System

Abstract

An object of a system according to an embodiment is to analyze customer behavior data, grasp a market trend, and optimize a marketing strategy and a sales plan.SOLUTION: A system includes a customer behavior data collection part, an analysis part, a trend analysis part, a marketing strategy optimization part, and a sales plan optimization part. The customer behavior data collection unit collects customer behavior data. The analysis unit analyzes the customer behavior data collected by the customer behavior data collection unit. The trend analysis unit analyzes a market trend based on the data analyzed by the analysis unit. The marketing strategy optimization unit optimizes a marketing strategy based on the market trend analyzed by the trend analysis unit. The sales plan optimization unit optimizes a sales plan based on the marketing strategy optimized by the marketing strategy optimization 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 analyze market trends or optimize marketing strategies based on customer behavior data, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze customer behavior data, grasp market trends, and optimize marketing strategies and sales plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a customer behavior data collection unit, an analysis unit, a trend analysis unit, a marketing strategy optimization unit, and a sales plan optimization unit. The customer behavior data collection unit collects customer behavior data. The analysis unit analyzes the customer behavior data collected by the customer behavior data collection unit. The trend analysis unit analyzes market trends based on the data analyzed by the analysis unit. The marketing strategy optimization unit optimizes a marketing strategy based on the market trend analyzed by the trend analysis unit. The sales plan optimization unit optimizes a sales plan based on the marketing strategy optimized by the marketing strategy optimization unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze customer behavior data, grasp market trends, and optimize marketing strategies and sales plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A customer behavior analysis system according to an embodiment of the present invention is a system that uses AI to analyze customer purchasing patterns, preferences, and behavioral trends, and optimizes marketing strategies and sales plans. As a result, the customer behavior analysis system can formulate effective marketing strategies and sales plans based on customer purchasing patterns and preferences.

[0029] A customer behavior analysis system according to an embodiment includes a customer behavior data collection unit, an analysis unit, a trend analysis unit, a marketing strategy optimization unit, and a sales plan optimization unit. The customer behavior data collection unit collects customer behavior data. For example, the customer behavior data collection unit collects customer purchase history, website browsing history, product reviews, social media activity, and the like. The customer behavior data collection unit can also collect real-time location information and weather data. The analysis unit analyzes the customer behavior data collected by the customer behavior data collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze customer purchasing patterns and preferences. The generation AI can also analyze customer behavior trends using a multimodal generation AI. The trend analysis unit analyzes market trends based on the data analyzed by the analysis unit. For example, the generation AI combines and analyzes the collected customer behavior data and market trend data to predict current market trends and future trends. The marketing strategy optimization unit optimizes a marketing strategy based on the market trends analyzed by the trend analysis unit. For example, the generation AI proposes effective promotions for specific customer segments and optimizes the timing and content of advertising campaigns. The sales plan optimization unit optimizes the sales plan based on the marketing strategy optimized by the marketing strategy optimization unit. For example, the generation AI supports the efficient improvement of inventory management and the formulation of production plans based on demand forecasts. As a result, the customer behavior analysis system according to the embodiment can formulate effective marketing strategies and sales plans based on customer purchasing patterns and preferences. For example, sales can be increased by conducting effective promotions for specific customer segments. Furthermore, cost reduction and profit maximization can be achieved by the efficient improvement of inventory management and the formulation of production plans based on demand forecasts.

[0030] The customer behavior data collection unit can collect real-time location information and weather data in addition to customer purchase history. The analysis unit can analyze the customer behavior data, real-time location information, and weather data. For example, the customer behavior data collection unit collects real-time location information in addition to customer purchase history, and analyzes which areas and products customers purchase. For example, it identifies trends in which specific products sell well in specific areas. The customer behavior data collection unit also collects weather data and analyzes the impact of weather on purchasing behavior. For example, it identifies trends in which specific products sell well on rainy days. The analysis unit analyzes the customer behavior data, real-time location information, and weather data to understand the background of customer purchasing behavior in more detail. This allows for a more detailed understanding of the background of customer purchasing behavior.

[0031] The customer behavior data collection unit can collect data from the voice assistant and smart home devices. The analysis unit can analyze the customer behavior data and the data from the voice assistant and smart home devices. The customer behavior data collection unit, for example, collects data from the voice assistant and analyzes customer purchasing behavior. For example, it identifies products that the customer has asked the voice assistant to "add to my shopping list." The customer behavior data collection unit also collects data from smart home devices and analyzes customer lifestyle habits. For example, it determines what foods the customer purchases based on data from a smart refrigerator. The analysis unit analyzes the customer behavior data and the data from the voice assistant and smart home devices to gain a detailed understanding of customer purchasing behavior. This makes it possible to analyze customer purchasing behavior by utilizing a wider variety of data sources.

[0032] The customer behavior data collection unit can collect data from different cultural spheres and regions. The analysis unit can analyze the customer behavior data and the data from different cultural spheres and regions. The customer behavior data collection unit, for example, collects data from different cultural spheres and analyzes customer purchasing patterns. For example, the purchasing patterns of customers in Japan and the United States are compared. The customer behavior data collection unit also collects data from different regions and analyzes purchasing behavior by region. For example, purchasing behavior in urban areas and rural areas is compared. The analysis unit analyzes the customer behavior data and the data from different cultural spheres and regions to understand customer purchasing patterns from a global perspective. This makes it possible to analyze customer purchasing patterns from a global perspective.

[0033] The trend analysis unit can analyze competitors' trends and new product release information in addition to market trend data. For example, the trend analysis unit analyzes competitors' trends in addition to market trend data to make a more comprehensive market forecast. For example, market trends are predicted based on new product release information from competitors. The trend analysis unit also analyzes new product release information to make a market trend. For example, the trend analysis unit analyzes the impact of new product releases on the market. The trend analysis unit also analyzes competitors' trends and new product release information to make a more comprehensive market forecast. For example, market trends are predicted based on new product release information from competitors. This makes it possible to make a market forecast that includes competitors' trends and new product release information.

[0034] The trend analysis unit can compare past market trends with current data and track trend changes. The trend analysis unit, for example, compares past market trends with current data and tracks trend changes. For example, future market trends are predicted based on market data from the past five years. The trend analysis unit also compares past market trends with current data and tracks trend changes. For example, future market trends are predicted based on market data from the past ten years. The trend analysis unit also compares past market trends with current data and tracks trend changes. For example, future market trends are predicted based on market data from the past five years. In this way, past and current data can be compared to track trend changes.

[0035] The trend analysis unit can analyze market trends by combining data from different industries. For example, the trend analysis unit combines data from different industries to analyze market trends and discover cross-industry trends. For example, data from the fashion industry and the technology industry can be combined to discover new trends. The trend analysis unit also combines data from different industries to analyze market trends. For example, data from the food industry and the healthcare industry can be combined to discover new trends. The trend analysis unit also combines data from different industries to analyze market trends. For example, data from the fashion industry and the technology industry can be combined to discover new trends. This makes it possible to analyze market trends by combining data from different industries.

[0036] The trend analysis unit can visualize market trend data and track trends in real time on an interactive dashboard. The trend analysis unit, for example, builds a system that visualizes market trend data and tracks trends in real time on an interactive dashboard. For example, market trends are visually displayed using graphs and charts. The trend analysis unit also visualizes market trend data and tracks trends in real time on an interactive dashboard. For example, trend fluctuations are checked in real time on the dashboard. The trend analysis unit also visualizes market trend data and tracks trends in real time on an interactive dashboard. For example, market trends are visually displayed using graphs and charts. This allows market trends to be visualized and tracked in real time.

[0037] The marketing strategy optimization unit can analyze the lifestyle data of customers and propose marketing strategies that suit individual life stages. The marketing strategy optimization unit, for example, analyzes the lifestyle data of customers and proposes marketing strategies that suit individual life stages. For example, it runs a promotion specialized for newlywed families. The marketing strategy optimization unit can also analyze the lifestyle data of customers and propose marketing strategies that suit individual life stages. For example, it runs a promotion specialized for the child-rearing generation. The marketing strategy optimization unit can also analyze the lifestyle data of customers and propose marketing strategies that suit individual life stages. For example, it runs a promotion specialized for newlywed families. This makes it possible to propose marketing strategies that suit the customer's life stages.

[0038] The marketing strategy optimization unit can generate personalized marketing messages by combining a customer's purchase history with their social media activity. The marketing strategy optimization unit, for example, combines a customer's purchase history with their social media activity to generate personalized marketing messages. For example, a message is created based on products that a customer has said they "want" on social media. The marketing strategy optimization unit can also generate personalized marketing messages by combining a customer's purchase history with their social media activity to generate personalized marketing messages. For example, a message is created based on products that a customer has said they "are interested" on social media. The marketing strategy optimization unit can also generate personalized marketing messages by combining a customer's purchase history with their social media activity to generate personalized marketing messages. For example, a message is created based on products that a customer has said they "want" on social media. This makes it possible to generate personalized marketing messages.

[0039] The marketing strategy optimization department can incorporate success stories from different industries to optimize the marketing strategy. The marketing strategy optimization department, for example, incorporates success stories from different industries to optimize the marketing strategy. For example, success stories from the technology industry are applied to the fashion industry. The marketing strategy optimization department also incorporates success stories from different industries to optimize the marketing strategy. For example, success stories from the food industry are applied to the healthcare industry. The marketing strategy optimization department also incorporates success stories from different industries to optimize the marketing strategy. For example, success stories from the technology industry are applied to the fashion industry. In this way, it is possible to optimize the marketing strategy by incorporating success stories from different industries.

[0040] The marketing strategy optimization unit can monitor the effectiveness of a marketing campaign in real time and adjust the strategy immediately. The marketing strategy optimization unit, for example, builds a system that monitors the effectiveness of a marketing campaign in real time and adjusts the strategy immediately. For example, it tracks the click rate and conversion rate of a campaign in real time. The marketing strategy optimization unit also monitors the effectiveness of a marketing campaign in real time and adjusts the strategy immediately. For example, it immediately changes the strategy if the effectiveness of a campaign is low. The marketing strategy optimization unit also monitors the effectiveness of a marketing campaign in real time and adjusts the strategy immediately. For example, it tracks the click rate and conversion rate of a campaign in real time. This allows the effectiveness of a marketing campaign to be monitored in real time and adjust the strategy immediately.

[0041] The sales plan optimization unit can combine supply chain data and create a sales plan that takes supply-side constraints into consideration. The sales plan optimization unit, for example, combines supply chain data into the optimization of the sales plan and creates a plan that takes supply-side constraints into consideration. For example, the sales plan is adjusted based on inventory status and supply capacity. The sales plan optimization unit also combines supply chain data and creates a sales plan that takes supply-side constraints into consideration. For example, the sales plan is adjusted based on logistics constraints. The sales plan optimization unit also combines supply chain data and creates a sales plan that takes supply-side constraints into consideration. For example, the sales plan is adjusted based on inventory status and supply capacity. In this way, a sales plan that takes supply-side constraints into consideration can be created.

[0042] The sales plan optimization unit can improve the accuracy of the sales plan by comparing past sales data with current market trends. The sales plan optimization unit, for example, compares past sales data with current market trends to improve the accuracy of the sales plan. For example, it predicts current market trends based on past data and adjusts the plan. The sales plan optimization unit also compares past sales data with current market trends to improve the accuracy of the sales plan. For example, it predicts current market trends based on past data and adjusts the plan. The sales plan optimization unit also compares past sales data with current market trends to improve the accuracy of the sales plan. For example, it predicts current market trends based on past data and adjusts the plan. In this way, the accuracy of the sales plan is improved by comparing past sales data with current market trends.

[0043] The sales plan optimization unit can combine data from different regions and create a sales plan that takes into account the demand characteristics of each region. For example, the sales plan optimization unit creates a sales plan by combining data from different regions and takes into account the demand characteristics of each region. For example, it compares the demand characteristics of urban and rural areas and adjusts the plan. Also, the sales plan optimization unit creates a sales plan by combining data from different regions and takes into account the demand characteristics of each region. For example, it compares the demand characteristics of urban and rural areas and adjusts the plan. Also, the sales plan optimization unit creates a sales plan by combining data from different regions and takes into account the demand characteristics of each region. For example, it compares the demand characteristics of urban and rural areas and adjusts the plan. In this way, it is possible to create a sales plan that takes into account the demand characteristics of each region.

[0044] The sales plan optimization unit visualizes the sales plan and can adjust the plan in real time using an interactive dashboard. The sales plan optimization unit, for example, builds a system that visualizes the sales plan and adjusts the plan in real time using an interactive dashboard. For example, the sales plan is visually displayed using graphs and charts. The sales plan optimization unit also visualizes the sales plan and adjusts the plan in real time using an interactive dashboard. For example, the progress of the plan is checked in real time on the dashboard. The sales plan optimization unit also visualizes the sales plan and adjusts the plan in real time using an interactive dashboard. For example, the sales plan is visually displayed using graphs and charts. This allows the sales plan to be visualized and adjusted in real time.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The customer behavior data collection unit can collect real-time location information and weather data in addition to customer purchase history. The analysis unit can analyze customer behavior data, real-time location information, and weather data. For example, in addition to customer purchase history, real-time location information can be collected to analyze which products customers purchase in which areas. This can identify trends in which specific products sell well in specific areas. Weather data can also be collected to analyze the impact of weather on purchasing behavior. For example, it can identify trends in which specific products sell well on rainy days. This allows for a more detailed understanding of the background to customer purchasing behavior.

[0047] The customer behavior data collection unit can collect data from voice assistants and smart home devices. The analysis unit can analyze customer behavior data and data from voice assistants and smart home devices. For example, data from the voice assistant can be collected to analyze customer purchasing behavior. Products that customers tell the voice assistant to "add to the shopping list" can be identified. Data from smart home devices can also be collected to analyze customer lifestyle habits. Based on data from smart refrigerators, the types of food that customers purchase can be identified. This makes it possible to analyze customer purchasing behavior by utilizing a wider variety of data sources.

[0048] The customer behavior data collection unit can collect data from different cultural spheres and regions. The analysis unit can analyze customer behavior data and data from different cultural spheres and regions. For example, data from different cultural spheres can be collected to analyze customer purchasing patterns. The purchasing patterns of customers in Japan and the United States can be compared. Data from different regions can also be collected to analyze purchasing behavior by region. Purchasing behavior in urban and rural areas can be compared. This makes it possible to analyze customer purchasing patterns from a global perspective.

[0049] The trend analysis unit can analyze competitor trends and new product release information in addition to market trend data. For example, in addition to market trend data, it can analyze competitor trends to make more comprehensive market forecasts. Market trends can be predicted based on competitor new product release information. It can also analyze new product release information and predict market trends. This makes it possible to make market forecasts that include competitor trends and new product release information.

[0050] The trend analysis unit can compare past market trends with current data and track changes in trends. For example, future market trends can be predicted based on market data from the past five years, or based on market data from the past 10 years. This allows past and current data to be compared to track changes in trends.

[0051] The Marketing Strategy Optimization Department can analyze customer lifestyle data and propose marketing strategies tailored to each individual life stage. For example, it can analyze customer lifestyle data and propose marketing strategies tailored to each individual life stage. It can carry out promotions tailored to newlywed families, and promotions tailored to the child-rearing generation. This makes it possible to propose marketing strategies tailored to each customer's life stage.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The customer behavior data collection unit collects customer behavior data, such as customer purchase history, website browsing history, product reviews, social media activity, etc. It can also collect real-time location information and weather data. Step 2: The analysis unit analyzes the customer behavior data collected by the customer behavior data collection unit. For example, the generation AI uses text generation AI (e.g., LLM) to analyze customer purchasing patterns and preferences. It can also use multimodal generation AI to analyze customer behavior trends. Step 3: The trend analysis unit analyzes market trends based on the data analyzed by the analysis unit. For example, the generation AI combines and analyzes the collected customer behavior data and market trend data to predict current market trends and future trends. Step 4: The Marketing Strategy Optimization Unit optimizes marketing strategies based on market trends analyzed by the Trend Analysis Unit. For example, generative AI can suggest effective promotions for specific customer segments and optimize the timing and content of advertising campaigns. Step 5: The sales plan optimization department optimizes the sales plan based on the marketing strategy optimized by the marketing strategy optimization department. For example, the generative AI helps improve the efficiency of inventory management and develop production plans based on demand forecasts.

[0054] (Example 2) A customer behavior analysis system according to an embodiment of the present invention is a system that uses AI to analyze customer purchasing patterns, preferences, and behavioral trends, and optimizes marketing strategies and sales plans. As a result, the customer behavior analysis system can formulate effective marketing strategies and sales plans based on customer purchasing patterns and preferences.

[0055] A customer behavior analysis system according to an embodiment includes a customer behavior data collection unit, an analysis unit, a trend analysis unit, a marketing strategy optimization unit, and a sales plan optimization unit. The customer behavior data collection unit collects customer behavior data. For example, the customer behavior data collection unit collects customer purchase history, website browsing history, product reviews, social media activity, and the like. The customer behavior data collection unit can also collect real-time location information and weather data. The analysis unit analyzes the customer behavior data collected by the customer behavior data collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze customer purchasing patterns and preferences. The generation AI can also analyze customer behavior trends using a multimodal generation AI. The trend analysis unit analyzes market trends based on the data analyzed by the analysis unit. For example, the generation AI combines and analyzes the collected customer behavior data and market trend data to predict current market trends and future trends. The marketing strategy optimization unit optimizes a marketing strategy based on the market trends analyzed by the trend analysis unit. For example, the generation AI proposes effective promotions for specific customer segments and optimizes the timing and content of advertising campaigns. The sales plan optimization unit optimizes the sales plan based on the marketing strategy optimized by the marketing strategy optimization unit. For example, the generation AI supports the efficient improvement of inventory management and the formulation of production plans based on demand forecasts. As a result, the customer behavior analysis system according to the embodiment can formulate effective marketing strategies and sales plans based on customer purchasing patterns and preferences. For example, sales can be increased by conducting effective promotions for specific customer segments. Furthermore, cost reduction and profit maximization can be achieved by the efficient improvement of inventory management and the formulation of production plans based on demand forecasts.

[0056] The customer behavior data collection unit can collect real-time location information and weather data in addition to customer purchase history. The analysis unit can analyze the customer behavior data, real-time location information, and weather data. For example, the customer behavior data collection unit collects real-time location information in addition to customer purchase history, and analyzes which areas and products customers purchase. For example, it identifies trends in which specific products sell well in specific areas. The customer behavior data collection unit also collects weather data and analyzes the impact of weather on purchasing behavior. For example, it identifies trends in which specific products sell well on rainy days. The analysis unit analyzes the customer behavior data, real-time location information, and weather data to understand the background of customer purchasing behavior in more detail. This allows for a more detailed understanding of the background of customer purchasing behavior.

[0057] The analysis unit can analyze customers' emotional expressions on social media and identify products or services that are associated with positive emotions. For example, the analysis unit analyzes customers' social media posts and identifies products or services that are associated with positive emotions. For example, it identifies products that customers have rated as "great." The analysis unit also uses a sentiment analysis algorithm to analyze customers' emotional expressions. For example, it uses text mining technology to extract posts that are associated with positive emotions. The analysis unit also analyzes customers' social media activities and identifies products or services that are associated with positive emotions. For example, it identifies services that customers have rated as "the best." This makes it possible to identify products or services that are associated with positive emotions.

[0058] The analysis unit uses the emotion estimation function to analyze the emotions of customers when purchasing products in real time and make product suggestions based on the emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of customers when purchasing products in real time and make suggestions for products that evoke positive emotions. For example, it suggests products that make the customer feel "happy." The analysis unit also uses the emotion estimation function to analyze the emotions of customers when purchasing products in real time and make suggestions that avoid products that evoke negative emotions. For example, it does not suggest products that make the customer feel "dissatisfied." The analysis unit also uses the emotion estimation function to analyze the emotions of customers when purchasing products in real time and make suggestions for products based on the emotions. For example, it suggests products that make the customer feel "excited." This makes it possible to make product suggestions based on the customer's emotions.

[0059] The customer behavior data collection unit can collect data from the voice assistant and smart home devices. The analysis unit can analyze the customer behavior data and the data from the voice assistant and smart home devices. The customer behavior data collection unit, for example, collects data from the voice assistant and analyzes customer purchasing behavior. For example, it identifies products that the customer has asked the voice assistant to "add to my shopping list." The customer behavior data collection unit also collects data from smart home devices and analyzes customer lifestyle habits. For example, it determines what foods the customer purchases based on data from a smart refrigerator. The analysis unit analyzes the customer behavior data and the data from the voice assistant and smart home devices to gain a detailed understanding of customer purchasing behavior. This makes it possible to analyze customer purchasing behavior by utilizing a wider variety of data sources.

[0060] The customer behavior data collection unit can collect data from different cultural spheres and regions. The analysis unit can analyze the customer behavior data and the data from different cultural spheres and regions. The customer behavior data collection unit, for example, collects data from different cultural spheres and analyzes customer purchasing patterns. For example, the purchasing patterns of customers in Japan and the United States are compared. The customer behavior data collection unit also collects data from different regions and analyzes purchasing behavior by region. For example, purchasing behavior in urban areas and rural areas is compared. The analysis unit analyzes the customer behavior data and the data from different cultural spheres and regions to understand customer purchasing patterns from a global perspective. This makes it possible to analyze customer purchasing patterns from a global perspective.

[0061] The analysis unit can use the emotion estimation function to analyze the emotions of customers when they write product reviews and provide feedback to encourage positive reviews. The analysis unit, for example, uses the emotion estimation function to analyze the emotions of customers when they write product reviews and provide feedback to encourage positive reviews. For example, for a product that a customer rated as "good," the analysis unit provides feedback that further emphasizes the good points. The analysis unit also uses the emotion estimation function to analyze the emotions of customers when they write product reviews and provide feedback to avoid writing negative reviews. For example, the analysis unit makes suggestions to improve points that the customer found "unsatisfactory." The analysis unit also uses the emotion estimation function to analyze the emotions of customers when they write product reviews and provide feedback to encourage positive reviews. For example, for a product that a customer rated as "the best," the analysis unit provides feedback that further emphasizes the good points. This makes it possible to provide feedback to encourage positive reviews.

[0062] The trend analysis unit can analyze competitors' trends and new product release information in addition to market trend data. For example, the trend analysis unit analyzes competitors' trends in addition to market trend data to make a more comprehensive market forecast. For example, market trends are predicted based on new product release information from competitors. The trend analysis unit also analyzes new product release information to make a market trend. For example, the trend analysis unit analyzes the impact of new product releases on the market. The trend analysis unit also analyzes competitors' trends and new product release information to make a more comprehensive market forecast. For example, market trends are predicted based on new product release information from competitors. This makes it possible to make a market forecast that includes competitors' trends and new product release information.

[0063] The trend analysis unit can compare past market trends with current data and track trend changes. The trend analysis unit, for example, compares past market trends with current data and tracks trend changes. For example, future market trends are predicted based on market data from the past five years. The trend analysis unit also compares past market trends with current data and tracks trend changes. For example, future market trends are predicted based on market data from the past ten years. The trend analysis unit also compares past market trends with current data and tracks trend changes. For example, future market trends are predicted based on market data from the past five years. In this way, past and current data can be compared to track trend changes.

[0064] The trend analysis unit can use the emotion estimation function to analyze fluctuations in market trends based on consumer emotions. The trend analysis unit, for example, uses the emotion estimation function to analyze fluctuations in market trends based on consumer emotions. For example, it analyzes the impact on the market of products that consumers feel "excited." The trend analysis unit also uses the emotion estimation function to analyze fluctuations in market trends based on consumer emotions. For example, it analyzes the impact on the market of products that consumers feel "delighted." The trend analysis unit also uses the emotion estimation function to analyze fluctuations in market trends based on consumer emotions. For example, it analyzes the impact on the market of products that consumers feel "excited." In this way, it is possible to analyze fluctuations in market trends based on consumer emotions.

[0065] The trend analysis unit can analyze market trends by combining data from different industries. For example, the trend analysis unit combines data from different industries to analyze market trends and discover cross-industry trends. For example, data from the fashion industry and the technology industry can be combined to discover new trends. The trend analysis unit also combines data from different industries to analyze market trends. For example, data from the food industry and the healthcare industry can be combined to discover new trends. The trend analysis unit also combines data from different industries to analyze market trends. For example, data from the fashion industry and the technology industry can be combined to discover new trends. This makes it possible to analyze market trends by combining data from different industries.

[0066] The trend analysis unit can visualize market trend data and track trends in real time on an interactive dashboard. The trend analysis unit, for example, builds a system that visualizes market trend data and tracks trends in real time on an interactive dashboard. For example, market trends are visually displayed using graphs and charts. The trend analysis unit also visualizes market trend data and tracks trends in real time on an interactive dashboard. For example, trend fluctuations are checked in real time on the dashboard. The trend analysis unit also visualizes market trend data and tracks trends in real time on an interactive dashboard. For example, market trends are visually displayed using graphs and charts. This allows market trends to be visualized and tracked in real time.

[0067] The trend analysis unit uses the emotion estimation function to make trend predictions based on consumer emotions and visualize the impact of emotional factors on the market. The trend analysis unit, for example, uses the emotion estimation function to make trend predictions based on consumer emotions. For example, it predicts the impact on the market of products that consumers feel "excited." The trend analysis unit also uses the emotion estimation function to make trend predictions based on consumer emotions and visualize the impact of emotional factors on the market. For example, it visualizes the impact on the market of products that consumers feel "delighted." The trend analysis unit also uses the emotion estimation function to make trend predictions based on consumer emotions and visualize the impact of emotional factors on the market. For example, it visualizes the impact on the market of products that consumers feel "excited." In this way, it is possible to make trend predictions based on consumer emotions and visualize the impact of emotional factors on the market.

[0068] The marketing strategy optimization unit can analyze the lifestyle data of customers and propose marketing strategies that suit individual life stages. The marketing strategy optimization unit, for example, analyzes the lifestyle data of customers and proposes marketing strategies that suit individual life stages. For example, it runs a promotion specialized for newlywed families. The marketing strategy optimization unit can also analyze the lifestyle data of customers and propose marketing strategies that suit individual life stages. For example, it runs a promotion specialized for the child-rearing generation. The marketing strategy optimization unit can also analyze the lifestyle data of customers and propose marketing strategies that suit individual life stages. For example, it runs a promotion specialized for newlywed families. This makes it possible to propose marketing strategies that suit the customer's life stages.

[0069] The marketing strategy optimization unit can generate personalized marketing messages by combining a customer's purchase history with their social media activity. The marketing strategy optimization unit, for example, combines a customer's purchase history with their social media activity to generate personalized marketing messages. For example, a message is created based on products that a customer has said they "want" on social media. The marketing strategy optimization unit can also generate personalized marketing messages by combining a customer's purchase history with their social media activity to generate personalized marketing messages. For example, a message is created based on products that a customer has said they "are interested" on social media. The marketing strategy optimization unit can also generate personalized marketing messages by combining a customer's purchase history with their social media activity to generate personalized marketing messages. For example, a message is created based on products that a customer has said they "want" on social media. This makes it possible to generate personalized marketing messages.

[0070] The marketing strategy optimization unit can use the emotion estimation function to design a marketing campaign based on customer emotions. The marketing strategy optimization unit, for example, uses the emotion estimation function to design a marketing campaign based on customer emotions. For example, a campaign is created that includes messages that make customers feel "happy." The marketing strategy optimization unit also uses the emotion estimation function to design a marketing campaign based on customer emotions. For example, a campaign is created that includes messages that make customers feel "excited." The marketing strategy optimization unit also uses the emotion estimation function to design a marketing campaign based on customer emotions. For example, a campaign is created that includes messages that make customers feel "happy." This makes it possible to design a marketing campaign based on customer emotions.

[0071] The marketing strategy optimization department can incorporate success stories from different industries to optimize the marketing strategy. The marketing strategy optimization department, for example, incorporates success stories from different industries to optimize the marketing strategy. For example, success stories from the technology industry are applied to the fashion industry. The marketing strategy optimization department also incorporates success stories from different industries to optimize the marketing strategy. For example, success stories from the food industry are applied to the healthcare industry. The marketing strategy optimization department also incorporates success stories from different industries to optimize the marketing strategy. For example, success stories from the technology industry are applied to the fashion industry. In this way, it is possible to optimize the marketing strategy by incorporating success stories from different industries.

[0072] The marketing strategy optimization unit can monitor the effectiveness of a marketing campaign in real time and adjust the strategy immediately. The marketing strategy optimization unit, for example, builds a system that monitors the effectiveness of a marketing campaign in real time and adjusts the strategy immediately. For example, it tracks the click rate and conversion rate of a campaign in real time. The marketing strategy optimization unit also monitors the effectiveness of a marketing campaign in real time and adjusts the strategy immediately. For example, it immediately changes the strategy if the effectiveness of a campaign is low. The marketing strategy optimization unit also monitors the effectiveness of a marketing campaign in real time and adjusts the strategy immediately. For example, it tracks the click rate and conversion rate of a campaign in real time. This allows the effectiveness of a marketing campaign to be monitored in real time and adjust the strategy immediately.

[0073] The marketing strategy optimization unit can use the emotion estimation function to generate a marketing message based on the customer's emotions. The marketing strategy optimization unit, for example, uses the emotion estimation function to generate a marketing message based on the customer's emotions. For example, a message that makes the customer feel "happy" is created. The marketing strategy optimization unit also uses the emotion estimation function to generate a marketing message based on the customer's emotions. For example, a message that makes the customer feel "excited." The marketing strategy optimization unit also uses the emotion estimation function to generate a marketing message based on the customer's emotions. For example, a message that makes the customer feel "happy" is created. In this way, a marketing message based on the customer's emotions can be generated.

[0074] The sales plan optimization unit can combine supply chain data and create a sales plan that takes supply-side constraints into consideration. The sales plan optimization unit, for example, combines supply chain data into the optimization of the sales plan and creates a plan that takes supply-side constraints into consideration. For example, the sales plan is adjusted based on inventory status and supply capacity. The sales plan optimization unit also combines supply chain data and creates a sales plan that takes supply-side constraints into consideration. For example, the sales plan is adjusted based on logistics constraints. The sales plan optimization unit also combines supply chain data and creates a sales plan that takes supply-side constraints into consideration. For example, the sales plan is adjusted based on inventory status and supply capacity. In this way, a sales plan that takes supply-side constraints into consideration can be created.

[0075] The sales plan optimization unit can improve the accuracy of the sales plan by comparing past sales data with current market trends. The sales plan optimization unit, for example, compares past sales data with current market trends to improve the accuracy of the sales plan. For example, it predicts current market trends based on past data and adjusts the plan. The sales plan optimization unit also compares past sales data with current market trends to improve the accuracy of the sales plan. For example, it predicts current market trends based on past data and adjusts the plan. The sales plan optimization unit also compares past sales data with current market trends to improve the accuracy of the sales plan. For example, it predicts current market trends based on past data and adjusts the plan. In this way, the accuracy of the sales plan is improved by comparing past sales data with current market trends.

[0076] The sales plan optimization unit uses the emotion estimation function to perform demand forecasting based on customer emotions and consider the impact of emotional factors on sales. The sales plan optimization unit, for example, uses the emotion estimation function to perform demand forecasting based on customer emotions. For example, it predicts the impact on sales of products that make customers feel "excited." The sales plan optimization unit also uses the emotion estimation function to perform demand forecasting based on customer emotions and consider the impact of emotional factors on sales. For example, it considers the impact on sales of products that make customers feel "delighted." The sales plan optimization unit also uses the emotion estimation function to perform demand forecasting based on customer emotions and consider the impact of emotional factors on sales. For example, it considers the impact on sales of products that make customers feel "excited." In this way, it is possible to perform demand forecasting based on customer emotions and consider the impact of emotional factors on sales.

[0077] The sales plan optimization unit can combine data from different regions and create a sales plan that takes into account the demand characteristics of each region. For example, the sales plan optimization unit creates a sales plan by combining data from different regions and takes into account the demand characteristics of each region. For example, it compares the demand characteristics of urban and rural areas and adjusts the plan. Also, the sales plan optimization unit creates a sales plan by combining data from different regions and takes into account the demand characteristics of each region. For example, it compares the demand characteristics of urban and rural areas and adjusts the plan. Also, the sales plan optimization unit creates a sales plan by combining data from different regions and takes into account the demand characteristics of each region. For example, it compares the demand characteristics of urban and rural areas and adjusts the plan. In this way, it is possible to create a sales plan that takes into account the demand characteristics of each region.

[0078] The sales plan optimization unit visualizes the sales plan and can adjust the plan in real time using an interactive dashboard. The sales plan optimization unit, for example, builds a system that visualizes the sales plan and adjusts the plan in real time using an interactive dashboard. For example, the sales plan is visually displayed using graphs and charts. The sales plan optimization unit also visualizes the sales plan and adjusts the plan in real time using an interactive dashboard. For example, the progress of the plan is checked in real time on the dashboard. The sales plan optimization unit also visualizes the sales plan and adjusts the plan in real time using an interactive dashboard. For example, the sales plan is visually displayed using graphs and charts. This allows the sales plan to be visualized and adjusted in real time.

[0079] The sales plan optimization unit can use the emotion estimation function to create a sales plan based on the emotions of the customer. The sales plan optimization unit, for example, uses the emotion estimation function to create a sales plan based on the emotions of the customer. For example, it considers the impact on sales of products that make the customer feel "excited." The sales plan optimization unit also uses the emotion estimation function to create a sales plan based on the emotions of the customer. For example, it considers the impact on sales of products that make the customer feel "delighted." The sales plan optimization unit also uses the emotion estimation function to create a sales plan based on the emotions of the customer. For example, it considers the impact on sales of products that make the customer feel "excited." In this way, it is possible to create a sales plan based on the emotions of the customer.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The customer behavior data collection unit can collect real-time location information and weather data in addition to customer purchase history. The analysis unit can analyze customer behavior data, real-time location information, and weather data. For example, in addition to customer purchase history, real-time location information can be collected to analyze which products customers purchase in which areas. This can identify trends in which specific products sell well in specific areas. Weather data can also be collected to analyze the impact of weather on purchasing behavior. For example, it can identify trends in which specific products sell well on rainy days. This allows for a more detailed understanding of the background to customer purchasing behavior.

[0082] The analysis unit can analyze customers' emotional expressions on social media and identify products or services that are associated with positive sentiment. For example, it can analyze customers' social media posts and identify products or services that express positive sentiment. It can also identify products that customers rate as "great." It can also analyze customers' emotional expressions using a sentiment analysis algorithm. It can also use text mining technology to extract posts that express positive sentiment. This makes it possible to identify products or services that are associated with positive sentiment.

[0083] The customer behavior data collection unit can collect data from voice assistants and smart home devices. The analysis unit can analyze customer behavior data and data from voice assistants and smart home devices. For example, data from the voice assistant can be collected to analyze customer purchasing behavior. Products that customers tell the voice assistant to "add to the shopping list" can be identified. Data from smart home devices can also be collected to analyze customer lifestyle habits. Based on data from smart refrigerators, the types of food that customers purchase can be identified. This makes it possible to analyze customer purchasing behavior by utilizing a wider variety of data sources.

[0084] The analysis unit uses the emotion estimation function to analyze the emotions of customers when they purchase products in real time and can make product suggestions based on those emotions. For example, the emotion estimation function can be used to analyze the emotions of customers when they purchase products in real time and suggest products that evoke positive emotions. Products that make the customer feel "happy" are suggested. Also, suggestions are made to avoid products that evoke negative emotions. Products that make the customer feel "unhappy" are not suggested. This makes it possible to make product suggestions based on the customer's emotions.

[0085] The customer behavior data collection unit can collect data from different cultural spheres and regions. The analysis unit can analyze customer behavior data and data from different cultural spheres and regions. For example, data from different cultural spheres can be collected to analyze customer purchasing patterns. The purchasing patterns of customers in Japan and the United States can be compared. Data from different regions can also be collected to analyze purchasing behavior by region. Purchasing behavior in urban and rural areas can be compared. This makes it possible to analyze customer purchasing patterns from a global perspective.

[0086] The analysis unit can use the emotion estimation function to analyze the emotions customers have when writing product reviews and provide feedback to encourage positive reviews. For example, the emotion estimation function can be used to analyze the emotions customers have when writing product reviews and provide feedback to encourage positive reviews. For products that customers have rated as "good," feedback that emphasizes the good points can be provided. Feedback to avoid negative reviews can also be provided. Suggestions can be made to improve points that customers find "unsatisfactory." In this way, feedback to encourage positive reviews can be provided.

[0087] The trend analysis unit can analyze competitor trends and new product release information in addition to market trend data. For example, in addition to market trend data, it can analyze competitor trends to make more comprehensive market forecasts. Market trends can be predicted based on competitor new product release information. It can also analyze new product release information and predict market trends. This makes it possible to make market forecasts that include competitor trends and new product release information.

[0088] The trend analysis unit can compare past market trends with current data and track changes in trends. For example, future market trends can be predicted based on market data from the past five years, or based on market data from the past 10 years. This allows past and current data to be compared to track changes in trends.

[0089] The trend analysis unit can use the emotion estimation function to analyze fluctuations in market trends based on consumer emotions. For example, the emotion estimation function is used to analyze fluctuations in market trends based on consumer emotions. The trend analysis unit analyzes the impact on the market of products that consumers find "exciting." Also, the trend analysis unit analyzes the impact on the market of products that consumers find "delightful." This makes it possible to analyze fluctuations in market trends based on consumer emotions.

[0090] The Marketing Strategy Optimization Department can analyze customer lifestyle data and propose marketing strategies tailored to each individual life stage. For example, it can analyze customer lifestyle data and propose marketing strategies tailored to each individual life stage. It can carry out promotions tailored to newlywed families, and promotions tailored to the child-rearing generation. This makes it possible to propose marketing strategies tailored to each customer's life stage.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The customer behavior data collection unit collects customer behavior data, such as customer purchase history, website browsing history, product reviews, social media activity, etc. It can also collect real-time location information and weather data. Step 2: The analysis unit analyzes the customer behavior data collected by the customer behavior data collection unit. For example, the generation AI uses text generation AI (e.g., LLM) to analyze customer purchasing patterns and preferences. It can also use multimodal generation AI to analyze customer behavior trends. Step 3: The trend analysis unit analyzes market trends based on the data analyzed by the analysis unit. For example, the generation AI combines and analyzes the collected customer behavior data and market trend data to predict current market trends and future trends. Step 4: The Marketing Strategy Optimization Unit optimizes marketing strategies based on market trends analyzed by the Trend Analysis Unit. For example, generative AI can suggest effective promotions for specific customer segments and optimize the timing and content of advertising campaigns. Step 5: The sales plan optimization department optimizes the sales plan based on the marketing strategy optimized by the marketing strategy optimization department. For example, the generative AI helps improve the efficiency of inventory management and develop production plans based on demand forecasts.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] 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]

[0160] 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 customer behavior data collection unit that collects customer behavior data; an analysis unit that analyzes the customer behavior data collected by the customer behavior data collection unit; a trend analysis unit that analyzes market trends based on the data analyzed by the analysis unit; a marketing strategy optimization unit that optimizes a marketing strategy based on the market trend analyzed by the trend analysis unit; a sales plan optimization unit that optimizes a sales plan based on the marketing strategy optimized by the marketing strategy optimization unit. A system characterized by:

2. The customer behavior data collection unit Collect real-time location and weather data in addition to customer purchase history, The analysis unit Analyzing the customer behavior data, real-time location information, and weather data 2. The system of claim 1.

3. The trend analysis unit Analyze market trend data, as well as competitor activity and new product release information 2. The system of claim 1.

4. The marketing strategy optimization unit Analyze customer lifestyle data and propose marketing strategies tailored to individual life stages 2. The system of claim 1.

5. The sales plan optimization unit Combine supply chain data to develop sales plans that take supply constraints into account 2. The system of claim 1.

6. The analysis unit Analyze customer social media sentiment to identify products or services with positive sentiment 2. The system of claim 1.

7. The trend analysis unit Use sentiment estimation to analyze fluctuations in market trends based on consumer sentiment 2. The system of claim 1.

8. The marketing strategy optimization unit Use sentiment estimation to design marketing campaigns based on customer sentiment 2. The system of claim 1.

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