System

Abstract

A system in accordance with an embodiment is directed to analyzing market and customer data to develop personalized promotions tailored to a target demographic.SOLUTION: A system includes a data collection part, an analysis part, a target selection part, a campaign creation part, a content generation part, and a promotion development part. The data collection component collects market and customer data. The analysis unit analyzes the data collected by the data collection unit. The target selection unit selects a target layer based on a result of the analysis by the analysis unit. The campaign creation unit creates a marketing campaign tailored to the target group selected by the target selection unit. The content generation unit generates marketing content based on the campaign created by the campaign creation unit. The promotion development unit develops the personalized promotion using the content generated by the content generation 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 do not adequately select target audiences based on market and customer data or develop personalized promotions, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze market and customer data and develop personalized promotions tailored to target demographics. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a target selection unit, a campaign creation unit, a content generation unit, and a promotion deployment unit. The data collection unit collects market and customer data. The analysis unit analyzes the data collected by the data collection unit. The target selection unit selects a target demographic based on the results of the analysis by the analysis unit. The campaign creation unit creates a marketing campaign tailored to the target demographic selected by the target selection unit. The content generation unit generates marketing content based on the campaign created by the campaign creation unit. The promotion deployment unit deploys personalized promotions using the content generated by the content generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze market and customer data and develop personalized promotions tailored to target demographics. [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 personalized promotion system according to an embodiment of the present invention is a system that uses AI to analyze market and customer data and develop personalized promotions tailored to a selected target demographic. As a result, the personalized promotion system develops promotions tailored to customer interests and concerns, enabling efficient customer acquisition.

[0029] A personalized promotion system according to an embodiment includes a data collection unit, an analysis unit, a target selection unit, a campaign creation unit, a content generation unit, and a promotion deployment unit. The data collection unit collects market and customer data. For example, the data collection unit collects customer purchase histories. The data collection unit can also collect website browsing histories. The data collection unit can also collect social media activity data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using natural language processing technology. The target selection unit selects a target demographic based on the results of the analysis by the analysis unit. For example, the target selection unit selects a customer demographic interested in a specific product. The target selection unit can also select a customer demographic likely to respond to a specific campaign. The target selection unit can also select a target demographic based on a specific age group or gender. The campaign creation unit creates a marketing campaign tailored to the target demographic selected by the target selection unit. For example, the campaign creation unit creates a campaign that provides discount coupons. The campaign creation unit can also create a campaign that provides special offers. The campaign creation unit can also create a campaign that provides a points program. The content generation unit generates marketing content based on the campaign created by the campaign creation unit. For example, the content generation unit generates advertising copy. The content generation unit can also generate images. The content generation unit can also generate videos. The promotion deployment unit deploys personalized promotions using the content generated by the content generation unit. For example, the promotion deployment unit deploys promotions through email marketing. The promotion deployment unit can also deploy promotions through social media advertising.The promotion development unit can also develop promotions on a website. As a result, the personalized promotion system according to the embodiment can acquire customers by analyzing market and customer data and developing personalized promotions tailored to the target demographic.

[0030] The analysis unit incorporates real-time social media trends into the data, allowing it to instantly reflect changes in customer interests. For example, the analysis unit uses AI to collect social media trend data in real time, integrate it with customer data, and analyze it. For example, it identifies customer interests based on trends in specific hashtags or keywords. The analysis unit can also use social media trend data to reflect changes in customer interests in real time. The analysis unit can also predict customer purchasing intent based on social media trend data. This makes it possible to reflect changes in customer interests in real time.

[0031] When analyzing customer data, the analysis unit takes into account the customer's life events and can classify the data according to their life stage. For example, the analysis unit uses AI to collect customer life event data and incorporate it into the analysis. For example, the analysis unit classifies customer data based on life events such as marriage and childbirth. The analysis unit can also use the life event data to classify data according to the customer's life stage. The analysis unit can also predict customer purchasing behavior based on the life event data. This makes it possible to classify data according to the customer's life stage.

[0032] When analyzing customer data, the analysis unit incorporates the customer's health and fitness data to develop marketing strategies based on their health status. For example, the analysis unit uses AI to collect health data from the customer's fitness app or wearable device and incorporates it into the analysis. For example, the analysis unit develops marketing strategies based on exercise habits and sleep patterns. The analysis unit can also use health data to develop marketing strategies based on the customer's health status. The analysis unit can also predict the customer's health status based on the fitness data. This makes it possible to develop marketing strategies based on the customer's health status.

[0033] When analyzing customer data, the analysis unit adds data on the customer's hobbies and preferences, enabling it to develop personalized promotions based on those preferences. For example, the analysis unit uses AI to collect data on the customer's hobbies and preferences and incorporates it into the analysis. For example, the analysis unit develops personalized promotions based on preferences in music and movies. The analysis unit can also identify customer interests using data on hobbies and preferences. The analysis unit can also predict a customer's purchasing intentions based on their hobbies and preferences. This makes it possible to develop personalized promotions based on the customer's hobbies.

[0034] The target selection unit can implement an algorithm that predicts not only the customer's purchasing history but also the customer's future purchasing intent. For example, the target selection unit implements an algorithm that uses AI to analyze the customer's purchasing history and predicts future purchasing intent. For example, future purchasing intent is predicted based on past purchasing patterns. The target selection unit can also predict customer purchasing behavior using an algorithm that predicts purchasing intent. The target selection unit can also analyze the customer's purchasing cycle using an algorithm that predicts purchasing intent. This makes it possible to predict the customer's future purchasing intent.

[0035] The target selection unit can select a target demographic according to the characteristics of each region, taking into account the geographical location information of customers. For example, the target selection unit uses AI to collect geographical location information of customers and incorporate it into analysis. For example, the target selection unit selects customers living in a specific region as a target demographic. The target selection unit can also use geographical location information to select a target demographic according to the characteristics of each region. The target selection unit can also predict customer purchasing behavior based on geographical location information. This makes it possible to select a target demographic according to the characteristics of each region.

[0036] The target selection unit can create segments based on customers' occupations and industries and develop marketing strategies for each occupation. For example, the target selection unit uses AI to collect data on customers' occupations and industries and incorporate it into analysis. For example, it selects customers who belong to a specific occupation or industry as a target demographic. The target selection unit can also create segments based on occupations and industries and develop marketing strategies for each occupation. The target selection unit can also predict customer purchasing behavior based on occupations and industries. This makes it possible to develop marketing strategies for each occupation.

[0037] The target selection unit can create segments based on customers' family structure and lifestyles and develop promotions for families. For example, the target selection unit uses AI to collect data on customers' family structure and lifestyles and incorporate it into analysis. For example, it selects families with children as the target demographic. The target selection unit can also create segments based on family structure and lifestyles and develop promotions for families. The target selection unit can also predict customer purchasing behavior based on family structure and lifestyle. This makes it possible to develop promotions for families.

[0038] The campaign creation unit can analyze past campaign response data of customers and extract the most effective campaign elements. For example, the campaign creation unit uses AI to collect past campaign response data of customers and incorporate it into the analysis. For example, it evaluates whether a particular campaign element was effective. The campaign creation unit can also use past campaign response data to extract the most effective campaign elements. The campaign creation unit can also optimize future campaigns based on past campaign response data. This makes it possible to extract the most effective campaign elements.

[0039] The campaign creation department can take into account the customer's purchasing cycle and launch a campaign at the optimal timing. For example, the campaign creation department uses AI to collect customer purchasing cycle data and incorporate it into analysis. For example, the campaign creation department can launch a campaign at a specific timing. The campaign creation department can also use the purchasing cycle data to launch a campaign at the optimal timing. The campaign creation department can also predict customer purchasing behavior based on the purchasing cycle data. This makes it possible to launch a campaign at the optimal timing.

[0040] The campaign creation department can customize based on the customer's cultural background and language and develop an international marketing strategy. For example, the campaign creation department uses AI to collect customer cultural background and language data and incorporate it into analysis. For example, it can develop a campaign tailored to a specific culture or language. The campaign creation department can also customize based on cultural background and language and develop an international marketing strategy. The campaign creation department can also predict customer purchasing behavior based on cultural background and language data. This makes it possible to develop an international marketing strategy.

[0041] The campaign creation unit can create campaigns optimized for digital channels by taking into account customers' digital behavior. For example, the campaign creation unit uses AI to collect customers' digital behavior data and incorporate it into analysis. For example, the campaign creation unit customizes campaigns based on website browsing history and app usage history. The campaign creation unit can also use the digital behavior data to create campaigns optimized for digital channels. The campaign creation unit can also predict customers' purchasing behavior based on the digital behavior data. This makes it possible to create campaigns optimized for digital channels.

[0042] The content generation unit can analyze past content response data of customers and extract the most effective content elements. For example, the content generation unit uses AI to collect past content response data of customers and incorporate it into the analysis. For example, it evaluates whether a particular content element was effective. The content generation unit can also use past content response data to extract the most effective content elements. The content generation unit can also optimize future content based on past content response data. This makes it possible to extract the most effective content elements.

[0043] The content generation unit can provide content at the optimal timing, taking into account the customer's purchasing cycle. For example, the content generation unit uses AI to collect customer purchasing cycle data and incorporate it into analysis. For example, the content generation unit provides content at a specific timing. The content generation unit can also provide content at the optimal timing using the purchasing cycle data. The content generation unit can also predict customer purchasing behavior based on the purchasing cycle data. This makes it possible to provide content at the optimal timing.

[0044] The content generation unit can customize content based on the customer's cultural background and language, and develop international marketing strategies. For example, the content generation unit uses AI to collect customer cultural background and language data and incorporate it into analysis. For example, it generates content tailored to a specific culture or language. The content generation unit can also customize content based on cultural background and language, and develop international marketing strategies. The content generation unit can also predict customer purchasing behavior based on cultural background and language data. This makes it possible to develop international marketing strategies.

[0045] The content generation unit can generate content optimized for digital channels by taking into account the digital behavior of customers. For example, the content generation unit uses AI to collect digital behavior data of customers and incorporate it into analysis. For example, the content generation unit generates content based on website browsing history and app usage history. The content generation unit can also use the digital behavior data to generate content optimized for digital channels. The content generation unit can also predict customer purchasing behavior based on the digital behavior data. This makes it possible to generate content optimized for digital channels.

[0046] The promotion development unit can analyze past promotion response data of customers and extract the most effective promotion elements. For example, the promotion development unit uses AI to collect past promotion response data of customers and incorporate it into the analysis. For example, it evaluates whether a particular promotion element was effective. The promotion development unit can also use past promotion response data to extract the most effective promotion elements. The promotion development unit can also optimize future promotions based on past promotion response data. This makes it possible to extract the most effective promotion elements.

[0047] The promotion development department can take into account the customer's purchasing cycle and develop promotions at the optimal timing. For example, the promotion development department uses AI to collect customer purchasing cycle data and incorporate it into analysis. For example, it develops promotions at specific times. The promotion development department can also use purchasing cycle data to develop promotions at the optimal timing. The promotion development department can also predict customer purchasing behavior based on purchasing cycle data. This allows promotions to be developed at the optimal timing.

[0048] The promotion development department can customize based on the customer's cultural background and language and develop an international marketing strategy. For example, the promotion development department uses AI to collect customer cultural background and language data and incorporate it into analysis. For example, the promotion development department can develop a promotion tailored to a specific culture or language. The promotion development department can also customize based on cultural background and language and develop an international marketing strategy. The promotion development department can also predict customer purchasing behavior based on cultural background and language data. This makes it possible to develop an international marketing strategy.

[0049] The promotion development department can develop promotions optimized for digital channels by taking into account customers' digital behavior. For example, the promotion development department uses AI to collect customers' digital behavior data and incorporate it into analysis. For example, the promotion development department can develop promotions based on website browsing history and app usage history. The promotion development department can also use digital behavior data to develop promotions optimized for digital channels. The promotion development department can also predict customer purchasing behavior based on digital behavior data. This makes it possible to develop promotions optimized for digital channels.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When analyzing customer data, the analysis department can incorporate customer health and fitness data to develop marketing strategies based on their health status. For example, AI can collect health data from customers' fitness apps and wearable devices and incorporate it into the analysis. Marketing strategies can be developed based on exercise habits and sleep patterns. Health data can also be used to develop marketing strategies based on the customer's health status. Fitness data can also be used to predict the customer's health status. This makes it possible to develop marketing strategies based on the customer's health status.

[0052] The target selection unit can select target demographics according to the characteristics of each region, taking into account the geographic location information of customers. For example, AI can collect customers' geographic location information and incorporate it into analysis. Customers living in a specific region can be selected as the target demographic. Geographic location information can also be used to select target demographics according to the characteristics of each region. Customer purchasing behavior can also be predicted based on geographic location information. This makes it possible to select target demographics according to the characteristics of each region.

[0053] The campaign creation department can customize based on the customer's cultural background and language to create an international marketing strategy. For example, AI can collect customer cultural background and language data and incorporate it into analysis. Campaigns tailored to specific cultures and languages ​​can be developed. It can also customize based on cultural background and language to create an international marketing strategy. It can also predict customer purchasing behavior based on cultural background and language data. This makes it possible to create an international marketing strategy.

[0054] The content generation unit can generate content optimized for digital channels by taking into account customers' digital behavior. For example, AI can collect customers' digital behavior data and incorporate it into analysis. Content can be generated based on website browsing history and app usage history. Digital behavior data can also be used to generate content optimized for digital channels. Customer purchasing behavior can also be predicted based on digital behavior data. This makes it possible to generate content optimized for digital channels.

[0055] The promotion development department can develop promotions at the optimal timing, taking into account the customer's purchasing cycle. For example, AI collects customer purchasing cycle data and incorporates it into analysis. Promotions can be developed at specific times. Purchasing cycle data can also be used to develop promotions at the optimal timing. Customer purchasing behavior can also be predicted based on purchasing cycle data. This makes it possible to develop promotions at the optimal timing.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection department collects market and customer data, such as customer purchase history, website browsing history, and social media activity data. Step 2: The analysis unit analyzes the data collected by the data collection unit, for example, using statistical analysis, machine learning algorithms, and natural language processing techniques. Step 3: The target selection unit selects a target demographic based on the results of the analysis by the analysis unit. For example, it may select a target demographic based on customers who are interested in a particular product, customers who are likely to respond to a particular campaign, or a specific age group or gender. Step 4: The campaign creation department creates a marketing campaign tailored to the target demographic selected by the target selection department, for example, a campaign offering discount coupons, special offers, or a loyalty program. Step 5: The content generation unit generates marketing content based on the campaign created by the campaign creation unit, such as advertising copy, images, and videos. Step 6: The promotion development unit develops personalized promotions using the content generated by the content generation unit, for example, through email marketing, social media advertising, and on the website.

[0058] (Example 2) The personalized promotion system according to an embodiment of the present invention is a system that uses AI to analyze market and customer data and develop personalized promotions tailored to a selected target demographic. As a result, the personalized promotion system develops promotions tailored to customer interests and concerns, enabling efficient customer acquisition.

[0059] A personalized promotion system according to an embodiment includes a data collection unit, an analysis unit, a target selection unit, a campaign creation unit, a content generation unit, and a promotion deployment unit. The data collection unit collects market and customer data. For example, the data collection unit collects customer purchase histories. The data collection unit can also collect website browsing histories. The data collection unit can also collect social media activity data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit performs statistical analysis. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using natural language processing technology. The target selection unit selects a target demographic based on the results of the analysis by the analysis unit. For example, the target selection unit selects a customer demographic interested in a specific product. The target selection unit can also select a customer demographic likely to respond to a specific campaign. The target selection unit can also select a target demographic based on a specific age group or gender. The campaign creation unit creates a marketing campaign tailored to the target demographic selected by the target selection unit. For example, the campaign creation unit creates a campaign that provides discount coupons. The campaign creation unit can also create a campaign that provides special offers. The campaign creation unit can also create a campaign that provides a points program. The content generation unit generates marketing content based on the campaign created by the campaign creation unit. For example, the content generation unit generates advertising copy. The content generation unit can also generate images. The content generation unit can also generate videos. The promotion deployment unit deploys personalized promotions using the content generated by the content generation unit. For example, the promotion deployment unit deploys promotions through email marketing. The promotion deployment unit can also deploy promotions through social media advertising.The promotion development unit can also develop promotions on a website. As a result, the personalized promotion system according to the embodiment can acquire customers by analyzing market and customer data and developing personalized promotions tailored to the target demographic.

[0060] When analyzing customer data, the analysis unit can use an emotion estimation function to estimate the emotional state of the customer and classify the data based on emotional fluctuations. For example, the analysis unit uses AI to analyze the customer's purchase history and social media posts, and then uses the emotion estimation function to estimate the customer's emotional state. For example, the analysis unit can identify customers who show positive emotions and classify their data into a separate category. The analysis unit can also use the emotion estimation function to analyze the emotional fluctuations of the customer and identify customers who show negative emotions. The analysis unit can also use the emotion estimation function to classify data in real time based on the customer's emotional fluctuations. This makes it possible to classify data taking into account the customer's emotional state.

[0061] The analysis unit incorporates real-time social media trends into the data, allowing it to instantly reflect changes in customer interests. For example, the analysis unit uses AI to collect social media trend data in real time, integrate it with customer data, and analyze it. For example, it identifies customer interests based on trends in specific hashtags or keywords. The analysis unit can also use social media trend data to reflect changes in customer interests in real time. The analysis unit can also predict customer purchasing intent based on social media trend data. This makes it possible to reflect changes in customer interests in real time.

[0062] When analyzing customer data, the analysis unit takes into account the customer's life events and can classify the data according to their life stage. For example, the analysis unit uses AI to collect customer life event data and incorporate it into the analysis. For example, the analysis unit classifies customer data based on life events such as marriage and childbirth. The analysis unit can also use the life event data to classify data according to the customer's life stage. The analysis unit can also predict customer purchasing behavior based on the life event data. This makes it possible to classify data according to the customer's life stage.

[0063] When analyzing customer data, the analysis unit incorporates the customer's health and fitness data to develop marketing strategies based on their health status. For example, the analysis unit uses AI to collect health data from the customer's fitness app or wearable device and incorporates it into the analysis. For example, the analysis unit develops marketing strategies based on exercise habits and sleep patterns. The analysis unit can also use health data to develop marketing strategies based on the customer's health status. The analysis unit can also predict the customer's health status based on the fitness data. This makes it possible to develop marketing strategies based on the customer's health status.

[0064] When analyzing customer data, the analysis unit adds data on the customer's hobbies and preferences, enabling it to develop personalized promotions based on those preferences. For example, the analysis unit uses AI to collect data on the customer's hobbies and preferences and incorporates it into the analysis. For example, the analysis unit develops personalized promotions based on preferences in music and movies. The analysis unit can also identify customer interests using data on hobbies and preferences. The analysis unit can also predict a customer's purchasing intentions based on their hobbies and preferences. This makes it possible to develop personalized promotions based on the customer's hobbies.

[0065] The analysis unit can use the emotion estimation function to predict how customers will feel about a specific promotion and classify data based on that emotion. For example, the analysis unit uses AI to predict how customers will feel about a specific promotion. For example, the analysis unit calculates an emotion score based on past response data. The analysis unit can also use the emotion estimation function to classify data based on customer emotions. The analysis unit can also use the emotion estimation function to analyze fluctuations in customer emotions in real time. This makes it possible to classify data based on customer emotions.

[0066] The target selection unit can use the emotion estimation function to consider the emotional state of the customer and preferentially select customer segments that show emotionally positive reactions. For example, the target selection unit uses AI to analyze the emotional state of the customer and preferentially select customer segments that show positive emotions. For example, the target selection unit calculates an emotion score based on past purchase history and social media posts. The target selection unit can also use the emotion estimation function to exclude customer segments that show negative emotions. The target selection unit can also use the emotion estimation function to analyze the emotional state of the customer in real time and select a target segment. This makes it possible to preferentially select customer segments that show emotionally positive reactions.

[0067] The target selection unit can implement an algorithm that predicts not only the customer's purchasing history but also the customer's future purchasing intent. For example, the target selection unit implements an algorithm that uses AI to analyze the customer's purchasing history and predicts future purchasing intent. For example, future purchasing intent is predicted based on past purchasing patterns. The target selection unit can also predict customer purchasing behavior using an algorithm that predicts purchasing intent. The target selection unit can also analyze the customer's purchasing cycle using an algorithm that predicts purchasing intent. This makes it possible to predict the customer's future purchasing intent.

[0068] The target selection unit can select a target demographic according to the characteristics of each region, taking into account the geographical location information of customers. For example, the target selection unit uses AI to collect geographical location information of customers and incorporate it into analysis. For example, the target selection unit selects customers living in a specific region as a target demographic. The target selection unit can also use geographical location information to select a target demographic according to the characteristics of each region. The target selection unit can also predict customer purchasing behavior based on geographical location information. This makes it possible to select a target demographic according to the characteristics of each region.

[0069] The target selection unit can create segments based on customers' occupations and industries and develop marketing strategies for each occupation. For example, the target selection unit uses AI to collect data on customers' occupations and industries and incorporate it into analysis. For example, it selects customers who belong to a specific occupation or industry as a target demographic. The target selection unit can also create segments based on occupations and industries and develop marketing strategies for each occupation. The target selection unit can also predict customer purchasing behavior based on occupations and industries. This makes it possible to develop marketing strategies for each occupation.

[0070] The target selection unit can create segments based on customers' family structure and lifestyles and develop promotions for families. For example, the target selection unit uses AI to collect data on customers' family structure and lifestyles and incorporate it into analysis. For example, it selects families with children as the target demographic. The target selection unit can also create segments based on family structure and lifestyles and develop promotions for families. The target selection unit can also predict customer purchasing behavior based on family structure and lifestyle. This makes it possible to develop promotions for families.

[0071] The campaign creation unit can use the emotion estimation function to consider the emotional state of the customer and create a campaign that elicits a positive emotional response. For example, the campaign creation unit uses the emotion estimation function with AI to analyze the emotional state of the customer and create a campaign that elicits positive emotions. For example, the campaign creation unit calculates an emotion score based on past campaign response data. The campaign creation unit can also use the emotion estimation function to exclude customers who show negative emotions. The campaign creation unit can also use the emotion estimation function to analyze the emotional state of the customer in real time and customize the campaign. This makes it possible to create a campaign that elicits a positive emotional response.

[0072] The campaign creation unit can analyze past campaign response data of customers and extract the most effective campaign elements. For example, the campaign creation unit uses AI to collect past campaign response data of customers and incorporate it into the analysis. For example, it evaluates whether a particular campaign element was effective. The campaign creation unit can also use past campaign response data to extract the most effective campaign elements. The campaign creation unit can also optimize future campaigns based on past campaign response data. This makes it possible to extract the most effective campaign elements.

[0073] The campaign creation department can take into account the customer's purchasing cycle and launch a campaign at the optimal timing. For example, the campaign creation department uses AI to collect customer purchasing cycle data and incorporate it into analysis. For example, the campaign creation department can launch a campaign at a specific timing. The campaign creation department can also use the purchasing cycle data to launch a campaign at the optimal timing. The campaign creation department can also predict customer purchasing behavior based on the purchasing cycle data. This makes it possible to launch a campaign at the optimal timing.

[0074] The campaign creation department can customize based on the customer's cultural background and language and develop an international marketing strategy. For example, the campaign creation department uses AI to collect customer cultural background and language data and incorporate it into analysis. For example, it can develop a campaign tailored to a specific culture or language. The campaign creation department can also customize based on cultural background and language and develop an international marketing strategy. The campaign creation department can also predict customer purchasing behavior based on cultural background and language data. This makes it possible to develop an international marketing strategy.

[0075] The campaign creation unit can create campaigns optimized for digital channels by taking into account customers' digital behavior. For example, the campaign creation unit uses AI to collect customers' digital behavior data and incorporate it into analysis. For example, the campaign creation unit customizes campaigns based on website browsing history and app usage history. The campaign creation unit can also use the digital behavior data to create campaigns optimized for digital channels. The campaign creation unit can also predict customers' purchasing behavior based on the digital behavior data. This makes it possible to create campaigns optimized for digital channels.

[0076] The campaign creation unit can use the emotion estimation function to predict how customers will feel about a specific campaign and customize the campaign based on that emotion. For example, the campaign creation unit uses the emotion estimation function with AI to predict how customers will feel about a specific campaign. For example, the campaign creation unit calculates an emotion score based on past response data. The campaign creation unit can also use the emotion estimation function to customize a campaign based on customer emotions. The campaign creation unit can also use the emotion estimation function to analyze fluctuations in customer emotions in real time. This makes it possible to customize a campaign based on customer emotions.

[0077] The content generation unit can analyze past content response data of customers and extract the most effective content elements. For example, the content generation unit uses AI to collect past content response data of customers and incorporate it into the analysis. For example, it evaluates whether a particular content element was effective. The content generation unit can also use past content response data to extract the most effective content elements. The content generation unit can also optimize future content based on past content response data. This makes it possible to extract the most effective content elements.

[0078] The content generation unit can provide content at the optimal timing, taking into account the customer's purchasing cycle. For example, the content generation unit uses AI to collect customer purchasing cycle data and incorporate it into analysis. For example, the content generation unit provides content at a specific timing. The content generation unit can also provide content at the optimal timing using the purchasing cycle data. The content generation unit can also predict customer purchasing behavior based on the purchasing cycle data. This makes it possible to provide content at the optimal timing.

[0079] The content generation unit can customize content based on the customer's cultural background and language, and develop international marketing strategies. For example, the content generation unit uses AI to collect customer cultural background and language data and incorporate it into analysis. For example, it generates content tailored to a specific culture or language. The content generation unit can also customize content based on cultural background and language, and develop international marketing strategies. The content generation unit can also predict customer purchasing behavior based on cultural background and language data. This makes it possible to develop international marketing strategies.

[0080] The content generation unit can generate content optimized for digital channels by taking into account the digital behavior of customers. For example, the content generation unit uses AI to collect digital behavior data of customers and incorporate it into analysis. For example, the content generation unit generates content based on website browsing history and app usage history. The content generation unit can also use the digital behavior data to generate content optimized for digital channels. The content generation unit can also predict customer purchasing behavior based on the digital behavior data. This makes it possible to generate content optimized for digital channels.

[0081] The content generation unit can use the emotion estimation function to predict how a customer will feel about specific content and generate content based on that emotion. For example, the content generation unit uses the emotion estimation function to predict how a customer will feel about specific content. For example, the content generation unit calculates an emotion score based on past reaction data. The content generation unit can also use the emotion estimation function to generate content based on the customer's emotions. The content generation unit can also use the emotion estimation function to analyze fluctuations in the customer's emotions in real time. This makes it possible to generate content based on the customer's emotions.

[0082] The promotion development unit can use the emotion estimation function to consider the emotional state of the customer and develop promotions that elicit a positive emotional response. For example, the promotion development unit uses AI to analyze the emotional state of the customer and develop promotions that elicit positive emotions. For example, the promotion development unit can calculate an emotion score based on past promotion response data. The promotion development unit can also use the emotion estimation function to exclude customers who show negative emotions. The promotion development unit can also use the emotion estimation function to analyze the emotional state of the customer in real time and customize promotions. This makes it possible to develop promotions that elicit a positive emotional response.

[0083] The promotion development unit can analyze past promotion response data of customers and extract the most effective promotion elements. For example, the promotion development unit uses AI to collect past promotion response data of customers and incorporate it into the analysis. For example, it evaluates whether a particular promotion element was effective. The promotion development unit can also use past promotion response data to extract the most effective promotion elements. The promotion development unit can also optimize future promotions based on past promotion response data. This makes it possible to extract the most effective promotion elements.

[0084] The promotion development department can take into account the customer's purchasing cycle and develop promotions at the optimal timing. For example, the promotion development department uses AI to collect customer purchasing cycle data and incorporate it into analysis. For example, it develops promotions at specific times. The promotion development department can also use purchasing cycle data to develop promotions at the optimal timing. The promotion development department can also predict customer purchasing behavior based on purchasing cycle data. This allows promotions to be developed at the optimal timing.

[0085] The promotion development department can customize based on the customer's cultural background and language and develop an international marketing strategy. For example, the promotion development department uses AI to collect customer cultural background and language data and incorporate it into analysis. For example, the promotion development department can develop a promotion tailored to a specific culture or language. The promotion development department can also customize based on cultural background and language and develop an international marketing strategy. The promotion development department can also predict customer purchasing behavior based on cultural background and language data. This makes it possible to develop an international marketing strategy.

[0086] The promotion development department can develop promotions optimized for digital channels by taking into account customers' digital behavior. For example, the promotion development department uses AI to collect customers' digital behavior data and incorporate it into analysis. For example, the promotion development department can develop promotions based on website browsing history and app usage history. The promotion development department can also use digital behavior data to develop promotions optimized for digital channels. The promotion development department can also predict customer purchasing behavior based on digital behavior data. This makes it possible to develop promotions optimized for digital channels.

[0087] The promotion development unit can use the emotion estimation function to predict how customers will feel about a particular promotion and develop the promotion based on that emotion. For example, the promotion development unit uses the emotion estimation function with AI to predict how customers will feel about a particular promotion. For example, it calculates an emotion score based on past response data. The promotion development unit can also use the emotion estimation function to develop a promotion based on the customer's emotion. The promotion development unit can also use the emotion estimation function to analyze fluctuations in customer emotion in real time. This makes it possible to develop a promotion based on the customer's emotion.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] When analyzing customer data, the analysis department can incorporate customer health and fitness data to develop marketing strategies based on their health status. For example, AI can collect health data from customers' fitness apps and wearable devices and incorporate it into the analysis. Marketing strategies can be developed based on exercise habits and sleep patterns. Health data can also be used to develop marketing strategies based on the customer's health status. Fitness data can also be used to predict the customer's health status. This makes it possible to develop marketing strategies based on the customer's health status.

[0090] The target selection unit can select target demographics according to the characteristics of each region, taking into account the geographic location information of customers. For example, AI can collect customers' geographic location information and incorporate it into analysis. Customers living in a specific region can be selected as the target demographic. Geographic location information can also be used to select target demographics according to the characteristics of each region. Customer purchasing behavior can also be predicted based on geographic location information. This makes it possible to select target demographics according to the characteristics of each region.

[0091] The campaign creation department can customize based on the customer's cultural background and language to create an international marketing strategy. For example, AI can collect customer cultural background and language data and incorporate it into analysis. Campaigns tailored to specific cultures and languages ​​can be developed. It can also customize based on cultural background and language to create an international marketing strategy. It can also predict customer purchasing behavior based on cultural background and language data. This makes it possible to create an international marketing strategy.

[0092] The content generation unit can generate content optimized for digital channels by taking into account customers' digital behavior. For example, AI can collect customers' digital behavior data and incorporate it into analysis. Content can be generated based on website browsing history and app usage history. Digital behavior data can also be used to generate content optimized for digital channels. Customer purchasing behavior can also be predicted based on digital behavior data. This makes it possible to generate content optimized for digital channels.

[0093] The promotion development department can develop promotions at the optimal timing, taking into account the customer's purchasing cycle. For example, AI collects customer purchasing cycle data and incorporates it into analysis. Promotions can be developed at specific times. Purchasing cycle data can also be used to develop promotions at the optimal timing. Customer purchasing behavior can also be predicted based on purchasing cycle data. This makes it possible to develop promotions at the optimal timing.

[0094] The analysis unit can use the emotion estimation function to predict how customers will feel about a specific promotion and classify data based on that emotion. For example, AI can use the emotion estimation function to predict how customers will feel about a specific promotion. An emotion score can be calculated based on past reaction data. The emotion estimation function can also be used to classify data based on customer emotions. The emotion estimation function can also be used to analyze fluctuations in customer emotions in real time. This makes it possible to classify data based on customer emotions.

[0095] The target selection unit can use the emotion estimation function to consider the emotional state of the customer and prioritize customer segments that show emotionally positive reactions. For example, AI can use the emotion estimation function to analyze the emotional state of the customer and prioritize customer segments that show positive emotions. An emotion score can be calculated based on past purchase history and social media posts. The emotion estimation function can also be used to exclude customer segments that show negative emotions. The emotion estimation function can also be used to analyze the emotional state of the customer in real time and select target segments. This makes it possible to prioritize customer segments that show emotionally positive reactions.

[0096] The campaign creation unit can use the emotion estimation function to consider the emotional state of the customer and create a campaign that elicits a positive emotional response. For example, AI can use the emotion estimation function to analyze the emotional state of the customer and create a campaign that elicits positive emotions. An emotion score can be calculated based on past campaign response data. The emotion estimation function can also be used to exclude customers who show negative emotions. The emotion estimation function can also be used to analyze the emotional state of the customer in real time and customize campaigns. This makes it possible to create a campaign that elicits a positive emotional response.

[0097] The content generation unit can use the emotion estimation function to predict how customers will feel about specific content and generate content based on those emotions. For example, AI can use the emotion estimation function to predict how customers will feel about specific content. An emotion score can be calculated based on past reaction data. The emotion estimation function can also be used to generate content based on customer emotions. The emotion estimation function can also be used to analyze fluctuations in customer emotions in real time. This makes it possible to generate content based on customer emotions.

[0098] The promotion development department can use the emotion estimation function to consider the emotional state of the customer and develop promotions that elicit a positive emotional response. For example, AI can use the emotion estimation function to analyze the customer's emotional state and develop promotions that elicit positive emotions. An emotion score can be calculated based on past promotion response data. The emotion estimation function can also be used to exclude customers who show negative emotions. The emotion estimation function can also be used to analyze the customer's emotional state in real time and customize promotions. This makes it possible to develop promotions that elicit a positive emotional response.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The data collection department collects market and customer data, such as customer purchase history, website browsing history, and social media activity data. Step 2: The analysis unit analyzes the data collected by the data collection unit, for example, using statistical analysis, machine learning algorithms, and natural language processing techniques. Step 3: The target selection unit selects a target demographic based on the results of the analysis by the analysis unit. For example, it may select a target demographic based on customers who are interested in a particular product, customers who are likely to respond to a particular campaign, or a specific age group or gender. Step 4: The campaign creation department creates a marketing campaign tailored to the target demographic selected by the target selection department, for example, a campaign offering discount coupons, special offers, or a loyalty program. Step 5: The content generation unit generates marketing content based on the campaign created by the campaign creation unit, such as advertising copy, images, and videos. Step 6: The promotion development unit develops personalized promotions using the content generated by the content generation unit, for example, through email marketing, social media advertising, and on the website.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] 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]

[0168] 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 department that collects market and customer data; an analysis unit that analyzes the data collected by the data collection unit; a target selection unit that selects a target demographic based on the results of the analysis by the analysis unit; a campaign creation unit that creates a marketing campaign tailored to the target demographic selected by the target selection unit; a content generation unit that generates marketing content based on the campaign created by the campaign creation unit; a promotion development unit that develops personalized promotions using the content generated by the content generation unit. A system characterized by:

2. The analysis unit When analyzing the customer data, an emotion estimation function is used to estimate the emotional state of the customer and classify the data based on the fluctuations in emotion.

2. The system of claim 1.

3. The analysis unit When analyzing the customer data, incorporate customer health and fitness data to develop marketing strategies based on health status.

2. The system of claim 1.

4. The target selection unit Using emotion estimation to consider the emotional state of the customer, prioritizing customer segments that show positive emotional responses 2. The system of claim 1.

5. The campaign creation unit Using a sentiment estimation function to predict how a customer will feel about a particular campaign and customize the campaign based on that sentiment.

2. The system of claim 1.

6. The content generation unit Using an emotion estimation function, the emotion that a customer will have toward a particular piece of content is predicted, and the content is generated based on that emotion.

2. The system of claim 1.

7. The promotion development unit Using emotion estimation capabilities, the promotion is developed to take into account the emotional state of the customer and elicit a positive emotional response.

2. The system of claim 1.

8. The promotion development unit Using an emotion estimation function, the customer's emotion toward the specific promotion is predicted, and the promotion is developed based on the emotion.

2. The system of claim 1.

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