system

Abstract

The system according to this embodiment aims to analyze sales performance and customer inquiries, propose the optimal next action, and present a sales strategy. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, and a strategy presentation unit. The analysis unit analyzes sales performance and customer inquiries. The proposal unit proposes the next action based on the data analyzed by the analysis unit. The strategy presentation unit learns from past success stories based on the actions proposed by the proposal unit and presents a sales strategy.

Description

Technical Field

[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, the method including 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 as a response 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, it has not been fully carried out to effectively utilize sales results and the content of inquiries from customers to propose the next action and present a sales strategy, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze sales results and the content of inquiries from customers, propose an optimal next action, and present a sales strategy. ​​​​​The system according to this embodiment comprises an analysis unit, a proposal unit, and a strategy presentation unit. The analysis unit analyzes sales performance and customer inquiries. The proposal unit proposes the next action based on the data analyzed by the analysis unit. The strategy presentation unit learns from past success stories based on the actions proposed by the proposal unit and presents a sales strategy. [Effects of the Invention]

[0007] The system according to this embodiment can analyze sales performance and customer inquiries, propose the optimal next action, and present a sales strategy. [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, etc. The communication I / F manages communication between multiple 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] 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) The sales support system according to an embodiment of the present invention is an optional service that eliminates lost sales opportunities for sales representatives and increases sales. This sales support system uses a generating AI to analyze sales performance and customer inquiries, and quickly proposes the optimal next action. Furthermore, the generating AI learns from past success stories and presents the most effective sales strategy. This enables stores lacking know-how or newly entered stores to smoothly achieve their sales targets. For example, the sales support system uses a generating AI to analyze sales performance and customer inquiries. Sales performance includes past sales data and sales figures for each product, while customer inquiries include questions and complaints about products. The generating AI analyzes this data to identify the causes of lost sales opportunities. Next, the sales support system uses a generating AI to quickly propose the optimal next action. For example, if a particular product is selling poorly, it may propose strengthening the promotion of that product. It may also suggest improvements to the product based on customer inquiries. This allows sales representatives to respond quickly and prevent lost sales opportunities. Furthermore, the sales support system uses a generating AI to learn from past success stories and present the most effective sales strategy. For example, the system learns what kind of promotions have been successful for a particular product in the past and suggests similar strategies. This allows sales representatives to implement effective sales strategies and increase sales. This system enables stores without know-how or newly established stores to smoothly achieve their sales targets. Sales representatives can implement effective sales strategies and prevent lost sales opportunities simply by following the suggestions of the generating AI. Furthermore, because the generating AI constantly analyzes the latest data and makes optimal suggestions, it can maximize the store's sales. In this way, the sales support system eliminates lost sales opportunities for sales representatives and enables increased sales.

[0029] The sales support system according to this embodiment comprises an analysis unit, a proposal unit, and a strategy presentation unit. The analysis unit analyzes sales performance and customer inquiries. Sales performance includes, but is not limited to, past sales data and sales figures for each product. Customer inquiries include, but are not limited to, questions and complaints about products. The analysis unit analyzes this data to identify the causes of lost sales opportunities. For example, the analysis unit can analyze sales performance data using data mining techniques. The analysis unit can also analyze customer inquiries using machine learning algorithms. Furthermore, the analysis unit can analyze trends in sales data using statistical analysis methods. The proposal unit proposes the following actions based on the data analyzed by the analysis unit. For example, if a particular product is underperforming, the proposal unit can propose strengthening the promotion of that product. The proposal unit can also propose improvements to products based on customer inquiries. For example, the proposal unit can analyze customer complaints and identify areas for product improvement. The proposal unit can also propose the development of new products based on customer requests. Furthermore, the proposal department can also propose revisions to the marketing strategy. The strategy presentation department learns from past success stories based on the actions proposed by the proposal department and presents a sales strategy. For example, the strategy presentation department can learn what kind of promotions have been successful for a particular product in the past and propose a similar strategy. The strategy presentation department can also analyze past sales data and identify the most effective sales strategy. For example, based on past success stories, the strategy presentation department can propose a promotion strategy tailored to the target market. The strategy presentation department can also propose the optimal sales channel based on past sales data. As a result, the sales support system according to this embodiment can eliminate lost sales opportunities and increase sales by analyzing sales performance and customer inquiries, proposing the optimal next action, and presenting the most effective sales strategy by learning from past success stories.

[0030] The analysis department analyzes sales performance and customer inquiries. Sales performance includes, but is not limited to, past sales data and sales figures for each product. This data is crucial for understanding the overall picture of a company's sales activities. The analysis department uses data mining techniques to analyze sales performance data in detail. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, and can identify specific trends and outliers within sales data. For example, it can analyze fluctuations in sales during specific seasons or events to identify the causes of lost sales opportunities. The analysis department also uses machine learning algorithms to analyze customer inquiries. Machine learning algorithms are techniques that learn from data and perform predictions and classifications, and can automatically classify customer inquiries to identify common problems and trends. For example, it can analyze customer complaints to reveal problems related to specific products or services. Furthermore, the analysis department uses statistical analysis methods to analyze trends in sales data. Statistical analysis methods are techniques for revealing the distribution and correlations of data, and can find significant trends and patterns within sales data. This allows the analysis unit to identify the causes of lost sales opportunities and provide the necessary data to propose the next course of action.

[0031] The proposal department proposes the following actions based on the data analyzed by the analysis department. For example, if a particular product is underperforming, the proposal department can propose strengthening the promotion of that product. Specifically, they may propose boosting sales by implementing advertising campaigns tailored to the target market or by offering discounts and benefits for underperforming products. The proposal department can also propose improvements to products based on customer inquiries. For example, they can analyze customer complaints to identify problems with product quality and functionality and propose improvements based on that. This can improve customer satisfaction and encourage repeat purchases. The proposal department can also propose the development of new products based on customer requests. By collecting customer feedback and proposing the development of new products that meet customer needs and requests, they can respond quickly to market needs. Furthermore, the proposal department can propose a review of the marketing strategy. For example, if the current marketing strategy is not effective, they can maximize sales opportunities by proposing new target markets and sales channels and reviewing the marketing strategy. In this way, the proposal department can propose specific and actionable next steps based on the data analyzed by the analysis department, minimizing lost sales opportunities.

[0032] The Strategy Presentation Department learns from past success stories based on the actions proposed by the Proposal Department and presents sales strategies. For example, the Strategy Presentation Department can learn what kind of promotions have been successful for a particular product in the past and propose similar strategies. Specifically, it analyzes past sales data to identify the conditions under which a particular promotion was successful and applies those success factors to the current situation to present an effective sales strategy. The Strategy Presentation Department can also identify the most effective sales strategy based on past sales data. For example, it can analyze which sales channels and promotion methods were most effective for a particular target market and propose the optimal sales strategy based on the results. Furthermore, the Strategy Presentation Department can propose promotion strategies tailored to the target market based on past success stories. For example, it can analyze which promotions were most effective for a particular region or customer segment and propose a promotion strategy tailored to the target market based on the results. The Strategy Presentation Department can also propose the optimal sales channel based on past sales data. For example, it can analyze which sales channel sold the most for a particular product and propose the optimal sales channel based on the results. This allows the strategy presentation department to learn from past success stories and apply them to the current situation, thereby presenting the most effective sales strategies, eliminating lost sales opportunities, and achieving increased sales.

[0033] The sales support system includes a data collection unit that collects sales performance data and customer inquiries. The data collection unit can, for example, automatically collect sales performance data. For example, the data collection unit obtains sales data from a sales management system. The data collection unit can also automatically collect customer inquiries. For example, the data collection unit obtains inquiry data from a customer support system. Furthermore, the data collection unit can collect customer feedback. For example, the data collection unit collects the results of customer surveys. This allows the analysis unit to analyze data more accurately by collecting sales performance data and customer inquiries. Some or all of the above-described processes in the data collection unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the data collection unit can input sales data obtained from a sales management system into a generation AI, and have the generation AI perform data collection and organization.

[0034] The proposal department can propose strengthening the promotion of a product if it is selling poorly. For example, the proposal department can propose running an advertising campaign for the underperforming product. For example, the proposal department can propose strengthening online advertising. The proposal department can also propose holding a discount sale for the underperforming product. For example, the proposal department can propose a limited-time discount sale. The proposal department can also propose holding a promotional event for the underperforming product. For example, the proposal department can propose an in-store demonstration event. This allows the proposal department to prevent lost sales opportunities by strengthening the promotion of underperforming products. Some or all of the above processes in the proposal department may be performed using or without a generation AI. For example, the proposal department can input data on underperforming products into a generation AI and have the generation AI propose the optimal promotion strategy.

[0035] The proposal department can suggest improvements to products based on customer inquiries. For example, the proposal department can analyze customer complaints to identify areas for product improvement. For example, the proposal department can analyze customer complaint data and make suggestions for improving product quality. The proposal department can also propose the development of new products based on customer requests. For example, the proposal department can analyze customer request data and propose the addition of new features to new products. Furthermore, the proposal department can propose design changes to existing products based on customer feedback. For example, the proposal department can analyze customer feedback data and propose design improvements to products. In this way, the proposal department can improve customer satisfaction by suggesting product improvements based on customer inquiries. Some or all of the above processes in the proposal department may be performed using or without a generation AI. For example, the proposal department can input customer inquiry data into a generation AI and have the generation AI identify areas for product improvement.

[0036] The strategy presentation unit can learn from past promotional successes of specific products and propose similar strategies. For example, the strategy presentation unit can analyze past promotional data to identify successful promotional methods. For example, it can analyze data from past advertising campaigns to propose the most effective advertising methods. It can also analyze data from past discount sales to propose successful discount strategies. For example, it can analyze data from past limited-time sales to propose the optimal sale period. It can also analyze data from past promotional events to propose successful event strategies. For example, it can analyze data from past in-store demonstration events to propose the most effective event methods. In this way, the strategy presentation unit can learn from past successes and propose similar strategies to implement effective sales strategies. Some or all of the above processing in the strategy presentation unit may be performed using generative AI, or not. For example, the strategy presentation unit can input past promotional data into a generative AI and have the generative AI identify successful promotional methods.

[0037] The strategy presentation unit can provide sales representatives with procedures for implementing effective sales strategies. For example, the strategy presentation unit can provide specific steps for implementing a sales strategy. For example, the strategy presentation unit can provide procedures for implementing a promotional campaign. The strategy presentation unit can also provide the resources necessary for implementing a sales strategy. For example, the strategy presentation unit can propose advertising budgets and staffing arrangements. The strategy presentation unit can also provide points to note when implementing a sales strategy. For example, the strategy presentation unit can propose risk management during promotion implementation. In this way, the strategy presentation unit can increase sales by providing sales representatives with specific procedures for implementing effective sales strategies. Some or all of the above processing in the strategy presentation unit may be performed using or without a generation AI. For example, the strategy presentation unit can input the sales strategy implementation procedures into a generation AI and have the generation AI provide the optimal procedures.

[0038] The analysis unit can improve the accuracy of its analysis based on seasonal fluctuations in sales performance. For example, the analysis unit can identify products that tend to sell well in a particular season based on seasonal sales data. For example, the analysis unit can analyze past sales data to identify best-selling products for each season. The analysis unit can also analyze the effectiveness of promotions in accordance with seasonal fluctuations and propose the optimal timing for promotions. For example, the analysis unit can analyze seasonal promotion data and propose the optimal timing for promotions. The analysis unit can also analyze seasonal customer purchasing trends and propose product lineups appropriate for each season. For example, the analysis unit can analyze seasonal customer data and propose products appropriate for each season. In this way, the analysis unit improves the accuracy of its analysis by considering seasonal fluctuations in sales performance. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input seasonal sales data into a generation AI and have the generation AI perform an improvement in the accuracy of the analysis based on seasonal fluctuations.

[0039] The analysis unit can perform analysis based on customer attribute information. For example, the analysis unit can analyze purchasing trends by customer age group and propose products tailored to the target group. For example, the analysis unit can analyze customer age data and identify purchasing trends by age group. The analysis unit can also analyze purchasing trends by customer gender and propose promotions tailored to gender. For example, the analysis unit can analyze customer gender data and identify purchasing trends by gender. The analysis unit can also analyze purchasing trends by customer region and propose sales strategies specific to that region. For example, the analysis unit can analyze customer region data and identify purchasing trends by region. This allows the analysis unit to perform more targeted analysis by considering customer attribute information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input customer attribute data into a generation AI and have the generation AI perform analysis based on the attribute information.

[0040] The analysis unit can improve the accuracy of its analysis by referring to the sales data of competitors during the analysis process. For example, the analysis unit can compare and analyze the sales of its own products based on the sales data of competitors. For example, the analysis unit can acquire the sales data of competitors and compare it with the sales data of its own products. The analysis unit can also analyze the promotional effectiveness of competitors and propose the optimal promotional strategy. For example, the analysis unit can analyze the promotional data of competitors and identify the optimal promotional strategy. The analysis unit can also analyze the customer base of competitors and identify the target audience for its own products. For example, the analysis unit can analyze the customer data of competitors and identify the target audience for its own products. As a result, the analysis unit improves the accuracy of its analysis by referring to the sales data of competitors. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the sales data of competitors into a generation AI and have the generation AI perform a comparative analysis of the data.

[0041] The analysis unit can identify relevant inquiries by analyzing the customer's purchase history during the analysis process. For example, the analysis unit can identify relevant inquiries based on the customer's past purchase history. For example, the analysis unit can analyze customer purchase history data and identify relevant inquiries. The analysis unit can also identify areas for product improvement from the customer's purchase history and reflect them in the inquiries. For example, the analysis unit can analyze customer purchase history data and identify areas for product improvement. The analysis unit can also analyze customer purchase history and make product suggestions based on the inquiries. For example, the analysis unit can analyze customer purchase history data and make relevant product suggestions. In this way, the analysis unit can identify relevant inquiries by analyzing the customer's purchase history. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input customer purchase history data into a generation AI and have the generation AI perform the identification of relevant inquiries.

[0042] The proposal department can suggest actions based on the inventory status of products when making a proposal. For example, the proposal department can suggest replenishing inventory for products with low stock levels. The proposal department can also suggest strengthening promotions for products with high inventory levels. The proposal department can also suggest adjusting sales prices according to inventory levels. For example, the proposal department can suggest lowering prices for products with high inventory levels. In this way, the proposal department can suggest the optimal action by considering the inventory status of products. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input product inventory data into a generation AI and have the generation AI execute action suggestions based on inventory levels.

[0043] The proposal unit can make personalized suggestions based on the customer's purchase history. For example, the proposal unit can suggest related products based on the customer's past purchase history. For example, the proposal unit can analyze the customer's purchase history data and suggest related products. The proposal unit can also suggest promotions for specific products based on the customer's purchase history. For example, the proposal unit can analyze the customer's purchase history data and suggest promotions for specific products. The proposal unit can also analyze the customer's purchase history and make personalized product suggestions. For example, the proposal unit can analyze the customer's purchase history data and make personalized product suggestions. This allows the proposal unit to make more effective suggestions by providing personalized suggestions based on the customer's purchase history. Some or all of the above processing in the proposal unit may be performed using generative AI, or it may be performed without generative AI. For example, the proposal unit can input customer purchase history data into generative AI and have the generative AI perform the execution of personalized suggestions.

[0044] The proposal department can analyze the customer's social media activity and make relevant suggestions when making proposals. For example, the proposal department can suggest relevant products based on the customer's interests on social media. For example, the proposal department can analyze the customer's social media data and make product suggestions based on their interests. The proposal department can also suggest product improvements based on the customer's feedback on social media. For example, the proposal department can analyze the customer's social media feedback and identify areas for product improvement. The proposal department can also analyze the customer's social media activity and make personalized suggestions. For example, the proposal department can analyze the customer's social media data and make personalized suggestions. This allows the proposal department to make relevant suggestions by analyzing the customer's social media activity. Some or all of the above processes in the proposal department may be performed using generative AI, or they may not be performed using generative AI. For example, the proposal department can input the customer's social media data into a generative AI and have the generative AI execute relevant suggestions.

[0045] The proposal department can adjust its proposals based on past customer feedback. For example, it can suggest improvements to a product based on past customer feedback. For example, it can analyze customer feedback data to identify areas for product improvement. The proposal department can also suggest promotions for specific products based on customer feedback. For example, it can analyze customer feedback data to suggest promotions for specific products. The proposal department can also analyze customer feedback to make personalized suggestions. For example, it can analyze customer feedback data to make individualized suggestions. This allows the proposal department to make more effective suggestions by considering past customer feedback. Some or all of the above processes in the proposal department may be performed using generative AI, or not. For example, the proposal department can input customer feedback data into generative AI and have the generative AI adjust the proposal content.

[0046] The strategy presentation unit can select the optimal strategy by referring to past sales data when presenting a strategy. For example, the strategy presentation unit can select the optimal promotion strategy based on past sales data. For example, the strategy presentation unit can analyze past sales data and identify the optimal promotion strategy. The strategy presentation unit can also select a sales strategy for a specific product from past sales data. For example, the strategy presentation unit can analyze past sales data and identify the optimal sales strategy for a specific product. The strategy presentation unit can also analyze past sales data and select the most effective sales strategy. For example, the strategy presentation unit can analyze past sales data and identify the most effective sales strategy. In this way, the strategy presentation unit can select the optimal strategy by referring to past sales data. Some or all of the above processing in the strategy presentation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the strategy presentation unit can input past sales data into a generation AI and have the generation AI perform the selection of the optimal strategy.

[0047] The strategy presentation unit can customize strategies based on customer attribute information when presenting strategies. For example, the strategy presentation unit can customize strategies to target groups based on purchasing trends by customer age group. For example, the strategy presentation unit can analyze customer age data and identify strategies based on purchasing trends by age group. The strategy presentation unit can also customize strategies according to gender based on purchasing trends by gender. For example, the strategy presentation unit can analyze customer gender data and identify strategies based on purchasing trends by gender. The strategy presentation unit can also customize region-specific strategies based on customer purchasing trends by region. For example, the strategy presentation unit can analyze customer region data and identify strategies based on purchasing trends by region. This allows the strategy presentation unit to customize strategies that are more tailored to the target audience by considering customer attribute information. Some or all of the above processing in the strategy presentation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the strategy presentation unit can input customer attribute data into a generation AI and have the generation AI perform the strategy customization.

[0048] The strategy presentation unit can optimize strategies by referring to successful case studies of competitors when presenting strategies. For example, the strategy presentation unit can propose the optimal promotion strategy based on successful case studies of competitors. For example, the strategy presentation unit can analyze successful case studies of competitors and identify the optimal promotion strategy. The strategy presentation unit can also propose sales strategies for specific products based on successful case studies of competitors. For example, the strategy presentation unit can analyze successful case studies of competitors and identify the optimal sales strategy for a specific product. The strategy presentation unit can also analyze successful case studies of competitors and propose the most effective sales strategy. For example, the strategy presentation unit can analyze successful case studies of competitors and identify the most effective sales strategy. In this way, the strategy presentation unit can select the optimal strategy by referring to successful case studies of competitors. Some or all of the above processing in the strategy presentation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the strategy presentation unit can input data on successful case studies of competitors into a generation AI and have the generation AI perform strategy optimization.

[0049] The strategy presentation unit can analyze the customer's purchase history and propose relevant strategies when presenting strategies. For example, the strategy presentation unit can propose relevant sales strategies based on the customer's past purchase history. For example, the strategy presentation unit can analyze customer purchase history data and identify relevant sales strategies. The strategy presentation unit can also propose promotion strategies for specific products based on the customer's purchase history. For example, the strategy presentation unit can analyze customer purchase history data and identify the optimal promotion strategy for a specific product. The strategy presentation unit can also analyze customer purchase history and propose the most effective sales strategies. For example, the strategy presentation unit can analyze customer purchase history data and identify the most effective sales strategies. In this way, the strategy presentation unit can propose relevant strategies by analyzing the customer's purchase history. Some or all of the above processing in the strategy presentation unit may be performed using or without a generative AI. For example, the strategy presentation unit can input customer purchase history data into a generative AI and have the generative AI execute the proposal of relevant strategies.

[0050] The data collection unit can filter data while considering customer attribute information. For example, the data collection unit can filter data by customer age group to collect data tailored to the target group. For example, the data collection unit can analyze customer age data and filter data by age group. The data collection unit can also filter data by customer gender to collect data tailored to gender. For example, the data collection unit can analyze customer gender data and filter data by gender. The data collection unit can also filter data by customer region to collect region-specific data. For example, the data collection unit can analyze customer region data and filter data by region. This allows the data collection unit to collect data that is more tailored to the target by considering customer attribute information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input customer attribute data into a generation AI and have the generation AI perform data filtering.

[0051] The data collection unit can analyze customers' social media activity and collect relevant data during the collection process. For example, the data collection unit can collect relevant data based on customers' interests on social media. For example, the data collection unit can analyze customers' social media data and collect data based on their interests. The data collection unit can also collect data on areas for product improvement based on customers' feedback on social media. For example, the data collection unit can analyze customers' social media feedback and collect data on areas for product improvement. The data collection unit can also analyze customers' social media activity and collect personalized data. For example, the data collection unit can analyze customers' social media data and collect personalized data. This allows the data collection unit to collect relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input customer social media data into a generative AI and have the generative AI perform the collection of relevant data.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The analysis unit can improve the accuracy of its analysis based on seasonal fluctuations in sales performance. For example, it can identify products that sell well in specific seasons based on seasonal sales data. The analysis unit can analyze past sales data to identify best-selling products for each season. It can also analyze the effectiveness of promotions in accordance with seasonal fluctuations and propose the optimal timing for promotions. The analysis unit can analyze seasonal promotion data and propose the optimal timing for promotions. It can also analyze seasonal customer purchasing trends and propose product lineups appropriate for each season. The analysis unit can analyze seasonal customer data and make product suggestions appropriate for each season. As a result, the analysis unit improves the accuracy of its analysis by considering seasonal fluctuations in sales performance.

[0054] The analysis unit can perform analysis based on customer attribute information. For example, it can analyze purchasing trends by customer age group and propose products tailored to the target group. The analysis unit can analyze customer age data and identify purchasing trends for each age group. It can also analyze purchasing trends by customer gender and propose promotions tailored to gender. The analysis unit can analyze customer gender data and identify purchasing trends for each gender. It can also analyze purchasing trends by customer region and propose sales strategies specific to that region. The analysis unit can analyze customer region data and identify purchasing trends for each region. As a result, the analysis unit can perform more targeted analysis by considering customer attribute information.

[0055] The proposal department can suggest actions based on the product's inventory status when making a proposal. For example, it can suggest replenishing inventory for products with low stock levels. The proposal department can also suggest strengthening promotions for products with high inventory levels. The proposal department can also suggest adjusting sales prices according to inventory levels. The proposal department can suggest lowering prices for products with high inventory levels. In this way, the proposal department can suggest the optimal action by considering the product's inventory status.

[0056] The proposal department can make personalized suggestions based on the customer's purchase history. For example, it can suggest related products based on the customer's past purchase history. The proposal department can analyze customer purchase history data and suggest related products. It can also suggest promotions for specific products based on the customer's purchase history. The proposal department can analyze customer purchase history data and suggest promotions for specific products. It can also analyze customer purchase history and make personalized product suggestions. The proposal department can analyze customer purchase history data and make personalized product suggestions. This allows the proposal department to make more effective suggestions by providing personalized suggestions based on the customer's purchase history.

[0057] The strategy presentation unit can select the optimal strategy by referring to past sales data when presenting a strategy. For example, it can select the optimal promotion strategy based on past sales data. The strategy presentation unit can analyze past sales data and identify the optimal promotion strategy. It can also select a sales strategy for a specific product from past sales data. The strategy presentation unit can analyze past sales data and identify the optimal sales strategy for a specific product. It can also analyze past sales data and select the most effective sales strategy. The strategy presentation unit can analyze past sales data and identify the most effective sales strategy. As a result, the strategy presentation unit can select the optimal strategy by referring to past sales data.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The analysis unit analyzes sales performance and customer inquiries. Sales performance includes past sales data and sales figures for each product, while customer inquiries include questions and complaints about products. The analysis unit analyzes this data to identify the causes of lost sales opportunities. For example, data mining techniques, machine learning algorithms, and statistical analysis methods can be used to analyze sales performance data and customer inquiries. Step 2: The proposal department proposes the next actions based on the data analyzed by the analysis department. For example, if a particular product is underperforming, they can propose strengthening its promotion, or suggest improvements to the product based on customer inquiries. They can also propose developing new products based on customer requests or suggest revising the marketing strategy. Step 3: The Strategy Presentation Department learns from past success stories based on the actions proposed by the Proposal Department and presents a sales strategy. For example, it can learn what kind of promotions have been successful for a particular product in the past and propose a similar strategy. It can also analyze past sales data to identify the most effective sales strategies and optimal sales channels.

[0060] (Example of form 2) The sales support system according to an embodiment of the present invention is an optional service that eliminates lost sales opportunities for sales representatives and increases sales. This sales support system uses a generating AI to analyze sales performance and customer inquiries, and quickly proposes the optimal next action. Furthermore, the generating AI learns from past success stories and presents the most effective sales strategy. This enables stores lacking know-how or newly entered stores to smoothly achieve their sales targets. For example, the sales support system uses a generating AI to analyze sales performance and customer inquiries. Sales performance includes past sales data and sales figures for each product, while customer inquiries include questions and complaints about products. The generating AI analyzes this data to identify the causes of lost sales opportunities. Next, the sales support system uses a generating AI to quickly propose the optimal next action. For example, if a particular product is selling poorly, it may propose strengthening the promotion of that product. It may also suggest improvements to the product based on customer inquiries. This allows sales representatives to respond quickly and prevent lost sales opportunities. Furthermore, the sales support system uses a generating AI to learn from past success stories and present the most effective sales strategy. For example, the system learns what kind of promotions have been successful for a particular product in the past and suggests similar strategies. This allows sales representatives to implement effective sales strategies and increase sales. This system enables stores without know-how or newly established stores to smoothly achieve their sales targets. Sales representatives can implement effective sales strategies and prevent lost sales opportunities simply by following the suggestions of the generating AI. Furthermore, because the generating AI constantly analyzes the latest data and makes optimal suggestions, it can maximize the store's sales. In this way, the sales support system eliminates lost sales opportunities for sales representatives and enables increased sales.

[0061] The sales support system according to this embodiment comprises an analysis unit, a proposal unit, and a strategy presentation unit. The analysis unit analyzes sales performance and customer inquiries. Sales performance includes, but is not limited to, past sales data and sales figures for each product. Customer inquiries include, but are not limited to, questions and complaints about products. The analysis unit analyzes this data to identify the causes of lost sales opportunities. For example, the analysis unit can analyze sales performance data using data mining techniques. The analysis unit can also analyze customer inquiries using machine learning algorithms. Furthermore, the analysis unit can analyze trends in sales data using statistical analysis methods. The proposal unit proposes the following actions based on the data analyzed by the analysis unit. For example, if a particular product is underperforming, the proposal unit can propose strengthening the promotion of that product. The proposal unit can also propose improvements to products based on customer inquiries. For example, the proposal unit can analyze customer complaints and identify areas for product improvement. The proposal unit can also propose the development of new products based on customer requests. Furthermore, the proposal department can also propose revisions to the marketing strategy. The strategy presentation department learns from past success stories based on the actions proposed by the proposal department and presents a sales strategy. For example, the strategy presentation department can learn what kind of promotions have been successful for a particular product in the past and propose a similar strategy. The strategy presentation department can also analyze past sales data and identify the most effective sales strategy. For example, based on past success stories, the strategy presentation department can propose a promotion strategy tailored to the target market. The strategy presentation department can also propose the optimal sales channel based on past sales data. As a result, the sales support system according to this embodiment can eliminate lost sales opportunities and increase sales by analyzing sales performance and customer inquiries, proposing the optimal next action, and presenting the most effective sales strategy by learning from past success stories.

[0062] The analysis department analyzes sales performance and customer inquiries. Sales performance includes, but is not limited to, past sales data and sales figures for each product. This data is crucial for understanding the overall picture of a company's sales activities. The analysis department uses data mining techniques to analyze sales performance data in detail. Data mining techniques are methods for extracting useful patterns and relationships from large amounts of data, and can identify specific trends and outliers within sales data. For example, it can analyze fluctuations in sales during specific seasons or events to identify the causes of lost sales opportunities. The analysis department also uses machine learning algorithms to analyze customer inquiries. Machine learning algorithms are techniques that learn from data and perform predictions and classifications, and can automatically classify customer inquiries to identify common problems and trends. For example, it can analyze customer complaints to reveal problems related to specific products or services. Furthermore, the analysis department uses statistical analysis methods to analyze trends in sales data. Statistical analysis methods are techniques for revealing the distribution and correlations of data, and can find significant trends and patterns within sales data. This allows the analysis unit to identify the causes of lost sales opportunities and provide the necessary data to propose the next course of action.

[0063] The proposal department proposes the following actions based on the data analyzed by the analysis department. For example, if a particular product is underperforming, the proposal department can propose strengthening the promotion of that product. Specifically, they may propose boosting sales by implementing advertising campaigns tailored to the target market or by offering discounts and benefits for underperforming products. The proposal department can also propose improvements to products based on customer inquiries. For example, they can analyze customer complaints to identify problems with product quality and functionality and propose improvements based on that. This can improve customer satisfaction and encourage repeat purchases. The proposal department can also propose the development of new products based on customer requests. By collecting customer feedback and proposing the development of new products that meet customer needs and requests, they can respond quickly to market needs. Furthermore, the proposal department can propose a review of the marketing strategy. For example, if the current marketing strategy is not effective, they can maximize sales opportunities by proposing new target markets and sales channels and reviewing the marketing strategy. In this way, the proposal department can propose specific and actionable next steps based on the data analyzed by the analysis department, minimizing lost sales opportunities.

[0064] The Strategy Presentation Department learns from past success stories based on the actions proposed by the Proposal Department and presents sales strategies. For example, the Strategy Presentation Department can learn what kind of promotions have been successful for a particular product in the past and propose similar strategies. Specifically, it analyzes past sales data to identify the conditions under which a particular promotion was successful and applies those success factors to the current situation to present an effective sales strategy. The Strategy Presentation Department can also identify the most effective sales strategy based on past sales data. For example, it can analyze which sales channels and promotion methods were most effective for a particular target market and propose the optimal sales strategy based on the results. Furthermore, the Strategy Presentation Department can propose promotion strategies tailored to the target market based on past success stories. For example, it can analyze which promotions were most effective for a particular region or customer segment and propose a promotion strategy tailored to the target market based on the results. The Strategy Presentation Department can also propose the optimal sales channel based on past sales data. For example, it can analyze which sales channel sold the most for a particular product and propose the optimal sales channel based on the results. This allows the strategy presentation department to learn from past success stories and apply them to the current situation, thereby presenting the most effective sales strategies, eliminating lost sales opportunities, and achieving increased sales.

[0065] The sales support system includes a data collection unit that collects sales performance data and customer inquiries. The data collection unit can, for example, automatically collect sales performance data. For example, the data collection unit obtains sales data from a sales management system. The data collection unit can also automatically collect customer inquiries. For example, the data collection unit obtains inquiry data from a customer support system. Furthermore, the data collection unit can collect customer feedback. For example, the data collection unit collects the results of customer surveys. This allows the analysis unit to analyze data more accurately by collecting sales performance data and customer inquiries. Some or all of the above-described processes in the data collection unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the data collection unit can input sales data obtained from a sales management system into a generation AI, and have the generation AI perform data collection and organization.

[0066] The proposal department can propose strengthening the promotion of a product if it is selling poorly. For example, the proposal department can propose running an advertising campaign for the underperforming product. For example, the proposal department can propose strengthening online advertising. The proposal department can also propose holding a discount sale for the underperforming product. For example, the proposal department can propose a limited-time discount sale. The proposal department can also propose holding a promotional event for the underperforming product. For example, the proposal department can propose an in-store demonstration event. This allows the proposal department to prevent lost sales opportunities by strengthening the promotion of underperforming products. Some or all of the above processes in the proposal department may be performed using or without a generation AI. For example, the proposal department can input data on underperforming products into a generation AI and have the generation AI propose the optimal promotion strategy.

[0067] The proposal department can suggest improvements to products based on customer inquiries. For example, the proposal department can analyze customer complaints to identify areas for product improvement. For example, the proposal department can analyze customer complaint data and make suggestions for improving product quality. The proposal department can also propose the development of new products based on customer requests. For example, the proposal department can analyze customer request data and propose the addition of new features to new products. Furthermore, the proposal department can propose design changes to existing products based on customer feedback. For example, the proposal department can analyze customer feedback data and propose design improvements to products. In this way, the proposal department can improve customer satisfaction by suggesting product improvements based on customer inquiries. Some or all of the above processes in the proposal department may be performed using or without a generation AI. For example, the proposal department can input customer inquiry data into a generation AI and have the generation AI identify areas for product improvement.

[0068] The strategy presentation unit can learn from past promotional successes of specific products and propose similar strategies. For example, the strategy presentation unit can analyze past promotional data to identify successful promotional methods. For example, it can analyze data from past advertising campaigns to propose the most effective advertising methods. It can also analyze data from past discount sales to propose successful discount strategies. For example, it can analyze data from past limited-time sales to propose the optimal sale period. It can also analyze data from past promotional events to propose successful event strategies. For example, it can analyze data from past in-store demonstration events to propose the most effective event methods. In this way, the strategy presentation unit can learn from past successes and propose similar strategies to implement effective sales strategies. Some or all of the above processing in the strategy presentation unit may be performed using generative AI, or not. For example, the strategy presentation unit can input past promotional data into a generative AI and have the generative AI identify successful promotional methods.

[0069] The strategy presentation unit can provide sales representatives with procedures for implementing effective sales strategies. For example, the strategy presentation unit can provide specific steps for implementing a sales strategy. For example, the strategy presentation unit can provide procedures for implementing a promotional campaign. The strategy presentation unit can also provide the resources necessary for implementing a sales strategy. For example, the strategy presentation unit can propose advertising budgets and staffing arrangements. The strategy presentation unit can also provide points to note when implementing a sales strategy. For example, the strategy presentation unit can propose risk management during promotion implementation. In this way, the strategy presentation unit can increase sales by providing sales representatives with specific procedures for implementing effective sales strategies. Some or all of the above processing in the strategy presentation unit may be performed using or without a generation AI. For example, the strategy presentation unit can input the sales strategy implementation procedures into a generation AI and have the generation AI provide the optimal procedures.

[0070] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing urgent inquiries. For instance, the analysis unit will quickly analyze inquiries from stressed users and propose solutions. Similarly, if the user is relaxed, the analysis unit can prioritize analyzing data related to long-term sales strategies. For example, the analysis unit will propose long-term sales strategies based on inquiries from relaxed users. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing sales performance data requiring immediate attention. For example, the analysis unit will quickly analyze sales performance data from urgent users and propose immediate solutions. This allows the analysis unit to perform more effective analysis by prioritizing analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI determine the priority of analysis based on emotion.

[0071] The analysis unit can improve the accuracy of its analysis based on seasonal fluctuations in sales performance. For example, the analysis unit can identify products that tend to sell well in a particular season based on seasonal sales data. For example, the analysis unit can analyze past sales data to identify best-selling products for each season. The analysis unit can also analyze the effectiveness of promotions in accordance with seasonal fluctuations and propose the optimal timing for promotions. For example, the analysis unit can analyze seasonal promotion data and propose the optimal timing for promotions. The analysis unit can also analyze seasonal customer purchasing trends and propose product lineups appropriate for each season. For example, the analysis unit can analyze seasonal customer data and propose products appropriate for each season. In this way, the analysis unit improves the accuracy of its analysis by considering seasonal fluctuations in sales performance. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input seasonal sales data into a generation AI and have the generation AI perform an improvement in the accuracy of the analysis based on seasonal fluctuations.

[0072] The analysis unit can perform analysis based on customer attribute information. For example, the analysis unit can analyze purchasing trends by customer age group and propose products tailored to the target group. For example, the analysis unit can analyze customer age data and identify purchasing trends by age group. The analysis unit can also analyze purchasing trends by customer gender and propose promotions tailored to gender. For example, the analysis unit can analyze customer gender data and identify purchasing trends by gender. The analysis unit can also analyze purchasing trends by customer region and propose sales strategies specific to that region. For example, the analysis unit can analyze customer region data and identify purchasing trends by region. This allows the analysis unit to perform more targeted analysis by considering customer attribute information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input customer attribute data into a generation AI and have the generation AI perform analysis based on the attribute information.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, the analysis unit can provide a simple display that highlights important information to a nervous user. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, the analysis unit can provide a display that includes detailed data to a relaxed user. The analysis unit can also provide a display method that gets straight to the point if the user is in a hurry. For example, the analysis unit can provide a display that concisely summarizes the key points to a user in a hurry. In this way, the analysis unit can provide a more highly visible display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the display method based on that emotion.

[0074] The analysis unit can improve the accuracy of its analysis by referring to the sales data of competitors during the analysis process. For example, the analysis unit can compare and analyze the sales of its own products based on the sales data of competitors. For example, the analysis unit can acquire the sales data of competitors and compare it with the sales data of its own products. The analysis unit can also analyze the promotional effectiveness of competitors and propose the optimal promotional strategy. For example, the analysis unit can analyze the promotional data of competitors and identify the optimal promotional strategy. The analysis unit can also analyze the customer base of competitors and identify the target audience for its own products. For example, the analysis unit can analyze the customer data of competitors and identify the target audience for its own products. As a result, the analysis unit improves the accuracy of its analysis by referring to the sales data of competitors. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the sales data of competitors into a generation AI and have the generation AI perform a comparative analysis of the data.

[0075] The analysis unit can identify relevant inquiries by analyzing the customer's purchase history during the analysis process. For example, the analysis unit can identify relevant inquiries based on the customer's past purchase history. For example, the analysis unit can analyze customer purchase history data and identify relevant inquiries. The analysis unit can also identify areas for product improvement from the customer's purchase history and reflect them in the inquiries. For example, the analysis unit can analyze customer purchase history data and identify areas for product improvement. The analysis unit can also analyze customer purchase history and make product suggestions based on the inquiries. For example, the analysis unit can analyze customer purchase history data and make relevant product suggestions. In this way, the analysis unit can identify relevant inquiries by analyzing the customer's purchase history. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input customer purchase history data into a generation AI and have the generation AI perform the identification of relevant inquiries.

[0076] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and highly visible suggestions. For example, it can present simple suggestions that highlight important information to a nervous user. If the user is relaxed, the suggestion unit can also present suggestions that include detailed information. For example, it can present suggestions that include detailed data to a relaxed user. If the user is in a hurry, the suggestion unit can present concise suggestions that get straight to the point. For example, it can present suggestions that summarize the key points to a user in a hurry. In this way, the suggestion unit can make more effective suggestions by adjusting the way it presents suggestions based on 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 generative AI or not. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way proposals are expressed based on those emotions.

[0077] The proposal department can suggest actions based on the inventory status of products when making a proposal. For example, the proposal department can suggest replenishing inventory for products with low stock levels. The proposal department can also suggest strengthening promotions for products with high inventory levels. The proposal department can also suggest adjusting sales prices according to inventory levels. For example, the proposal department can suggest lowering prices for products with high inventory levels. In this way, the proposal department can suggest the optimal action by considering the inventory status of products. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input product inventory data into a generation AI and have the generation AI execute action suggestions based on inventory levels.

[0078] The proposal unit can make personalized suggestions based on the customer's purchase history. For example, the proposal unit can suggest related products based on the customer's past purchase history. For example, the proposal unit can analyze the customer's purchase history data and suggest related products. The proposal unit can also suggest promotions for specific products based on the customer's purchase history. For example, the proposal unit can analyze the customer's purchase history data and suggest promotions for specific products. The proposal unit can also analyze the customer's purchase history and make personalized product suggestions. For example, the proposal unit can analyze the customer's purchase history data and make personalized product suggestions. This allows the proposal unit to make more effective suggestions by providing personalized suggestions based on the customer's purchase history. Some or all of the above processing in the proposal unit may be performed using generative AI, or it may be performed without generative AI. For example, the proposal unit can input customer purchase history data into generative AI and have the generative AI perform the execution of personalized suggestions.

[0079] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize urgent suggestions. For instance, it will provide urgent suggestions to a stressed user. The suggestion unit can also prioritize long-term suggestions if the user is relaxed. For instance, it will provide long-term suggestions to a relaxed user. The suggestion unit can also prioritize suggestions requiring immediate attention if the user is in a hurry. For instance, it will provide suggestions requiring immediate attention to a user in a hurry. This allows the suggestion unit to make more effective suggestions by prioritizing suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using generative AI or not. For example, the proposal department can input user emotion data into a generating AI and have the AI ​​determine the priority of proposals based on those emotions.

[0080] The proposal department can analyze the customer's social media activity and make relevant suggestions when making proposals. For example, the proposal department can suggest relevant products based on the customer's interests on social media. For example, the proposal department can analyze the customer's social media data and make product suggestions based on their interests. The proposal department can also suggest product improvements based on the customer's feedback on social media. For example, the proposal department can analyze the customer's social media feedback and identify areas for product improvement. The proposal department can also analyze the customer's social media activity and make personalized suggestions. For example, the proposal department can analyze the customer's social media data and make personalized suggestions. This allows the proposal department to make relevant suggestions by analyzing the customer's social media activity. Some or all of the above processes in the proposal department may be performed using generative AI, or they may not be performed using generative AI. For example, the proposal department can input the customer's social media data into a generative AI and have the generative AI execute relevant suggestions.

[0081] The proposal department can adjust its proposals based on past customer feedback. For example, it can suggest improvements to a product based on past customer feedback. For example, it can analyze customer feedback data to identify areas for product improvement. The proposal department can also suggest promotions for specific products based on customer feedback. For example, it can analyze customer feedback data to suggest promotions for specific products. The proposal department can also analyze customer feedback to make personalized suggestions. For example, it can analyze customer feedback data to make individualized suggestions. This allows the proposal department to make more effective suggestions by considering past customer feedback. Some or all of the above processes in the proposal department may be performed using generative AI, or not. For example, the proposal department can input customer feedback data into generative AI and have the generative AI adjust the proposal content.

[0082] The strategy presentation unit can estimate the user's emotions and adjust the way strategies are presented based on those emotions. For example, if the user is nervous, the strategy presentation unit can present a simple and highly visible strategy. For example, it can present a simple strategy that highlights important information to a nervous user. Also, if the user is relaxed, the strategy presentation unit can present a strategy that includes detailed information. For example, it can present a strategy that includes detailed data to a relaxed user. Furthermore, if the user is in a hurry, the strategy presentation unit can present a strategy that gets straight to the point. For example, it can present a strategy that summarizes the key points concisely to a user in a hurry. In this way, the strategy presentation unit can provide more effective strategies by adjusting the way strategies are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the strategy presentation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the strategy presentation unit can input user emotion data into a generative AI and have the generative AI adjust the strategy presentation method based on emotion.

[0083] The strategy presentation unit can estimate the user's emotions and adjust the way strategies are presented based on those emotions. For example, if the user is nervous, the strategy presentation unit can present a simple and highly visible strategy. For example, it can present a simple strategy that highlights important information to a nervous user. Also, if the user is relaxed, the strategy presentation unit can present a strategy that includes detailed information. For example, it can present a strategy that includes detailed data to a relaxed user. Furthermore, if the user is in a hurry, the strategy presentation unit can present a strategy that gets straight to the point. For example, it can present a strategy that summarizes the key points concisely to a user in a hurry. In this way, the strategy presentation unit can provide more effective strategies by adjusting the way strategies are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the strategy presentation unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the strategy presentation unit can input user emotion data into a generative AI and have the generative AI adjust the strategy presentation method based on emotion.

[0084] The strategy presentation unit can select the optimal strategy by referring to past sales data when presenting a strategy. For example, the strategy presentation unit can select the optimal promotion strategy based on past sales data. For example, the strategy presentation unit can analyze past sales data and identify the optimal promotion strategy. The strategy presentation unit can also select a sales strategy for a specific product from past sales data. For example, the strategy presentation unit can analyze past sales data and identify the optimal sales strategy for a specific product. The strategy presentation unit can also analyze past sales data and select the most effective sales strategy. For example, the strategy presentation unit can analyze past sales data and identify the most effective sales strategy. In this way, the strategy presentation unit can select the optimal strategy by referring to past sales data. Some or all of the above processing in the strategy presentation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the strategy presentation unit can input past sales data into a generation AI and have the generation AI perform the selection of the optimal strategy.

[0085] The strategy presentation unit can customize strategies based on customer attribute information when presenting strategies. For example, the strategy presentation unit can customize strategies to target groups based on purchasing trends by customer age group. For example, the strategy presentation unit can analyze customer age data and identify strategies based on purchasing trends by age group. The strategy presentation unit can also customize strategies according to gender based on purchasing trends by gender. For example, the strategy presentation unit can analyze customer gender data and identify strategies based on purchasing trends by gender. The strategy presentation unit can also customize region-specific strategies based on customer purchasing trends by region. For example, the strategy presentation unit can analyze customer region data and identify strategies based on purchasing trends by region. This allows the strategy presentation unit to customize strategies that are more tailored to the target audience by considering customer attribute information. Some or all of the above processing in the strategy presentation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the strategy presentation unit can input customer attribute data into a generation AI and have the generation AI perform the strategy customization.

[0086] The strategy presentation unit can estimate the user's emotions and determine the priority of strategies based on the estimated emotions. For example, if the user is stressed, the strategy presentation unit will prioritize urgent strategies. For example, the strategy presentation unit will present urgent strategies to a stressed user. The strategy presentation unit can also prioritize long-term strategies if the user is relaxed. For example, the strategy presentation unit will present long-term strategies to a relaxed user. The strategy presentation unit can also prioritize strategies requiring immediate action if the user is in a hurry. For example, the strategy presentation unit will present strategies requiring immediate action to a user in a hurry. In this way, the strategy presentation unit can provide more effective strategies by determining the priority of strategies based on 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 strategy presentation unit may be performed using generative AI or not. For example, the strategy presentation unit can input user emotion data into a generating AI and have the generating AI determine the priority of strategies based on those emotions.

[0087] The strategy presentation unit can optimize strategies by referring to successful case studies of competitors when presenting strategies. For example, the strategy presentation unit can propose the optimal promotion strategy based on successful case studies of competitors. For example, the strategy presentation unit can analyze successful case studies of competitors and identify the optimal promotion strategy. The strategy presentation unit can also propose sales strategies for specific products based on successful case studies of competitors. For example, the strategy presentation unit can analyze successful case studies of competitors and identify the optimal sales strategy for a specific product. The strategy presentation unit can also analyze successful case studies of competitors and propose the most effective sales strategy. For example, the strategy presentation unit can analyze successful case studies of competitors and identify the most effective sales strategy. In this way, the strategy presentation unit can select the optimal strategy by referring to successful case studies of competitors. Some or all of the above processing in the strategy presentation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the strategy presentation unit can input data on successful case studies of competitors into a generation AI and have the generation AI perform strategy optimization.

[0088] The strategy presentation unit can analyze the customer's purchase history and propose relevant strategies when presenting strategies. For example, the strategy presentation unit can propose relevant sales strategies based on the customer's past purchase history. For example, the strategy presentation unit can analyze customer purchase history data and identify relevant sales strategies. The strategy presentation unit can also propose promotion strategies for specific products based on the customer's purchase history. For example, the strategy presentation unit can analyze customer purchase history data and identify the optimal promotion strategy for a specific product. The strategy presentation unit can also analyze customer purchase history and propose the most effective sales strategies. For example, the strategy presentation unit can analyze customer purchase history data and identify the most effective sales strategies. In this way, the strategy presentation unit can propose relevant strategies by analyzing the customer's purchase history. Some or all of the above processing in the strategy presentation unit may be performed using or without a generative AI. For example, the strategy presentation unit can input customer purchase history data into a generative AI and have the generative AI execute the proposal of relevant strategies.

[0089] 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. For example, the data collection unit can suggest to stressed users that the frequency of data collection be reduced. The data collection unit can also increase the frequency of data collection if the user is relaxed. For example, the data collection unit can suggest to relaxed users that the frequency of data collection be increased. The data collection unit can also prioritize the collection of data requiring immediate attention if the user is in a hurry. For example, the data collection unit can suggest to urgent users that the collection of data requiring immediate attention be collected. In this way, the data collection unit can perform more effective data collection by adjusting the timing of data collection based on 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 generative AI or not. For example, the data collection unit can input user emotion data into a generating AI, which can then adjust the timing of data collection based on those emotions.

[0090] The data collection unit can filter data while considering customer attribute information. For example, the data collection unit can filter data by customer age group to collect data tailored to the target group. For example, the data collection unit can analyze customer age data and filter data by age group. The data collection unit can also filter data by customer gender to collect data tailored to gender. For example, the data collection unit can analyze customer gender data and filter data by gender. The data collection unit can also filter data by customer region to collect region-specific data. For example, the data collection unit can analyze customer region data and filter data by region. This allows the data collection unit to collect data that is more tailored to the target by considering customer attribute information. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input customer attribute data into a generation AI and have the generation AI perform data filtering.

[0091] 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 urgent data. For example, the data collection unit will prioritize collecting urgent data for stressed users. The data collection unit can also prioritize collecting long-term data if the user is relaxed. For example, the data collection unit will prioritize collecting long-term data for relaxed users. The data collection unit can also prioritize collecting data requiring immediate attention if the user is in a hurry. For example, the data collection unit will prioritize collecting data requiring immediate attention for urgent users. This allows the data collection unit to collect data more effectively by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 generative AI or not. For example, the data collection unit can input user emotion data into a generating AI, which can then perform the task of prioritizing the data based on those emotions.

[0092] The data collection unit can analyze customers' social media activity and collect relevant data during the collection process. For example, the data collection unit can collect relevant data based on customers' interests on social media. For example, the data collection unit can analyze customers' social media data and collect data based on their interests. The data collection unit can also collect data on areas for product improvement based on customers' feedback on social media. For example, the data collection unit can analyze customers' social media feedback and collect data on areas for product improvement. The data collection unit can also analyze customers' social media activity and collect personalized data. For example, the data collection unit can analyze customers' social media data and collect personalized data. This allows the data collection unit to collect relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input customer social media data into a generative AI and have the generative AI perform the collection of relevant data.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The analytics unit can estimate the user's emotions and prioritize analysis based on those emotions. For example, if a user is stressed, it will prioritize analyzing urgent inquiries. The analytics unit will quickly analyze inquiries from stressed users and propose solutions. If a user is relaxed, it can also prioritize analyzing data related to long-term sales strategies. Based on inquiries from relaxed users, the analytics unit will propose long-term sales strategies. Furthermore, if a user is in a hurry, it can prioritize analyzing sales performance data that requires immediate attention. The analytics unit will quickly analyze sales performance data from urgent users and propose immediate solutions. In this way, the analytics unit can perform more effective analysis by prioritizing analysis based on the user's emotions.

[0095] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, it can present simple and highly visible suggestions. The suggestion function provides simple suggestions that highlight important information to nervous users. If the user is relaxed, it can also present suggestions that include detailed information. The suggestion function provides suggestions that include detailed data to relaxed users. If the user is in a hurry, it can present suggestions that get straight to the point. The suggestion function provides suggestions that summarize the key points concisely to users in a hurry. In this way, the suggestion function can make more effective suggestions by adjusting the way it presents suggestions based on the user's emotions.

[0096] The strategy presentation unit can estimate the user's emotions and adjust the strategy presentation method based on those emotions. For example, if the user is nervous, it can present a simple and highly visible strategy. The strategy presentation unit presents a simple strategy that emphasizes important information to nervous users. If the user is relaxed, it can also present a strategy that includes detailed information. The strategy presentation unit presents a strategy that includes detailed data to relaxed users. If the user is in a hurry, it can also present a strategy that gets straight to the point. The strategy presentation unit presents a strategy that summarizes the key points concisely to users in a hurry. In this way, the strategy presentation unit can provide more effective strategy presentations by adjusting the strategy presentation method based on the user's emotions.

[0097] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if a user is stressed, the frequency of data collection can be reduced. The data collection unit suggests reducing the frequency of data collection to users who are stressed. Conversely, if a user is relaxed, the frequency of data collection can be increased. The data collection unit suggests increasing the frequency of data collection to users who are relaxed. Furthermore, if a user is in a hurry, the data collection unit can prioritize the collection of data that requires immediate attention. The data collection unit suggests collecting data that requires immediate attention to users who are in a hurry. In this way, the data collection unit can perform more effective data collection by adjusting the timing of data collection based on the user's emotions.

[0098] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if a user is stressed, the unit will prioritize collecting urgent data. The data collection unit prioritizes collecting urgent data for stressed users. If a user is relaxed, the unit can also prioritize collecting long-term data. The data collection unit prioritizes collecting long-term data for relaxed users. If a user is in a hurry, the unit can also prioritize collecting data that requires immediate attention. The data collection unit prioritizes collecting data that requires immediate attention for urgent users. This allows the data collection unit to collect data more effectively by prioritizing data collection based on the user's emotions.

[0099] The analysis unit can improve the accuracy of its analysis based on seasonal fluctuations in sales performance. For example, it can identify products that sell well in specific seasons based on seasonal sales data. The analysis unit can analyze past sales data to identify best-selling products for each season. It can also analyze the effectiveness of promotions in accordance with seasonal fluctuations and propose the optimal timing for promotions. The analysis unit can analyze seasonal promotion data and propose the optimal timing for promotions. It can also analyze seasonal customer purchasing trends and propose product lineups appropriate for each season. The analysis unit can analyze seasonal customer data and make product suggestions appropriate for each season. As a result, the analysis unit improves the accuracy of its analysis by considering seasonal fluctuations in sales performance.

[0100] The analysis unit can perform analysis based on customer attribute information. For example, it can analyze purchasing trends by customer age group and propose products tailored to the target group. The analysis unit can analyze customer age data and identify purchasing trends for each age group. It can also analyze purchasing trends by customer gender and propose promotions tailored to gender. The analysis unit can analyze customer gender data and identify purchasing trends for each gender. It can also analyze purchasing trends by customer region and propose sales strategies specific to that region. The analysis unit can analyze customer region data and identify purchasing trends for each region. As a result, the analysis unit can perform more targeted analysis by considering customer attribute information.

[0101] The proposal department can suggest actions based on the product's inventory status when making a proposal. For example, it can suggest replenishing inventory for products with low stock levels. The proposal department can also suggest strengthening promotions for products with high inventory levels. The proposal department can also suggest adjusting sales prices according to inventory levels. The proposal department can suggest lowering prices for products with high inventory levels. In this way, the proposal department can suggest the optimal action by considering the product's inventory status.

[0102] The proposal department can make personalized suggestions based on the customer's purchase history. For example, it can suggest related products based on the customer's past purchase history. The proposal department can analyze customer purchase history data and suggest related products. It can also suggest promotions for specific products based on the customer's purchase history. The proposal department can analyze customer purchase history data and suggest promotions for specific products. It can also analyze customer purchase history and make personalized product suggestions. The proposal department can analyze customer purchase history data and make personalized product suggestions. This allows the proposal department to make more effective suggestions by providing personalized suggestions based on the customer's purchase history.

[0103] The strategy presentation unit can select the optimal strategy by referring to past sales data when presenting a strategy. For example, it can select the optimal promotion strategy based on past sales data. The strategy presentation unit can analyze past sales data and identify the optimal promotion strategy. It can also select a sales strategy for a specific product from past sales data. The strategy presentation unit can analyze past sales data and identify the optimal sales strategy for a specific product. It can also analyze past sales data and select the most effective sales strategy. The strategy presentation unit can analyze past sales data and identify the most effective sales strategy. As a result, the strategy presentation unit can select the optimal strategy by referring to past sales data.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The analysis unit analyzes sales performance and customer inquiries. Sales performance includes past sales data and sales figures for each product, while customer inquiries include questions and complaints about products. The analysis unit analyzes this data to identify the causes of lost sales opportunities. For example, data mining techniques, machine learning algorithms, and statistical analysis methods can be used to analyze sales performance data and customer inquiries. Step 2: The proposal department proposes the next actions based on the data analyzed by the analysis department. For example, if a particular product is underperforming, they can propose strengthening its promotion, or suggest improvements to the product based on customer inquiries. They can also propose developing new products based on customer requests or suggest revising the marketing strategy. Step 3: The Strategy Presentation Department learns from past success stories based on the actions proposed by the Proposal Department and presents a sales strategy. For example, it can learn what kind of promotions have been successful for a particular product in the past and propose a similar strategy. It can also analyze past sales data to identify the most effective sales strategies and optimal sales channels.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the analysis unit, proposal unit, strategy presentation unit, and data collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The strategy presentation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the analysis unit, proposal unit, strategy presentation unit, and data collection unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The strategy presentation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The data collection unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the analysis unit, proposal unit, strategy presentation unit, and data collection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The strategy presentation unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The data collection unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the analysis unit, proposal unit, strategy presentation unit, and data collection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The strategy presentation unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The data collection unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) The analysis department analyzes sales performance and customer inquiries, Based on the data analyzed by the aforementioned analysis unit, a proposal unit proposes the next action, The system includes a strategy presentation unit that learns from past success stories based on the actions proposed by the proposal unit and presents a sales strategy. A system characterized by the following features. (Note 2) It has a data collection department that collects sales performance data and customer inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system described in Appendix 1, characterized in that the proposal unit makes a proposal to strengthen the promotion of a particular product if that product is not selling well. (Note 4) The system described in Appendix 1 is characterized in that the proposal unit proposes improvements to the product based on customer inquiries. (Note 5) The system described in Appendix 1 is characterized in that the strategy presentation unit learns what kind of promotions have been successful for a particular product in the past and proposes similar strategies. (Note 6) The system according to Appendix 1, wherein the strategy presentation unit presents procedures for sales personnel to implement an effective sales strategy. (Note 7) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The system described in Appendix 1 is characterized in that the analysis unit improves the accuracy of the analysis based on seasonal fluctuations in sales performance during the analysis. (Note 9) The system described in Appendix 1, wherein the analysis unit performs the analysis based on customer attribute information during the analysis. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The system described in Appendix 1 is characterized in that the analysis unit improves the accuracy of the analysis based on sales data of competitors during the analysis. (Note 12) The system described in Appendix 1 is characterized in that the analysis unit analyzes the customer's purchase history during analysis and identifies the content of related inquiries. (Note 13) 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 14) The system described in Appendix 1 is characterized in that the proposal unit proposes an action based on the product inventory status at the time of proposal. (Note 15) The system described in Appendix 1, characterized in that the proposal unit makes personalized proposals based on the customer's purchase history at the time of proposal. (Note 16) 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 17) The system described in Appendix 1, characterized in that the proposal unit analyzes the customer's social media activity and makes relevant proposals when making a proposal. (Note 18) The system described in Appendix 1, characterized in that the proposal unit adjusts the proposal content based on past customer feedback when making a proposal. (Note 19) The aforementioned strategy presentation unit is, It estimates user sentiment and adjusts the way strategies are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned strategy presentation unit is, It estimates user sentiment and adjusts the way strategies are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The system described in Appendix 1, wherein the strategy presentation unit selects a strategy based on past sales data when presenting a strategy. (Note 22) The system described in Appendix 1, wherein the strategy presentation unit customizes the strategy based on customer attribute information when presenting the strategy. (Note 23) The aforementioned strategy presentation unit is, We estimate user sentiment and prioritize strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The system described in Appendix 1 is characterized in that the strategy presentation unit optimizes the strategy based on successful case studies of competitors when presenting the strategy. (Note 25) The system according to Appendix 1, wherein the strategy presentation unit analyzes the customer's purchase history and proposes relevant strategies when presenting a strategy. (Note 26) 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 2, characterized by the features described herein. (Note 27) The aforementioned collection unit is When collecting data, filter it considering customer attribute information. The system described in Appendix 2, characterized by the features described herein. (Note 28) 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 2, characterized by the features described herein. (Note 29) The aforementioned collection unit is During data collection, we analyze customers' social media activity to gather relevant data. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0178] 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 analysis department analyzes sales performance and customer inquiries, Based on the data analyzed by the aforementioned analysis unit, a proposal unit proposes the next action, The system includes a strategy presentation unit that learns from past success stories based on the actions proposed by the proposal unit and presents a sales strategy. A system characterized by the following features.

2. It has a data collection department that collects sales performance data and customer inquiries. The system according to feature 1.

3. The system according to claim 1, characterized in that the suggestion unit makes suggestions to strengthen the promotion of a particular product if that product is not selling well.

4. The system according to claim 1, characterized in that the proposal unit proposes improvements to the product based on customer inquiries.

5. The system according to claim 1, characterized in that the strategy presentation unit learns what kind of promotions have been successful for a particular product in the past and proposes similar strategies.

6. The system according to claim 1, characterized in that the strategy presentation unit presents procedures for sales personnel to implement an effective sales strategy.

7. The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system according to feature 1.

8. The system according to claim 1, characterized in that the analysis unit improves the accuracy of the analysis based on seasonal fluctuations in sales performance during the analysis.

9. The system according to claim 1, characterized in that the analysis unit performs the analysis based on customer attribute information during the analysis.

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