System

Abstract

An object of the system according to the embodiment is to perform dynamic pricing and advertisement adjustment based on market demand and competition information.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects market demand and competition information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate pricing on the basis of the analysis result obtained by the analysis unit. The adjustor automatically adjusts the advertisement based on the pricing suggested by the suggester.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Current technologies do not adequately address dynamic pricing and advertising adjustments based on market demand and competitive information, leaving room for improvement.

[0005] The system of the embodiment aims to dynamically set pricing and adjust advertising based on market demand and competitive information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects market demand and competitive information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate pricing based on the analysis results obtained by the analysis unit. The adjustment unit automatically adjusts advertisements based on the pricing proposed by the proposal unit. [Effects of the Invention]

[0007] Embodiments of the system allow for dynamic pricing and advertising adjustments based on market demand and competitive information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A dynamic pricing system according to an embodiment of the present invention collects market demand and competitor information, analyzes it using a generation AI, proposes optimal pricing, and automatically adjusts advertising. The dynamic pricing system aims to enable companies to maintain appropriate pricing strategies and improve their competitiveness in highly competitive markets. For example, the dynamic pricing system collects market demand and competitor pricing information, and the generation AI analyzes this data. The generation AI then proposes optimal pricing based on the analysis results, and advertising is automatically adjusted according to market fluctuations. This helps companies maximize profits and expand market share. For example, the generation AI can analyze market demand and raise prices for products with increased demand. Furthermore, if a competitor lowers its price, the generation AI adjusts prices accordingly to maintain competitiveness. Furthermore, advertising is updated in real time to reflect the latest price information. This allows companies to quickly respond to market fluctuations and maximize profits. This allows companies to constantly monitor market trends and maintain optimal pricing strategies. For example, the generation AI can analyze market demand and raise prices for products with increased demand. Additionally, if a competitor lowers their price, Generative AI will adjust their price accordingly to stay competitive. Furthermore, ads are updated in real time to reflect the latest pricing information, allowing businesses to quickly respond to market fluctuations and maximize revenue.

[0029] A dynamic price change system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects market demand and competitive information. Market demand includes, but is not limited to, demand indicators and data sources. The collection unit can also collect, for example, competitor pricing information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and proposes optimal pricing. The proposal unit proposes appropriate pricing based on the analysis results obtained by the analysis unit. For example, if a competitor lowers their price, the proposal unit can adjust the price accordingly. The adjustment unit automatically adjusts advertisements based on the pricing proposed by the proposal unit. For example, the adjustment unit can raise prices when demand increases. The adjustment unit can also update advertisements in real time to reflect the latest price information. As a result, the dynamic price change system according to an embodiment collects and analyzes market demand and competitive information, proposes optimal pricing, and automatically adjusts advertisements, thereby helping companies maximize their profits and expand their market share.

[0030] The collection unit can collect market demand or competitor price information. Market demand includes, but is not limited to, demand indicators and data sources. The collection unit can also collect competitor price information, for example. For example, the collection unit collects price information from online marketplaces and updates it in real time. The collection unit can also monitor competitor price fluctuations and collect price information. This allows for more accurate pricing by collecting market demand and competitor price information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input price information from online marketplaces to the generation AI and cause the generation AI to collect price information.

[0031] The analysis unit can analyze the collected information and propose appropriate pricing. The analysis unit, for example, analyzes the collected data and proposes optimal pricing. For example, the analysis unit can analyze collected market demand data and adjust prices based on demand fluctuations. The analysis unit can also analyze competitors' price information and adjust prices in response to competitors' price fluctuations. Furthermore, the analysis unit can apply a pricing algorithm based on the collected data and propose optimal prices. This improves a company's competitiveness by analyzing the collected information and proposing optimal pricing. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI execute a pricing proposal.

[0032] If a competitor lowers its price, the proposal unit can adjust the price based on the competitor's price. For example, the proposal unit detects a competitor's price fluctuation and adjusts the price accordingly. For example, if a competitor lowers its price, the proposal unit can apply a pricing algorithm based on the competitor's price information to propose an optimal price. Furthermore, the proposal unit can also build a system for quickly responding to a competitor's price fluctuation. This allows the company to maintain its competitiveness by quickly responding to a competitor's price fluctuation. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input competitor's price information into the generation AI and have the generation AI execute a price adjustment proposal.

[0033] The adjustment unit can raise prices when demand increases. For example, the adjustment unit detects an increase in demand and raises prices. For example, the adjustment unit raises prices when demand increases. The adjustment unit can also adjust prices based on fluctuations in demand. Furthermore, the adjustment unit can build a system for quickly responding to increases in demand. This maximizes the company's profits by raising prices when demand increases. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on increased demand into the generation AI and have the generation AI adjust the price to increase.

[0034] The adjustment unit can instantly update advertisements to reflect the latest price information. The adjustment unit, for example, updates advertisements in real time to reflect the latest price information. For example, the adjustment unit instantly updates advertisements in response to fluctuations in price information. The adjustment unit can also adjust the timing of advertisement display to reflect the latest price information. Furthermore, the adjustment unit can build a system for automatically updating advertisement content to reflect the latest price information. By updating advertisements in real time, the latest price information is always reflected, improving the competitiveness of companies. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input fluctuation data of price information into the generation AI and cause the generation AI to update the advertisement.

[0035] The collection unit can analyze past market data and select an appropriate information collection method. The collection unit, for example, analyzes past market data and selects the optimal information collection method. For example, the collection unit selects a collection method for a specific time period based on the past market data. The collection unit can also select a collection method for a specific region based on the past market data. Furthermore, the collection unit can select a collection method from a specific data source based on the past market data. This allows the optimal information collection method to be selected by analyzing past market data, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input past market data into a generation AI and have the generation AI select an information collection method.

[0036] The collection unit can filter market information based on a specific region or segment when collecting the market information. For example, the collection unit filters market information based on a specific region or segment when collecting the market information. For example, the collection unit prioritizes collecting market information for a specific region. The collection unit can also prioritize collecting market information for a specific segment (such as age group or gender). Furthermore, the collection unit can filter market information based on a combination of a specific region and segment. In this way, by filtering market information based on a specific region or segment, more relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data for a specific region or segment to a generation AI and have the generation AI perform filtering.

[0037] When collecting market information, the collection unit can select an appropriate collection means depending on the user's input method. The collection unit selects the optimal collection means depending on, for example, the user's input method (voice, text, image, etc.). For example, when the user uses voice input, the collection unit collects market information using voice recognition technology. Also, when the user uses text input, the collection unit can collect market information using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect market information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the collection means.

[0038] When collecting market information, the collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. The collection unit, for example, collects market information by taking geographical location information into consideration. For example, the collection unit prioritizes collecting highly relevant market information based on the user's current location. The collection unit can also prioritize collecting market information for a specific region. Furthermore, the collection unit can filter highly relevant market information based on the geographical location information. In this way, highly relevant market information can be prioritized by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to prioritize information collection.

[0039] The collection unit can analyze social media activities and collect related information when collecting market information. The collection unit, for example, analyzes social media activities and collects market information. For example, the collection unit analyzes trends on social media and collects related market information. The collection unit can also analyze user posts on social media and collect related market information. Furthermore, the collection unit can also collect related market information based on user activities on social media. In this way, related market information can be efficiently collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect information.

[0040] When collecting market information, the collection unit can customize the collection method by reflecting past feedback. The collection unit, for example, customizes the collection method based on past feedback. For example, the collection unit improves the collection method based on past feedback. The collection unit can also prioritize collection from a specific data source based on past feedback. Furthermore, the collection unit can adjust the collection timing based on past feedback. In this way, by reflecting past feedback, the collection method can be customized, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and have the generation AI customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit applies different analysis algorithms depending on, for example, the category of information. For example, the analysis unit applies a price fluctuation analysis algorithm to price information. The analysis unit can also apply a demand forecasting algorithm to demand information. Furthermore, the analysis unit can apply a competitive analysis algorithm to competitive information. This enables more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, improves the accuracy of the analysis based on past analysis results. For example, the analysis unit improves the analysis algorithm based on past analysis results. The analysis unit can also adjust analysis parameters based on past analysis results. Furthermore, the analysis unit can dynamically improve the accuracy of the analysis based on past analysis results. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the information was collected. The analysis unit determines the analysis priority based on, for example, the time when the information was collected. For example, the analysis unit prioritizes analyzing the most recent information. The analysis unit can also prioritize analyzing information collected within a specific period. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the information was collected. This allows the most recent information to be analyzed preferentially by determining the analysis priority based on the time when the information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information collection time data to the generation AI and have the generation AI execute the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of information. In this way, by adjusting the order of analysis based on the relevance of information, more relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to a generation AI and have the generation AI execute the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can dynamically adjust the way the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terminology.

[0047] The proposal unit can adjust the level of detail of the proposal based on the importance of pricing when making a proposal. The proposal unit adjusts the level of detail of the proposal based on, for example, the importance of pricing. For example, the proposal unit provides a detailed proposal when the importance of pricing is high. The proposal unit can also provide a simplified proposal when the importance of pricing is low. Furthermore, the proposal unit can dynamically adjust the level of detail of the proposal depending on the importance of pricing. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of pricing. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input pricing importance data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] The proposal unit can apply different proposal algorithms depending on the pricing category when making a proposal. The proposal unit applies different proposal algorithms depending on, for example, the pricing category. For example, the proposal unit applies a competitive analysis algorithm to competitive prices. The proposal unit can also apply a demand forecasting algorithm to demand prices. Furthermore, the proposal unit can apply a market trend analysis algorithm to market prices. This enables more accurate proposals by applying different proposal algorithms depending on the pricing category. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input pricing category data into a generation AI and cause the generation AI to apply a proposal algorithm.

[0049] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results. The proposal unit, for example, improves the accuracy of the proposal based on past proposal results. For example, the proposal unit improves the proposal algorithm based on past proposal results. The proposal unit can also adjust proposal parameters based on past proposal results. Furthermore, the proposal unit can dynamically improve the accuracy of the proposal based on past proposal results. In this way, the accuracy of the proposal can be improved by referring to past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] The proposal unit can determine the priority of proposals based on the time of submission of pricing settings when making proposals. The proposal unit determines the priority of proposals based on, for example, the time of submission of pricing settings. For example, the proposal unit prioritizes proposals based on the most recent pricing settings. The proposal unit can also prioritize proposals based on pricing settings submitted within a specific period. Furthermore, the proposal unit can dynamically adjust the priority of proposals based on the time of submission of pricing settings. This allows the most recent pricing settings to be prioritized by determining the priority of proposals based on the time of submission of pricing settings. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input pricing settings submission time data into a generation AI and have the generation AI execute the proposal priority.

[0051] The suggestion unit can adjust the order of proposals based on the relevance of pricing when making a proposal. The suggestion unit adjusts the order of proposals based on, for example, the relevance of pricing. For example, the suggestion unit prioritizes proposing highly relevant pricing. The suggestion unit can also postpone proposing less relevant pricing. Furthermore, the suggestion unit can dynamically adjust the order of proposals based on the relevance of pricing. In this way, by adjusting the order of proposals based on the relevance of pricing, more relevant pricing can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs pricing relevance data to a generation AI and causes the generation AI to execute the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can provide a proposal in simple language. Furthermore, the suggestion unit can dynamically adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to provide a proposal that is easier to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to execute the use of technical terminology.

[0053] During adjustment, the adjustment unit can analyze past ad adjustment results and select the optimal adjustment method. The adjustment unit, for example, selects the adjustment method based on past ad adjustment results. For example, the adjustment unit improves the adjustment method based on past ad adjustment results. The adjustment unit can also prioritize a specific ad format based on past ad adjustment results. Furthermore, the adjustment unit can adjust the adjustment timing based on past ad adjustment results. This enables the optimal adjustment method to be selected by analyzing past ad adjustment results, thereby enabling efficient ad adjustment. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past ad adjustment result data to a generation AI and have the generation AI select an adjustment method.

[0054] During the adjustment, the adjustment unit can adjust the advertisement based on a specific market segment. The adjustment unit adjusts the advertisement based on, for example, a specific market segment. For example, the adjustment unit provides an advertisement appropriate for a specific age group. The adjustment unit can also provide an advertisement appropriate for a specific region. Furthermore, the adjustment unit can also provide an advertisement appropriate for users with specific interests. By adjusting the advertisement based on a specific market segment, it is possible to provide an advertisement that is more targeted. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input specific market segment data into the generation AI and cause the generation AI to adjust the advertisement.

[0055] During adjustment, the adjustment unit can improve the advertisement adjustment method by reflecting user feedback. The adjustment unit, for example, improves the advertisement adjustment method based on user feedback. For example, the adjustment unit improves the advertisement content based on user feedback. The adjustment unit can also adjust the advertisement display timing based on user feedback. Furthermore, the adjustment unit can improve the advertisement format based on user feedback. In this way, by reflecting user feedback, the advertisement adjustment method can be improved and more effective advertisements can be provided. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input user feedback data into the generation AI and cause the generation AI to improve the advertisement adjustment method.

[0056] During adjustment, the adjustment unit can select an optimal advertisement adjustment method by taking geographical location information into consideration. The adjustment unit adjusts advertisements, for example, by taking geographical location information into consideration. For example, the adjustment unit prioritizes displaying highly relevant advertisements based on the user's current location. The adjustment unit can also provide appropriate advertisements to users in specific areas. Furthermore, the adjustment unit can adjust the timing of advertisement display based on the geographical location information. This makes it possible to prioritize displaying highly relevant advertisements by taking geographical location information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input geographical location information data to a generation AI and cause the generation AI to adjust the advertisements.

[0057] During adjustment, the adjustment unit can analyze social media activity and suggest ways to adjust the advertisement. The adjustment unit, for example, analyzes social media activity and suggests ways to adjust the advertisement. For example, the adjustment unit analyzes trends on social media and provides relevant advertisements. The adjustment unit can also analyze content posted by users on social media and provide relevant advertisements. Furthermore, the adjustment unit can adjust the timing of advertisement display based on users' activities on social media. In this way, by analyzing social media activity, relevant advertisements can be efficiently provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input social media data into a generation AI and cause the generation AI to suggest ways to adjust the advertisement.

[0058] During adjustment, the adjustment unit can customize the advertisement adjustment method by reflecting past feedback. The adjustment unit customizes the advertisement adjustment method based on, for example, past feedback. For example, the adjustment unit customizes the content of the advertisement based on past feedback. The adjustment unit can also customize the timing of advertisement display based on past feedback. Furthermore, the adjustment unit can also customize the format of the advertisement based on past feedback. In this way, by reflecting past feedback, the advertisement adjustment method can be customized and more effective advertisements can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past feedback data into the generation AI and cause the generation AI to customize the advertisement adjustment method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze the user's purchase history and determine which market information to collect based on the analyzed purchase history. For example, the collection unit can analyze the trends of products purchased by the user in the past and prioritize collecting related market information. The collection unit can also analyze the user's purchase frequency and collect market information on products purchased frequently. Furthermore, the collection unit can collect market information on specific brands or categories based on the user's purchase history. In this way, by determining which market information to collect based on the user's purchase history, more relevant information can be collected.

[0061] The suggestion unit can analyze the user's purchasing history and suggest pricing based on the analyzed purchasing history. For example, the suggestion unit can analyze the price trends of products purchased by the user in the past and suggest pricing for related products. The suggestion unit can also analyze the user's purchasing frequency and suggest pricing for frequently purchased products. Furthermore, the suggestion unit can suggest pricing for specific brands or categories based on the user's purchasing history. This allows for more relevant suggestions by suggesting pricing based on the user's purchasing history.

[0062] The collection unit can analyze the user's social media activity and determine the target of market information collection based on the analyzed social media activity. For example, the collection unit can prioritize collecting information on products in which the user has shown interest on social media. The collection unit can also analyze the content of the user's social media posts and collect related market information. Furthermore, the collection unit can collect market information related to specific trends or topics based on the user's social media activity. In this way, by determining the target of market information collection based on the user's social media activity, more relevant information can be collected.

[0063] The suggestion unit can analyze a user's purchase history and suggest advertisement content based on the analyzed purchase history. For example, the suggestion unit can analyze the trends of products purchased by the user in the past and suggest advertisements for related products. The suggestion unit can also analyze a user's purchase frequency and suggest advertisements for frequently purchased products. Furthermore, the suggestion unit can suggest advertisements for specific brands or categories based on the user's purchase history. This allows for more relevant advertisements by suggesting advertisement content based on the user's purchase history.

[0064] The collection unit can analyze the user's geographical location information and determine the market information to be collected based on the analyzed geographical location information. For example, the collection unit can prioritize collecting highly relevant market information based on the user's current location. The collection unit can also prioritize collecting market information for a specific region. Furthermore, the collection unit can also collect market information related to a specific region or area based on the geographical location information. In this way, by determining the market information to be collected based on the user's geographical location information, more relevant information can be collected.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects market demand and competitive information. Market demand includes, but is not limited to, demand indicators and data sources. The collection unit may also collect, for example, competitor pricing information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and proposes optimal pricing. Step 3: The proposal unit proposes appropriate pricing based on the analysis results obtained by the analysis unit. For example, if a competitor lowers their price, the proposal unit can adjust the price accordingly. Step 4: The adjuster automatically adjusts the advertisement based on the pricing suggested by the suggester. For example, the adjuster can increase the price if demand increases. The adjuster can also update the advertisement in real time to reflect the latest pricing information.

[0067] (Example 2) A dynamic pricing system according to an embodiment of the present invention collects market demand and competitor information, analyzes it using a generation AI, proposes optimal pricing, and automatically adjusts advertising. The dynamic pricing system aims to enable companies to maintain appropriate pricing strategies and improve their competitiveness in highly competitive markets. For example, the dynamic pricing system collects market demand and competitor pricing information, and the generation AI analyzes this data. The generation AI then proposes optimal pricing based on the analysis results, and advertising is automatically adjusted according to market fluctuations. This helps companies maximize profits and expand market share. For example, the generation AI can analyze market demand and raise prices for products with increased demand. Furthermore, if a competitor lowers its price, the generation AI adjusts prices accordingly to maintain competitiveness. Furthermore, advertising is updated in real time to reflect the latest price information. This allows companies to quickly respond to market fluctuations and maximize profits. This allows companies to constantly monitor market trends and maintain optimal pricing strategies. For example, the generation AI can analyze market demand and raise prices for products with increased demand. Additionally, if a competitor lowers their price, Generative AI will adjust their price accordingly to stay competitive. Furthermore, ads are updated in real time to reflect the latest pricing information, allowing businesses to quickly respond to market fluctuations and maximize revenue.

[0068] A dynamic price change system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects market demand and competitive information. Market demand includes, but is not limited to, demand indicators and data sources. The collection unit can also collect, for example, competitor pricing information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and proposes optimal pricing. The proposal unit proposes appropriate pricing based on the analysis results obtained by the analysis unit. For example, if a competitor lowers their price, the proposal unit can adjust the price accordingly. The adjustment unit automatically adjusts advertisements based on the pricing proposed by the proposal unit. For example, the adjustment unit can raise prices when demand increases. The adjustment unit can also update advertisements in real time to reflect the latest price information. As a result, the dynamic price change system according to an embodiment collects and analyzes market demand and competitive information, proposes optimal pricing, and automatically adjusts advertisements, thereby helping companies maximize their profits and expand their market share.

[0069] The collection unit can collect market demand or competitor price information. Market demand includes, but is not limited to, demand indicators and data sources. The collection unit can also collect competitor price information, for example. For example, the collection unit collects price information from online marketplaces and updates it in real time. The collection unit can also monitor competitor price fluctuations and collect price information. This allows for more accurate pricing by collecting market demand and competitor price information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input price information from online marketplaces to the generation AI and cause the generation AI to collect price information.

[0070] The analysis unit can analyze the collected information and propose appropriate pricing. The analysis unit, for example, analyzes the collected data and proposes optimal pricing. For example, the analysis unit can analyze collected market demand data and adjust prices based on demand fluctuations. The analysis unit can also analyze competitors' price information and adjust prices in response to competitors' price fluctuations. Furthermore, the analysis unit can apply a pricing algorithm based on the collected data and propose optimal prices. This improves a company's competitiveness by analyzing the collected information and proposing optimal pricing. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI execute a pricing proposal.

[0071] If a competitor lowers its price, the proposal unit can adjust the price based on the competitor's price. For example, the proposal unit detects a competitor's price fluctuation and adjusts the price accordingly. For example, if a competitor lowers its price, the proposal unit can apply a pricing algorithm based on the competitor's price information to propose an optimal price. Furthermore, the proposal unit can also build a system for quickly responding to a competitor's price fluctuation. This allows the company to maintain its competitiveness by quickly responding to a competitor's price fluctuation. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input competitor's price information into the generation AI and have the generation AI execute a price adjustment proposal.

[0072] The adjustment unit can raise prices when demand increases. For example, the adjustment unit detects an increase in demand and raises prices. For example, the adjustment unit raises prices when demand increases. The adjustment unit can also adjust prices based on fluctuations in demand. Furthermore, the adjustment unit can build a system for quickly responding to increases in demand. This maximizes the company's profits by raising prices when demand increases. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on increased demand into the generation AI and have the generation AI adjust the price to increase.

[0073] The adjustment unit can instantly update advertisements to reflect the latest price information. The adjustment unit, for example, updates advertisements in real time to reflect the latest price information. For example, the adjustment unit instantly updates advertisements in response to fluctuations in price information. The adjustment unit can also adjust the timing of advertisement display to reflect the latest price information. Furthermore, the adjustment unit can build a system for automatically updating advertisement content to reflect the latest price information. By updating advertisements in real time, the latest price information is always reflected, improving the competitiveness of companies. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input fluctuation data of price information into the generation AI and cause the generation AI to update the advertisement.

[0074] The collection unit can analyze the user's emotions and adjust the timing of collecting market information based on the analyzed user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the collection timing. For example, if the user is feeling stressed, the collection unit delays the collection timing to collect information when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can also advance the collection timing to quickly collect market information. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to quickly collect necessary information. This enables more appropriate information collection by adjusting the timing of collecting market information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0075] The collection unit can analyze past market data and select an appropriate information collection method. The collection unit, for example, analyzes past market data and selects the optimal information collection method. For example, the collection unit selects a collection method for a specific time period based on the past market data. The collection unit can also select a collection method for a specific region based on the past market data. Furthermore, the collection unit can select a collection method from a specific data source based on the past market data. This allows the optimal information collection method to be selected by analyzing past market data, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input past market data into a generation AI and have the generation AI select an information collection method.

[0076] The collection unit can filter market information based on a specific region or segment when collecting the market information. For example, the collection unit filters market information based on a specific region or segment when collecting the market information. For example, the collection unit prioritizes collecting market information for a specific region. The collection unit can also prioritize collecting market information for a specific segment (such as age group or gender). Furthermore, the collection unit can filter market information based on a combination of a specific region and segment. In this way, by filtering market information based on a specific region or segment, more relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data for a specific region or segment to a generation AI and have the generation AI perform filtering.

[0077] When collecting market information, the collection unit can select an appropriate collection means depending on the user's input method. The collection unit selects the optimal collection means depending on, for example, the user's input method (voice, text, image, etc.). For example, when the user uses voice input, the collection unit collects market information using voice recognition technology. Also, when the user uses text input, the collection unit can collect market information using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect market information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the collection means.

[0078] The collection unit can estimate the user's emotions and determine the priority of market information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of market information to be collected. For example, if the user is feeling stressed, the collection unit can prioritize collecting important market information. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting detailed market information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting market information that can be collected quickly. Thus, by determining the priority of market information according to the user's emotions, more important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0079] When collecting market information, the collection unit can prioritize collecting highly relevant information by taking geographical location information into consideration. The collection unit, for example, collects market information by taking geographical location information into consideration. For example, the collection unit prioritizes collecting highly relevant market information based on the user's current location. The collection unit can also prioritize collecting market information for a specific region. Furthermore, the collection unit can filter highly relevant market information based on the geographical location information. In this way, highly relevant market information can be prioritized by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to prioritize information collection.

[0080] The collection unit can analyze social media activities and collect related information when collecting market information. The collection unit, for example, analyzes social media activities and collects market information. For example, the collection unit analyzes trends on social media and collects related market information. The collection unit can also analyze user posts on social media and collect related market information. Furthermore, the collection unit can also collect related market information based on user activities on social media. In this way, related market information can be efficiently collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to collect information.

[0081] When collecting market information, the collection unit can customize the collection method by reflecting past feedback. The collection unit, for example, customizes the collection method based on past feedback. For example, the collection unit improves the collection method based on past feedback. The collection unit can also prioritize collection from a specific data source based on past feedback. Furthermore, the collection unit can adjust the collection timing based on past feedback. In this way, by reflecting past feedback, the collection method can be customized, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and have the generation AI customize the collection method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the presentation method of the analysis. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit applies different analysis algorithms depending on, for example, the category of information. For example, the analysis unit applies a price fluctuation analysis algorithm to price information. The analysis unit can also apply a demand forecasting algorithm to demand information. Furthermore, the analysis unit can apply a competitive analysis algorithm to competitive information. This enables more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, improves the accuracy of the analysis based on past analysis results. For example, the analysis unit improves the analysis algorithm based on past analysis results. The analysis unit can also adjust analysis parameters based on past analysis results. Furthermore, the analysis unit can dynamically improve the accuracy of the analysis based on past analysis results. In this way, the accuracy of the analysis can be improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time when the information was collected. The analysis unit determines the analysis priority based on, for example, the time when the information was collected. For example, the analysis unit prioritizes analyzing the most recent information. The analysis unit can also prioritize analyzing information collected within a specific period. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time when the information was collected. This allows the most recent information to be analyzed preferentially by determining the analysis priority based on the time when the information was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information collection time data to the generation AI and have the generation AI execute the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of information. In this way, by adjusting the order of analysis based on the relevance of information, more relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to a generation AI and have the generation AI execute the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can dynamically adjust the way the analysis results are presented according to the user's level of expertise. This allows for the provision of analysis results that are easier to understand by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terminology.

[0090] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the way the suggestions are expressed. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can also provide suggestions that focus on the main points. This allows for adjusting the way the suggestions are expressed based on the user's emotions, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0091] The proposal unit can adjust the level of detail of the proposal based on the importance of pricing when making a proposal. The proposal unit adjusts the level of detail of the proposal based on, for example, the importance of pricing. For example, the proposal unit provides a detailed proposal when the importance of pricing is high. The proposal unit can also provide a simplified proposal when the importance of pricing is low. Furthermore, the proposal unit can dynamically adjust the level of detail of the proposal depending on the importance of pricing. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of pricing. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input pricing importance data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0092] The proposal unit can apply different proposal algorithms depending on the pricing category when making a proposal. The proposal unit applies different proposal algorithms depending on, for example, the pricing category. For example, the proposal unit applies a competitive analysis algorithm to competitive prices. The proposal unit can also apply a demand forecasting algorithm to demand prices. Furthermore, the proposal unit can apply a market trend analysis algorithm to market prices. This enables more accurate proposals by applying different proposal algorithms depending on the pricing category. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI, for example. For example, the proposal unit can input pricing category data into a generation AI and cause the generation AI to apply a proposal algorithm.

[0093] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results. The proposal unit, for example, improves the accuracy of the proposal based on past proposal results. For example, the proposal unit improves the proposal algorithm based on past proposal results. The proposal unit can also adjust proposal parameters based on past proposal results. Furthermore, the proposal unit can dynamically improve the accuracy of the proposal based on past proposal results. In this way, the accuracy of the proposal can be improved by referring to past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the length of the suggestions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows for adjusting the length of the suggestions according to the user's emotions, thereby providing more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0095] The proposal unit can determine the priority of proposals based on the time of submission of pricing settings when making proposals. The proposal unit determines the priority of proposals based on, for example, the time of submission of pricing settings. For example, the proposal unit prioritizes proposals based on the most recent pricing settings. The proposal unit can also prioritize proposals based on pricing settings submitted within a specific period. Furthermore, the proposal unit can dynamically adjust the priority of proposals based on the time of submission of pricing settings. This allows the most recent pricing settings to be prioritized by determining the priority of proposals based on the time of submission of pricing settings. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input pricing settings submission time data into a generation AI and have the generation AI execute the proposal priority.

[0096] The suggestion unit can adjust the order of proposals based on the relevance of pricing when making a proposal. The suggestion unit adjusts the order of proposals based on, for example, the relevance of pricing. For example, the suggestion unit prioritizes proposing highly relevant pricing. The suggestion unit can also postpone proposing less relevant pricing. Furthermore, the suggestion unit can dynamically adjust the order of proposals based on the relevance of pricing. In this way, by adjusting the order of proposals based on the relevance of pricing, more relevant pricing can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs pricing relevance data to a generation AI and causes the generation AI to execute the order of proposals.

[0097] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can provide a proposal in simple language. Furthermore, the suggestion unit can dynamically adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to provide a proposal that is easier to understand by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to execute the use of technical terminology.

[0098] The adjustment unit can estimate a user's emotions and adjust the advertisement adjustment method based on the estimated user emotions. The adjustment unit, for example, estimates a user's emotions and adjusts the advertisement adjustment method. For example, if the user is nervous, the adjustment unit can provide a simple, highly visible advertisement. Furthermore, if the user is relaxed, the adjustment unit can provide a detailed advertisement. Furthermore, if the user is in a hurry, the adjustment unit can provide an advertisement that focuses on the main points. This allows for adjusting the advertisement adjustment method according to the user's emotions to provide more appropriate advertisements. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using an AI, for example, or without an AI. For example, the adjustment unit can input user emotion data into the generation AI and cause the generation AI to adjust the advertisement adjustment method.

[0099] During adjustment, the adjustment unit can analyze past ad adjustment results and select the optimal adjustment method. The adjustment unit, for example, selects the adjustment method based on past ad adjustment results. For example, the adjustment unit improves the adjustment method based on past ad adjustment results. The adjustment unit can also prioritize a specific ad format based on past ad adjustment results. Furthermore, the adjustment unit can adjust the adjustment timing based on past ad adjustment results. This enables the optimal adjustment method to be selected by analyzing past ad adjustment results, thereby enabling efficient ad adjustment. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past ad adjustment result data to a generation AI and have the generation AI select an adjustment method.

[0100] During the adjustment, the adjustment unit can adjust the advertisement based on a specific market segment. The adjustment unit adjusts the advertisement based on, for example, a specific market segment. For example, the adjustment unit provides an advertisement appropriate for a specific age group. The adjustment unit can also provide an advertisement appropriate for a specific region. Furthermore, the adjustment unit can also provide an advertisement appropriate for users with specific interests. By adjusting the advertisement based on a specific market segment, it is possible to provide an advertisement that is more targeted. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input specific market segment data into the generation AI and cause the generation AI to adjust the advertisement.

[0101] During adjustment, the adjustment unit can improve the advertisement adjustment method by reflecting user feedback. The adjustment unit, for example, improves the advertisement adjustment method based on user feedback. For example, the adjustment unit improves the advertisement content based on user feedback. The adjustment unit can also adjust the advertisement display timing based on user feedback. Furthermore, the adjustment unit can improve the advertisement format based on user feedback. In this way, by reflecting user feedback, the advertisement adjustment method can be improved and more effective advertisements can be provided. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input user feedback data into the generation AI and cause the generation AI to improve the advertisement adjustment method.

[0102] The adjustment unit can estimate the user's emotions and determine the priority of advertisements based on the estimated user emotions. The adjustment unit, for example, estimates the user's emotions and determines the priority of advertisements. For example, when the user is feeling stressed, the adjustment unit can prioritize displaying advertisements with high importance. Furthermore, when the user is relaxed, the adjustment unit can also prioritize displaying detailed advertisements. Furthermore, when the user is in a hurry, the adjustment unit can prioritize displaying advertisements that can be displayed quickly. Thus, by determining the priority of advertisements according to the user's emotions, more important advertisements can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 can be performed using, for example, an AI, or without an AI. For example, the adjustment unit can input user emotion data into the generation AI and cause the generation AI to determine the priority of advertisements.

[0103] During adjustment, the adjustment unit can select an optimal advertisement adjustment method by taking geographical location information into consideration. The adjustment unit adjusts advertisements, for example, by taking geographical location information into consideration. For example, the adjustment unit prioritizes displaying highly relevant advertisements based on the user's current location. The adjustment unit can also provide appropriate advertisements to users in specific areas. Furthermore, the adjustment unit can adjust the timing of advertisement display based on the geographical location information. This makes it possible to prioritize displaying highly relevant advertisements by taking geographical location information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input geographical location information data to a generation AI and cause the generation AI to adjust the advertisements.

[0104] During adjustment, the adjustment unit can analyze social media activity and suggest ways to adjust the advertisement. The adjustment unit, for example, analyzes social media activity and suggests ways to adjust the advertisement. For example, the adjustment unit analyzes trends on social media and provides relevant advertisements. The adjustment unit can also analyze content posted by users on social media and provide relevant advertisements. Furthermore, the adjustment unit can adjust the timing of advertisement display based on users' activities on social media. In this way, by analyzing social media activity, relevant advertisements can be efficiently provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input social media data into a generation AI and cause the generation AI to suggest ways to adjust the advertisement.

[0105] During adjustment, the adjustment unit can customize the advertisement adjustment method by reflecting past feedback. The adjustment unit customizes the advertisement adjustment method based on, for example, past feedback. For example, the adjustment unit customizes the content of the advertisement based on past feedback. The adjustment unit can also customize the timing of advertisement display based on past feedback. Furthermore, the adjustment unit can also customize the format of the advertisement based on past feedback. In this way, by reflecting past feedback, the advertisement adjustment method can be customized and more effective advertisements can be provided. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past feedback data into the generation AI and cause the generation AI to customize the advertisement adjustment method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and adjustment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects market demand and competitive information using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal pricing based on the analysis results. The adjustment unit is realized, for example, by the control unit 46A of the smart device 14 and automatically adjusts advertisements to reflect the latest price information. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects market demand and competitive information using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal pricing based on the analysis results. The adjustment unit is realized, for example, by the control unit 46A of the smart glasses 214 and automatically adjusts advertisements to reflect the latest price information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and adjustment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects market demand and competitive information using the camera 42 and communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal pricing based on the analysis results. The adjustment unit is realized, for example, by the control unit 46A of the headset type terminal 314, and automatically adjusts advertisements to reflect the latest price information. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects market demand and competitive information using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal pricing based on the analysis results. The adjustment unit is realized, for example, by the control unit 46A of the robot 414 and automatically adjusts advertisements to reflect the latest price information.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can analyze the user's purchase history and determine which market information to collect based on the analyzed purchase history. For example, the collection unit can analyze the trends of products purchased by the user in the past and prioritize collecting related market information. The collection unit can also analyze the user's purchase frequency and collect market information on products purchased frequently. Furthermore, the collection unit can collect market information on specific brands or categories based on the user's purchase history. In this way, by determining which market information to collect based on the user's purchase history, more relevant information can be collected.

[0108] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can delay the timing of the analysis to perform the analysis when the user is relaxed. Also, if the user is relaxed, the analysis unit can advance the timing of the analysis to perform the analysis quickly. Furthermore, if the user is in a hurry, the analysis unit can optimize the timing of the analysis to quickly analyze the necessary information. This allows for more appropriate analysis by adjusting the timing of the analysis according to the user's emotions.

[0109] The suggestion unit can analyze the user's purchasing history and suggest pricing based on the analyzed purchasing history. For example, the suggestion unit can analyze the price trends of products purchased by the user in the past and suggest pricing for related products. The suggestion unit can also analyze the user's purchasing frequency and suggest pricing for frequently purchased products. Furthermore, the suggestion unit can suggest pricing for specific brands or categories based on the user's purchasing history. This allows for more relevant suggestions by suggesting pricing based on the user's purchasing history.

[0110] The adjustment unit can estimate the user's emotions and adjust the timing of advertisement display based on the estimated user emotions. For example, if the user is feeling stressed, the adjustment unit delays the timing of advertisement display to display the advertisement when the user is relaxed. Also, if the user is relaxed, the adjustment unit can advance the timing of advertisement display to quickly display the advertisement. Furthermore, if the user is in a hurry, the adjustment unit can optimize the timing of advertisement display to quickly display necessary information. This allows for more appropriate advertisement display by adjusting the timing of advertisement display according to the user's emotions.

[0111] The collection unit can analyze the user's social media activity and determine the target of market information collection based on the analyzed social media activity. For example, the collection unit can prioritize collecting information on products in which the user has shown interest on social media. The collection unit can also analyze the content of the user's social media posts and collect related market information. Furthermore, the collection unit can collect market information related to specific trends or topics based on the user's social media activity. In this way, by determining the target of market information collection based on the user's social media activity, more relevant information can be collected.

[0112] The analysis unit can estimate the user's emotions and adjust the level of analysis detail based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can also provide a summary analysis result. In this way, by adjusting the level of analysis detail according to the user's emotions, more appropriate analysis results can be provided.

[0113] The suggestion unit can analyze a user's purchase history and suggest advertisement content based on the analyzed purchase history. For example, the suggestion unit can analyze the trends of products purchased by the user in the past and suggest advertisements for related products. The suggestion unit can also analyze a user's purchase frequency and suggest advertisements for frequently purchased products. Furthermore, the suggestion unit can suggest advertisements for specific brands or categories based on the user's purchase history. This allows for more relevant advertisements by suggesting advertisement content based on the user's purchase history.

[0114] The adjustment unit can estimate the user's emotions and adjust the content of the advertisement based on the estimated user's emotions. For example, if the user is nervous, the adjustment unit can provide a simple, highly visible advertisement. If the user is relaxed, the adjustment unit can also provide a detailed advertisement. Furthermore, if the user is in a hurry, the adjustment unit can also provide an advertisement that focuses on the main points. In this way, by adjusting the content of the advertisement according to the user's emotions, more appropriate advertisements can be provided.

[0115] The collection unit can analyze the user's geographical location information and determine the market information to be collected based on the analyzed geographical location information. For example, the collection unit can prioritize collecting highly relevant market information based on the user's current location. The collection unit can also prioritize collecting market information for a specific region. Furthermore, the collection unit can also collect market information related to a specific region or area based on the geographical location information. In this way, by determining the market information to be collected based on the user's geographical location information, more relevant information can be collected.

[0116] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing information of high importance. Also, if the user is relaxed, the analysis unit can prioritize analyzing detailed information. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing information that can be analyzed quickly. In this way, by determining the analysis priority according to the user's emotions, more important information can be analyzed preferentially.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects market demand and competitive information. Market demand includes, but is not limited to, demand indicators and data sources. The collection unit may also collect, for example, competitor pricing information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected data and proposes optimal pricing. Step 3: The proposal unit proposes appropriate pricing based on the analysis results obtained by the analysis unit. For example, if a competitor lowers their price, the proposal unit can adjust the price accordingly. Step 4: The adjuster automatically adjusts the advertisement based on the pricing suggested by the suggester. For example, the adjuster can increase the price if demand increases. The adjuster can also update the advertisement in real time to reflect the latest pricing information.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects market demand and competitive information; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes appropriate pricing based on the analysis results obtained by the analysis unit; an adjustment unit that automatically adjusts the advertisement based on the pricing proposed by the proposal unit. A system characterized by:

2. The collecting unit Gather market demand or competitor pricing information 2. The system of claim 1.

3. The analysis unit Analyze the collected information and propose appropriate pricing 2. The system of claim 1.

4. The proposal unit If a competitor lowers their price, adjust your price based on theirs 2. The system of claim 1.

5. The adjustment unit If demand increases, raise prices 2. The system of claim 1.

6. The adjustment unit Instantly update your ads to reflect the latest pricing information 2. The system of claim 1.

7. The collecting unit Analyze user sentiment and adjust the timing of collecting market information based on the analyzed user sentiment.

2. The system of claim 1.

8. The collecting unit Analyze past market data and select appropriate information gathering methods 2. The system of claim 1.

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