System

Abstract

An object of a system according to an embodiment is to collect and analyze customer behavior data and provide useful insights for product development.SOLUTION: A system includes a data collection part, a data analysis part, and an insight provision part. The data collection unit collects data from the sensor and the camera. The data analysis unit analyzes the data collected by the data collection unit. The insight provider provides the results analyzed by the data analyzer.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 faced the challenge of making it difficult to effectively collect and analyze customer behavior data in stores and online shops and to gain insights useful for product development.

[0005] The system according to the embodiment aims to collect and analyze customer behavior data and provide useful insights for product development. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and an insight provision unit. The data collection unit collects data from sensors and cameras. The data analysis unit analyzes the data collected by the data collection unit. The insight provision unit provides the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect and analyze customer behavior data and provide useful insights for product development. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 customer behavior analysis system according to an embodiment of the present invention uses AI to analyze data collected from in-store surveillance cameras and sensors installed in the user interface of an online shop, understands customer behavior patterns and product interests, and provides useful insights to product development teams. As a result, the customer behavior analysis system can obtain a detailed understanding of customer behavior patterns and product interests, and provide useful insights to product development teams.

[0029] A customer behavior analysis system according to an embodiment includes a data collection unit, a data analysis unit, and an insight provision unit. The data collection unit collects data from sensors and cameras. For example, the data collection unit records customer behavior using in-store surveillance cameras. The data collection unit can also collect user behavior data using sensors installed in the user interface of an online shop. The data collection unit can also collect environmental data using, for example, temperature sensors and motion sensors. The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit analyzes customer behavior patterns using a generation AI. The data analysis unit can also analyze customer interest levels using the generation AI. The data analysis unit can also analyze customer reviews and comments using, for example, natural language processing technology. The insight provision unit provides the results of the analysis by the data analysis unit. For example, the insight provision unit provides useful insights to a product development team based on the results of the analysis by the generation AI. The insight provision unit can also provide insights to a marketing team or a sales team based on the results of the analysis by the generation AI. The insight providing unit can also display insights in real time, for example, using a dashboard. This allows the customer behavior analysis system according to the embodiment to gain a detailed understanding of customer behavior patterns and product interest, and provide useful insights to the product development team. For example, if customers show a high level of interest in a particular product, this information can be used to develop or improve a new product. Furthermore, information can be provided to optimize store layout and product placement. This is expected to improve customer satisfaction and increase sales.

[0030] The data collection unit can dynamically change the installation locations of sensors and cameras, automatically adjusting them to the optimal positions according to customer behavior patterns. For example, the data collection unit can introduce a system that can dynamically change the installation locations of sensors and cameras, and automatically adjust them to the optimal positions according to customer behavior patterns. For example, if customers are concentrated in a specific area, the sensors and cameras can be automatically moved to that area. The data collection unit can also, for example, analyze customer behavior patterns in real time and adjust the positions of sensors and cameras based on the results. This makes it possible to optimize the positions of sensors and cameras according to customer behavior patterns.

[0031] The data collection unit can simultaneously collect not only customer movements but also facial expressions and eye movements, thereby obtaining more detailed behavioral data. The data collection unit uses, for example, sensors and cameras to build a system that simultaneously collects not only customer movements but also facial expressions and eye movements. For example, it records which products customers are looking at and what facial expressions they are making. The data collection unit can also analyze customer gaze movements using, for example, eye tracking technology. The data collection unit can also analyze customer facial expressions using, for example, facial recognition technology. This makes it possible to obtain detailed behavioral data about customers.

[0032] The data collection unit can identify products of interest by collecting voice data within the store and analyzing what customers are saying about products. The data collection unit, for example, builds a system that collects voice data within the store and analyzes what customers are saying about products. For example, when a customer talks about a particular product, the data collection unit records that data. The data collection unit can also use, for example, voice recognition technology to convert the content of the customer's conversation into text data and analyze it. The data collection unit can also build a system that identifies products of interest based on, for example, the customer's voice data. This makes it possible to identify products that the customer is interested in.

[0033] The data collection unit can collect users' mouse movements and click patterns in the user interface of the online shop and analyze them as behavioral data. The data collection unit, for example, builds a system that collects users' mouse movements and click patterns in the user interface of the online shop. For example, it records which products the users click on. The data collection unit can also track users' mouse movements in real time and analyze the data. The data collection unit can also build a system that analyzes users' click patterns and collects them as behavioral data. This makes it possible to collect and analyze users' behavioral data in the online shop.

[0034] When analyzing behavioral data, the data analysis unit also takes into account past purchase history and reviews, enabling more accurate interest analysis. For example, the data analysis unit builds a system in which, when the generation AI analyzes customer behavioral data, it also takes into account past purchase history and reviews. For example, it analyzes interest levels based on the products a customer has previously purchased and the content of their reviews. The data analysis unit can also predict products that a customer is likely to purchase next, for example, based on the customer's purchase history. The data analysis unit can also analyze customer reviews and evaluate products, for example. This enables more accurate interest analysis by taking into account past purchase history and reviews.

[0035] When analyzing behavioral patterns, the data analysis unit can also take into account external factors such as seasons and weather, and identify factors that cause behavioral fluctuations. For example, the data analysis unit builds a system in which external factors such as seasons and weather are taken into account when the generation AI analyzes customer behavioral patterns. For example, it analyzes seasonal purchasing trends and fluctuations in store visit frequency due to weather. The data analysis unit can also analyze customer behavioral patterns by taking into account external factors such as economic conditions and social events. The data analysis unit can also build a system that integrates and analyzes external factors and customer behavioral data. This makes it possible to identify factors that cause behavioral fluctuations by taking into account external factors such as seasons and weather.

[0036] The data analysis unit can predict behavioral patterns and future trends based on the analyzed data. For example, the data analysis unit predicts customer behavioral patterns based on data analyzed by the generation AI, and builds a system that predicts future trends. For example, it predicts popular products for the next season based on past data. The data analysis unit can also predict future purchasing trends based on customer behavioral data. The data analysis unit can also predict future market trends using, for example, a trend prediction algorithm. This makes it possible to predict future trends.

[0037] The data analysis unit can cluster behavioral patterns based on the analyzed data and identify the level of interest for each different customer segment. The data analysis unit, for example, builds a system that clusters customer behavioral patterns based on data analyzed by the generation AI and identifies the level of interest for each different customer segment. For example, it analyzes the level of interest by age and gender. The data analysis unit can also analyze purchasing trends for each different segment based on customer purchase history, for example. The data analysis unit can also cluster customer behavioral data and develop marketing strategies for specific segments, for example. This makes it possible to identify the level of interest for each different customer segment.

[0038] The insights provision department can share the insights provided by the generation AI not only with the product development team but also with the marketing team and sales team, and utilize them in company-wide strategy planning. The insights provision department, for example, builds a system that shares the insights provided by the generation AI not only with the product development team but also with the marketing team and sales team. For example, it creates a dashboard to be used in company-wide strategy planning. The insights provision department can also, for example, develop marketing strategies and sales strategies based on the insights provided by the generation AI. The insights provision department can also, for example, determine the direction of product development based on the insights provided by the generation AI. This makes it possible to utilize insights in company-wide strategy planning.

[0039] The insight providing unit can display the insights provided by the generation AI on a dashboard in real time to support immediate decision-making. The insight providing unit, for example, builds a system that displays the insights provided by the generation AI on a dashboard in real time to support immediate decision-making. For example, it displays sales data and customer interest in real time. The insight providing unit can also make quick decisions based on the insights displayed in real time. The insight providing unit can also customize the dashboard to highlight specific data, for example. This makes it possible to display insights in real time to support immediate decision-making.

[0040] The insight providing unit can integrate the insights provided by the generation AI with other data sources to provide more comprehensive insights. For example, the insight providing unit can integrate the insights provided by the generation AI with social media trend data to build a system that provides more comprehensive insights. For example, the insight providing unit can compare customer interest levels with social media trends. The insight providing unit can also integrate the insights provided by the generation AI with sales data and market research data. The insight providing unit can also integrate multiple data sources to build a system that provides comprehensive insights. This makes it possible to provide more comprehensive insights by integrating with other data sources.

[0041] The insight providing unit can compare the insights provided by the generation AI with data from different industries and markets and utilize them for competitive analysis and market forecasting. The insight providing unit, for example, builds a system that compares the insights provided by the generation AI with data from different industries and markets and utilizes them for competitive analysis and market forecasting. For example, it compares a competitor's product data with the company's own insights. The insight providing unit can also predict future market trends based on data from different markets, for example. The insight providing unit can also perform competitive analysis based on data from different industries, for example. This allows comparison with data from different industries and markets to be utilized for competitive analysis and market forecasting.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data collection unit collects customers' purchase histories and online browsing histories, and can analyze customers' interests based on this data. For example, it collects data on products that customers have purchased in the past and product pages that they have viewed. The data collection unit can also record keywords that customers have searched for in online shops, for example, and identify product categories in which they are interested. The data collection unit can also collect product information that customers have shared on social media, for example, and analyze their level of interest. This enables more detailed interest analysis based on customers' purchase histories and online behavior data.

[0044] The data collection unit collects environmental data such as the temperature, humidity, and lighting brightness in the store, and can analyze customer behavior patterns based on this data. For example, it analyzes the tendency for customers to stay longer in the store when the temperature is high. The data collection unit can also analyze, for example, the impact of lighting brightness on customers' willingness to purchase. The data collection unit can also analyze customer behavior patterns when the humidity is high, for example, and identify optimal environmental conditions. This makes it possible to analyze customer behavior patterns in detail based on environmental data.

[0045] The data collection unit can collect location information from customers' smartphones or wearable devices and analyze their movement patterns within the store. For example, it can identify which areas customers spend the most time in. The data collection unit can also record the amount of time customers spend standing in front of specific products and identify products they are interested in. The data collection unit can also analyze the congestion situation within the store based on customers' movement patterns and propose optimal layouts. This makes it possible to analyze detailed behavioral patterns based on customers' location information.

[0046] The data collection unit can dynamically adjust environmental elements such as music and fragrances in the store to increase customer purchasing motivation. For example, music that increases purchasing motivation can be played when a customer is in a specific area. The data collection unit can also increase purchasing motivation by, for example, diffusing a relaxing fragrance in the store. The data collection unit can also build a system that dynamically adjusts optimal environmental elements based on, for example, customer behavior patterns. This makes it possible to dynamically adjust environmental elements to increase customer purchasing motivation.

[0047] The data collection unit can make personalized product recommendations based on the customer's purchase history and behavioral data. For example, it can recommend related products based on products the customer has purchased in the past. The data collection unit can also recommend products that the customer is interested in based on the customer's online browsing history, for example. The data collection unit can also build a system that analyzes the customer's behavioral patterns and recommends products at the optimal time. This makes it possible to make personalized product recommendations based on the customer's purchase history and behavioral data.

[0048] The data analysis unit can propose optimal product placement within a store based on customer behavior data. For example, popular products can be placed in areas where customers frequently visit. The data analysis unit can also analyze customer behavior patterns and optimize product placement. The data analysis unit can also build a system that places related products nearby based on customer purchase history, for example. This makes it possible to propose optimal product placement within a store based on customer behavior data.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data collection unit collects data from sensors and cameras. For example, the data collection unit records customer behavior using surveillance cameras in the store. The data collection unit can also collect user behavior data using sensors installed in the user interface of the online shop. Furthermore, the data collection unit can also collect environmental data using temperature sensors and motion sensors. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit uses generative AI to analyze customer behavior patterns and interest levels. It can also analyze customer reviews and comments using natural language processing technology. Step 3: The insight provider provides the results analyzed by the data analysis unit. For example, the insight provider provides useful insights to the product development team, marketing team, and sales team based on the results analyzed by the generation AI. Insights can also be displayed in real time using a dashboard.

[0051] (Example 2) A customer behavior analysis system according to an embodiment of the present invention uses AI to analyze data collected from in-store surveillance cameras and sensors installed in the user interface of an online shop, understands customer behavior patterns and product interests, and provides useful insights to product development teams. As a result, the customer behavior analysis system can obtain a detailed understanding of customer behavior patterns and product interests, and provide useful insights to product development teams.

[0052] A customer behavior analysis system according to an embodiment includes a data collection unit, a data analysis unit, and an insight provision unit. The data collection unit collects data from sensors and cameras. For example, the data collection unit records customer behavior using in-store surveillance cameras. The data collection unit can also collect user behavior data using sensors installed in the user interface of an online shop. The data collection unit can also collect environmental data using, for example, temperature sensors and motion sensors. The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit analyzes customer behavior patterns using a generation AI. The data analysis unit can also analyze customer interest levels using the generation AI. The data analysis unit can also analyze customer reviews and comments using, for example, natural language processing technology. The insight provision unit provides the results of the analysis by the data analysis unit. For example, the insight provision unit provides useful insights to a product development team based on the results of the analysis by the generation AI. The insight provision unit can also provide insights to a marketing team or a sales team based on the results of the analysis by the generation AI. The insight providing unit can also display insights in real time, for example, using a dashboard. This allows the customer behavior analysis system according to the embodiment to gain a detailed understanding of customer behavior patterns and product interest, and provide useful insights to the product development team. For example, if customers show a high level of interest in a particular product, this information can be used to develop or improve a new product. Furthermore, information can be provided to optimize store layout and product placement. This is expected to improve customer satisfaction and increase sales.

[0053] The data collection unit can dynamically change the installation locations of sensors and cameras, automatically adjusting them to the optimal positions according to customer behavior patterns. For example, the data collection unit can introduce a system that can dynamically change the installation locations of sensors and cameras, and automatically adjust them to the optimal positions according to customer behavior patterns. For example, if customers are concentrated in a specific area, the sensors and cameras can be automatically moved to that area. The data collection unit can also, for example, analyze customer behavior patterns in real time and adjust the positions of sensors and cameras based on the results. This makes it possible to optimize the positions of sensors and cameras according to customer behavior patterns.

[0054] The data collection unit can simultaneously collect not only customer movements but also facial expressions and eye movements, thereby obtaining more detailed behavioral data. The data collection unit uses, for example, sensors and cameras to build a system that simultaneously collects not only customer movements but also facial expressions and eye movements. For example, it records which products customers are looking at and what facial expressions they are making. The data collection unit can also analyze customer gaze movements using, for example, eye tracking technology. The data collection unit can also analyze customer facial expressions using, for example, facial recognition technology. This makes it possible to obtain detailed behavioral data about customers.

[0055] The data collection unit can use the emotion estimation function to collect emotions of customers when they view products in real time, and integrate and analyze the emotion data and behavioral data. The data collection unit, for example, uses the emotion estimation function to build a system that collects emotions of customers when they view products in real time. For example, the data collection unit analyzes facial expressions and voices of customers when they view a particular product to obtain emotion data. The data collection unit can also collect biometric data of customers (heart rate and electrodermal activity) using a sensor, for example, and analyze emotions using an emotion estimation algorithm. The data collection unit can also build a system that integrates and analyzes emotion data and behavioral data, for example. This allows for the integration and analysis of customer emotion data and behavioral data.

[0056] The data collection unit can identify products of interest by collecting voice data within the store and analyzing what customers are saying about products. The data collection unit, for example, builds a system that collects voice data within the store and analyzes what customers are saying about products. For example, when a customer talks about a particular product, the data collection unit records that data. The data collection unit can also use, for example, voice recognition technology to convert the content of the customer's conversation into text data and analyze it. The data collection unit can also build a system that identifies products of interest based on, for example, the customer's voice data. This makes it possible to identify products that the customer is interested in.

[0057] The data collection unit can collect users' mouse movements and click patterns in the user interface of the online shop and analyze them as behavioral data. The data collection unit, for example, builds a system that collects users' mouse movements and click patterns in the user interface of the online shop. For example, it records which products the users click on. The data collection unit can also track users' mouse movements in real time and analyze the data. The data collection unit can also build a system that analyzes users' click patterns and collects them as behavioral data. This makes it possible to collect and analyze users' behavioral data in the online shop.

[0058] The data collection unit can use the emotion estimation function to collect emotions of online shop users in real time when they are viewing product pages, and integrate and analyze the emotion data and behavioral data. The data collection unit, for example, uses the emotion estimation function to build a system that collects emotions of online shop users in real time when they are viewing product pages. For example, the data collection unit analyzes the user's facial expressions and voice to obtain emotion data. The data collection unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor, for example, and analyze the emotion using an emotion estimation algorithm. The data collection unit can also build a system that integrates and analyzes emotion data and behavioral data, for example. This makes it possible to integrate and analyze the emotion data and behavioral data of online shop users.

[0059] When analyzing behavioral data, the data analysis unit also takes into account past purchase history and reviews, enabling more accurate interest analysis. For example, the data analysis unit builds a system in which, when the generation AI analyzes customer behavioral data, it also takes into account past purchase history and reviews. For example, it analyzes interest levels based on the products a customer has previously purchased and the content of their reviews. The data analysis unit can also predict products that a customer is likely to purchase next, for example, based on the customer's purchase history. The data analysis unit can also analyze customer reviews and evaluate products, for example. This enables more accurate interest analysis by taking into account past purchase history and reviews.

[0060] When analyzing behavioral patterns, the data analysis unit can also take into account external factors such as seasons and weather, and identify factors that cause behavioral fluctuations. For example, the data analysis unit builds a system in which external factors such as seasons and weather are taken into account when the generation AI analyzes customer behavioral patterns. For example, it analyzes seasonal purchasing trends and fluctuations in store visit frequency due to weather. The data analysis unit can also analyze customer behavioral patterns by taking into account external factors such as economic conditions and social events. The data analysis unit can also build a system that integrates and analyzes external factors and customer behavioral data. This makes it possible to identify factors that cause behavioral fluctuations by taking into account external factors such as seasons and weather.

[0061] The data analysis unit can use the emotion estimation function to integrate the behavioral data and the emotion data and analyze behavioral patterns based on changes in emotion. The data analysis unit, for example, uses the emotion estimation function to integrate customer behavioral data and emotion data and build a system that analyzes behavioral patterns based on changes in emotion. For example, the data analysis unit analyzes changes in emotion when a customer looks at a specific product. The data analysis unit can also cluster behavioral patterns based on customer emotion data. The data analysis unit can also build a system that integrates and analyzes changes in emotion and behavioral data, for example. This makes it possible to analyze behavioral patterns based on changes in emotion.

[0062] The data analysis unit can predict behavioral patterns and future trends based on the analyzed data. For example, the data analysis unit predicts customer behavioral patterns based on data analyzed by the generation AI, and builds a system that predicts future trends. For example, it predicts popular products for the next season based on past data. The data analysis unit can also predict future purchasing trends based on customer behavioral data. The data analysis unit can also predict future market trends using, for example, a trend prediction algorithm. This makes it possible to predict future trends.

[0063] The data analysis unit can cluster behavioral patterns based on the analyzed data and identify the level of interest for each different customer segment. The data analysis unit, for example, builds a system that clusters customer behavioral patterns based on data analyzed by the generation AI and identifies the level of interest for each different customer segment. For example, it analyzes the level of interest by age and gender. The data analysis unit can also analyze purchasing trends for each different segment based on customer purchase history, for example. The data analysis unit can also cluster customer behavioral data and develop marketing strategies for specific segments, for example. This makes it possible to identify the level of interest for each different customer segment.

[0064] The data analysis unit can use the emotion estimation function to integrate the behavioral data and the emotion data and perform clustering of behavioral patterns based on changes in emotion. The data analysis unit, for example, uses the emotion estimation function to integrate customer behavioral data and emotion data and build a system that performs clustering of behavioral patterns based on changes in emotion. For example, clustering is performed based on changes in emotion when a customer views a specific product. The data analysis unit can also cluster behavioral patterns based on customer emotion data, for example. The data analysis unit can also build a system that integrates and analyzes changes in emotion and behavioral data, for example. This makes it possible to cluster behavioral patterns based on changes in emotion.

[0065] The insights provision department can share the insights provided by the generation AI not only with the product development team but also with the marketing team and sales team, and utilize them in company-wide strategy planning. The insights provision department, for example, builds a system that shares the insights provided by the generation AI not only with the product development team but also with the marketing team and sales team. For example, it creates a dashboard to be used in company-wide strategy planning. The insights provision department can also, for example, develop marketing strategies and sales strategies based on the insights provided by the generation AI. The insights provision department can also, for example, determine the direction of product development based on the insights provided by the generation AI. This makes it possible to utilize insights in company-wide strategy planning.

[0066] The insight providing unit can display the insights provided by the generation AI on a dashboard in real time to support immediate decision-making. The insight providing unit, for example, builds a system that displays the insights provided by the generation AI on a dashboard in real time to support immediate decision-making. For example, it displays sales data and customer interest in real time. The insight providing unit can also make quick decisions based on the insights displayed in real time. The insight providing unit can also customize the dashboard to highlight specific data, for example. This makes it possible to display insights in real time to support immediate decision-making.

[0067] The insight providing unit uses the emotion estimation function to provide insights based on customer emotion data, and can plan emotion-based product development and marketing strategies. The insight providing unit, for example, uses the emotion estimation function to build a system that provides insights based on customer emotion data. For example, product development and marketing strategies are planned based on emotions expressed by customers toward a specific product. The insight providing unit can also, for example, understand customer needs and improve products based on the emotion data. The insight providing unit can also, for example, optimize marketing campaigns based on the emotion data. This makes it possible to provide insights based on emotion data and plan emotion-based product development and marketing strategies.

[0068] The insight providing unit can integrate the insights provided by the generation AI with other data sources to provide more comprehensive insights. For example, the insight providing unit can integrate the insights provided by the generation AI with social media trend data to build a system that provides more comprehensive insights. For example, the insight providing unit can compare customer interest levels with social media trends. The insight providing unit can also integrate the insights provided by the generation AI with sales data and market research data. The insight providing unit can also integrate multiple data sources to build a system that provides comprehensive insights. This makes it possible to provide more comprehensive insights by integrating with other data sources.

[0069] The insight providing unit can compare the insights provided by the generation AI with data from different industries and markets and utilize them for competitive analysis and market forecasting. The insight providing unit, for example, builds a system that compares the insights provided by the generation AI with data from different industries and markets and utilizes them for competitive analysis and market forecasting. For example, it compares a competitor's product data with the company's own insights. The insight providing unit can also predict future market trends based on data from different markets, for example. The insight providing unit can also perform competitive analysis based on data from different industries, for example. This allows comparison with data from different industries and markets to be utilized for competitive analysis and market forecasting.

[0070] The insight providing unit can use the emotion estimation function to compare insights based on customer emotion data with data from different industries and markets, and perform emotion-based competitive analysis and market forecasts. The insight providing unit, for example, uses the emotion estimation function to compare insights based on customer emotion data with data from different industries and markets, and build a system for emotion-based competitive analysis and market forecasts. For example, the emotion data of different markets can be compared. The insight providing unit can also compare a competitor's products with a company's products, for example, based on the emotion data. The insight providing unit can also predict future market trends, for example, based on the emotion data. This makes it possible to compare insights based on emotion data with data from different industries and markets, and perform emotion-based competitive analysis and market forecasts.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The data collection unit collects customers' purchase histories and online browsing histories, and can analyze customers' interests based on this data. For example, it collects data on products that customers have purchased in the past and product pages that they have viewed. The data collection unit can also record keywords that customers have searched for in online shops, for example, and identify product categories in which they are interested. The data collection unit can also collect product information that customers have shared on social media, for example, and analyze their level of interest. This enables more detailed interest analysis based on customers' purchase histories and online behavior data.

[0073] The data collection unit collects environmental data such as the temperature, humidity, and lighting brightness in the store, and can analyze customer behavior patterns based on this data. For example, it analyzes the tendency for customers to stay longer in the store when the temperature is high. The data collection unit can also analyze, for example, the impact of lighting brightness on customers' willingness to purchase. The data collection unit can also analyze customer behavior patterns when the humidity is high, for example, and identify optimal environmental conditions. This makes it possible to analyze customer behavior patterns in detail based on environmental data.

[0074] The data collection unit can collect location information from customers' smartphones or wearable devices and analyze their movement patterns within the store. For example, it can identify which areas customers spend the most time in. The data collection unit can also record the amount of time customers spend standing in front of specific products and identify products they are interested in. The data collection unit can also analyze the congestion situation within the store based on customers' movement patterns and propose optimal layouts. This makes it possible to analyze detailed behavioral patterns based on customers' location information.

[0075] The data collection unit uses the emotion estimation function to collect in real time the stress and satisfaction felt by customers during their in-store experience, and can identify areas for service improvement based on this data. For example, the data collection unit analyzes the stress felt by customers while waiting in line at the register. The data collection unit can also analyze the satisfaction felt by customers in specific areas and identify popular areas. The data collection unit can also suggest areas for service improvement within the store based on, for example, the customer's stress level. This makes it possible to identify areas for service improvement based on customer emotion data.

[0076] The data collection unit can dynamically adjust environmental elements such as music and fragrances in the store to increase customer purchasing motivation. For example, music that increases purchasing motivation can be played when a customer is in a specific area. The data collection unit can also increase purchasing motivation by, for example, diffusing a relaxing fragrance in the store. The data collection unit can also build a system that dynamically adjusts optimal environmental elements based on, for example, customer behavior patterns. This makes it possible to dynamically adjust environmental elements to increase customer purchasing motivation.

[0077] The data collection unit can use the emotion estimation function to collect emotions of customers when they pick up a specific product in real time and evaluate the product's appeal based on the emotion data. For example, the data collection unit can analyze the customer's facial expression when they pick up the product and evaluate the product's appeal. The data collection unit can also analyze the customer's voice data and evaluate the product's appeal. The data collection unit can also build a system for evaluating the product's appeal based on the customer's biometric data, for example. This makes it possible to evaluate the product's appeal based on the emotion data.

[0078] The data collection unit can make personalized product recommendations based on the customer's purchase history and behavioral data. For example, it can recommend related products based on products the customer has purchased in the past. The data collection unit can also recommend products that the customer is interested in based on the customer's online browsing history, for example. The data collection unit can also build a system that analyzes the customer's behavioral patterns and recommends products at the optimal time. This makes it possible to make personalized product recommendations based on the customer's purchase history and behavioral data.

[0079] The data analysis unit can use the emotion estimation function to analyze changes in behavioral patterns based on customer emotion data and develop marketing strategies in response to these changes in emotion. For example, it can analyze changes in emotion when a customer views a particular product and optimize a marketing strategy. The data analysis unit can also evaluate the effectiveness of an advertising campaign based on customer emotion data, for example. The data analysis unit can also develop strategies to increase customer purchasing motivation based on emotion data, for example. This makes it possible to develop marketing strategies based on emotion data.

[0080] The data analysis unit can propose optimal product placement within a store based on customer behavior data. For example, popular products can be placed in areas where customers frequently visit. The data analysis unit can also analyze customer behavior patterns and optimize product placement. The data analysis unit can also build a system that places related products nearby based on customer purchase history, for example. This makes it possible to propose optimal product placement within a store based on customer behavior data.

[0081] The data analysis unit uses the emotion estimation function to cluster behavioral patterns based on customer emotion data and develop marketing strategies tailored to different emotional states. For example, clustering is performed based on the emotional changes customers experience when viewing a particular product. The data analysis unit can also develop advertising campaigns tailored to different emotional states based on customer emotion data. The data analysis unit can also develop strategies to increase customer purchasing motivation based on emotion data. This makes it possible to cluster behavioral patterns and develop marketing strategies based on emotion data.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data collection unit collects data from sensors and cameras. For example, the data collection unit records customer behavior using surveillance cameras in the store. The data collection unit can also collect user behavior data using sensors installed in the user interface of the online shop. Furthermore, the data collection unit can also collect environmental data using temperature sensors and motion sensors. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit uses generative AI to analyze customer behavior patterns and interest levels. It can also analyze customer reviews and comments using natural language processing technology. Step 3: The insight provider provides the results analyzed by the data analysis unit. For example, the insight provider provides useful insights to the product development team, marketing team, and sales team based on the results analyzed by the generation AI. Insights can also be displayed in real time using a dashboard.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 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 data collection unit that collects data from sensors and cameras; a data analysis unit that analyzes the data collected by the data collection unit; an insight providing unit that provides the results analyzed by the data analysis unit. A system characterized by:

2. The data collection unit The installation locations of the sensors and cameras are dynamically changed and automatically adjusted to the optimal positions according to the customer's behavior patterns. The system of claim 1 .

3. The data collection unit Collecting voice data in-store and analyzing what customers are saying about products to identify products of interest The system of claim 1 .

4. The data analysis unit When analyzing behavioral data, past purchase history and reviews are also taken into account to perform more accurate interest analysis. The system of claim 1 .

5. The insight providing unit The insights provided by generative AI can be shared not only with product development teams but also with marketing and sales teams, and used to formulate company-wide strategies. The system of claim 1 .

6. The data collection unit Using emotion estimation functionality, we collect real-time data on customers' emotions when they view products, and then integrate and analyze the emotional data and behavioral data. The system of claim 1 .

7. The data analysis unit Using emotion estimation functionality, behavioral data and emotional data are integrated to analyze behavioral patterns based on changes in emotions. The system of claim 1 .

8. The insight providing unit Using emotion estimation capabilities, we provide insights based on customer emotion data and develop emotion-based product development and marketing strategies. The system of claim 1 .

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