system

Abstract

The system according to this embodiment aims to maximize sales and profits by responding to fluctuations in demand and price changes by competitors. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a pricing unit, a monitoring unit, and an adjustment unit. The data collection unit collects sales data, competitor data, and market trends. The analysis unit analyzes the data collected by the data collection unit. The pricing unit sets prices based on the analysis results obtained by the analysis unit. The monitoring unit adjusts the prices set by the pricing unit in accordance with price fluctuations of competitors. The adjustment unit further adjusts the prices adjusted by the monitoring unit based on market seasonality and trends.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to quickly respond to fluctuations in demand and competitive price changes, and it is difficult to maximize sales and profits.

[0005] The system according to the embodiment aims to respond to fluctuations in demand and competitive price changes and maximize sales and profits.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a data collection unit, an analysis unit, a pricing unit, a monitoring unit, and an adjustment unit. The data collection unit collects sales data, competitor data, and market trends. The analysis unit analyzes the data collected by the data collection unit. The pricing unit sets prices based on the analysis results obtained by the analysis unit. The monitoring unit adjusts the prices set by the pricing unit in accordance with price fluctuations of competitors. The adjustment unit further adjusts the prices adjusted by the monitoring unit based on market seasonality and trends. [Effects of the Invention]

[0007] The system according to this embodiment can respond to fluctuations in demand and price changes by competitors, and maximize sales and profits. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] 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 pricing system according to an embodiment of the present invention is a system that utilizes generative AI to automatically set the optimal price according to supply and demand, thereby maximizing sales and profits. This pricing system analyzes sales data, competitor data, market trends, etc., and has the following functions. First, it predicts demand by analyzing sales trends and user purchasing behavior. The generative AI analyzes past sales data and user purchase history to predict future demand. For example, it predicts how much of a particular product will sell at a particular time and sets the price based on that demand. Next, it monitors competitor price fluctuations and sets a competitive price. The generative AI monitors competitor prices in real time and automatically adjusts prices accordingly. For example, if a competitor lowers its price, it maintains competitiveness by lowering its price in response. Furthermore, it performs price adjustments that take into account market seasonality and trends. The generative AI analyzes seasonal demand fluctuations and market trends and adjusts prices based on them. For example, it maximizes sales and profits by raising the price of products for which demand increases during the Christmas season. This pricing system allows businesses to respond to fluctuations in demand and price changes by competitors that cannot be handled by static pricing, thereby maximizing sales and profits. It also significantly reduces the effort and time required for manual pricing. As a result, the pricing system automatically sets the optimal price based on supply and demand, maximizing sales and profits.

[0029] The pricing system according to this embodiment comprises a data collection unit, an analysis unit, a pricing unit, a monitoring unit, and an adjustment unit. The data collection unit collects sales data, competitor data, and market trends. For example, the data collection unit collects sales data and sales volume data as sales data. The data collection unit can also collect competitor pricing information and sales strategies as competitor data. Furthermore, the data collection unit can also collect consumer purchasing trends and economic indicators as market trends. For example, the data collection unit obtains sales data from online databases and collects pricing information from competitor websites. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using statistical analysis or machine learning algorithms. For example, the analysis unit forecasts demand based on sales data and sets competitive prices based on competitor data. The pricing unit sets prices based on the analysis results obtained by the analysis unit. For example, the pricing unit performs cost-based pricing or demand-based pricing. For example, the pricing unit sets prices based on the cost of the product and adjusts prices according to fluctuations in demand. The monitoring unit adjusts the price set by the pricing unit in response to price fluctuations of competitors. For example, the monitoring unit monitors competitor prices in real time and automatically adjusts prices. For example, if a competitor lowers their price, the monitoring unit maintains competitiveness by lowering its price in response. The adjustment unit further adjusts the price adjusted by the monitoring unit based on market seasonality and trends. For example, the adjustment unit analyzes seasonal demand fluctuations and market trends and adjusts prices accordingly. For example, during the Christmas season, the adjustment unit maximizes sales and profits by raising prices for products with high demand. As a result, the pricing system according to this embodiment can automatically set the optimal price in response to supply and demand, thereby maximizing sales and profits.

[0030] The data collection department collects sales data, competitor data, and market trends. Specifically, it collects sales data and sales volume data as sales data. This includes daily, weekly, and monthly sales figures and sales volumes for each product, and this data is obtained from POS systems and online sales platforms. The data collection department can also collect competitor pricing information and sales strategies as competitor data. For example, it can collect pricing information from competitors' websites and online marketplaces using scraping techniques, and also obtain information on competitor promotions and discounts. Furthermore, the data collection department can collect consumer purchasing trends and economic indicators as market trends. This includes online consumer reviews and social media posts, and economic indicators such as unemployment rates and consumer confidence indices. For example, the data collection department can obtain sales data from online databases and collect pricing information from competitor websites. This allows the data collection department to collect a wide range of information from diverse data sources and understand market conditions in real time. Furthermore, the data collection department can centrally manage this data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analytics department and the pricing department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the data using statistical analysis and machine learning algorithms. For example, the analysis unit forecasts demand based on sales data and sets competitive prices based on competitor data. For demand forecasting, it uses time series analysis and regression analysis to predict future demand from past sales data. It also uses machine learning algorithms to analyze consumer purchasing patterns and market trends and predict fluctuations in demand. Furthermore, by analyzing competitor data, it understands the pricing strategies and market share of competitors and reflects this in its own pricing. For example, by analyzing the price fluctuations of competitors and evaluating the effectiveness of their pricing strategies, it is possible to set optimal prices. Based on these analysis results, the analysis unit provides specific pricing instructions to the pricing unit. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past sales data, it can predict fluctuations in demand during specific seasons or events and formulate future pricing strategies. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The pricing department sets prices based on the analysis results obtained by the analysis department. Specifically, it uses cost-based pricing and demand-based pricing. In cost-based pricing, it considers the manufacturing costs, logistics costs, and marketing costs of the product, and sets the price by adding an appropriate profit margin. On the other hand, in demand-based pricing, it adjusts the price according to fluctuations in demand. For example, it maximizes sales and profits by raising prices when demand is high and lowering prices when demand is low. The pricing department combines these pricing methods to set the optimal price. In addition, the pricing department sets competitive prices based on competitive data and market trends provided by the analysis department. For example, it sets prices while considering the company's strengths and points of differentiation, while referencing the prices of competitors. Furthermore, the pricing department monitors how the set price is received in the market and adjusts the price as needed. In this way, the pricing department can always provide the optimal price according to market conditions and maintain competitiveness.

[0033] The monitoring unit adjusts the prices set by the pricing unit in response to price fluctuations by competitors. Specifically, it monitors competitor prices in real time and automatically adjusts prices. For example, if a competitor lowers its price, the monitoring unit will lower its price in response to maintain competitiveness. The monitoring unit uses web scraping technology and APIs to obtain competitor price information in real time and monitor price fluctuations. The monitoring unit can also analyze price fluctuation patterns and trends and predict future price fluctuations. This allows the monitoring unit to respond quickly to competitor pricing strategies and maintain optimal prices. Furthermore, the monitoring unit evaluates the impact of price fluctuations and verifies the effectiveness of price adjustments. For example, it analyzes changes in sales and profits when prices are lowered to evaluate the effectiveness of pricing strategies. This allows the monitoring unit to improve the accuracy of pricing strategies and maintain competitiveness.

[0034] The adjustment department further adjusts prices set by the monitoring department based on market seasonality and trends. Specifically, it analyzes seasonal demand fluctuations and market trends and adjusts prices accordingly. For example, during the Christmas season, it maximizes sales and profits by raising prices for products with high demand. The adjustment department can also adjust prices to coincide with specific events and promotions. For example, it sets promotional prices when launching new products to attract consumer interest. Furthermore, the adjustment department develops long-term pricing strategies based on historical data and market trends. For example, it analyzes historical sales data to predict demand fluctuations during specific seasons and events and adjusts prices accordingly. This allows the adjustment department to always provide optimal pricing that is in line with market conditions, maximizing sales and profits. In addition, the adjustment department continuously monitors the effects of price adjustments and reviews pricing strategies as needed. This allows the adjustment department to maintain competitiveness by always performing highly accurate price adjustments based on the latest market information.

[0035] The data collection unit can evaluate the accuracy of past collected data and select the optimal data collection method. For example, the data collection unit can evaluate the accuracy of past sales data and select a reliable data source. It can also evaluate the accuracy of competitor data and select the most accurate data collection method. Furthermore, the data collection unit can evaluate the accuracy of market trend data and determine the optimal collection frequency. In this way, the optimal data collection method can be selected by evaluating the accuracy of past collected data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select a data collection method.

[0036] The data collection unit can prioritize the collection of data during specific events or campaign periods. For example, it can prioritize the collection of sales data during the Christmas season. It can also prioritize the collection of competitive data during new product launches. Furthermore, it can prioritize the collection of market trend data during large-scale sales periods. This allows for the efficient collection of important data by prioritizing the collection of data during specific events or campaign periods. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from specific events or campaign periods into a generating AI and have the generating AI perform the priority collection of that data.

[0037] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of sales data for that region. Similarly, if the user is in a specific city, the data collection unit can prioritize the collection of competitive data for that city. Furthermore, if the user is in a specific country, the data collection unit can prioritize the collection of market trend data for that country. This allows for the efficient collection of region-specific data by prioritizing the collection of highly relevant data while considering the user's geographical location information. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if the user mentions a specific product, the data collection unit can collect sales data for that product. It can also collect competitive data for a specific brand if the user mentions that brand. Furthermore, if the user mentions a specific trend, the data collection unit can collect market trend data related to that trend. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important sales data. The analysis unit can also adjust the level of detail of the analysis based on the importance of competitor data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of market trend data. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analytical methods depending on the data category during analysis. For example, the analysis unit can apply time series analysis to sales data. It can also apply clustering analysis to competitor data. Furthermore, it can apply trend analysis to market trend data. By applying different analytical methods depending on the data category, it is possible to provide more appropriate analytical results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analytical method.

[0041] The analysis unit can determine the priority of analysis based on the timing of data submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent sales data. The analysis unit can also determine the priority of analysis based on the timing of competitor data submission. Furthermore, the analysis unit can also determine the priority of analysis based on the timing of market trend data submission. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the timing of data submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of data submission into a generating AI and have the generating AI perform the determination of analysis priorities.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can adjust the order of analysis based on the relevance of sales data. It can also adjust the order of analysis based on the relevance of competitor data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of market trend data. By adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The pricing unit can adjust the level of detail in pricing based on the importance of the product when setting prices. For example, the pricing unit can provide a detailed explanation for the pricing of important products. It can also provide a concise explanation for the pricing of general products. Furthermore, it can provide a detailed explanation for the pricing of expensive products. By adjusting the level of detail in pricing based on the importance of the product, it is possible to provide a detailed explanation for the pricing of important products. Some or all of the above processing in the pricing unit may be performed using AI, for example, or not using AI. For example, the pricing unit can input the importance of the product into a generating AI and have the generating AI perform the adjustment of the level of detail in pricing.

[0044] The pricing unit can apply different pricing algorithms depending on the product category when setting prices. For example, for electronic products, the pricing unit can apply a pricing algorithm based on demand forecasting. For clothing, the pricing unit can also apply a pricing algorithm that takes seasonality into account. Furthermore, for food products, the pricing unit can apply a pricing algorithm that takes expiration dates into account. By applying different pricing algorithms depending on the product category, more appropriate pricing can be achieved. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the product category into a generating AI and have the generating AI execute the application of the pricing algorithm.

[0045] The pricing unit can determine pricing priorities based on the product submission date when setting prices. For example, the pricing unit may prioritize pricing for the newest products. It can also prioritize pricing for seasonal products. Furthermore, the pricing unit may determine pricing priorities based on the submission date of expensive products. This allows for prioritizing pricing for the newest products by determining pricing priorities based on the product submission date. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the product submission date into a generating AI and have the generating AI determine the pricing priorities.

[0046] The pricing unit can adjust the pricing order based on the relevance of the products when setting prices. For example, the pricing unit may prioritize pricing important products. It can also postpone pricing general products. Furthermore, it can prioritize pricing expensive products. In this way, by adjusting the pricing order based on the relevance of the products, it is possible to prioritize pricing important products. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the relevance of products into a generating AI and have the generating AI perform the adjustment of the pricing order.

[0047] The monitoring unit can improve the accuracy of monitoring by analyzing the price fluctuation patterns of competitors during monitoring. For example, the monitoring unit can analyze the price fluctuation patterns of competitors and optimize the timing of price adjustments. The monitoring unit can also analyze the price fluctuation patterns of competitors and optimize the frequency of price adjustments. Furthermore, the monitoring unit can analyze the price fluctuation patterns of competitors and optimize the magnitude of price adjustments. In this way, the accuracy of monitoring can be improved by analyzing the price fluctuation patterns of competitors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the price fluctuation patterns of competitors into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0048] The monitoring unit can perform monitoring while considering the attribute information of competitors. For example, the monitoring unit can adjust the level of detail of monitoring according to the size of the competitor. The monitoring unit can also adjust the frequency of monitoring according to the market share of the competitor. Furthermore, the monitoring unit can adjust the scope of monitoring according to the regional characteristics of the competitor. This allows for more detailed monitoring by considering the attribute information of competitors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input competitor attribute information into a generating AI and have the generating AI perform the adjustment of monitoring.

[0049] The monitoring unit can perform monitoring while considering the geographical distribution of competitors. For example, the monitoring unit can prioritize monitoring price fluctuations in areas where competitors are concentrated. It can also periodically monitor price fluctuations in areas with few competitors. Furthermore, the monitoring unit can focus its monitoring on price fluctuations in areas where competitors have newly entered the market. This allows for region-specific monitoring by considering the geographical distribution of competitors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical distribution of competitors into a generating AI and have the generating AI perform adjustments to the monitoring.

[0050] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on competitors during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to literature on competitors' pricing strategies. It can also improve the accuracy of its monitoring by referring to literature on competitors' market trends. Furthermore, the monitoring unit can improve the accuracy of its monitoring by referring to literature on competitors' consumer behavior. In this way, the accuracy of monitoring can be improved by referring to relevant literature on competitors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature on competitors into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0051] The adjustment unit can optimize the adjustment algorithm by referring to past adjustment data during the adjustment process. For example, the adjustment unit can apply the optimal adjustment algorithm by referring to past price adjustment data. The adjustment unit can also apply the optimal adjustment algorithm by referring to past competition data. Furthermore, the adjustment unit can apply the optimal adjustment algorithm by referring to past market trend data. In this way, the adjustment algorithm can be optimized by referring to past adjustment data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input past adjustment data into a generating AI and have the generating AI perform the optimization of the adjustment algorithm.

[0052] The adjustment unit can apply different adjustment methods to each product category during the adjustment process. For example, for electronic products, the adjustment unit can apply an adjustment method based on demand forecasts. Furthermore, for clothing, the adjustment unit can apply an adjustment method that takes seasonality into account. In addition, for food products, the adjustment unit can apply an adjustment method that considers the expiration date. This allows for more appropriate adjustments by applying different adjustment methods to each product category. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input product categories into a generating AI and have the generating AI execute the application of adjustment methods.

[0053] The adjustment unit can analyze changes in adjustments based on the product submission timing during the adjustment process. For example, the adjustment unit can prioritize adjustments for the newest products. It can also prioritize adjustments for seasonal products. Furthermore, the adjustment unit can determine the priority of adjustments based on the submission timing of expensive products. This allows for prioritizing adjustments for the newest products by analyzing changes in adjustments based on the product submission timing. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the product submission timing into a generating AI and have the generating AI perform an analysis of changes in adjustments.

[0054] The adjustment unit can analyze adjustments by referring to relevant market data for the product during the adjustment process. For example, the adjustment unit can perform adjustments by referring to relevant market data for important products. It can also perform adjustments by referring to relevant market data for general products. Furthermore, the adjustment unit can perform adjustments by referring to relevant market data for expensive products. This allows for more appropriate adjustments by referring to relevant market data for the product. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input relevant market data for the product into a generating AI and have the generating AI perform the adjustment analysis.

[0055] The adjustment unit can analyze adjustments by referring to relevant market data for the product during the adjustment process. For example, the adjustment unit can perform adjustments by referring to relevant market data for important products. It can also perform adjustments by referring to relevant market data for general products. Furthermore, the adjustment unit can perform adjustments by referring to relevant market data for expensive products. This allows for more appropriate adjustments by referring to relevant market data for the product. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input relevant market data for the product into a generating AI and have the generating AI perform the adjustment analysis.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0058] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0059] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0060] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0061] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects sales data, competitor data, and market trends. For example, it collects sales data and sales volume data as sales data, and competitor data such as competitor pricing information and sales strategies. It also collects consumer purchasing trends and economic indicators as market trends. The data collection unit obtains sales data from online databases and collects pricing information from competitor websites. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using statistical analysis and machine learning algorithms, forecasts demand based on sales data, and sets competitive prices based on competitor data. Step 3: The pricing unit sets prices based on the analysis results obtained by the analysis unit. For example, it may perform cost-based pricing or demand-based pricing, setting prices based on the cost of the product and adjusting prices according to fluctuations in demand. Step 4: The monitoring unit adjusts the price set by the pricing unit in response to price fluctuations by competitors. For example, it monitors competitor prices in real time and automatically adjusts the price. If a competitor lowers their price, it lowers its price in response to maintain its competitiveness. Step 5: The adjustment unit further adjusts the prices set by the monitoring unit based on market seasonality and trends. For example, it analyzes seasonal demand fluctuations and market trends and adjusts prices accordingly. During the Christmas season, it maximizes sales and profits by raising prices for products with high demand.

[0064] (Example of form 2) The pricing system according to an embodiment of the present invention is a system that utilizes generative AI to automatically set the optimal price according to supply and demand, thereby maximizing sales and profits. This pricing system analyzes sales data, competitor data, market trends, etc., and has the following functions. First, it predicts demand by analyzing sales trends and user purchasing behavior. The generative AI analyzes past sales data and user purchase history to predict future demand. For example, it predicts how much of a particular product will sell at a particular time and sets the price based on that demand. Next, it monitors competitor price fluctuations and sets a competitive price. The generative AI monitors competitor prices in real time and automatically adjusts prices accordingly. For example, if a competitor lowers its price, it maintains competitiveness by lowering its price in response. Furthermore, it performs price adjustments that take into account market seasonality and trends. The generative AI analyzes seasonal demand fluctuations and market trends and adjusts prices based on them. For example, it maximizes sales and profits by raising the price of products for which demand increases during the Christmas season. This pricing system allows businesses to respond to fluctuations in demand and price changes by competitors that cannot be handled by static pricing, thereby maximizing sales and profits. It also significantly reduces the effort and time required for manual pricing. As a result, the pricing system automatically sets the optimal price based on supply and demand, maximizing sales and profits.

[0065] The pricing system according to this embodiment comprises a data collection unit, an analysis unit, a pricing unit, a monitoring unit, and an adjustment unit. The data collection unit collects sales data, competitor data, and market trends. For example, the data collection unit collects sales data and sales volume data as sales data. The data collection unit can also collect competitor pricing information and sales strategies as competitor data. Furthermore, the data collection unit can also collect consumer purchasing trends and economic indicators as market trends. For example, the data collection unit obtains sales data from online databases and collects pricing information from competitor websites. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using statistical analysis or machine learning algorithms. For example, the analysis unit forecasts demand based on sales data and sets competitive prices based on competitor data. The pricing unit sets prices based on the analysis results obtained by the analysis unit. For example, the pricing unit performs cost-based pricing or demand-based pricing. For example, the pricing unit sets prices based on the cost of the product and adjusts prices according to fluctuations in demand. The monitoring unit adjusts the price set by the pricing unit in response to price fluctuations of competitors. For example, the monitoring unit monitors competitor prices in real time and automatically adjusts prices. For example, if a competitor lowers their price, the monitoring unit maintains competitiveness by lowering its price in response. The adjustment unit further adjusts the price adjusted by the monitoring unit based on market seasonality and trends. For example, the adjustment unit analyzes seasonal demand fluctuations and market trends and adjusts prices accordingly. For example, during the Christmas season, the adjustment unit maximizes sales and profits by raising prices for products with high demand. As a result, the pricing system according to this embodiment can automatically set the optimal price in response to supply and demand, thereby maximizing sales and profits.

[0066] The data collection department collects sales data, competitor data, and market trends. Specifically, it collects sales data and sales volume data as sales data. This includes daily, weekly, and monthly sales figures and sales volumes for each product, and this data is obtained from POS systems and online sales platforms. The data collection department can also collect competitor pricing information and sales strategies as competitor data. For example, it can collect pricing information from competitors' websites and online marketplaces using scraping techniques, and also obtain information on competitor promotions and discounts. Furthermore, the data collection department can collect consumer purchasing trends and economic indicators as market trends. This includes online consumer reviews and social media posts, and economic indicators such as unemployment rates and consumer confidence indices. For example, the data collection department can obtain sales data from online databases and collect pricing information from competitor websites. This allows the data collection department to collect a wide range of information from diverse data sources and understand market conditions in real time. Furthermore, the data collection department can centrally manage this data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analytics department and the pricing department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the data using statistical analysis and machine learning algorithms. For example, the analysis unit forecasts demand based on sales data and sets competitive prices based on competitor data. For demand forecasting, it uses time series analysis and regression analysis to predict future demand from past sales data. It also uses machine learning algorithms to analyze consumer purchasing patterns and market trends and predict fluctuations in demand. Furthermore, by analyzing competitor data, it understands the pricing strategies and market share of competitors and reflects this in its own pricing. For example, by analyzing the price fluctuations of competitors and evaluating the effectiveness of their pricing strategies, it is possible to set optimal prices. Based on these analysis results, the analysis unit provides specific pricing instructions to the pricing unit. Furthermore, the analysis unit can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past sales data, it can predict fluctuations in demand during specific seasons or events and formulate future pricing strategies. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The pricing department sets prices based on the analysis results obtained by the analysis department. Specifically, it uses cost-based pricing and demand-based pricing. In cost-based pricing, it considers the manufacturing costs, logistics costs, and marketing costs of the product, and sets the price by adding an appropriate profit margin. On the other hand, in demand-based pricing, it adjusts the price according to fluctuations in demand. For example, it maximizes sales and profits by raising prices when demand is high and lowering prices when demand is low. The pricing department combines these pricing methods to set the optimal price. In addition, the pricing department sets competitive prices based on competitive data and market trends provided by the analysis department. For example, it sets prices while considering the company's strengths and points of differentiation, while referencing the prices of competitors. Furthermore, the pricing department monitors how the set price is received in the market and adjusts the price as needed. In this way, the pricing department can always provide the optimal price according to market conditions and maintain competitiveness.

[0069] The monitoring unit adjusts the prices set by the pricing unit in response to price fluctuations by competitors. Specifically, it monitors competitor prices in real time and automatically adjusts prices. For example, if a competitor lowers its price, the monitoring unit will lower its price in response to maintain competitiveness. The monitoring unit uses web scraping technology and APIs to obtain competitor price information in real time and monitor price fluctuations. The monitoring unit can also analyze price fluctuation patterns and trends and predict future price fluctuations. This allows the monitoring unit to respond quickly to competitor pricing strategies and maintain optimal prices. Furthermore, the monitoring unit evaluates the impact of price fluctuations and verifies the effectiveness of price adjustments. For example, it analyzes changes in sales and profits when prices are lowered to evaluate the effectiveness of pricing strategies. This allows the monitoring unit to improve the accuracy of pricing strategies and maintain competitiveness.

[0070] The adjustment department further adjusts prices set by the monitoring department based on market seasonality and trends. Specifically, it analyzes seasonal demand fluctuations and market trends and adjusts prices accordingly. For example, during the Christmas season, it maximizes sales and profits by raising prices for products with high demand. The adjustment department can also adjust prices to coincide with specific events and promotions. For example, it sets promotional prices when launching new products to attract consumer interest. Furthermore, the adjustment department develops long-term pricing strategies based on historical data and market trends. For example, it analyzes historical sales data to predict demand fluctuations during specific seasons and events and adjusts prices accordingly. This allows the adjustment department to always provide optimal pricing that is in line with market conditions, maximizing sales and profits. In addition, the adjustment department continuously monitors the effects of price adjustments and reviews pricing strategies as needed. This allows the adjustment department to maintain competitiveness by always performing highly accurate price adjustments based on the latest market information.

[0071] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting real-time sales data. If the user is relaxed, the data collection unit may also collect long-term market trend data. Furthermore, if the user is stressed, the data collection unit may collect only concise and important data. This allows for the collection of more relevant data by adjusting the types of data collected 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the types of data to be collected.

[0072] The data collection unit can evaluate the accuracy of past collected data and select the optimal data collection method. For example, the data collection unit can evaluate the accuracy of past sales data and select a reliable data source. It can also evaluate the accuracy of competitor data and select the most accurate data collection method. Furthermore, the data collection unit can evaluate the accuracy of market trend data and determine the optimal collection frequency. In this way, the optimal data collection method can be selected by evaluating the accuracy of past collected data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI and have the generating AI select a data collection method.

[0073] The data collection unit can prioritize the collection of data during specific events or campaign periods. For example, it can prioritize the collection of sales data during the Christmas season. It can also prioritize the collection of competitive data during new product launches. Furthermore, it can prioritize the collection of market trend data during large-scale sales periods. This allows for the efficient collection of important data by prioritizing the collection of data during specific events or campaign periods. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data from specific events or campaign periods into a generating AI and have the generating AI perform the priority collection of that data.

[0074] 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 excited, the data collection unit may prioritize collecting real-time sales data. It may also prioritize collecting long-term market trend data if the user is relaxed. Furthermore, if the user is stressed, the data collection unit may prioritize collecting concise and important data. This allows for the priority collection of important 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0075] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of sales data for that region. Similarly, if the user is in a specific city, the data collection unit can prioritize the collection of competitive data for that city. Furthermore, if the user is in a specific country, the data collection unit can prioritize the collection of market trend data for that country. This allows for the efficient collection of region-specific data by prioritizing the collection of highly relevant data while considering the user's geographical location information. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For instance, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data.

[0076] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if the user mentions a specific product, the data collection unit can collect sales data for that product. It can also collect competitive data for a specific brand if the user mentions that brand. Furthermore, if the user mentions a specific trend, the data collection unit can collect market trend data related to that trend. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can apply an algorithm that performs a detailed analysis. It can also apply an algorithm that performs a rapid analysis if the user is in a hurry. Furthermore, if the user is excited, the analysis unit can apply an algorithm that provides visually stimulating analysis results. This allows for more appropriate analysis results by adjusting the analysis algorithm 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 may be, 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 AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important sales data. The analysis unit can also adjust the level of detail of the analysis based on the importance of competitor data. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of market trend data. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0079] The analysis unit can apply different analytical methods depending on the data category during analysis. For example, the analysis unit can apply time series analysis to sales data. It can also apply clustering analysis to competitor data. Furthermore, it can apply trend analysis to market trend data. By applying different analytical methods depending on the data category, it is possible to provide more appropriate analytical results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analytical method.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method of the analysis results based on the user's emotions, a more appropriate display method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0081] The analysis unit can determine the priority of analysis based on the timing of data submission during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent sales data. The analysis unit can also determine the priority of analysis based on the timing of competitor data submission. Furthermore, the analysis unit can also determine the priority of analysis based on the timing of market trend data submission. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the timing of data submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of data submission into a generating AI and have the generating AI perform the determination of analysis priorities.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can adjust the order of analysis based on the relevance of sales data. It can also adjust the order of analysis based on the relevance of competitor data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of market trend data. By adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0083] The pricing unit can estimate the user's emotions and adjust the way pricing is presented based on those emotions. For example, if the user is relaxed, the pricing unit can provide a detailed explanation of the pricing. If the user is in a hurry, the pricing unit can provide a concise explanation. Furthermore, if the user is excited, the pricing unit can provide a visually stimulating way of presenting the pricing. By adjusting the way pricing is presented based on the user's emotions, a more appropriate way of presenting pricing can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pricing unit may be performed using AI, or not using AI. For example, the pricing unit can input user emotion data into a generative AI and have the generative AI adjust the way pricing is presented.

[0084] The pricing unit can adjust the level of detail in pricing based on the importance of the product when setting prices. For example, the pricing unit can provide a detailed explanation for the pricing of important products. It can also provide a concise explanation for the pricing of general products. Furthermore, it can provide a detailed explanation for the pricing of expensive products. By adjusting the level of detail in pricing based on the importance of the product, it is possible to provide a detailed explanation for the pricing of important products. Some or all of the above processing in the pricing unit may be performed using AI, for example, or not using AI. For example, the pricing unit can input the importance of the product into a generating AI and have the generating AI perform the adjustment of the level of detail in pricing.

[0085] The pricing unit can apply different pricing algorithms depending on the product category when setting prices. For example, for electronic products, the pricing unit can apply a pricing algorithm based on demand forecasting. For clothing, the pricing unit can also apply a pricing algorithm that takes seasonality into account. Furthermore, for food products, the pricing unit can apply a pricing algorithm that takes expiration dates into account. By applying different pricing algorithms depending on the product category, more appropriate pricing can be achieved. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the product category into a generating AI and have the generating AI execute the application of the pricing algorithm.

[0086] The pricing unit can estimate the user's emotions and adjust the length of the pricing based on the estimated emotions. For example, if the user is relaxed, the pricing unit can provide a detailed explanation of the pricing. If the user is in a hurry, the pricing unit can provide a concise explanation. Furthermore, if the user is excited, the pricing unit can provide a visually stimulating way of presenting the pricing. This allows for the provision of a more appropriate explanation of the pricing by adjusting the length of the pricing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the pricing unit may be performed using AI or not using AI. For example, the pricing unit can input user emotion data into a generative AI and have the generative AI adjust the length of the pricing.

[0087] The pricing unit can determine pricing priorities based on the product submission date when setting prices. For example, the pricing unit may prioritize pricing for the newest products. It can also prioritize pricing for seasonal products. Furthermore, the pricing unit may determine pricing priorities based on the submission date of expensive products. This allows for prioritizing pricing for the newest products by determining pricing priorities based on the product submission date. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the product submission date into a generating AI and have the generating AI determine the pricing priorities.

[0088] The pricing unit can adjust the pricing order based on the relevance of the products when setting prices. For example, the pricing unit may prioritize pricing important products. It can also postpone pricing general products. Furthermore, it can prioritize pricing expensive products. In this way, by adjusting the pricing order based on the relevance of the products, it is possible to prioritize pricing important products. Some or all of the above processing in the pricing unit may be performed using AI, for example, or without AI. For example, the pricing unit can input the relevance of products into a generating AI and have the generating AI perform the adjustment of the pricing order.

[0089] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the user is relaxed, the monitoring unit can provide detailed monitoring criteria. If the user is in a hurry, the monitoring unit can also provide concise monitoring criteria. Furthermore, if the user is excited, the monitoring unit can provide visually stimulating monitoring criteria. This allows for the provision of more appropriate monitoring criteria by adjusting them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the monitoring criteria.

[0090] The monitoring unit can improve the accuracy of monitoring by analyzing the price fluctuation patterns of competitors during monitoring. For example, the monitoring unit can analyze the price fluctuation patterns of competitors and optimize the timing of price adjustments. The monitoring unit can also analyze the price fluctuation patterns of competitors and optimize the frequency of price adjustments. Furthermore, the monitoring unit can analyze the price fluctuation patterns of competitors and optimize the magnitude of price adjustments. In this way, the accuracy of monitoring can be improved by analyzing the price fluctuation patterns of competitors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the price fluctuation patterns of competitors into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0091] The monitoring unit can perform monitoring while considering the attribute information of competitors. For example, the monitoring unit can adjust the level of detail of monitoring according to the size of the competitor. The monitoring unit can also adjust the frequency of monitoring according to the market share of the competitor. Furthermore, the monitoring unit can adjust the scope of monitoring according to the regional characteristics of the competitor. This allows for more detailed monitoring by considering the attribute information of competitors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input competitor attribute information into a generating AI and have the generating AI perform the adjustment of monitoring.

[0092] The monitoring unit can estimate the user's emotions and adjust the display order of monitoring results based on the estimated emotions. For example, if the user is stressed, the monitoring unit can prioritize displaying important monitoring results. It can also display detailed monitoring results if the user is relaxed. Furthermore, if the user is in a hurry, the monitoring unit can display concise monitoring results. This allows for prioritizing the display of important information by adjusting the display order of monitoring results 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the display order of monitoring results.

[0093] The monitoring unit can perform monitoring while considering the geographical distribution of competitors. For example, the monitoring unit can prioritize monitoring price fluctuations in areas where competitors are concentrated. It can also periodically monitor price fluctuations in areas with few competitors. Furthermore, the monitoring unit can focus its monitoring on price fluctuations in areas where competitors have newly entered the market. This allows for region-specific monitoring by considering the geographical distribution of competitors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the geographical distribution of competitors into a generating AI and have the generating AI perform adjustments to the monitoring.

[0094] The monitoring unit can improve the accuracy of its monitoring by referring to relevant literature on competitors during monitoring. For example, the monitoring unit can improve the accuracy of its monitoring by referring to literature on competitors' pricing strategies. It can also improve the accuracy of its monitoring by referring to literature on competitors' market trends. Furthermore, the monitoring unit can improve the accuracy of its monitoring by referring to literature on competitors' consumer behavior. In this way, the accuracy of monitoring can be improved by referring to relevant literature on competitors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input relevant literature on competitors into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0095] The adjustment unit can estimate the user's emotions and adjust the adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit can provide a detailed adjustment method. It can also provide a concise adjustment method if the user is in a hurry. Furthermore, if the user is excited, the adjustment unit can provide a visually stimulating adjustment method. This allows for the provision of a more appropriate adjustment method by adjusting the adjustment method 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the adjustment method.

[0096] The adjustment unit can optimize the adjustment algorithm by referring to past adjustment data during the adjustment process. For example, the adjustment unit can apply the optimal adjustment algorithm by referring to past price adjustment data. The adjustment unit can also apply the optimal adjustment algorithm by referring to past competition data. Furthermore, the adjustment unit can apply the optimal adjustment algorithm by referring to past market trend data. In this way, the adjustment algorithm can be optimized by referring to past adjustment data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input past adjustment data into a generating AI and have the generating AI perform the optimization of the adjustment algorithm.

[0097] The adjustment unit can apply different adjustment methods to each product category during the adjustment process. For example, for electronic products, the adjustment unit can apply an adjustment method based on demand forecasts. Furthermore, for clothing, the adjustment unit can apply an adjustment method that takes seasonality into account. In addition, for food products, the adjustment unit can apply an adjustment method that considers the expiration date. This allows for more appropriate adjustments by applying different adjustment methods to each product category. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input product categories into a generating AI and have the generating AI execute the application of adjustment methods.

[0098] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated emotions. For example, if the user is relaxed, the adjustment unit can provide a detailed adjustment method. If the user is in a hurry, the adjustment unit can also provide a concise adjustment method. Furthermore, if the user is excited, the adjustment unit can provide a visually stimulating adjustment method. This allows important adjustments to be prioritized by determining the priority of adjustments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the priority of adjustments.

[0099] The adjustment unit can analyze changes in adjustments based on the product submission timing during the adjustment process. For example, the adjustment unit can prioritize adjustments for the newest products. It can also prioritize adjustments for seasonal products. Furthermore, the adjustment unit can determine the priority of adjustments based on the submission timing of expensive products. This allows for prioritizing adjustments for the newest products by analyzing changes in adjustments based on the product submission timing. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the product submission timing into a generating AI and have the generating AI perform an analysis of changes in adjustments.

[0100] The adjustment unit can analyze adjustments by referring to relevant market data for the product during the adjustment process. For example, the adjustment unit can perform adjustments by referring to relevant market data for important products. It can also perform adjustments by referring to relevant market data for general products. Furthermore, the adjustment unit can perform adjustments by referring to relevant market data for expensive products. This allows for more appropriate adjustments by referring to relevant market data for the product. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input relevant market data for the product into a generating AI and have the generating AI perform the adjustment analysis.

[0101] The adjustment unit can analyze adjustments by referring to relevant market data for the product during the adjustment process. For example, the adjustment unit can perform adjustments by referring to relevant market data for important products. It can also perform adjustments by referring to relevant market data for general products. Furthermore, the adjustment unit can perform adjustments by referring to relevant market data for expensive products. This allows for more appropriate adjustments by referring to relevant market data for the product. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input relevant market data for the product into a generating AI and have the generating AI perform the adjustment analysis.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, it can provide a notification containing detailed analysis results. If the user is in a hurry, it can provide a notification containing a concise summary. Furthermore, if the user is excited, it can provide a visually stimulating notification. In this way, by adjusting the notification method of the analysis results based on the user's emotions, a more appropriate notification method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the notification method.

[0104] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0105] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback can be provided. Furthermore, if the user is excited, visually stimulating feedback can be provided. In this way, by adjusting the feedback method of the analysis results based on the user's emotions, a more appropriate feedback method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the feedback method.

[0106] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0107] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback can be provided. Furthermore, if the user is excited, visually stimulating feedback can be provided. In this way, by adjusting the feedback method of the analysis results based on the user's emotions, a more appropriate feedback method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the feedback method.

[0108] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0109] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback can be provided. Furthermore, if the user is excited, visually stimulating feedback can be provided. In this way, by adjusting the feedback method of the analysis results based on the user's emotions, a more appropriate feedback method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the feedback method.

[0110] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0111] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback can be provided. Furthermore, if the user is excited, visually stimulating feedback can be provided. In this way, by adjusting the feedback method of the analysis results based on the user's emotions, a more appropriate feedback method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the feedback method.

[0112] The data collection unit can analyze the user's purchase history and determine the priority of data collection based on specific purchase patterns. For example, if a user frequently purchases products from a particular brand, the unit can prioritize collecting sales data for that brand. Similarly, if a user regularly purchases products from a specific category, the unit can prioritize collecting market trend data for that category. Furthermore, if a user tends to purchase specific products during certain seasons, the unit can prioritize collecting data for those seasons. This allows for the collection of more relevant data by prioritizing data collection based on the user's purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history data into a generating AI and have the generating AI determine the priority of data collection.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects sales data, competitor data, and market trends. For example, it collects sales data and sales volume data as sales data, and competitor data such as competitor pricing information and sales strategies. It also collects consumer purchasing trends and economic indicators as market trends. The data collection unit obtains sales data from online databases and collects pricing information from competitor websites. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using statistical analysis and machine learning algorithms, forecasts demand based on sales data, and sets competitive prices based on competitor data. Step 3: The pricing unit sets prices based on the analysis results obtained by the analysis unit. For example, it may perform cost-based pricing or demand-based pricing, setting prices based on the cost of the product and adjusting prices according to fluctuations in demand. Step 4: The monitoring unit adjusts the price set by the pricing unit in response to price fluctuations by competitors. For example, it monitors competitor prices in real time and automatically adjusts the price. If a competitor lowers their price, it lowers its price in response to maintain its competitiveness. Step 5: The adjustment unit further adjusts the prices set by the monitoring unit based on market seasonality and trends. For example, it analyzes seasonal demand fluctuations and market trends and adjusts prices accordingly. During the Christmas season, it maximizes sales and profits by raising prices for products with high demand.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, pricing unit, monitoring unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects sales data and competitor data using the camera 42 and communication I / F 44 of the smart device 14, and collects market trends using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The pricing unit is implemented in the specific processing unit 290 of the data processing unit 12 and sets prices based on the analysis results. The monitoring unit is implemented in the control unit 46A of the smart device 14 and monitors price fluctuations of competitors. The adjustment unit is implemented in the specific processing unit 290 of the data processing unit 12 and adjusts prices based on seasonality and trends. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, pricing unit, monitoring unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects sales data and competitor data using the camera 42 and communication I / F 44 of the smart glasses 214, and collects market trends using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The pricing unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and sets prices based on the analysis results. The monitoring unit is implemented, for example, in the control unit 46A of the smart glasses 214, and monitors price fluctuations of competitors. The adjustment unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and adjusts prices based on seasonality and trends. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, pricing unit, monitoring unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects sales data and competitor data using the camera 42 and communication I / F 44 of the headset terminal 314, and collects market trends using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The pricing unit is implemented in the specific processing unit 290 of the data processing unit 12 and sets prices based on the analysis results. The monitoring unit is implemented in the control unit 46A of the headset terminal 314 and monitors price fluctuations of competitors. The adjustment unit is implemented in the specific processing unit 290 of the data processing unit 12 and adjusts prices based on seasonality and trends. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, pricing unit, monitoring unit, and adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects sales data and competitor data using the camera 42 and communication I / F 44 of the robot 414, and collects market trends using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The pricing unit is implemented in the specific processing unit 290 of the data processing unit 12 and sets prices based on the analysis results. The monitoring unit is implemented in the control unit 46A of the robot 414 and monitors price fluctuations of competitors. The adjustment unit is implemented in the specific processing unit 290 of the data processing unit 12 and adjusts prices based on seasonality and trends. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) The data collection department collects sales data, competitor data, and market trends. An analysis unit analyzes the data collected by the aforementioned collection unit, A pricing unit sets a price based on the analysis results obtained by the aforementioned analysis unit, A monitoring unit adjusts the price set by the pricing unit in accordance with price fluctuations of competitors, The system includes an adjustment unit that further adjusts the price adjusted by the monitoring unit based on market seasonality and trends. A system characterized by the following features. (Note 2) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Evaluate the accuracy of past collected data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting data, prioritize collecting data from specific events or campaign periods. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned pricing unit is It estimates user sentiment and adjusts the way pricing is presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned pricing unit is When setting prices, adjust the level of detail in pricing based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned pricing unit is When setting prices, different pricing algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned pricing unit is It estimates user sentiment and adjusts the length of the pricing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned pricing unit is When setting prices, prioritize pricing based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned pricing unit is When setting prices, adjust the order of prices based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, The system estimates user sentiment and adjusts monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, During monitoring, analyze competitor price fluctuation patterns to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, During monitoring, the monitoring process takes into account the attribute information of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, It estimates the user's emotions and adjusts the display order of monitoring results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The monitoring unit, When monitoring, consider the geographical distribution of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 25) The monitoring unit, During monitoring, we improve the accuracy of monitoring by referring to relevant literature from competitors. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, It estimates the user's emotions and adjusts the adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During adjustment, the adjustment algorithm is optimized by referring to past adjustment data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, During adjustments, different adjustment methods are applied to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, It estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, we analyze changes in adjustments based on the product submission date. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, During adjustments, we analyze the adjustments by referring to relevant market data for the product. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, During adjustments, we analyze the adjustments by referring to relevant market data for the product. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects sales data, competitor data, and market trends. An analysis unit analyzes the data collected by the aforementioned collection unit, A pricing unit sets a price based on the analysis results obtained by the aforementioned analysis unit, A monitoring unit adjusts the price set by the pricing unit in accordance with price fluctuations of competitors, The system includes an adjustment unit that further adjusts the price adjusted by the monitoring unit based on market seasonality and trends. A system characterized by the following features.

2. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Evaluate the accuracy of past collected data and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting data, prioritize collecting data from specific events or campaign periods. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analytical methods are applied depending on the data category. The system according to feature 1.

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