveness of different display layouts based on, for example, the age group and gender of the customer. For example, the analysis unit can analyze the effectiveness of different display layouts based on the age group of the customer and propose an optimal layout. The analysis unit can also analyze the effectiveness of different display layouts based on the gender of the customer and propose an optimal layout. The analysis unit can also analyze the effectiveness of different display layouts by combining the age group and gender of the customer and propose an optimal layout. In this way, by analyzing the effectiveness of display layouts based on the age group and gender of the customer, a more effective layout can be proposed. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer age group and gender data into AI and have the AI ​​analyze the effectiveness of different display layouts.

[0081] The analysis unit can analyze the display layout in response to changes in the lighting and acoustic environment in the store. The analysis unit analyzes the display layout in response to changes in the lighting and acoustic environment in the store, for example. For example, when the lighting in the store changes, the analysis unit analyzes the display layout and analyzes the effect of the lighting change. The analysis unit can also analyze the display layout in response to changes in the acoustic environment in the store and analyze the effect of the acoustic change. The analysis unit can also analyze the display layout in a specific area in response to changes in the lighting and acoustic environment in the store. In this way, by analyzing the display layout in response to changes in the lighting and acoustic environment in the store, it is possible to understand changes in customer behavior due to environmental changes. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the lighting and acoustic environment in the store into AI and have the AI ​​perform an analysis of the display layout.

[0082] The analysis unit can combine customer purchase history and movement line data to analyze a layout that will increase interest in a specific product. For example, the analysis unit combines customer purchase history and movement line data to analyze a layout that will increase interest in a specific product. For example, the analysis unit combines customer purchase history and movement line data to identify an area where interest in a specific product is high and analyze the optimal layout for that area. The analysis unit can also analyze the movement line data based on the customer purchase history and propose a layout that will increase interest in a specific product. The analysis unit can also combine customer purchase history and movement line data to analyze a layout that will increase interest in a specific product. In this way, by combining customer purchase history and movement line data, it is possible to analyze a layout that will increase interest in a specific product. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input customer purchase history and movement line data into AI and have the AI ​​analyze a layout that will increase interest in a specific product.

[0083] The analysis unit can analyze data from specific in-store events or promotional periods and propose optimal layouts for each event. For example, the analysis unit can analyze data from specific in-store events or promotional periods and propose optimal layouts for each event. For example, when a specific event is being held in the store, the analysis unit can analyze data from that area and propose optimal layouts. The analysis unit can also analyze data around specific products during promotional periods and propose layouts that enhance the effectiveness of the promotion. The analysis unit can also analyze data from events or promotional periods and propose layouts that increase customer purchasing motivation. In this way, by analyzing data from specific events or promotional periods, optimal layouts for each event can be proposed. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from events or promotional periods into AI and have the AI ​​propose optimal layouts.

[0084] The suggestion unit can estimate the customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. For example, the suggestion unit can estimate the customer's emotions and adjust the way the suggestion is presented based on the estimated customer emotions. For example, if the customer is excited, the suggestion unit can provide detailed suggestions and visually display their behavioral patterns. Furthermore, if the customer is relaxed, the suggestion unit can provide simple suggestions and display their behavior over time. Furthermore, if the customer is stressed, the suggestion unit can provide suggestions that identify the cause of the stress. This enables more effective suggestions by adjusting the way the suggestion is presented based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input customer emotion data into an AI and have the AI ​​adjust the way the suggestion is presented.

[0085] The suggestion unit can adjust the level of detail of the suggestion based on product sales data when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on product sales data when making a suggestion. For example, the suggestion unit makes a detailed suggestion for a product with high sales, specifically indicating how to arrange and display the product. Furthermore, the suggestion unit can make a simple suggestion for a product with low sales, indicating a basic arrangement and display method. Furthermore, the suggestion unit can adjust the arrangement and display method of the product based on the sales data and make an optimal suggestion. In this way, adjusting the level of detail of the suggestion based on the product sales data enables more effective suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input product sales data into AI and cause the AI ​​to adjust the level of detail of the suggestion.

[0086] The proposal unit can apply different proposal algorithms according to different store layouts when making a proposal. For example, the proposal unit can apply different proposal algorithms according to different store layouts when making a proposal. For example, if the store layout is large, the proposal unit can apply a proposal algorithm suitable for large spaces. Furthermore, if the store layout is small, the proposal unit can apply a proposal algorithm suitable for small spaces. Furthermore, if the store layout is complex, the proposal unit can apply a proposal algorithm suitable for complex layouts. This enables more appropriate proposals by applying proposal algorithms according to different store layouts. Some or all of the above-described processing in the proposal unit can be performed using AI, for example, or without AI. For example, the proposal unit can input store layout data into AI and cause the AI ​​to apply different proposal algorithms.

[0087] The proposal unit can improve the accuracy of a proposal by referring to past proposal results when making a proposal. For example, the proposal unit can improve the accuracy of a proposal by referring to past proposal results when making a proposal. For example, the proposal unit applies an algorithm that improves the accuracy of a proposal based on past proposal results. The proposal unit can also analyze past proposal results and extract successful proposal patterns to improve the accuracy of the proposal. The proposal unit can also build a feedback loop for improving the accuracy of a proposal by referring to past proposal results. In this way, the accuracy of a proposal can be improved by referring to past proposal results. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal result data into AI and cause the AI ​​to improve the accuracy of the proposal.

[0088] The suggestion unit can estimate the customer's emotions and prioritize suggestions based on the estimated customer emotions. For example, the suggestion unit can estimate the customer's emotions and prioritize suggestions based on the estimated customer emotions. For example, if the customer is excited, the suggestion unit can prioritize detailed suggestions and visually display their behavioral patterns. Furthermore, if the customer is relaxed, the suggestion unit can prioritize simple suggestions and display their behavior over time. Furthermore, if the customer is stressed, the suggestion unit can prioritize suggestions that identify the cause of the stress. This enables more effective suggestions by prioritizing suggestions based on the customer's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input customer emotion data into an AI and have the AI ​​determine the priority of suggestions.

[0089] The suggestion unit can adjust the suggestion content taking into account the lighting and acoustic environment in the store when making a suggestion. For example, the suggestion unit adjusts the suggestion content taking into account the lighting and acoustic environment in the store when making a suggestion. For example, if the lighting in the store is bright, the suggestion unit adjusts the suggestion content to be suitable for a bright environment. Furthermore, if the acoustic environment in the store is quiet, the suggestion unit can adjust the suggestion content to be suitable for a quiet environment. Furthermore, the suggestion unit can adjust the suggestion content for a specific area depending on the lighting and acoustic environment in the store. This enables more appropriate suggestions by taking into account the lighting and acoustic environment in the store. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the lighting and acoustic environment in the store into AI and have the AI ​​adjust the suggestion content.

[0090] The suggestion unit can combine the customer's purchase history and movement data to make a suggestion that will increase interest in a specific product. For example, the suggestion unit can combine the customer's purchase history and movement data to make a suggestion that will increase interest in a specific product. For example, the suggestion unit can combine the customer's purchase history and movement data to identify areas where interest in a specific product is high and make optimal suggestions for those areas. The suggestion unit can also analyze the movement data based on the customer's purchase history to make a suggestion that will increase interest in a specific product. The suggestion unit can also combine the customer's purchase history and movement data to make a suggestion that will increase interest in a specific product. By combining the customer's purchase history and movement data, it is possible to make a suggestion that will increase interest in a specific product. Some or all of the above-described processing by the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the customer's purchase history and movement data into AI and cause the AI ​​to execute a suggestion that will increase interest in a specific product.

[0091] The suggestion unit can make optimal suggestions for each event based on data from a specific event or promotion period when making a suggestion. For example, the suggestion unit can make optimal suggestions for each event based on data from a specific event or promotion period when making a suggestion. For example, when a specific event is being held in a store, the suggestion unit can make optimal suggestions based on data from that area. Furthermore, during a promotion period, the suggestion unit can make suggestions that enhance the effectiveness of the promotion based on data from around a specific product. Furthermore, the suggestion unit can make suggestions that increase customer purchasing motivation based on data from the event or promotion period. This makes it possible to make optimal suggestions for each event based on data from the specific event or promotion period. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data from the event or promotion period into AI and have the AI ​​execute optimal suggestions. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can use the camera 42 and sensors of the smart device 14 to record detailed data on customer traffic and product placement. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect data such as which aisles customers pass through and which products they are interested in. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and suggests efficient and attractive display and layout. The suggestion unit, realized, for example, by the control unit 46A of the smart device 14, optimizes store design based on the display and layout suggested by AI. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can use the camera 42 and sensors of the smart glasses 214 to record detailed data on customer traffic and product placement. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect data such as which aisles customers pass through and which products they are interested in. The analysis unit, for example, can be realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to suggest efficient and attractive displays and layouts. The suggestion unit, for example, can be realized by the control unit 46A of the smart glasses 214 and optimizes store design based on displays and layouts suggested by AI. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can use the camera 42 and sensors of the headset terminal 314 to record detailed data on customer movement and product placement. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect data such as which aisles customers pass through and which products they are interested in. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and suggests efficient and attractive display and placement. The suggestion unit, realized, for example, by the control unit 46A of the headset terminal 314, optimizes store design based on the display and placement suggested by AI. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can use the camera 42 and sensors of the robot 414 to record detailed data on customer movement and product placement. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect data such as which aisles customers pass through and which products they are interested in. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and proposes efficient and attractive displays and layouts. The proposal unit, realized, for example, by the control unit 46A of the robot 414, optimizes store design based on displays and layouts proposed by AI.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The suggestion unit can estimate the customer's emotions and adjust the music and lighting settings in the store based on the estimated customer's emotions. For example, if the customer is relaxed, the suggestion unit can suggest calm music and soft lighting. If the customer is excited, the suggestion unit can suggest lively music and bright lighting. Furthermore, if the customer is stressed, the suggestion unit can suggest relaxing music and calm lighting. This makes it possible to improve the customer experience by adjusting the store environment according to the customer's emotions.

[0094] The collection unit can select a collection method that combines a customer's purchase history with movement line data to increase the level of interest in a specific product. For example, if the collection unit estimates from the customer's purchase history that the customer is interested in a specific product, it will focus on collecting movement line data around that product. The collection unit can also collect movement line data for products related to products the customer has previously purchased to increase the level of interest. The collection unit can also combine the customer's purchase history with movement line data to identify areas where interest in a specific product is high and strengthen data collection in those areas. In this way, by combining the customer's purchase history with movement line data, it is possible to increase the level of interest in a specific product.

[0095] The analysis unit can estimate the customer's emotions and adjust the product placement based on the estimated customer emotions. For example, if the customer is excited, the analysis unit can suggest placing popular products in prominent locations. Also, if the customer is relaxed, the analysis unit can suggest expanding the product placement to provide a more spacious space. Furthermore, if the customer is feeling stressed, the analysis unit can suggest product placement to reduce stress. In this way, the customer experience can be improved by adjusting the product placement according to the customer's emotions.

[0096] When changing the store layout, the suggestion unit can propose an optimal layout by combining customer movement line data and purchase history. For example, the suggestion unit can propose placing popular products in aisles that customers frequently pass through. The suggestion unit can also propose placing related products nearby based on the customer's purchase history. Furthermore, the suggestion unit can combine customer movement line data and purchase history to propose placing specific products in specific areas. In this way, by combining customer movement line data and purchase history, it is possible to propose an optimal store layout.

[0097] The collection unit can collect customer movement data in response to changes in the temperature and lighting in the store. For example, if the temperature in the store rises, the collection unit collects customer movement data and records changes in behavior due to the temperature change. The collection unit can also collect customer movement data in response to changes in the lighting in the store and record changes in behavior due to the change in lighting. The collection unit can also focus on collecting movement data in specific areas in response to changes in the temperature and lighting in the store. In this way, by collecting movement data in response to changes in the temperature and lighting in the store, it is possible to understand changes in customer behavior due to environmental changes.

[0098] The analysis unit can estimate the customer's emotions and adjust the product placement based on the estimated customer emotions. For example, if the customer is excited, the analysis unit can suggest placing popular products in prominent locations. Also, if the customer is relaxed, the analysis unit can suggest expanding the product placement to provide a more spacious space. Furthermore, if the customer is feeling stressed, the analysis unit can suggest product placement to reduce stress. In this way, the customer experience can be improved by adjusting the product placement according to the customer's emotions.

[0099] The suggestion unit can estimate the customer's emotions and adjust the music and lighting settings in the store based on the estimated customer's emotions. For example, if the customer is relaxed, the suggestion unit can suggest calm music and soft lighting. If the customer is excited, the suggestion unit can suggest lively music and bright lighting. Furthermore, if the customer is stressed, the suggestion unit can suggest relaxing music and calm lighting. This makes it possible to improve the customer experience by adjusting the store environment according to the customer's emotions.

[0100] The collection unit can monitor the congestion status of the store in real time and change the frequency of data collection depending on the level of congestion. For example, when the store is crowded, the collection unit increases the frequency of data collection and acquires detailed movement line data. In addition, when the store is empty, the collection unit can set the frequency of data collection to a low level and acquire movement line data over a long period of time. In addition, when a specific area is crowded, the collection unit can increase the frequency of data collection for that area and decrease the frequency for other areas. This allows for efficient data collection by changing the frequency of data collection depending on the congestion status of the store.

[0101] When making a proposal, the suggestion unit can combine the customer's purchase history with the movement line data to make a proposal that will increase the customer's interest in a specific product. For example, the suggestion unit can combine the customer's purchase history with the movement line data to identify areas where the customer has a high level of interest in a specific product and make the optimal proposal for that area. The suggestion unit can also analyze the movement line data based on the customer's purchase history to make a proposal that will increase the customer's interest in a specific product. The suggestion unit can also combine the customer's purchase history with the movement line data to make a proposal that will increase the customer's interest in a specific product. In this way, by combining the customer's purchase history with the movement line data, it is possible to make a proposal that will increase the customer's interest in a specific product.

[0102] When making a proposal, the proposal unit can make the optimal proposal for each event based on data from a specific event or promotion period. For example, if a specific event is being held in the store, the proposal unit can make the optimal proposal based on data from that area. Furthermore, during a promotion period, the proposal unit can make proposals that enhance the effectiveness of the promotion based on data from around a specific product. Furthermore, the proposal unit can make proposals that increase the customer's desire to purchase based on data from the event or promotion period. In this way, the optimal proposal for each event can be made based on data from the specific event or promotion period.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects in-store traffic flow data and product display information. For example, the collection unit uses cameras and sensors inside the store to record detailed data on customer traffic flow and product placement. Specifically, it collects data such as which aisles customers passed through, which products they showed interest in, and their movement routes and dwell times. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes efficient and attractive displays and layouts. Based on customer movement data and product display information, the analysis unit makes suggestions, such as placing popular products in aisles frequently visited by customers, or arranging products in a way that will increase customer purchasing motivation. Step 3: The proposal unit reflects the displays and layouts proposed by the analysis unit in the actual store design. For example, the proposal unit optimizes the store design based on the displays and layouts proposed by the AI, making suggestions such as widening aisles and arranging products so that customers can find products easily. This improves the customer experience and sales efficiency.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects in-store traffic flow data and product display information; an analysis unit that analyzes the data collected by the collection unit and proposes specific display and layout; a proposal unit that reflects the display and layout proposed by the analysis unit in an actual store design. A system characterized by:

2. The collecting unit In-store cameras and sensors are used to record specific customer movements and product placement.

2. The system of claim 1.

3. The analysis unit Propose specific display and layout ideas based on customer traffic data and product display information 2. The system of claim 1.

4. The proposal unit Specific store design adjustments based on displays and layouts suggested by AI 2. The system of claim 1.

5. The analysis unit Propose placement of popular products in aisles frequently visited by customers 2. The system of claim 1.

6. The proposal unit Propose ways to widen aisles and improve product placement so that customers can find products more easily.

2. The system of claim 1.

7. The collecting unit Estimate customer sentiment and adjust the timing of collection of movement data based on the estimated sentiment 2. The system of claim 1.

8. The collecting unit Monitor in-store congestion in real time and change the frequency of data collection depending on the level of congestion.

2. The system of claim 1.

9. The collecting unit Select a collection method that combines customer purchase history and movement data to increase interest in specific products 2. The system of claim 1.

10. The collecting unit Collect customer movement data according to changes in temperature and lighting in the store 2. The system of claim 1.

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