System

Abstract

An object of a system according to an embodiment is to analyze customer data, predict a purchase probability, and propose an optimal marketing strategy.SOLUTION: A system includes a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects customer data. The analysis unit analyzes the customer data collected by the data collection unit. The prediction unit predicts a purchase probability of each customer based on the data analyzed by the analysis unit. The proposal unit proposes an optimal marketing strategy based on the purchase probability predicted by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively utilize customer data to predict purchase probability and propose optimal marketing strategies.

[0005] The system according to the embodiment aims to analyze customer data, predict purchase probabilities, and propose optimal marketing strategies. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects customer data. The analysis unit analyzes the customer data collected by the data collection unit. The prediction unit predicts the purchase probability of each customer based on the data analyzed by the analysis unit. The proposal unit proposes an optimal marketing strategy based on the purchase probability predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze customer data, predict purchase probabilities, and propose optimal marketing strategies. [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) The AI ​​tool according to the embodiment of the present invention is a system that identifies customers with a high probability of purchasing and takes an effective approach to those customers. In this system, AI analyzes customer data, predicts customers with a high probability of purchasing, and proposes optimal marketing strategies for those customers. This enables the AI ​​tool to take an effective approach to customers with a high probability of purchasing.

[0029] An AI tool according to an embodiment includes a data collection unit, an analysis unit, a prediction unit, and a suggestion unit. The data collection unit collects customer data, such as customer purchase history, website browsing history, and social media activity. The data collection unit can also collect data for analyzing customer behavior patterns, such as information on what products customers have purchased in the past, which pages they have viewed, and what comments they have posted. The analysis unit analyzes the customer data collected by the data collection unit. For example, an AI analyzes customer behavior patterns to identify customers with a high probability of purchasing. The analysis unit can also use a machine learning algorithm to model and predict the relationship between customer behavior patterns and purchase probability. The prediction unit predicts each customer's purchase probability based on the data analyzed by the analysis unit. For example, the AI ​​predicts each customer's purchase probability based on the collected data. The prediction unit can also predict customers who are likely to purchase a specific product. The suggestion unit proposes an optimal marketing strategy based on the purchase probability predicted by the prediction unit. For example, this can include providing a discount coupon for a specific product, sending personalized emails, or inviting customers to a specific campaign. The suggestion unit can also propose the most effective approach based on customer preferences and behavioral patterns. This allows the AI ​​tool according to the embodiment to take an effective approach to customers who are likely to purchase. For example, by providing timely discount coupons to customers who are likely to purchase a specific product, it is possible to increase their purchasing motivation. In addition, by sending personalized emails, it is possible to strengthen relationships with customers and increase repeat customers. Furthermore, by measuring the effectiveness of marketing strategies and reflecting this in the next approach, it is possible to continuously implement effective marketing.

[0030] The data collection unit can analyze not only customer purchase histories, but also return histories and customer support inquiry histories. For example, the data collection unit collects customer return histories and analyzes what products are likely to be returned. For example, if a particular product is frequently returned, it suggests that there may be a problem with that product. The data collection unit also collects customer support inquiry histories and analyzes what types of problems are common. For example, if there are many inquiries about a particular product, providing information about that product can improve customer satisfaction. The data collection unit can also combine a customer's purchase history, return history, and customer support inquiry history to analyze the customer's overall behavioral patterns. For example, it can analyze the series of actions of a customer purchasing a particular product, then returning it, and then contacting customer support, to identify the cause of the problem. This allows for a more accurate understanding of the customer's overall behavioral patterns.

[0031] The data collection unit can collect customer location information in real time and incorporate store visit history and movement patterns into the analysis. For example, the data collection unit collects location information from customers' smartphones and analyzes which stores the customers visited. For example, a customer who frequently visits a particular store can be determined to be interested in the products of that store. The data collection unit also analyzes the customer's movement patterns to determine which area the customer lives in and the route the customer takes. For example, if a customer lives in a specific area, it can suggest products and services related to that area. The data collection unit can also collect customer location information in real time and analyze store visit history and movement patterns to gain a more detailed understanding of customer behavior patterns. For example, if customers tend to visit certain stores during certain times of the day, it can develop a marketing strategy tailored to those times. This allows for the development of more effective marketing strategies based on customer location information.

[0032] The data collection unit can analyze a customer's social media followers and friendships to identify influential customers. For example, the data collection unit analyzes a customer's social media account to identify the number of followers and friendships. For example, a customer with a large number of followers can be determined to be highly influential. The data collection unit also collects data on the customer's social media activity and analyzes the types of posts and reactions that are most common. For example, a customer who posts frequently about a particular product can be determined to be interested in that product. The data collection unit can also analyze a customer's social media followers and friendships to identify influential customers, thereby enabling the development of effective marketing strategies. For example, by offering special promotions to influential customers, other customers can be approached through them. This allows the identification of influential customers and the development of effective marketing strategies.

[0033] The data collection unit can identify the purchasing motive by analyzing reviews and ratings from other customers who purchased the same product based on the customer's purchase history. The data collection unit, for example, analyzes the customer's purchase history and collects reviews from other customers who purchased the same product. For example, it analyzes the content of the reviews to identify the purchasing motive. The data collection unit also collects customer rating data and analyzes what types of ratings are most common. For example, if a particular product has many positive ratings, it is determined that the product is popular. The data collection unit can also identify the purchasing motive by combining the customer's purchase history, reviews, and ratings. For example, if a customer purchases a particular product and then posts a positive review, it is determined that the product provides high customer satisfaction. This allows the customer's purchasing motive to be identified and a more effective marketing strategy to be developed.

[0034] The prediction unit can predict the purchase probability using not only the customer's past purchasing behavior but also data related to seasons and events. The prediction unit, for example, predicts the purchase probability by analyzing the customer's past purchasing history in combination with seasonal data. For example, it determines that a customer who tends to purchase a particular product in the summer is likely to purchase the same product again in the following summer. The prediction unit also predicts the purchase probability by analyzing the customer's past purchasing history in combination with event data. For example, it determines that a customer who tends to purchase a particular product during the Christmas season is likely to purchase the same product again in the following Christmas season. The prediction unit can also more accurately predict the customer's purchasing behavior by using data related to seasons and events. This makes it possible to more accurately predict the purchase probability based on the seasons and events.

[0035] The prediction unit can take into account customer life events (marriage, childbirth, moving, etc.) when predicting the purchase probability. The prediction unit, for example, collects customer life event data and incorporates it into the purchase probability prediction. For example, it determines that a customer who is about to get married is likely to purchase wedding-related products. The prediction unit can also predict customers who are likely to purchase specific products based on the customer life event data. For example, it determines that a customer who is about to give birth is likely to purchase baby products. The prediction unit can also use the life event data to more accurately predict customer purchasing behavior. In this way, by taking into account the customer's life events, a more accurate purchase probability can be predicted.

[0036] The prediction unit can incorporate customer health and fitness data into the purchase probability prediction. The prediction unit, for example, collects customer health data and incorporates it into the purchase probability prediction. For example, it determines that a health-conscious customer is more likely to purchase health foods and fitness equipment. The prediction unit also collects customer fitness data and incorporates it into the purchase probability prediction. For example, it determines that a customer who exercises a lot is more likely to purchase fitness-related products. The prediction unit can also use the health and fitness data to more accurately predict customer purchasing behavior. As a result, by taking the customer's health and fitness data into consideration, a more accurate purchase probability prediction can be made.

[0037] The proposal unit can analyze data on customers' responses to past marketing campaigns and identify the most effective approach. For example, the proposal unit collects data on customers' responses to past marketing campaigns and analyzes which campaign was most effective. For example, a similar approach can be repeated for customers who showed a high response rate to a particular campaign. The proposal unit can also propose the most effective marketing strategy based on customer response data. For example, if a particular promotion was effective, the promotion can be implemented again. In this way, the most effective marketing strategy can be proposed based on past response data.

[0038] The proposal unit can analyze data related to the customer's hobbies and interests as well as the customer's purchase history, and make personalized proposals. For example, the proposal unit combines and analyzes the customer's purchase history with data related to the customer's hobbies and interests, and makes personalized marketing proposals. For example, a customer who purchased outdoor equipment can be proposed a promotion for camping equipment. The proposal unit can also propose the most effective marketing strategy based on the customer's hobbies and interests. For example, a customer who likes music can be proposed a promotion for concert tickets. This makes it possible to propose more personalized marketing strategies based on the customer's hobbies and interests.

[0039] The proposal unit can analyze the purchasing patterns of other customers who have purchased the same product based on the customer's purchasing history and make cross-selling and up-selling proposals. The proposal unit, for example, analyzes the customer's purchasing history and identifies the purchasing patterns of other customers who have purchased the same product. For example, to a customer who has purchased a specific product, it proposes related products. The proposal unit can also make cross-selling and up-selling proposals based on the customer's purchasing patterns. For example, to a customer who has purchased a specific product, it proposes a higher-end model or additional services. This makes it possible to increase sales by proposing cross-selling and up-selling.

[0040] The suggestion unit can analyze the customer's social media activity data as well as the customer's purchase history to suggest an approach at the optimal time. The suggestion unit, for example, analyzes the customer's social media activity data to identify the optimal time to approach. For example, if the customer is active during a specific time period, a marketing message is sent during that time period. The suggestion unit can also combine the customer's purchase history with the social media activity data to suggest an approach at the optimal time. For example, if the customer is attending a specific event, products related to that event are suggested. This makes it possible to approach the customer at the optimal time based on the customer's social media activity data.

[0041] The proposal unit can take into account customer post-purchase satisfaction and repeat purchase rate when measuring marketing effectiveness. For example, the proposal unit collects customer post-purchase satisfaction data when measuring the effectiveness of a marketing campaign. For example, it analyzes post-purchase survey results and evaluates satisfaction. The proposal unit can also collect customer repeat purchase rates and evaluate marketing effectiveness. For example, it analyzes repurchase rates and frequency of repeat purchases and evaluates the effectiveness of a campaign. The proposal unit can also reflect customer satisfaction and repeat purchase rate in the next marketing strategy. This makes it possible to evaluate marketing effectiveness based on customer satisfaction and repeat purchase rate and reflect it in the next strategy.

[0042] The suggestion unit can analyze the content of customer word-of-mouth and reviews and emphasize positive feedback in measuring marketing effectiveness. For example, the suggestion unit collects the content of customer word-of-mouth and reviews when measuring the effectiveness of a marketing campaign. For example, the suggestion unit analyzes reviews of products related to the campaign and identifies positive feedback. The suggestion unit can also evaluate marketing effectiveness based on the content of customer word-of-mouth and reviews. For example, the suggestion unit analyzes positive comments and ratings and evaluates the effectiveness of the campaign. The suggestion unit can also emphasize positive feedback and reflect it in the next marketing strategy. In this way, positive feedback can be emphasized based on the content of word-of-mouth and reviews and reflected in the next marketing strategy.

[0043] The proposal unit can include customer post-purchase behavior (e.g., referring to friends or sharing on social media) in measuring marketing effectiveness. For example, the proposal unit collects data on customers' post-purchase behavior when measuring the effectiveness of a marketing campaign. For example, it analyzes whether the customer referred the product to friends after purchasing. The proposal unit can also collect customer share data on social media to evaluate marketing effectiveness. For example, it can analyze the number of times the product was shared on social media after purchase to evaluate the effectiveness of the campaign. The proposal unit can also reflect the customer's post-purchase behavior in the next marketing strategy. This makes it possible to evaluate marketing effectiveness based on the customer's post-purchase behavior and reflect it in the next strategy.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The data collection unit can collect not only customer purchase history but also their health and fitness data and include it in the analysis. For example, if a customer uses a fitness app, that data can be collected to suggest health foods and fitness products to health-conscious customers. It can also collect customer health data to suggest products related to specific health conditions. For example, if a customer has an allergy, it can suggest allergy-friendly products. This allows for the creation of more personalized marketing strategies based on the customer's health and fitness data.

[0046] The data collection department can analyze customers' social media followers and friendships to identify influential customers. For example, customers with a large number of followers can be determined to be highly influential, and by offering them special promotions, it is possible to reach other customers through them. In addition, by collecting data on customers' social media activity and analyzing the types of posts and responses that are most common, it is possible to understand customers' interests and propose optimal marketing strategies. This allows influential customers to be identified and effective marketing strategies to be developed.

[0047] The data collection unit can collect customer location information in real time and incorporate store visit history and movement patterns into analysis. For example, by collecting location information from customers' smartphones and analyzing which stores they visited, it is possible to propose promotions related to the products of those stores to customers who frequently visit a particular store. In addition, by analyzing customers' movement patterns and determining which area they live in and the routes they take, it is possible to propose products and services related to that area. This makes it possible to develop more effective marketing strategies based on customer location information.

[0048] The data collection unit can identify the purchasing motivation by analyzing reviews and ratings from other customers who have purchased the same product based on the customer's purchasing history. For example, by analyzing the customer's purchasing history and collecting reviews from other customers who have purchased the same product, the content of the reviews can be analyzed to identify the purchasing motivation. In addition, by collecting customer evaluation data and analyzing the most common types of evaluation, it can be determined that a particular product is popular if it has many positive reviews. This allows the identification of the customer's purchasing motivation and the development of more effective marketing strategies.

[0049] The data collection unit can analyze data related to a customer's hobbies and interests as well as their purchase history to make personalized proposals. For example, by combining and analyzing a customer's purchase history with data related to their hobbies and interests, a promotion on camping equipment can be proposed to a customer who has purchased outdoor equipment. Also, a promotion on concert tickets can be proposed to a customer who likes music. This makes it possible to propose more personalized marketing strategies based on the customer's hobbies and interests.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection unit collects customer data. For example, it collects data such as customer purchase history, website browsing history, and social media activity. The data collection unit can also collect data to analyze customer behavior patterns. For example, it collects information such as what products customers have purchased in the past, which pages they have viewed, and what comments they have posted. Step 2: The analysis unit analyzes the customer data collected by the data collection unit. For example, AI can analyze customer behavior patterns and identify customers with a high probability of purchasing. The analysis unit can also use machine learning algorithms to model the relationship between customer behavior patterns and purchase probability and make predictions. Step 3: The prediction unit predicts the purchase probability of each customer based on the data analyzed by the analysis unit. For example, the AI ​​predicts the purchase probability of each customer based on the collected data. The prediction unit can also predict customers who are likely to purchase a specific product. Step 4: The proposal unit proposes the optimal marketing strategy based on the purchase probability predicted by the prediction unit. For example, it could offer discount coupons for specific products, send personalized emails, invite customers to specific campaigns, etc. The proposal unit can also suggest the most effective approach based on customer preferences and behavioral patterns.

[0052] (Example 2) The AI ​​tool according to the embodiment of the present invention is a system that identifies customers with a high probability of purchasing and takes an effective approach to those customers. In this system, AI analyzes customer data, predicts customers with a high probability of purchasing, and proposes optimal marketing strategies for those customers. This enables the AI ​​tool to take an effective approach to customers with a high probability of purchasing.

[0053] An AI tool according to an embodiment includes a data collection unit, an analysis unit, a prediction unit, and a suggestion unit. The data collection unit collects customer data, such as customer purchase history, website browsing history, and social media activity. The data collection unit can also collect data for analyzing customer behavior patterns, such as information on what products customers have purchased in the past, which pages they have viewed, and what comments they have posted. The analysis unit analyzes the customer data collected by the data collection unit. For example, an AI analyzes customer behavior patterns to identify customers with a high probability of purchasing. The analysis unit can also use a machine learning algorithm to model and predict the relationship between customer behavior patterns and purchase probability. The prediction unit predicts each customer's purchase probability based on the data analyzed by the analysis unit. For example, the AI ​​predicts each customer's purchase probability based on the collected data. The prediction unit can also predict customers who are likely to purchase a specific product. The suggestion unit proposes an optimal marketing strategy based on the purchase probability predicted by the prediction unit. For example, this can include providing a discount coupon for a specific product, sending personalized emails, or inviting customers to a specific campaign. The suggestion unit can also propose the most effective approach based on customer preferences and behavioral patterns. This allows the AI ​​tool according to the embodiment to take an effective approach to customers who are likely to purchase. For example, by providing timely discount coupons to customers who are likely to purchase a specific product, it is possible to increase their purchasing motivation. In addition, by sending personalized emails, it is possible to strengthen relationships with customers and increase repeat customers. Furthermore, by measuring the effectiveness of marketing strategies and reflecting this in the next approach, it is possible to continuously implement effective marketing.

[0054] The data collection unit can collect customer voice data and facial recognition data and analyze the customer's emotional state using an emotion estimation function. For example, the data collection unit collects voice data when a customer calls customer support and estimates the customer's emotional state using voice analysis technology. For example, it analyzes whether the customer is angry or satisfied and adds the data to the customer profile. The data collection unit also collects customer facial recognition data and estimates the customer's emotional state using facial expression analysis technology. For example, it analyzes whether the customer is smiling or angry and adds the data to the customer profile. The data collection unit can also combine the customer's voice data and facial recognition data to more accurately analyze the customer's emotional state. For example, it analyzes both the voice data and the facial recognition data to comprehensively determine the customer's emotional state. This enables a more personalized approach by analyzing the customer's emotional state.

[0055] The data collection unit can analyze not only customer purchase histories, but also return histories and customer support inquiry histories. For example, the data collection unit collects customer return histories and analyzes what products are likely to be returned. For example, if a particular product is frequently returned, it suggests that there may be a problem with that product. The data collection unit also collects customer support inquiry histories and analyzes what types of problems are common. For example, if there are many inquiries about a particular product, providing information about that product can improve customer satisfaction. The data collection unit can also combine a customer's purchase history, return history, and customer support inquiry history to analyze the customer's overall behavioral patterns. For example, it can analyze the series of actions of a customer purchasing a particular product, then returning it, and then contacting customer support, to identify the cause of the problem. This allows for a more accurate understanding of the customer's overall behavioral patterns.

[0056] The data collection unit can collect customer location information in real time and incorporate store visit history and movement patterns into the analysis. For example, the data collection unit collects location information from customers' smartphones and analyzes which stores the customers visited. For example, a customer who frequently visits a particular store can be determined to be interested in the products of that store. The data collection unit also analyzes the customer's movement patterns to determine which area the customer lives in and the route the customer takes. For example, if a customer lives in a specific area, it can suggest products and services related to that area. The data collection unit can also collect customer location information in real time and analyze store visit history and movement patterns to gain a more detailed understanding of customer behavior patterns. For example, if customers tend to visit certain stores during certain times of the day, it can develop a marketing strategy tailored to those times. This allows for the development of more effective marketing strategies based on customer location information.

[0057] The data collection unit can analyze a customer's social media followers and friendships to identify influential customers. For example, the data collection unit analyzes a customer's social media account to identify the number of followers and friendships. For example, a customer with a large number of followers can be determined to be highly influential. The data collection unit also collects data on the customer's social media activity and analyzes the types of posts and reactions that are most common. For example, a customer who posts frequently about a particular product can be determined to be interested in that product. The data collection unit can also analyze a customer's social media followers and friendships to identify influential customers, thereby enabling the development of effective marketing strategies. For example, by offering special promotions to influential customers, other customers can be approached through them. This allows the identification of influential customers and the development of effective marketing strategies.

[0058] The data collection unit can identify the purchasing motive by analyzing reviews and ratings from other customers who purchased the same product based on the customer's purchase history. The data collection unit, for example, analyzes the customer's purchase history and collects reviews from other customers who purchased the same product. For example, it analyzes the content of the reviews to identify the purchasing motive. The data collection unit also collects customer rating data and analyzes what types of ratings are most common. For example, if a particular product has many positive ratings, it is determined that the product is popular. The data collection unit can also identify the purchasing motive by combining the customer's purchase history, reviews, and ratings. For example, if a customer purchases a particular product and then posts a positive review, it is determined that the product provides high customer satisfaction. This allows the customer's purchasing motive to be identified and a more effective marketing strategy to be developed.

[0059] The data collection unit uses the emotion estimation function to analyze the emotions of customers in real time while they are browsing products, and can identify products of interest. For example, when a customer is browsing products on a website, the data collection unit uses a camera to analyze their facial expressions and estimate their emotions. For example, if it detects an expression of interest, it determines that the customer is interested in that product. The data collection unit also collects the customer's voice data and estimates their emotions using voice analysis technology. For example, if the customer is excited, it determines that the customer is interested in that product. The data collection unit also collects the customer's biometric data (heart rate and electrodermal activity) using a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations. This allows the customer's interests to be understood in real time, enabling more effective marketing strategies to be developed.

[0060] The prediction unit can predict the purchase probability using not only the customer's past purchasing behavior but also data related to seasons and events. The prediction unit, for example, predicts the purchase probability by analyzing the customer's past purchasing history in combination with seasonal data. For example, it determines that a customer who tends to purchase a particular product in the summer is likely to purchase the same product again in the following summer. The prediction unit also predicts the purchase probability by analyzing the customer's past purchasing history in combination with event data. For example, it determines that a customer who tends to purchase a particular product during the Christmas season is likely to purchase the same product again in the following Christmas season. The prediction unit can also more accurately predict the customer's purchasing behavior by using data related to seasons and events. This makes it possible to more accurately predict the purchase probability based on the seasons and events.

[0061] The prediction unit can use the emotion estimation function to analyze the emotion a customer has toward a specific product and predict the purchase probability based on that emotion. For example, the prediction unit uses a camera to analyze facial expressions while the customer is viewing a product page and estimates the emotion. For example, if an excited expression is detected, the prediction unit determines that the customer is likely to purchase the product. The prediction unit also collects customer voice data and estimates the emotion using voice analysis technology. For example, if the customer is excited, the prediction unit determines that the customer is likely to purchase the product. The prediction unit also collects customer biometric data (heart rate and electrodermal activity) using a sensor and analyzes the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on heart rate fluctuations. This enables more accurate predictions by predicting the purchase probability based on the customer's emotion.

[0062] The prediction unit can take into account customer life events (marriage, childbirth, moving, etc.) when predicting the purchase probability. The prediction unit, for example, collects customer life event data and incorporates it into the purchase probability prediction. For example, it determines that a customer who is about to get married is likely to purchase wedding-related products. The prediction unit can also predict customers who are likely to purchase specific products based on the customer life event data. For example, it determines that a customer who is about to give birth is likely to purchase baby products. The prediction unit can also use the life event data to more accurately predict customer purchasing behavior. In this way, by taking into account the customer's life events, a more accurate purchase probability can be predicted.

[0063] The prediction unit can incorporate customer health and fitness data into the purchase probability prediction. The prediction unit, for example, collects customer health data and incorporates it into the purchase probability prediction. For example, it determines that a health-conscious customer is more likely to purchase health foods and fitness equipment. The prediction unit also collects customer fitness data and incorporates it into the purchase probability prediction. For example, it determines that a customer who exercises a lot is more likely to purchase fitness-related products. The prediction unit can also use the health and fitness data to more accurately predict customer purchasing behavior. As a result, by taking the customer's health and fitness data into consideration, a more accurate purchase probability prediction can be made.

[0064] The prediction unit can use the emotion estimation function to analyze the emotions a customer has toward a specific advertisement or promotion and predict the probability of purchase based on those emotions. For example, the prediction unit uses a camera to analyze facial expressions while a customer is viewing an advertisement and estimates emotions. For example, if a positive expression is detected, it determines that the customer is likely to purchase the advertised product. The prediction unit also collects customer voice data and estimates emotions using voice analysis technology. For example, if the customer is excited, it determines that the customer is likely to purchase the advertised product. The prediction unit also collects customer biometric data (heart rate and electrodermal activity) using a sensor and analyzes emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations. This enables more accurate predictions by predicting the purchase probability based on the customer's emotions.

[0065] The proposal unit can analyze data on customers' responses to past marketing campaigns and identify the most effective approach. For example, the proposal unit collects data on customers' responses to past marketing campaigns and analyzes which campaign was most effective. For example, a similar approach can be repeated for customers who showed a high response rate to a particular campaign. The proposal unit can also propose the most effective marketing strategy based on customer response data. For example, if a particular promotion was effective, the promotion can be implemented again. In this way, the most effective marketing strategy can be proposed based on past response data.

[0066] The proposal unit can analyze data related to the customer's hobbies and interests as well as the customer's purchase history, and make personalized proposals. For example, the proposal unit combines and analyzes the customer's purchase history with data related to the customer's hobbies and interests, and makes personalized marketing proposals. For example, a customer who purchased outdoor equipment can be proposed a promotion for camping equipment. The proposal unit can also propose the most effective marketing strategy based on the customer's hobbies and interests. For example, a customer who likes music can be proposed a promotion for concert tickets. This makes it possible to propose more personalized marketing strategies based on the customer's hobbies and interests.

[0067] The suggestion unit can use the emotion estimation function to analyze the emotions customers have toward a specific marketing message and propose an optimal strategy based on those emotions. For example, the suggestion unit analyzes the emotions customers have when they receive a marketing message and proposes an optimal strategy based on those emotions. For example, the suggestion unit may send a similar message again to customers who have positive emotions. The suggestion unit can also propose the most effective marketing strategy based on customer emotion data. For example, it may try a different approach for customers who have negative emotions. This makes it possible to propose a more effective marketing strategy based on customer emotions.

[0068] The proposal unit can analyze the purchasing patterns of other customers who have purchased the same product based on the customer's purchasing history and make cross-selling and up-selling proposals. The proposal unit, for example, analyzes the customer's purchasing history and identifies the purchasing patterns of other customers who have purchased the same product. For example, to a customer who has purchased a specific product, it proposes related products. The proposal unit can also make cross-selling and up-selling proposals based on the customer's purchasing patterns. For example, to a customer who has purchased a specific product, it proposes a higher-end model or additional services. This makes it possible to increase sales by proposing cross-selling and up-selling.

[0069] The suggestion unit can analyze the customer's social media activity data as well as the customer's purchase history to suggest an approach at the optimal time. The suggestion unit, for example, analyzes the customer's social media activity data to identify the optimal time to approach. For example, if the customer is active during a specific time period, a marketing message is sent during that time period. The suggestion unit can also combine the customer's purchase history with the social media activity data to suggest an approach at the optimal time. For example, if the customer is attending a specific event, products related to that event are suggested. This makes it possible to approach the customer at the optimal time based on the customer's social media activity data.

[0070] The suggestion unit can use the emotion estimation function to identify the timing when a customer feels most positive emotions during a specific time period or day of the week, and propose an approach at that timing. The suggestion unit, for example, analyzes customer emotion data to identify the timing when a customer feels most positive emotions during a specific time period or day of the week. For example, if a customer feels positive emotions on the weekend, the suggestion unit can send a marketing message at that timing. The suggestion unit can also propose an approach at the most effective timing based on the customer emotion data. For example, if a customer feels positive emotions during a specific time period, the suggestion unit can create a marketing strategy tailored to that time period. This makes it possible to maximize marketing effectiveness by approaching customers when they feel most positive emotions.

[0071] The proposal unit can monitor changes in customer emotions in real time and reflect these changes in feedback to measure marketing effectiveness. The proposal unit, for example, builds a system that monitors changes in customer emotions in real time while a marketing campaign is being implemented. For example, it analyzes facial expressions while customers are viewing advertisements and records changes in emotion. The proposal unit can also evaluate the effectiveness of a marketing strategy based on changes in customer emotions. For example, if there are a lot of positive emotions, it can determine that the campaign was effective. The proposal unit can also reflect changes in customer emotions in feedback and use this in the next marketing strategy. This makes it possible to monitor changes in customer emotions in real time and reflect them in the next marketing strategy, enabling an effective approach.

[0072] The proposal unit can take into account customer post-purchase satisfaction and repeat purchase rate when measuring marketing effectiveness. For example, the proposal unit collects customer post-purchase satisfaction data when measuring the effectiveness of a marketing campaign. For example, it analyzes post-purchase survey results and evaluates satisfaction. The proposal unit can also collect customer repeat purchase rates and evaluate marketing effectiveness. For example, it analyzes repurchase rates and frequency of repeat purchases and evaluates the effectiveness of a campaign. The proposal unit can also reflect customer satisfaction and repeat purchase rate in the next marketing strategy. This makes it possible to evaluate marketing effectiveness based on customer satisfaction and repeat purchase rate and reflect it in the next strategy.

[0073] The suggestion unit can analyze the content of customer word-of-mouth and reviews and emphasize positive feedback in measuring marketing effectiveness. For example, the suggestion unit collects the content of customer word-of-mouth and reviews when measuring the effectiveness of a marketing campaign. For example, the suggestion unit analyzes reviews of products related to the campaign and identifies positive feedback. The suggestion unit can also evaluate marketing effectiveness based on the content of customer word-of-mouth and reviews. For example, the suggestion unit analyzes positive comments and ratings and evaluates the effectiveness of the campaign. The suggestion unit can also emphasize positive feedback and reflect it in the next marketing strategy. In this way, positive feedback can be emphasized based on the content of word-of-mouth and reviews and reflected in the next marketing strategy.

[0074] The proposal unit can include customer post-purchase behavior (e.g., referring to friends or sharing on social media) in measuring marketing effectiveness. For example, the proposal unit collects data on customers' post-purchase behavior when measuring the effectiveness of a marketing campaign. For example, it analyzes whether the customer referred the product to friends after purchasing. The proposal unit can also collect customer share data on social media to evaluate marketing effectiveness. For example, it can analyze the number of times the product was shared on social media after purchase to evaluate the effectiveness of the campaign. The proposal unit can also reflect the customer's post-purchase behavior in the next marketing strategy. This makes it possible to evaluate marketing effectiveness based on the customer's post-purchase behavior and reflect it in the next strategy.

[0075] The proposal unit can use the emotion estimation function to analyze the emotions customers have toward a marketing campaign when measuring its effectiveness and improve the next approach based on those emotions. For example, the proposal unit analyzes customer emotions in real time while a marketing campaign is being conducted and evaluates its effectiveness based on that data. For example, if there are a lot of positive emotions, it determines that the campaign was effective. The proposal unit can also improve the next approach based on the customer emotion data. For example, if there are a lot of negative emotions, it can try a different approach. The proposal unit can also reflect the customer emotion data in feedback and use it in the next marketing strategy. This makes it possible to develop a more effective marketing strategy by evaluating the effectiveness of a marketing campaign based on customer emotions and improving the next approach.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The data collection unit can collect not only customer purchase history but also their health and fitness data and include it in the analysis. For example, if a customer uses a fitness app, that data can be collected to suggest health foods and fitness products to health-conscious customers. It can also collect customer health data to suggest products related to specific health conditions. For example, if a customer has an allergy, it can suggest allergy-friendly products. This allows for the creation of more personalized marketing strategies based on the customer's health and fitness data.

[0078] The data collection department can analyze customers' social media followers and friendships to identify influential customers. For example, customers with a large number of followers can be determined to be highly influential, and by offering them special promotions, it is possible to reach other customers through them. In addition, by collecting data on customers' social media activity and analyzing the types of posts and responses that are most common, it is possible to understand customers' interests and propose optimal marketing strategies. This allows influential customers to be identified and effective marketing strategies to be developed.

[0079] The data collection unit can collect customer location information in real time and incorporate store visit history and movement patterns into analysis. For example, by collecting location information from customers' smartphones and analyzing which stores they visited, it is possible to propose promotions related to the products of those stores to customers who frequently visit a particular store. In addition, by analyzing customers' movement patterns and determining which area they live in and the routes they take, it is possible to propose products and services related to that area. This makes it possible to develop more effective marketing strategies based on customer location information.

[0080] The data collection unit can identify the purchasing motivation by analyzing reviews and ratings from other customers who have purchased the same product based on the customer's purchasing history. For example, by analyzing the customer's purchasing history and collecting reviews from other customers who have purchased the same product, the content of the reviews can be analyzed to identify the purchasing motivation. In addition, by collecting customer evaluation data and analyzing the most common types of evaluation, it can be determined that a particular product is popular if it has many positive reviews. This allows the identification of the customer's purchasing motivation and the development of more effective marketing strategies.

[0081] The data collection unit can analyze data related to a customer's hobbies and interests as well as their purchase history to make personalized proposals. For example, by combining and analyzing a customer's purchase history with data related to their hobbies and interests, a promotion on camping equipment can be proposed to a customer who has purchased outdoor equipment. Also, a promotion on concert tickets can be proposed to a customer who likes music. This makes it possible to propose more personalized marketing strategies based on the customer's hobbies and interests.

[0082] The prediction unit uses the emotion estimation function to analyze the emotion a customer has toward a specific product and predict the purchase probability based on that emotion. For example, when a customer is browsing a product page, the prediction unit can analyze their facial expression using a camera and determine that they are likely to purchase the product if an excited expression is detected. In addition, by collecting customer voice data and estimating their emotion using voice analysis technology, it can determine that if a customer is excited, they are likely to purchase the product. This allows for more accurate predictions by predicting the purchase probability based on the customer's emotion.

[0083] The prediction unit uses the emotion estimation function to analyze the emotions that customers have toward a specific advertisement or promotion, and can predict the probability of purchase based on those emotions. For example, when a customer is viewing an advertisement, the prediction unit can analyze their facial expressions using a camera, and if a positive expression is detected, it can determine that the customer is likely to purchase the advertised product. In addition, by collecting customer voice data and estimating their emotions using voice analysis technology, it can determine that if the customer is excited, the customer is likely to purchase the advertised product. This allows for more accurate predictions by predicting the purchase probability based on the customer's emotions.

[0084] The suggestion unit uses the emotion estimation function to analyze the emotions customers have toward specific marketing messages and propose optimal strategies based on those emotions. For example, it can analyze the emotions customers felt when they received a marketing message and send a similar message again to customers who had positive emotions. It can also propose the most effective marketing strategy based on customer emotion data. For example, it can try a different approach for customers who have negative emotions. This makes it possible to propose more effective marketing strategies based on customer emotions.

[0085] The suggestion unit can use the emotion estimation function to identify when a customer feels most positive emotions during a particular time period or day of the week, and propose an approach at that time. For example, by analyzing customer emotion data and identifying when a customer feels most positive emotions during a particular time period or day of the week, if a customer feels positive emotions on the weekend, it can send a marketing message at that time. It can also propose the most effective timing of an approach based on the customer emotion data. This makes it possible to maximize marketing effectiveness by approaching customers when they feel most positive emotions.

[0086] The proposal unit uses the emotion estimation function to analyze the emotions customers have toward a marketing campaign when measuring its effectiveness, and can improve the next approach based on those emotions. For example, by analyzing customer emotions in real time while a marketing campaign is being conducted and evaluating its effectiveness based on that data, it can be determined that the campaign was effective if there are many positive emotions. It can also improve the next approach based on the customer emotion data. This allows for the development of a more effective marketing strategy by evaluating the effectiveness of a marketing campaign based on customer emotions and improving the next approach.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The data collection unit collects customer data. For example, it collects data such as customer purchase history, website browsing history, and social media activity. The data collection unit can also collect data to analyze customer behavior patterns. For example, it collects information such as what products customers have purchased in the past, which pages they have viewed, and what comments they have posted. Step 2: The analysis unit analyzes the customer data collected by the data collection unit. For example, AI can analyze customer behavior patterns and identify customers with a high probability of purchasing. The analysis unit can also use machine learning algorithms to model the relationship between customer behavior patterns and purchase probability and make predictions. Step 3: The prediction unit predicts the purchase probability of each customer based on the data analyzed by the analysis unit. For example, the AI ​​predicts the purchase probability of each customer based on the collected data. The prediction unit can also predict customers who are likely to purchase a specific product. Step 4: The proposal unit proposes the optimal marketing strategy based on the purchase probability predicted by the prediction unit. For example, it could offer discount coupons for specific products, send personalized emails, invite customers to specific campaigns, etc. The proposal unit can also suggest the most effective approach based on customer preferences and behavioral patterns.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 customer data; an analysis unit that analyzes the customer data collected by the data collection unit; a prediction unit that predicts the purchase probability of each customer based on the data analyzed by the analysis unit; a proposal unit that proposes an optimal marketing strategy based on the purchase probability predicted by the prediction unit. A system characterized by:

2. The data collection unit Collect customer voice and facial recognition data to analyze their emotional state 2. The system of claim 1.

3. The data collection unit Analyze customers' social media followers and friendships to identify influential customers 2. The system of claim 1.

4. The prediction unit Predict purchase probability using data related to seasons and events, as well as past customer purchasing behavior 2. The system of claim 1.

5. The proposal unit Analyze data on customer responses to past marketing campaigns to identify the most effective approach 2. The system of claim 1.

6. The prediction unit Analyze customer sentiment toward a particular product and predict the likelihood of purchase based on that sentiment 2. The system of claim 1.

7. The proposal unit Analyze how customers feel about specific marketing messages and suggest optimal strategies based on those feelings 2. The system of claim 1.

8. The proposal unit To measure marketing effectiveness, monitor changes in customer sentiment in real time and reflect those changes in feedback.

2. The system of claim 1.

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