system

Abstract

The system according to this embodiment aims to effectively utilize sales data, propose sales strategies, and provide real-time feedback. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a feedback unit. The collection unit collects sales data. The analysis unit analyzes the sales data collected by the collection unit. The proposal unit proposes a sales strategy based on the analysis results obtained by the analysis unit. The feedback unit provides real-time feedback based on the sales strategy proposed by the proposal unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, sales data has not been effectively utilized to formulate a sales strategy and provide real-time feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize sales data, propose a sales strategy, and provide real-time feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects sales data. The analysis unit analyzes the sales data collected by the data collection unit. The proposal unit proposes a sales strategy based on the analysis results obtained by the analysis unit. The feedback unit provides real-time feedback based on the sales strategy proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively utilize sales data, propose sales strategies, and provide real-time feedback. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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] [[ID=ID=18]]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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An autonomous AI agent system according to an embodiment of the present invention is a system that comprehensively supports the sales promotion activities of agents. This system contributes to improving the performance of agents by analyzing sales data, predicting trends, and proposing appropriate sales strategies. The autonomous AI agent system automatically analyzes sales data and predicts trends. Next, based on the predicted trends, it proposes a customized sales strategy to each agent. Furthermore, it uses a real-time feedback function to quickly correct and improve sales activities. This mechanism contributes to improving the performance of agents. For example, the autonomous AI agent system automatically analyzes sales data. In this process, it collects sales data from each agent and the AI ​​analyzes it. For example, by collecting data such as customer behavior patterns and purchase history in each region and analyzing it, the AI ​​can predict sales trends. Next, based on the predicted trends, it proposes a customized sales strategy to each agent. Specifically, the AI ​​creates an optimal sales promotion plan based on the sales data of each agent. For example, it predicts the times and regions in which a particular product is likely to sell well and proposes a sales strategy based on that information. Furthermore, it uses a real-time feedback function to quickly correct and improve sales activities. For example, the AI ​​monitors sales data in real time and immediately suggests improvement measures if sales targets are not being met. In this way, the agency's performance can be maximized. This system makes the agency's sales activities more efficient and is expected to improve performance. It can also help sales representatives focus on more strategic tasks. For example, by having the AI ​​analyze sales data and predict trends, sales representatives can use that information to develop effective sales strategies. In this way, the autonomous AI agent system can comprehensively support the agency's sales promotion activities and contribute to improved performance.

[0029] The autonomous AI agent system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a feedback unit. The collection unit collects sales data. The collection unit collects, for example, customer behavior patterns and purchase history in each region. The collection unit stores, for example, customer purchase history in a database so that it can be analyzed later. The collection unit monitors, for example, customer behavior patterns in real time and collects data. The collection unit collects, for example, online customer reviews and analyzes product evaluations and areas for improvement. The analysis unit analyzes the sales data collected by the collection unit. The analysis unit predicts sales trends based on the collected data. The analysis unit predicts trends based on past data and formulates future sales strategies. The analysis unit predicts sales trends considering seasonality and the impact of events. The analysis unit predicts sales trends by analyzing customer purchase history and behavior patterns. The proposal unit proposes sales strategies based on the analysis results obtained by the analysis unit. The proposal unit predicts, for example, the times and regions in which a particular product is likely to sell well and proposes sales strategies based on that information. The proposal department provides customized sales strategies to each agency, for example. The proposal department creates optimal sales promotion plans based on customer purchase history, for example. The proposal department proposes optimal sales strategies for specific customer segments, for example. The feedback department provides real-time feedback based on the sales strategies proposed by the proposal department. The feedback department proposes improvement measures, for example, if sales targets are not met. The feedback department monitors sales data in real time and immediately proposes improvement measures. The feedback department analyzes the success factors when sales targets are exceeded and shares this information with other agencies, for example. The feedback department provides not only real-time feedback but also periodic reports, for example. As a result, the autonomous AI agent system according to the embodiment can comprehensively support the sales promotion activities of agencies and contribute to improved performance.

[0030] The data collection unit collects sales data. For example, it collects customer behavior patterns and purchase history in each region. Specifically, it collects detailed data such as what products customers purchased, purchase frequency, and purchase circumstances. This includes data from online shopping sites and point-of-sale (POS) systems in physical stores. The data collection unit stores customer purchase history in a database for later analysis. The database stores each customer's purchase history chronologically and allows searching by specific periods and conditions. The data collection unit monitors customer behavior patterns in real time and collects data. This includes website browsing history, clickstream data, and social media activity. The data collection unit collects online customer reviews and analyzes product evaluations and areas for improvement. Online reviews are analyzed using text mining techniques to identify trends in positive and negative evaluations. This allows the data collection unit to gather a wide range of information from diverse data sources and gain a detailed understanding of customer behavior and preferences. Furthermore, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes sales data collected by the data collection department. For example, the analysis department predicts sales trends based on the collected data. Specifically, it uses AI to analyze past sales data and predict future sales trends. Using machine learning algorithms, the AI ​​learns data patterns and trends, enabling it to predict future sales trends with high accuracy. For example, the analysis department predicts trends based on past data and formulates future sales strategies. This includes predictions that take into account seasonality and the impact of events. For example, it identifies products that sell well or poorly during specific seasons or event periods and adjusts sales strategies accordingly. For example, the analysis department analyzes customer purchase history and behavioral patterns to predict sales trends. This allows for the optimization of sales strategies for specific customer segments. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This enables the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The Proposal Department proposes sales strategies based on the analysis results obtained by the Analysis Department. For example, the Proposal Department predicts the best time and region for selling a particular product and proposes a sales strategy based on that information. Specifically, it uses AI to analyze sales data from each region and develops sales strategies that take into account the characteristics and demand of each region. For example, the Proposal Department provides customized sales strategies to each agency. This includes strategies that take into account each agency's sales performance and customer base. For example, the Proposal Department creates optimal sales promotion plans based on customer purchase history. This includes promotional activities for specific products or services, as well as cross-selling and upselling suggestions. For example, the Proposal Department proposes optimal sales strategies for specific customer segments. This includes personalized suggestions based on customer preferences and purchase history. Furthermore, the Proposal Department can use AI to simulate multiple scenarios and identify the most effective sales strategy. This allows the Proposal Department to improve the accuracy and effectiveness of sales strategies and contribute to improving the performance of agencies.

[0033] The Feedback Department provides real-time feedback based on the sales strategies proposed by the Proposal Department. For example, the Feedback Department proposes improvement measures if sales targets are not met. Specifically, it uses AI to monitor sales data in real time and evaluate the status of target achievement. If sales targets are not met, the AI ​​analyzes the cause and proposes improvement measures. The Feedback Department monitors sales data in real time and immediately proposes improvement measures. This includes reviewing sales promotion activities and adjusting the timing of promotions. For example, the Feedback Department analyzes the success factors when sales targets are exceeded and shares this with other agencies. This allows successful cases to be spread to other agencies, improving overall performance. In addition to real-time feedback, the Feedback Department also provides periodic reports. These include monthly and quarterly sales performance, areas for improvement, and success stories. Furthermore, the Feedback Department can collect user feedback and use it to improve the system. In this way, the Feedback Department can contribute to improving the performance of agencies by continuously evaluating the effectiveness of sales strategies and making improvements.

[0034] The data collection unit can collect customer behavior patterns and purchase history in each region. For example, the data collection unit can store customer purchase frequency and visit count in a database. For example, the data collection unit can record customer purchase timing and use it for analysis. For example, the data collection unit can store customer purchased items and purchase dates and times in a database. By collecting customer behavior patterns and purchase history in each region, it is possible to formulate sales strategies for each region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer behavior patterns and purchase history into a generating AI and have the generating AI perform data collection and analysis.

[0035] The analysis department can predict sales trends based on the collected data. For example, the analysis department can predict trends based on historical data. For example, the analysis department can predict sales trends by considering the influence of seasonality and events. For example, the analysis department can predict sales trends by analyzing customer purchase history and behavioral patterns. By predicting sales trends, effective sales strategies can be formulated. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected data into a generating AI and have the generating AI perform sales trend predictions.

[0036] The proposal department can predict the times and regions in which a particular product is likely to sell well, and propose sales strategies based on that information. For example, the proposal department can predict the times when a particular product is likely to sell well. For example, the proposal department can predict the regions in which a particular product is likely to sell well. For example, the proposal department can propose sales strategies based on the times and regions in which a particular product is likely to sell well. In this way, by predicting the times and regions in which a particular product is likely to sell well, it is possible to propose effective sales strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input predictions of the times and regions in which a particular product is likely to sell well into a generating AI, and have the generating AI execute sales strategy proposals.

[0037] The feedback unit can propose improvement measures if sales targets are not being met. For example, the feedback unit can monitor sales data in real time and immediately propose improvement measures if sales targets are not being met. For example, the feedback unit can propose changes to promotions if sales targets are not being met. For example, the feedback unit can propose price adjustments if sales targets are not being met. This enables a rapid response by immediately proposing improvement measures when sales targets are not being met. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input sales data into a generating AI and have the generating AI execute the proposal of improvement measures.

[0038] The proposal department can provide each agency with a customized sales strategy. For example, the proposal department can provide a sales strategy tailored to the characteristics and needs of each agency. For example, the proposal department can provide a customized sales strategy based on each agency's sales data. For example, the proposal department can provide a sales strategy tailored to each agency's resources and budget. In this way, by providing each agency with a customized sales strategy, the needs of each agency can be met. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input each agency's sales data into a generating AI and have the generating AI execute the provision of a customized sales strategy.

[0039] The data collection unit can collect customer behavior patterns and purchase history in each region in real time and convert them into a format that can be immediately analyzed. For example, the data collection unit can collect customer purchase history in real time and immediately store it in a database. For example, the data collection unit can monitor customer behavior patterns in real time and immediately convert them into a format that can be immediately analyzed. For example, the data collection unit can collect online customer reviews in real time and immediately convert them into a format that can be immediately analyzed. This enables rapid response by collecting data in real time and immediately converting it into a format that can be immediately analyzed. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data collected in real time into a generating AI and have the generating AI perform the process of converting it into a format that can be immediately analyzed.

[0040] The data collection unit can expand the types of data it collects to include customer social media activity and online reviews. For example, the data collection unit can collect customer social media activity and use it to predict purchasing behavior. For example, the data collection unit can collect customer online reviews and analyze product evaluations and areas for improvement. For example, the data collection unit can collect customer blog and forum posts and analyze opinions and impressions of products. By collecting customer social media activity and online reviews, a more multifaceted data analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer social media activity and online reviews into a generating AI and have the generating AI perform data collection and analysis.

[0041] The data collection unit can collect data specific to a particular region, taking into account the geographical location of customers. For example, the data collection unit can collect the purchase history of customers in a specific region and analyze sales trends in that region. For example, the data collection unit can collect the behavioral patterns of customers in a specific region and formulate marketing strategies for that region. For example, the data collection unit can collect online reviews of customers in a specific region and analyze product evaluations in that region. By collecting data specific to a particular region, it is possible to formulate sales strategies for each region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the geographical location information of customers into a generating AI and have the generating AI perform data collection specific to a particular region.

[0042] The data collection unit can collect not only customer purchase history but also data related to customer lifestyles and hobbies. For example, the data collection unit can collect data on customer lifestyles and use it to predict purchasing behavior. For example, the data collection unit can collect data on customer hobbies and use it for product targeting. For example, the data collection unit can collect data on customer health status and use it for sales strategies for health-related products. By collecting data on customer lifestyles and hobbies, it becomes possible to formulate more accurate sales strategies. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on customer lifestyles and hobbies into a generating AI and have the generating AI perform data collection and analysis.

[0043] The analysis department can predict not only short-term sales trends but also long-term market trends based on the collected data. For example, the analysis department can predict short-term sales trends and formulate immediate sales strategies. For example, the analysis department can predict long-term market trends and formulate future sales strategies. For example, the analysis department can combine short-term and long-term forecasts to formulate a comprehensive sales strategy. This allows for the formulation of a comprehensive sales strategy by predicting not only short-term sales trends but also long-term market trends. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected data into a generating AI and have the generating AI perform short-term and long-term forecasts.

[0044] The analysis department can integrate information from different data sources to make more accurate trend predictions. For example, the analysis department can integrate customer purchase history and social media activity to make trend predictions. For example, the analysis department can integrate customer behavior patterns and online reviews to make trend predictions. For example, the analysis department can integrate customer geographic location information and purchase history to make trend predictions. This enables more accurate trend predictions by integrating information from different data sources. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input information from different data sources into a generating AI and have the generating AI perform trend predictions.

[0045] The analytics department can include not only customer purchase history but also customer feedback and reviews in its analysis. For example, the analytics department can analyze customer purchase history and feedback to evaluate products. For example, the analytics department can analyze customer purchase history and online reviews to identify areas for product improvement. For example, the analytics department can analyze customer purchase history and social media posts to predict product trends. By including customer feedback and reviews in the analysis, a more multifaceted data analysis becomes possible. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input customer feedback and reviews into a generating AI and have the generating AI perform the data analysis.

[0046] The analysis department can also collect sales data from competitors and conduct comparative analysis. For example, the analysis department can collect sales data from competitors and compare it with its own sales data. For example, the analysis department can analyze the sales trends of competitors and reflect them in its own sales strategy. For example, the analysis department can analyze the product evaluations of competitors and identify areas for improvement in its own products. By collecting and comparing sales data from competitors, it is possible to formulate a more effective sales strategy. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input competitor sales data into a generating AI and have the generating AI perform a comparative analysis.

[0047] The proposal department can propose not only the best times and regions for selling a particular product, but also optimal sales strategies for specific customer segments. For example, the proposal department can propose an optimal sales strategy for a specific customer segment. For example, the proposal department can predict the best times and regions for selling a particular product and propose a sales strategy based on that information. For example, the proposal department can analyze customer purchase history and propose an optimal sales strategy for a specific customer segment. This enables effective sales promotion by proposing an optimal sales strategy for a specific customer segment. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have a generation AI execute the proposal of a sales strategy for a specific customer segment.

[0048] The proposal department can simulate future sales strategies based on past sales data. For example, the proposal department can simulate future sales strategies based on past sales data. For example, the proposal department can simulate future sales strategies based on past sales trends. For example, the proposal department can simulate future sales strategies based on past sales data and market trends. By simulating future sales strategies based on past sales data, more effective sales strategies can be formulated. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past sales data into a generating AI and have the generating AI run a simulation of future sales strategies.

[0049] The proposal department can propose not only sales strategies but also marketing campaigns. For example, the proposal department can propose marketing campaigns in addition to sales strategies. For example, the proposal department can propose the optimal marketing campaign based on customer purchase history. For example, the proposal department can propose an effective marketing campaign based on customer behavior patterns. By proposing marketing campaigns in addition to sales strategies, comprehensive sales promotion becomes possible. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have a generation AI execute the generation of marketing campaign proposals.

[0050] The proposal department can provide customized proposals tailored to the agency's resources and budget. For example, the proposal department can provide customized proposals based on the agency's resources. For example, the proposal department can provide customized proposals based on the agency's budget. For example, the proposal department can provide optimal proposals considering the agency's resources and budget. This allows for the provision of effective sales strategies by providing customized proposals tailored to the agency's resources and budget. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can have a generation AI generate proposals tailored to the agency's resources and budget.

[0051] The feedback department can analyze and share not only the success factors when sales targets are not met, but also when targets are exceeded. For example, if sales targets are not met, the feedback department analyzes the causes and proposes improvement measures. For example, if sales targets are exceeded, the feedback department analyzes the success factors and shares them with other agents. For example, the feedback department analyzes both cases where sales targets are not met and where targets are exceeded, and provides comprehensive feedback. This enables comprehensive sales promotion by analyzing and sharing not only improvement measures when sales targets are not met, but also the success factors when targets are exceeded. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input sales data into a generating AI and have the generating AI perform the analysis and sharing of success factors.

[0052] The feedback unit can provide not only real-time feedback but also periodic reports. For example, the feedback unit can provide real-time feedback and propose immediate improvement measures. For example, the feedback unit can provide periodic reports and propose long-term improvement measures. For example, the feedback unit can combine real-time feedback and periodic reports to provide comprehensive feedback. This allows for the proposal of comprehensive improvement measures by providing both real-time feedback and periodic reports. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generating AI perform the provision of real-time feedback and periodic reports.

[0053] The feedback department can propose improvement measures by considering not only sales data but also customer feedback and reviews. For example, the feedback department can analyze sales data and customer feedback to propose improvement measures. For example, the feedback department can analyze sales data and online reviews to identify areas for product improvement. For example, the feedback department can analyze sales data and social media posts to predict product trends. By considering customer feedback and reviews when proposing improvement measures, it is possible to provide more effective improvement measures. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input customer feedback and reviews into a generating AI and have the generating AI execute improvement measure proposals.

[0054] The feedback department can provide feedback not only on the sales activities of agencies but also on their marketing activities. For example, the feedback department can provide feedback on the agencies' sales activities and propose improvement measures. For example, the feedback department can provide feedback on the agencies' marketing activities and propose improvement measures. For example, the feedback department can provide feedback on both the agencies' sales and marketing activities and propose comprehensive improvement measures. In this way, by providing feedback on both the agencies' sales and marketing activities, comprehensive improvement measures can be proposed. Some or all of the above processing in the feedback department may be performed using AI, for example, or without AI. For example, the feedback department can input data on the agencies' sales and marketing activities into a generating AI and have the generating AI perform the provision of feedback.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can collect not only customer purchase history but also data related to customer lifestyles and hobbies. For example, the data collection unit can collect data on customer lifestyles and use it to predict purchasing behavior. For example, the data collection unit can collect data on customer hobbies and use it for product targeting. For example, the data collection unit can collect data on customer health status and use it for sales strategies for health-related products. By collecting data on customer lifestyles and hobbies, it becomes possible to formulate more accurate sales strategies. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on customer lifestyles and hobbies into a generating AI and have the generating AI perform data collection and analysis.

[0057] The analysis department can integrate information from different data sources to make more accurate trend predictions. For example, the analysis department can integrate customer purchase history and social media activity to make trend predictions. For example, the analysis department can integrate customer behavior patterns and online reviews to make trend predictions. For example, the analysis department can integrate customer geographic location information and purchase history to make trend predictions. This enables more accurate trend predictions by integrating information from different data sources. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input information from different data sources into a generating AI and have the generating AI perform trend predictions.

[0058] The proposal department can propose not only the best times and regions for selling a particular product, but also optimal sales strategies for specific customer segments. For example, the proposal department can propose an optimal sales strategy for a specific customer segment. For example, the proposal department can predict the best times and regions for selling a particular product and propose a sales strategy based on that information. For example, the proposal department can analyze customer purchase history and propose an optimal sales strategy for a specific customer segment. This enables effective sales promotion by proposing an optimal sales strategy for a specific customer segment. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have a generation AI execute the proposal of a sales strategy for a specific customer segment.

[0059] The feedback department can analyze and share not only the success factors when sales targets are not met, but also when targets are exceeded. For example, if sales targets are not met, the feedback department analyzes the causes and proposes improvement measures. For example, if sales targets are exceeded, the feedback department analyzes the success factors and shares them with other agents. For example, the feedback department analyzes both cases where sales targets are not met and where targets are exceeded, and provides comprehensive feedback. This enables comprehensive sales promotion by analyzing and sharing not only improvement measures when sales targets are not met, but also the success factors when targets are exceeded. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input sales data into a generating AI and have the generating AI perform the analysis and sharing of success factors.

[0060] The feedback department can propose improvement measures by considering not only sales data but also customer feedback and reviews. For example, the feedback department can analyze sales data and customer feedback to propose improvement measures. For example, the feedback department can analyze sales data and online reviews to identify areas for product improvement. For example, the feedback department can analyze sales data and social media posts to predict product trends. By considering customer feedback and reviews when proposing improvement measures, it is possible to provide more effective improvement measures. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input customer feedback and reviews into a generating AI and have the generating AI execute improvement measure proposals.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects sales data. For example, it collects customer behavior patterns and purchase history in each region and stores them in a database. It also monitors customer behavior patterns in real time and collects online reviews to analyze product evaluations and areas for improvement. Step 2: The analysis department analyzes the sales data collected by the data collection department. For example, they predict sales trends based on the collected data and formulate future sales strategies considering past data, seasonality, and the impact of events. Step 3: The proposal department proposes sales strategies based on the analysis results obtained by the analysis department. For example, they predict the best time and region for selling a particular product and provide customized sales strategies to each agency. They also create optimal sales promotion plans based on customer purchase history and propose optimal sales strategies for specific customer segments. Step 4: The Feedback Department provides real-time feedback based on the sales strategy proposed by the Proposal Department. For example, if sales targets are not met, they propose improvement measures and monitor sales data in real time to immediately propose improvements. They also analyze the success factors when sales targets are exceeded and share this information with other agencies.

[0063] (Example of form 2) An autonomous AI agent system according to an embodiment of the present invention is a system that comprehensively supports the sales promotion activities of agents. This system contributes to improving the performance of agents by analyzing sales data, predicting trends, and proposing appropriate sales strategies. The autonomous AI agent system automatically analyzes sales data and predicts trends. Next, based on the predicted trends, it proposes a customized sales strategy to each agent. Furthermore, it uses a real-time feedback function to quickly correct and improve sales activities. This mechanism contributes to improving the performance of agents. For example, the autonomous AI agent system automatically analyzes sales data. In this process, it collects sales data from each agent and the AI ​​analyzes it. For example, by collecting data such as customer behavior patterns and purchase history in each region and analyzing it, the AI ​​can predict sales trends. Next, based on the predicted trends, it proposes a customized sales strategy to each agent. Specifically, the AI ​​creates an optimal sales promotion plan based on the sales data of each agent. For example, it predicts the times and regions in which a particular product is likely to sell well and proposes a sales strategy based on that information. Furthermore, it uses a real-time feedback function to quickly correct and improve sales activities. For example, the AI ​​monitors sales data in real time and immediately suggests improvement measures if sales targets are not being met. In this way, the agency's performance can be maximized. This system makes the agency's sales activities more efficient and is expected to improve performance. It can also help sales representatives focus on more strategic tasks. For example, by having the AI ​​analyze sales data and predict trends, sales representatives can use that information to develop effective sales strategies. In this way, the autonomous AI agent system can comprehensively support the agency's sales promotion activities and contribute to improved performance.

[0064] The autonomous AI agent system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a feedback unit. The collection unit collects sales data. The collection unit collects, for example, customer behavior patterns and purchase history in each region. The collection unit stores, for example, customer purchase history in a database so that it can be analyzed later. The collection unit monitors, for example, customer behavior patterns in real time and collects data. The collection unit collects, for example, online customer reviews and analyzes product evaluations and areas for improvement. The analysis unit analyzes the sales data collected by the collection unit. The analysis unit predicts sales trends based on the collected data. The analysis unit predicts trends based on past data and formulates future sales strategies. The analysis unit predicts sales trends considering seasonality and the impact of events. The analysis unit predicts sales trends by analyzing customer purchase history and behavior patterns. The proposal unit proposes sales strategies based on the analysis results obtained by the analysis unit. The proposal unit predicts, for example, the times and regions in which a particular product is likely to sell well and proposes sales strategies based on that information. The proposal department provides customized sales strategies to each agency, for example. The proposal department creates optimal sales promotion plans based on customer purchase history, for example. The proposal department proposes optimal sales strategies for specific customer segments, for example. The feedback department provides real-time feedback based on the sales strategies proposed by the proposal department. The feedback department proposes improvement measures, for example, if sales targets are not met. The feedback department monitors sales data in real time and immediately proposes improvement measures. The feedback department analyzes the success factors when sales targets are exceeded and shares this information with other agencies, for example. The feedback department provides not only real-time feedback but also periodic reports, for example. As a result, the autonomous AI agent system according to the embodiment can comprehensively support the sales promotion activities of agencies and contribute to improved performance.

[0065] The data collection unit collects sales data. For example, it collects customer behavior patterns and purchase history in each region. Specifically, it collects detailed data such as what products customers purchased, purchase frequency, and purchase circumstances. This includes data from online shopping sites and point-of-sale (POS) systems in physical stores. The data collection unit stores customer purchase history in a database for later analysis. The database stores each customer's purchase history chronologically and allows searching by specific periods and conditions. The data collection unit monitors customer behavior patterns in real time and collects data. This includes website browsing history, clickstream data, and social media activity. The data collection unit collects online customer reviews and analyzes product evaluations and areas for improvement. Online reviews are analyzed using text mining techniques to identify trends in positive and negative evaluations. This allows the data collection unit to gather a wide range of information from diverse data sources and gain a detailed understanding of customer behavior and preferences. Furthermore, the data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis department analyzes sales data collected by the data collection department. For example, the analysis department predicts sales trends based on the collected data. Specifically, it uses AI to analyze past sales data and predict future sales trends. Using machine learning algorithms, the AI ​​learns data patterns and trends, enabling it to predict future sales trends with high accuracy. For example, the analysis department predicts trends based on past data and formulates future sales strategies. This includes predictions that take into account seasonality and the impact of events. For example, it identifies products that sell well or poorly during specific seasons or event periods and adjusts sales strategies accordingly. For example, the analysis department analyzes customer purchase history and behavioral patterns to predict sales trends. This allows for the optimization of sales strategies for specific customer segments. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This enables the analysis department to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0067] The Proposal Department proposes sales strategies based on the analysis results obtained by the Analysis Department. For example, the Proposal Department predicts the best time and region for selling a particular product and proposes a sales strategy based on that information. Specifically, it uses AI to analyze sales data from each region and develops sales strategies that take into account the characteristics and demand of each region. For example, the Proposal Department provides customized sales strategies to each agency. This includes strategies that take into account each agency's sales performance and customer base. For example, the Proposal Department creates optimal sales promotion plans based on customer purchase history. This includes promotional activities for specific products or services, as well as cross-selling and upselling suggestions. For example, the Proposal Department proposes optimal sales strategies for specific customer segments. This includes personalized suggestions based on customer preferences and purchase history. Furthermore, the Proposal Department can use AI to simulate multiple scenarios and identify the most effective sales strategy. This allows the Proposal Department to improve the accuracy and effectiveness of sales strategies and contribute to improving the performance of agencies.

[0068] The Feedback Department provides real-time feedback based on the sales strategies proposed by the Proposal Department. For example, the Feedback Department proposes improvement measures if sales targets are not met. Specifically, it uses AI to monitor sales data in real time and evaluate the status of target achievement. If sales targets are not met, the AI ​​analyzes the cause and proposes improvement measures. The Feedback Department monitors sales data in real time and immediately proposes improvement measures. This includes reviewing sales promotion activities and adjusting the timing of promotions. For example, the Feedback Department analyzes the success factors when sales targets are exceeded and shares this with other agencies. This allows successful cases to be spread to other agencies, improving overall performance. In addition to real-time feedback, the Feedback Department also provides periodic reports. These include monthly and quarterly sales performance, areas for improvement, and success stories. Furthermore, the Feedback Department can collect user feedback and use it to improve the system. In this way, the Feedback Department can contribute to improving the performance of agencies by continuously evaluating the effectiveness of sales strategies and making improvements.

[0069] The data collection unit can collect customer behavior patterns and purchase history in each region. For example, the data collection unit can store customer purchase frequency and visit count in a database. For example, the data collection unit can record customer purchase timing and use it for analysis. For example, the data collection unit can store customer purchased items and purchase dates and times in a database. By collecting customer behavior patterns and purchase history in each region, it is possible to formulate sales strategies for each region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer behavior patterns and purchase history into a generating AI and have the generating AI perform data collection and analysis.

[0070] The analysis department can predict sales trends based on the collected data. For example, the analysis department can predict trends based on historical data. For example, the analysis department can predict sales trends by considering the influence of seasonality and events. For example, the analysis department can predict sales trends by analyzing customer purchase history and behavioral patterns. By predicting sales trends, effective sales strategies can be formulated. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected data into a generating AI and have the generating AI perform sales trend predictions.

[0071] The proposal department can predict the times and regions in which a particular product is likely to sell well, and propose sales strategies based on that information. For example, the proposal department can predict the times when a particular product is likely to sell well. For example, the proposal department can predict the regions in which a particular product is likely to sell well. For example, the proposal department can propose sales strategies based on the times and regions in which a particular product is likely to sell well. In this way, by predicting the times and regions in which a particular product is likely to sell well, it is possible to propose effective sales strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input predictions of the times and regions in which a particular product is likely to sell well into a generating AI, and have the generating AI execute sales strategy proposals.

[0072] The feedback unit can propose improvement measures if sales targets are not being met. For example, the feedback unit can monitor sales data in real time and immediately propose improvement measures if sales targets are not being met. For example, the feedback unit can propose changes to promotions if sales targets are not being met. For example, the feedback unit can propose price adjustments if sales targets are not being met. This enables a rapid response by immediately proposing improvement measures when sales targets are not being met. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input sales data into a generating AI and have the generating AI execute the proposal of improvement measures.

[0073] The proposal department can provide each agency with a customized sales strategy. For example, the proposal department can provide a sales strategy tailored to the characteristics and needs of each agency. For example, the proposal department can provide a customized sales strategy based on each agency's sales data. For example, the proposal department can provide a sales strategy tailored to each agency's resources and budget. In this way, by providing each agency with a customized sales strategy, the needs of each agency can be met. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input each agency's sales data into a generating AI and have the generating AI execute the provision of a customized sales strategy.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0075] The data collection unit can collect customer behavior patterns and purchase history in each region in real time and convert them into a format that can be immediately analyzed. For example, the data collection unit can collect customer purchase history in real time and immediately store it in a database. For example, the data collection unit can monitor customer behavior patterns in real time and immediately convert them into a format that can be immediately analyzed. For example, the data collection unit can collect online customer reviews in real time and immediately convert them into a format that can be immediately analyzed. This enables rapid response by collecting data in real time and immediately converting it into a format that can be immediately analyzed. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data collected in real time into a generating AI and have the generating AI perform the process of converting it into a format that can be immediately analyzed.

[0076] The data collection unit can expand the types of data it collects to include customer social media activity and online reviews. For example, the data collection unit can collect customer social media activity and use it to predict purchasing behavior. For example, the data collection unit can collect customer online reviews and analyze product evaluations and areas for improvement. For example, the data collection unit can collect customer blog and forum posts and analyze opinions and impressions of products. By collecting customer social media activity and online reviews, a more multifaceted data analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer social media activity and online reviews into a generating AI and have the generating AI perform data collection and analysis.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0078] The data collection unit can collect data specific to a particular region, taking into account the geographical location of customers. For example, the data collection unit can collect the purchase history of customers in a specific region and analyze sales trends in that region. For example, the data collection unit can collect the behavioral patterns of customers in a specific region and formulate marketing strategies for that region. For example, the data collection unit can collect online reviews of customers in a specific region and analyze product evaluations in that region. By collecting data specific to a particular region, it is possible to formulate sales strategies for each region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the geographical location information of customers into a generating AI and have the generating AI perform data collection specific to a particular region.

[0079] The data collection unit can collect not only customer purchase history but also data related to customer lifestyles and hobbies. For example, the data collection unit can collect data on customer lifestyles and use it to predict purchasing behavior. For example, the data collection unit can collect data on customer hobbies and use it for product targeting. For example, the data collection unit can collect data on customer health status and use it for sales strategies for health-related products. By collecting data on customer lifestyles and hobbies, it becomes possible to formulate more accurate sales strategies. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on customer lifestyles and hobbies into a generating AI and have the generating AI perform data collection and analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis results according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis results.

[0081] The analysis department can predict not only short-term sales trends but also long-term market trends based on the collected data. For example, the analysis department can predict short-term sales trends and formulate immediate sales strategies. For example, the analysis department can predict long-term market trends and formulate future sales strategies. For example, the analysis department can combine short-term and long-term forecasts to formulate a comprehensive sales strategy. This allows for the formulation of a comprehensive sales strategy by predicting not only short-term sales trends but also long-term market trends. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected data into a generating AI and have the generating AI perform short-term and long-term forecasts.

[0082] The analysis department can integrate information from different data sources to make more accurate trend predictions. For example, the analysis department can integrate customer purchase history and social media activity to make trend predictions. For example, the analysis department can integrate customer behavior patterns and online reviews to make trend predictions. For example, the analysis department can integrate customer geographic location information and purchase history to make trend predictions. This enables more accurate trend predictions by integrating information from different data sources. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input information from different data sources into a generating AI and have the generating AI perform trend predictions.

[0083] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize providing only important analysis results. For example, if the user is relaxed, the analysis unit will prioritize providing detailed analysis results. For example, if the user is in a hurry, the analysis unit will prioritize providing analysis results that can be delivered quickly. This enables efficient data analysis by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0084] The analytics department can include not only customer purchase history but also customer feedback and reviews in its analysis. For example, the analytics department can analyze customer purchase history and feedback to evaluate products. For example, the analytics department can analyze customer purchase history and online reviews to identify areas for product improvement. For example, the analytics department can analyze customer purchase history and social media posts to predict product trends. By including customer feedback and reviews in the analysis, a more multifaceted data analysis becomes possible. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input customer feedback and reviews into a generating AI and have the generating AI perform the data analysis.

[0085] The analysis department can also collect sales data from competitors and conduct comparative analysis. For example, the analysis department can collect sales data from competitors and compare it with its own sales data. For example, the analysis department can analyze the sales trends of competitors and reflect them in its own sales strategy. For example, the analysis department can analyze the product evaluations of competitors and identify areas for improvement in its own products. By collecting and comparing sales data from competitors, it is possible to formulate a more effective sales strategy. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input competitor sales data into a generating AI and have the generating AI perform a comparative analysis.

[0086] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0087] The proposal department can propose not only the best times and regions for selling a particular product, but also optimal sales strategies for specific customer segments. For example, the proposal department can propose an optimal sales strategy for a specific customer segment. For example, the proposal department can predict the best times and regions for selling a particular product and propose a sales strategy based on that information. For example, the proposal department can analyze customer purchase history and propose an optimal sales strategy for a specific customer segment. This enables effective sales promotion by proposing an optimal sales strategy for a specific customer segment. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have a generation AI execute the proposal of a sales strategy for a specific customer segment.

[0088] The proposal department can simulate future sales strategies based on past sales data. For example, the proposal department can simulate future sales strategies based on past sales data. For example, the proposal department can simulate future sales strategies based on past sales trends. For example, the proposal department can simulate future sales strategies based on past sales data and market trends. By simulating future sales strategies based on past sales data, more effective sales strategies can be formulated. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past sales data into a generating AI and have the generating AI run a simulation of future sales strategies.

[0089] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit will prioritize providing only important suggestions. For example, if the user is relaxed, the suggestion unit will prioritize providing detailed suggestions. For example, if the user is in a hurry, the suggestion unit will prioritize providing suggestions that can be delivered quickly. This enables efficient suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0090] The proposal department can propose not only sales strategies but also marketing campaigns. For example, the proposal department can propose marketing campaigns in addition to sales strategies. For example, the proposal department can propose the optimal marketing campaign based on customer purchase history. For example, the proposal department can propose an effective marketing campaign based on customer behavior patterns. By proposing marketing campaigns in addition to sales strategies, comprehensive sales promotion becomes possible. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have a generation AI execute the generation of marketing campaign proposals.

[0091] The proposal department can provide customized proposals tailored to the agency's resources and budget. For example, the proposal department can provide customized proposals based on the agency's resources. For example, the proposal department can provide customized proposals based on the agency's budget. For example, the proposal department can provide optimal proposals considering the agency's resources and budget. This allows for the provision of effective sales strategies by providing customized proposals tailored to the agency's resources and budget. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can have a generation AI generate proposals tailored to the agency's resources and budget.

[0092] The feedback unit can estimate the user's emotions and adjust the way it presents the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit provides simple and easily understandable feedback. For example, if the user is relaxed, the feedback unit provides detailed feedback. For example, if the user is in a hurry, the feedback unit provides concise feedback. By adjusting the way it presents the feedback according to the user's emotions, it is possible to provide feedback that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way it presents the feedback.

[0093] The feedback department can analyze and share not only the success factors when sales targets are not met, but also when targets are exceeded. For example, if sales targets are not met, the feedback department analyzes the causes and proposes improvement measures. For example, if sales targets are exceeded, the feedback department analyzes the success factors and shares them with other agents. For example, the feedback department analyzes both cases where sales targets are not met and where targets are exceeded, and provides comprehensive feedback. This enables comprehensive sales promotion by analyzing and sharing not only improvement measures when sales targets are not met, but also the success factors when targets are exceeded. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input sales data into a generating AI and have the generating AI perform the analysis and sharing of success factors.

[0094] The feedback unit can provide not only real-time feedback but also periodic reports. For example, the feedback unit can provide real-time feedback and propose immediate improvement measures. For example, the feedback unit can provide periodic reports and propose long-term improvement measures. For example, the feedback unit can combine real-time feedback and periodic reports to provide comprehensive feedback. This allows for the proposal of comprehensive improvement measures by providing both real-time feedback and periodic reports. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generating AI perform the provision of real-time feedback and periodic reports.

[0095] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize providing only important feedback. For example, if the user is relaxed, the feedback unit will prioritize providing detailed feedback. For example, if the user is in a hurry, the feedback unit will prioritize providing feedback that can be delivered quickly. This enables efficient feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0096] The feedback department can propose improvement measures by considering not only sales data but also customer feedback and reviews. For example, the feedback department can analyze sales data and customer feedback to propose improvement measures. For example, the feedback department can analyze sales data and online reviews to identify areas for product improvement. For example, the feedback department can analyze sales data and social media posts to predict product trends. By considering customer feedback and reviews when proposing improvement measures, it is possible to provide more effective improvement measures. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input customer feedback and reviews into a generating AI and have the generating AI execute improvement measure proposals.

[0097] The feedback department can provide feedback not only on the sales activities of agencies but also on their marketing activities. For example, the feedback department can provide feedback on the agencies' sales activities and propose improvement measures. For example, the feedback department can provide feedback on the agencies' marketing activities and propose improvement measures. For example, the feedback department can provide feedback on both the agencies' sales and marketing activities and propose comprehensive improvement measures. In this way, by providing feedback on both the agencies' sales and marketing activities, comprehensive improvement measures can be proposed. Some or all of the above processing in the feedback department may be performed using AI, for example, or without AI. For example, the feedback department can input data on the agencies' sales and marketing activities into a generating AI and have the generating AI perform the provision of feedback.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can collect not only customer purchase history but also data related to customer lifestyles and hobbies. For example, the data collection unit can collect data on customer lifestyles and use it to predict purchasing behavior. For example, the data collection unit can collect data on customer hobbies and use it for product targeting. For example, the data collection unit can collect data on customer health status and use it for sales strategies for health-related products. By collecting data on customer lifestyles and hobbies, it becomes possible to formulate more accurate sales strategies. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on customer lifestyles and hobbies into a generating AI and have the generating AI perform data collection and analysis.

[0100] The analysis department can integrate information from different data sources to make more accurate trend predictions. For example, the analysis department can integrate customer purchase history and social media activity to make trend predictions. For example, the analysis department can integrate customer behavior patterns and online reviews to make trend predictions. For example, the analysis department can integrate customer geographic location information and purchase history to make trend predictions. This enables more accurate trend predictions by integrating information from different data sources. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input information from different data sources into a generating AI and have the generating AI perform trend predictions.

[0101] The proposal department can propose not only the best times and regions for selling a particular product, but also optimal sales strategies for specific customer segments. For example, the proposal department can propose an optimal sales strategy for a specific customer segment. For example, the proposal department can predict the best times and regions for selling a particular product and propose a sales strategy based on that information. For example, the proposal department can analyze customer purchase history and propose an optimal sales strategy for a specific customer segment. This enables effective sales promotion by proposing an optimal sales strategy for a specific customer segment. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have a generation AI execute the proposal of a sales strategy for a specific customer segment.

[0102] The feedback department can analyze and share not only the success factors when sales targets are not met, but also when targets are exceeded. For example, if sales targets are not met, the feedback department analyzes the causes and proposes improvement measures. For example, if sales targets are exceeded, the feedback department analyzes the success factors and shares them with other agents. For example, the feedback department analyzes both cases where sales targets are not met and where targets are exceeded, and provides comprehensive feedback. This enables comprehensive sales promotion by analyzing and sharing not only improvement measures when sales targets are not met, but also the success factors when targets are exceeded. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input sales data into a generating AI and have the generating AI perform the analysis and sharing of success factors.

[0103] The feedback department can propose improvement measures by considering not only sales data but also customer feedback and reviews. For example, the feedback department can analyze sales data and customer feedback to propose improvement measures. For example, the feedback department can analyze sales data and online reviews to identify areas for product improvement. For example, the feedback department can analyze sales data and social media posts to predict product trends. By considering customer feedback and reviews when proposing improvement measures, it is possible to provide more effective improvement measures. Some or all of the above processes in the feedback department may be performed using AI, for example, or not using AI. For example, the feedback department can input customer feedback and reviews into a generating AI and have the generating AI execute improvement measure proposals.

[0104] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0105] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis results according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis results.

[0106] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0107] The feedback unit can estimate the user's emotions and adjust the way it presents the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit provides simple and easily understandable feedback. For example, if the user is relaxed, the feedback unit provides detailed feedback. For example, if the user is in a hurry, the feedback unit provides concise feedback. By adjusting the way it presents the feedback according to the user's emotions, it is possible to provide feedback that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way it presents the feedback.

[0108] The analysis unit can estimate the user's emotions and prioritize analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize providing only important analysis results. For example, if the user is relaxed, the analysis unit will prioritize providing detailed analysis results. For example, if the user is in a hurry, the analysis unit will prioritize providing analysis results that can be delivered quickly. This enables efficient data analysis by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects sales data. For example, it collects customer behavior patterns and purchase history in each region and stores them in a database. It also monitors customer behavior patterns in real time and collects online reviews to analyze product evaluations and areas for improvement. Step 2: The analysis department analyzes the sales data collected by the data collection department. For example, they predict sales trends based on the collected data and formulate future sales strategies considering past data, seasonality, and the impact of events. Step 3: The proposal department proposes sales strategies based on the analysis results obtained by the analysis department. For example, they predict the best time and region for selling a particular product and provide customized sales strategies to each agency. They also create optimal sales promotion plans based on customer purchase history and propose optimal sales strategies for specific customer segments. Step 4: The Feedback Department provides real-time feedback based on the sales strategy proposed by the Proposal Department. For example, if sales targets are not met, they propose improvement measures and monitor sales data in real time to immediately propose improvements. They also analyze the success factors when sales targets are exceeded and share this information with other agencies.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect customer behavior patterns and purchase history, and stores them in the database 24 by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts sales trends based on the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes customized sales strategies to each agent based on the predicted trends. The feedback unit is implemented in the specific processing unit 46A of the smart device 14 and monitors sales data in real time and immediately proposes improvement measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect customer behavior patterns and purchase history, and stores them in the database 24 by the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and predicts sales trends based on the collected data. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes a customized sales strategy to each agent based on the predicted trends. The feedback unit is implemented, for example, in the control unit 46A of the smart glasses 214, and monitors sales data in real time and immediately proposes improvement measures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect customer behavior patterns and purchase history, and the control unit 46A stores them in the database 24. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts sales trends based on the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes customized sales strategies to each agent based on the predicted trends. The feedback unit is implemented in the specific processing unit 46A of the headset terminal 314 and monitors sales data in real time and immediately proposes improvement measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and feedback unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect customer behavior patterns and purchase history, and stores them in the database 24 by the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and predicts sales trends based on the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes a customized sales strategy to each agent based on the predicted trends. The feedback unit is implemented, for example, by the control unit 46A of the robot 414, and monitors sales data in real time and immediately proposes improvement measures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The data collection department collects sales data, An analysis unit analyzes the sales data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a sales strategy. The system includes a feedback unit that provides real-time feedback based on the sales strategy proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect customer behavior patterns and purchase history in each region. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Predicting sales trends based on collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We predict the best times and regions for selling specific products and propose sales strategies based on that information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is We propose improvement measures if sales targets have not been met. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We provide each agency with a customized sales strategy. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We collect customer behavior patterns and purchase history in each region in real time and convert them into a format that can be immediately analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Expand the types of data collected to include customer social media activity and online reviews. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We collect data specifically tailored to a particular region, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is In addition to customer purchase history, data on customer lifestyles and hobbies is also collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Based on the collected data, we predict not only short-term sales trends but also long-term market trends. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Integrating information from different data sources to perform more accurate trend forecasts. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is In addition to customer purchase history, customer feedback and reviews will also be included in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We also collect and compare sales data from our competitors. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, We propose not only the best times and regions for selling specific products, but also optimal sales strategies for specific customer segments. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Based on past sales data, we will simulate future sales strategies. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, We offer not only sales strategies but also marketing campaign proposals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We provide customized proposals tailored to the agency's resources and budget. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is We will analyze and share not only the factors contributing to failures in sales targeting, but also the factors contributing to success in cases where targets were exceeded. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is We provide not only real-time feedback but also regular reports. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is We propose improvement measures that take into account not only sales data, but also customer feedback and reviews. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is We provide feedback not only on the sales activities of our agencies, but also on their marketing activities. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects sales data, An analysis unit analyzes the sales data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a sales strategy. The system includes a feedback unit that provides real-time feedback based on the sales strategy proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect customer behavior patterns and purchase history in each region. The system according to feature 1.

3. The aforementioned analysis unit is Predicting sales trends based on collected data. The system according to feature 1.

4. The aforementioned proposal section is, We predict the best times and regions for selling specific products and propose sales strategies based on that information. The system according to feature 1.

5. The aforementioned feedback unit is We propose improvement measures if sales targets have not been met. The system according to feature 1.

6. The aforementioned proposal section is, We provide each agency with a customized sales strategy. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is We collect customer behavior patterns and purchase history in each region in real time and convert them into a format that can be immediately analyzed. The system according to feature 1.

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