System

Abstract

An object of a system according to an embodiment is to predict price fluctuations of raw materials in real time and provide the predicted price fluctuations to consumers and companies.SOLUTION: A system includes a price data collection part, a price fluctuation analysis part, a prediction part, and a provision part. The price data collection unit collects price data of various raw materials. The price fluctuation analysis unit analyzes the price data collected by the price data collection unit. The prediction unit predicts the price fluctuation based on the data analyzed by the price fluctuation analysis unit. The providing unit provides the price change information predicted by the prediction unit to the consumer and the company.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are not sufficient in predicting raw material price fluctuations in real time and providing this information to consumers and businesses, and there is room for improvement.

[0005] The system according to the embodiment aims to provide consumers and businesses with real-time forecasts of raw material price fluctuations. [Means for solving the problem]

[0006] The system according to the embodiment includes a price data collection unit, a price fluctuation analysis unit, a prediction unit, and a providing unit. The price data collection unit collects price data for various raw materials. The price fluctuation analysis unit analyzes the price data collected by the price data collection unit. The prediction unit predicts price fluctuations based on the data analyzed by the price fluctuation analysis unit. The providing unit provides price fluctuation information predicted by the prediction unit to consumers and businesses. [Effects of the Invention]

[0007] The system according to the embodiment can predict raw material price fluctuations in real time and provide the forecast to consumers and businesses. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) The price prediction system according to an embodiment of the present invention uses a generative AI to predict price fluctuations of various raw materials in real time and forecast price increases for each product on the market. This allows the price prediction system to provide consumers and businesses with predicted future prices, helping them make smart decisions.

[0029] A price prediction system according to an embodiment includes a price data collection unit, a price fluctuation analysis unit, a prediction unit, and a providing unit. The price data collection unit collects price data for various raw materials. For example, it collects price data for oil, metals, agricultural products, and the like. The price data collection unit can also collect data from public databases on the Internet and market research reports. For example, the price data collection unit acquires data in real time via an API. The price fluctuation analysis unit analyzes the collected price data. For example, the price fluctuation analysis unit extracts price fluctuation patterns using statistical analysis. The price fluctuation analysis unit can also build a price fluctuation prediction model using a machine learning algorithm. For example, the price fluctuation analysis unit performs regression analysis based on past price data to predict future prices. The prediction unit predicts price fluctuations based on the analyzed data. For example, the prediction unit predicts future price fluctuations using time series prediction. The prediction unit can also generate price fluctuation scenarios using a generation AI. For example, the prediction unit uses a generation AI to analyze market trends and make price predictions based on multiple scenarios. The providing unit provides predicted price fluctuation information to consumers and businesses. For example, the providing unit provides the prediction information through a website or a mobile app. The providing unit can also link the prediction information to a business system via an API. For example, the providing unit displays the prediction information in a dashboard format so that users can intuitively understand it. As a result, the price prediction system according to the embodiment can provide future price predictions to consumers and businesses and support smart decision-making.

[0030] In addition to predicting price fluctuations, the prediction unit can quantify the reliability of the prediction and provide it to the user. For example, when the generation AI predicts price fluctuations, the prediction unit quantifies the reliability of the prediction and provides it to the user. For example, it indicates that the reliability of an oil price fluctuation prediction is 80%. The prediction unit can also use confidence intervals and probability values ​​as a method of calculating reliability. For example, the prediction unit calculates the reliability of the prediction based on the confidence interval and provides it to the user. This allows the user to understand the reliability of the prediction and make smarter decisions.

[0031] The price fluctuation analysis unit can also take into account external factors such as weather data and political events when predicting price fluctuations. For example, the price fluctuation analysis unit takes weather data into account when the generation AI predicts price fluctuations. For example, when predicting the price of agricultural products, it analyzes the impact of weather fluctuations on harvest yields. The price fluctuation analysis unit can also take political events into account. For example, when predicting oil prices, it analyzes the impact of policy changes in oil-producing countries on prices. By taking external factors into account, more accurate price fluctuation predictions are possible.

[0032] The prediction unit can take into account the product's life cycle and seasonality when predicting product price increases. For example, when the generation AI predicts product price increases, the prediction unit takes into account the product's life cycle. For example, it predicts price fluctuations according to the introduction, growth, maturity, and decline periods of a new product. The prediction unit can also take into account the seasonality of a product. For example, it predicts price fluctuations of toys during the Christmas season. In this way, taking into account the product's life cycle and seasonality makes it possible to make more accurate price increase predictions.

[0033] The prediction unit can take into account the pricing strategies and market share of competitors when predicting product price increases. For example, when the generation AI predicts product price increases, the prediction unit takes into account the pricing strategies of competitors. For example, it analyzes the price change history of major competitors and predicts future price trends. The prediction unit can also take market share into account. For example, if a particular product has a high market share, it analyzes the tendency for that product's price to rise. This makes it possible to make more accurate price increase predictions by taking into account the pricing strategies and market share of competitors.

[0034] The provision unit can take into account the consumer's purchasing history and preferences when providing a predicted price to the consumer. For example, when the generation AI provides a predicted price to the consumer, the provision unit takes into account the consumer's purchasing history. For example, it predicts future prices based on price fluctuations of products purchased in the past. The provision unit can also take into account the consumer's preferences. For example, it provides a predicted price based on preferences for specific brands or product categories. In this way, by taking into account the consumer's purchasing history and preferences, it is possible to provide a more personalized predicted price.

[0035] The provision unit can take into account regional price differences when providing predicted prices to consumers. For example, when the generation AI provides predicted prices to consumers, the provision unit takes into account regional price differences. For example, it makes a prediction based on the price difference between urban and rural areas. The provision unit can also take into account logistics costs. For example, it provides predicted prices based on regional logistics costs. In this way, by taking into account regional price differences, it is possible to provide more accurate predicted prices.

[0036] The provision department can take into account the company's cost structure and profit margin when supporting price optimization for a company. For example, when the generative AI supports price optimization for a company, the provision department takes into account the company's cost structure. For example, it proposes an appropriate price based on manufacturing costs and logistics costs. The provision department can also take into account the company's profit margin. For example, it proposes pricing based on gross profit margin and operating profit margin. By taking into account the company's cost structure and profit margin, more appropriate pricing becomes possible.

[0037] The provision department can take into account the pricing strategies and market share of competitors when supporting price optimization for companies. For example, when the generation AI supports price optimization for companies, the provision department takes into account the pricing strategies of competitors. For example, it analyzes the price change history of major competitors and predicts future price trends. The provision department can also take market share into account. For example, if a particular product has a high market share, it analyzes the tendency for that product's price to rise. This makes it possible to set more appropriate prices by taking into account the pricing strategies and market share of competitors.

[0038] The prediction unit can incorporate the latest market news and event information when predicting price fluctuations in real time. For example, the prediction unit incorporates the latest market news when the generation AI predicts price fluctuations in real time. For example, when predicting oil prices, the prediction unit takes into account the latest policy changes in oil-producing countries. The prediction unit can also incorporate event information. For example, it analyzes the impact of international conferences and policy announcements on prices. By incorporating the latest market news and event information, more accurate real-time predictions are possible.

[0039] The prediction unit can compare past data with current data and detect outliers when predicting price fluctuations in real time. For example, when the generation AI predicts price fluctuations in real time, the prediction unit compares past data with current data and detects outliers. For example, in predicting oil prices, the prediction unit compares past price fluctuation patterns with current prices. The prediction unit can also use statistical outlier detection or anomaly detection using machine learning as anomaly detection methods. For example, the prediction unit detects outliers based on statistical outlier detection and notifies the user. This enables more accurate real-time predictions by comparing past data with current data and detecting outliers.

[0040] The prediction unit can incorporate the latest market news and event information when predicting price fluctuations in real time. For example, the prediction unit incorporates the latest market news when the generation AI predicts price fluctuations in real time. For example, when predicting oil prices, the prediction unit takes into account the latest policy changes in oil-producing countries. The prediction unit can also incorporate event information. For example, it analyzes the impact of international conferences and policy announcements on prices. By incorporating the latest market news and event information, more accurate real-time predictions are possible.

[0041] The prediction unit can compare past data with current data and detect outliers when predicting price fluctuations in real time. For example, when the generation AI predicts price fluctuations in real time, the prediction unit compares past data with current data and detects outliers. For example, in predicting oil prices, the prediction unit compares past price fluctuation patterns with current prices. The prediction unit can also use statistical outlier detection or anomaly detection using machine learning as anomaly detection methods. For example, the prediction unit detects outliers based on statistical outlier detection and notifies the user. This enables more accurate real-time predictions by comparing past data with current data and detecting outliers.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The price prediction system may further include an energy analysis unit that collects energy consumption data and analyzes the impact of energy price fluctuations on raw material prices. For example, the energy analysis unit may collect data on electricity consumption and gas consumption and analyze the impact of these energy prices on the prices of oil and metals. The energy analysis unit may also take into account the spread of renewable energy. For example, it may analyze the impact of the spread of solar power generation and wind power generation on energy prices. By taking energy consumption data into consideration, more accurate price predictions are possible.

[0044] In addition to predicting price fluctuations, the prediction unit can quantify the reliability of the prediction and provide it to the user. For example, when the generation AI predicts price fluctuations, the prediction unit quantifies the reliability of the prediction and provides it to the user. For example, it shows that the reliability of an oil price fluctuation prediction is 80%. The prediction unit can also use confidence intervals and probability values ​​as a method of calculating reliability. For example, the prediction unit calculates the reliability of the prediction based on the confidence interval and provides it to the user. This allows the user to understand the reliability of the prediction and make smarter decisions.

[0045] The price fluctuation analysis unit can also take into account external factors such as weather data and political events when predicting price fluctuations. For example, the price fluctuation analysis unit takes weather data into account when the generation AI predicts price fluctuations. For example, when predicting the price of agricultural products, it analyzes the impact of weather fluctuations on harvest yields. The price fluctuation analysis unit can also take political events into account. For example, when predicting oil prices, it analyzes the impact of policy changes in oil-producing countries on prices. By taking external factors into account, more accurate price fluctuation predictions are possible.

[0046] The prediction unit can take into account the product's life cycle and seasonality when predicting price increases. For example, the prediction unit takes into account the product's life cycle when the generation AI predicts price increases. For example, it predicts price fluctuations according to the introduction, growth, maturity, and decline periods of a new product. The prediction unit can also take into account the seasonality of a product. For example, it predicts price fluctuations of toys during the Christmas season. In this way, taking into account the product's life cycle and seasonality makes it possible to make more accurate price increase predictions.

[0047] The prediction unit can take into account the pricing strategies and market share of competitors when predicting product price increases. For example, the prediction unit takes into account the pricing strategies of competitors when the generation AI predicts product price increases. For example, it analyzes the price change history of major competitors to predict future price trends. The prediction unit can also take market share into account. For example, if a particular product has a high market share, it analyzes the tendency for that product's price to rise. This allows for more accurate price increase predictions by taking into account the pricing strategies and market share of competitors.

[0048] The provision unit can take into account the consumer's purchasing history and preferences when providing a predicted price to the consumer. For example, the provision unit takes into account the consumer's purchasing history when the generation AI provides a predicted price to the consumer. For example, the provision unit predicts future prices based on price fluctuations of products purchased in the past. The provision unit can also take into account the consumer's preferences. For example, the provision unit provides a predicted price based on preferences for specific brands or product categories. In this way, by taking into account the consumer's purchasing history and preferences, it is possible to provide a more personalized predicted price.

[0049] The provision unit can take into account regional price differences when providing predicted prices to consumers. For example, the provision unit takes into account regional price differences when the generation AI provides predicted prices to consumers. For example, the provision unit makes predictions based on price differences between urban and rural areas. The provision unit can also take into account logistics costs. For example, the provision unit provides predicted prices based on regional logistics costs. This allows for more accurate predicted prices to be provided by taking into account regional price differences.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The price data collection unit collects price data for various raw materials. For example, it collects price data for oil, metals, agricultural products, etc. The price data collection unit can also collect data from public databases on the Internet and market research reports. Furthermore, the price data collection unit obtains data in real time through APIs. Step 2: The price fluctuation analysis unit analyzes the collected price data. For example, the price fluctuation analysis unit extracts price fluctuation patterns using statistical analysis. The price fluctuation analysis unit can also build a price fluctuation prediction model using a machine learning algorithm. For example, the price fluctuation analysis unit performs regression analysis based on past price data to predict future prices. Step 3: The forecasting unit predicts price fluctuations based on the analyzed data. For example, the forecasting unit predicts future price fluctuations using time series forecasting. The forecasting unit can also generate price fluctuation scenarios using a generation AI. For example, the forecasting unit uses a generation AI to analyze market trends and make price predictions based on multiple scenarios. Step 4: The provider provides the predicted price fluctuation information to consumers and businesses. For example, the provider provides the forecast information through a website or mobile app. The provider can also link the forecast information to a company's system via an API. For example, the provider displays the forecast information in a dashboard format so that users can understand it intuitively.

[0052] (Example 2) The price prediction system according to an embodiment of the present invention uses a generative AI to predict price fluctuations of various raw materials in real time and forecast price increases for each product on the market. This allows the price prediction system to provide consumers and businesses with predicted future prices, helping them make smart decisions.

[0053] A price prediction system according to an embodiment includes a price data collection unit, a price fluctuation analysis unit, a prediction unit, and a providing unit. The price data collection unit collects price data for various raw materials. For example, it collects price data for oil, metals, agricultural products, and the like. The price data collection unit can also collect data from public databases on the Internet and market research reports. For example, the price data collection unit acquires data in real time via an API. The price fluctuation analysis unit analyzes the collected price data. For example, the price fluctuation analysis unit extracts price fluctuation patterns using statistical analysis. The price fluctuation analysis unit can also build a price fluctuation prediction model using a machine learning algorithm. For example, the price fluctuation analysis unit performs regression analysis based on past price data to predict future prices. The prediction unit predicts price fluctuations based on the analyzed data. For example, the prediction unit predicts future price fluctuations using time series prediction. The prediction unit can also generate price fluctuation scenarios using a generation AI. For example, the prediction unit uses a generation AI to analyze market trends and make price predictions based on multiple scenarios. The providing unit provides predicted price fluctuation information to consumers and businesses. For example, the providing unit provides the prediction information through a website or a mobile app. The providing unit can also link the prediction information to a business system via an API. For example, the providing unit displays the prediction information in a dashboard format so that users can intuitively understand it. As a result, the price prediction system according to the embodiment can provide future price predictions to consumers and businesses and support smart decision-making.

[0054] In addition to predicting price fluctuations, the prediction unit can quantify the reliability of the prediction and provide it to the user. For example, when the generation AI predicts price fluctuations, the prediction unit quantifies the reliability of the prediction and provides it to the user. For example, it indicates that the reliability of an oil price fluctuation prediction is 80%. The prediction unit can also use confidence intervals and probability values ​​as a method of calculating reliability. For example, the prediction unit calculates the reliability of the prediction based on the confidence interval and provides it to the user. This allows the user to understand the reliability of the prediction and make smarter decisions.

[0055] The price fluctuation analysis unit can also take into account external factors such as weather data and political events when predicting price fluctuations. For example, the price fluctuation analysis unit takes weather data into account when the generation AI predicts price fluctuations. For example, when predicting the price of agricultural products, it analyzes the impact of weather fluctuations on harvest yields. The price fluctuation analysis unit can also take political events into account. For example, when predicting oil prices, it analyzes the impact of policy changes in oil-producing countries on prices. By taking external factors into account, more accurate price fluctuation predictions are possible.

[0056] The price fluctuation analysis unit can use the emotion estimation function to analyze the emotions of consumers and companies and reflect the impact of emotional fluctuations on prices in predictions. The price fluctuation analysis unit, for example, uses the emotion estimation function to analyze consumer emotions and reflect the impact of those emotions on raw material prices in predictions. For example, it analyzes the tendency for gold prices to rise when consumer anxiety increases. The price fluctuation analysis unit can also analyze corporate emotions and reflect the impact of those emotions on prices in predictions. For example, it analyzes the tendency for investment to increase and raw material prices to rise when corporate optimism increases. This allows for more accurate price fluctuation predictions by taking into account the impact of emotional fluctuations on prices.

[0057] The prediction unit can take into account the product's life cycle and seasonality when predicting product price increases. For example, when the generation AI predicts product price increases, the prediction unit takes into account the product's life cycle. For example, it predicts price fluctuations according to the introduction, growth, maturity, and decline periods of a new product. The prediction unit can also take into account the seasonality of a product. For example, it predicts price fluctuations of toys during the Christmas season. In this way, taking into account the product's life cycle and seasonality makes it possible to make more accurate price increase predictions.

[0058] The prediction unit can take into account the pricing strategies and market share of competitors when predicting product price increases. For example, when the generation AI predicts product price increases, the prediction unit takes into account the pricing strategies of competitors. For example, it analyzes the price change history of major competitors and predicts future price trends. The prediction unit can also take market share into account. For example, if a particular product has a high market share, it analyzes the tendency for that product's price to rise. This makes it possible to make more accurate price increase predictions by taking into account the pricing strategies and market share of competitors.

[0059] The price fluctuation analysis unit can use the emotion estimation function to analyze consumer purchasing willingness and reflect the impact of that willingness on product price increases in the prediction. The price fluctuation analysis unit can, for example, use the emotion estimation function to analyze consumer purchasing willingness and reflect the impact of that willingness on product price increases in the prediction. For example, it can analyze the tendency for the price of a specific product to rise when consumer purchasing willingness increases. The price fluctuation analysis unit can also analyze the purchasing willingness of companies and reflect the impact of that willingness on product price increases in the prediction. For example, it can analyze the tendency for raw material prices to rise when corporate purchasing willingness increases. In this way, by taking consumer purchasing willingness into consideration, more accurate price increase predictions are possible.

[0060] The provision unit can take into account the consumer's purchasing history and preferences when providing a predicted price to the consumer. For example, when the generation AI provides a predicted price to the consumer, the provision unit takes into account the consumer's purchasing history. For example, it predicts future prices based on price fluctuations of products purchased in the past. The provision unit can also take into account the consumer's preferences. For example, it provides a predicted price based on preferences for specific brands or product categories. In this way, by taking into account the consumer's purchasing history and preferences, it is possible to provide a more personalized predicted price.

[0061] The provision unit can take into account regional price differences when providing predicted prices to consumers. For example, when the generation AI provides predicted prices to consumers, the provision unit takes into account regional price differences. For example, it makes a prediction based on the price difference between urban and rural areas. The provision unit can also take into account logistics costs. For example, it provides predicted prices based on regional logistics costs. In this way, by taking into account regional price differences, it is possible to provide more accurate predicted prices.

[0062] The providing unit can analyze consumer emotions using the emotion estimation function and provide a price forecast based on the emotions. The providing unit, for example, uses the emotion estimation function to analyze consumer emotions and provide a price forecast based on the emotions. For example, it analyzes the tendency for the price of a specific product to rise as consumer anxiety increases. The providing unit can also provide a price forecast based on consumer emotions. For example, it analyzes the tendency for prices to stabilize as consumer optimism increases. In this way, by taking consumer emotions into consideration, a more accurate price forecast can be provided.

[0063] The provision department can take into account the company's cost structure and profit margin when supporting price optimization for a company. For example, when the generative AI supports price optimization for a company, the provision department takes into account the company's cost structure. For example, it proposes an appropriate price based on manufacturing costs and logistics costs. The provision department can also take into account the company's profit margin. For example, it proposes pricing based on gross profit margin and operating profit margin. By taking into account the company's cost structure and profit margin, more appropriate pricing becomes possible.

[0064] The provision department can take into account the pricing strategies and market share of competitors when supporting price optimization for companies. For example, when the generation AI supports price optimization for companies, the provision department takes into account the pricing strategies of competitors. For example, it analyzes the price change history of major competitors and predicts future price trends. The provision department can also take market share into account. For example, if a particular product has a high market share, it analyzes the tendency for that product's price to rise. This makes it possible to set more appropriate prices by taking into account the pricing strategies and market share of competitors.

[0065] The provision unit can analyze consumer emotions using the emotion estimation function and propose a pricing strategy based on those emotions. The provision unit, for example, analyzes consumer emotions using the emotion estimation function and proposes a pricing strategy based on those emotions. For example, when a consumer's purchasing motivation increases, the provision unit proposes a strategy to raise prices. The provision unit can also propose a pricing strategy based on consumer emotions. For example, when consumer anxiety increases, the provision unit proposes a strategy to stabilize prices. This makes it possible to implement a more appropriate pricing strategy by taking consumer emotions into consideration.

[0066] The prediction unit can incorporate the latest market news and event information when predicting price fluctuations in real time. For example, the prediction unit incorporates the latest market news when the generation AI predicts price fluctuations in real time. For example, when predicting oil prices, the prediction unit takes into account the latest policy changes in oil-producing countries. The prediction unit can also incorporate event information. For example, it analyzes the impact of international conferences and policy announcements on prices. By incorporating the latest market news and event information, more accurate real-time predictions are possible.

[0067] The prediction unit can compare past data with current data and detect outliers when predicting price fluctuations in real time. For example, when the generation AI predicts price fluctuations in real time, the prediction unit compares past data with current data and detects outliers. For example, in predicting oil prices, the prediction unit compares past price fluctuation patterns with current prices. The prediction unit can also use statistical outlier detection or anomaly detection using machine learning as anomaly detection methods. For example, the prediction unit detects outliers based on statistical outlier detection and notifies the user. This enables more accurate real-time predictions by comparing past data with current data and detecting outliers.

[0068] The price fluctuation analysis unit can use the emotion estimation function to analyze the emotions of consumers and companies in real time and reflect the impact of those emotions on price fluctuations in predictions. The price fluctuation analysis unit, for example, uses the emotion estimation function to analyze consumer emotions in real time and reflect the impact of those emotions on price fluctuations in predictions. For example, it analyzes the tendency for gold prices to rise when consumer anxiety increases. The price fluctuation analysis unit can also analyze corporate emotions in real time and reflect the impact of those emotions on price fluctuations in predictions. For example, it analyzes the tendency for investment to increase and raw material prices to rise when corporate optimism increases. In this way, by analyzing consumer and corporate emotions in real time and reflecting their impact in predictions, more accurate real-time predictions are possible.

[0069] The prediction unit can incorporate the latest market news and event information when predicting price fluctuations in real time. For example, the prediction unit incorporates the latest market news when the generation AI predicts price fluctuations in real time. For example, when predicting oil prices, the prediction unit takes into account the latest policy changes in oil-producing countries. The prediction unit can also incorporate event information. For example, it analyzes the impact of international conferences and policy announcements on prices. By incorporating the latest market news and event information, more accurate real-time predictions are possible.

[0070] The prediction unit can compare past data with current data and detect outliers when predicting price fluctuations in real time. For example, when the generation AI predicts price fluctuations in real time, the prediction unit compares past data with current data and detects outliers. For example, in predicting oil prices, the prediction unit compares past price fluctuation patterns with current prices. The prediction unit can also use statistical outlier detection or anomaly detection using machine learning as anomaly detection methods. For example, the prediction unit detects outliers based on statistical outlier detection and notifies the user. This enables more accurate real-time predictions by comparing past data with current data and detecting outliers.

[0071] The price fluctuation analysis unit can use the emotion estimation function to analyze the emotions of consumers and companies in real time and reflect the impact of those emotions on price fluctuations in predictions. The price fluctuation analysis unit, for example, uses the emotion estimation function to analyze consumer emotions in real time and reflect the impact of those emotions on price fluctuations in predictions. For example, it analyzes the tendency for gold prices to rise when consumer anxiety increases. The price fluctuation analysis unit can also analyze corporate emotions in real time and reflect the impact of those emotions on price fluctuations in predictions. For example, it analyzes the tendency for investment to increase and raw material prices to rise when corporate optimism increases. In this way, by analyzing consumer and corporate emotions in real time and reflecting their impact in predictions, more accurate real-time predictions are possible.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The price prediction system may further include an energy analysis unit that collects energy consumption data and analyzes the impact of energy price fluctuations on raw material prices. For example, the energy analysis unit may collect data on electricity consumption and gas consumption and analyze the impact of these energy prices on the prices of oil and metals. The energy analysis unit may also take into account the spread of renewable energy. For example, it may analyze the impact of the spread of solar power generation and wind power generation on energy prices. By taking energy consumption data into consideration, more accurate price predictions are possible.

[0074] In addition to predicting price fluctuations, the prediction unit can quantify the reliability of the prediction and provide it to the user. For example, when the generation AI predicts price fluctuations, the prediction unit quantifies the reliability of the prediction and provides it to the user. For example, it shows that the reliability of an oil price fluctuation prediction is 80%. The prediction unit can also use confidence intervals and probability values ​​as a method of calculating reliability. For example, the prediction unit calculates the reliability of the prediction based on the confidence interval and provides it to the user. This allows the user to understand the reliability of the prediction and make smarter decisions.

[0075] The price fluctuation analysis unit can also take into account external factors such as weather data and political events when predicting price fluctuations. For example, the price fluctuation analysis unit takes weather data into account when the generation AI predicts price fluctuations. For example, when predicting the price of agricultural products, it analyzes the impact of weather fluctuations on harvest yields. The price fluctuation analysis unit can also take political events into account. For example, when predicting oil prices, it analyzes the impact of policy changes in oil-producing countries on prices. By taking external factors into account, more accurate price fluctuation predictions are possible.

[0076] The price fluctuation analysis unit can use the emotion estimation function to analyze the emotions of consumers and companies and reflect the impact of emotional fluctuations on prices in predictions. For example, the price fluctuation analysis unit can use the emotion estimation function to analyze consumer emotions and reflect the impact of those emotions on raw material prices in predictions. For example, it can analyze the tendency for gold prices to rise when consumer anxiety increases. The price fluctuation analysis unit can also analyze corporate emotions and reflect the impact of those emotions on prices in predictions. For example, it can analyze the tendency for investment to increase and raw material prices to rise when corporate optimism increases. This allows for more accurate price fluctuation predictions by taking into account the impact of emotional fluctuations on prices.

[0077] The prediction unit can take into account the product's life cycle and seasonality when predicting price increases. For example, the prediction unit takes into account the product's life cycle when the generation AI predicts price increases. For example, it predicts price fluctuations according to the introduction, growth, maturity, and decline periods of a new product. The prediction unit can also take into account the seasonality of a product. For example, it predicts price fluctuations of toys during the Christmas season. In this way, taking into account the product's life cycle and seasonality makes it possible to make more accurate price increase predictions.

[0078] The prediction unit can take into account the pricing strategies and market share of competitors when predicting product price increases. For example, the prediction unit takes into account the pricing strategies of competitors when the generation AI predicts product price increases. For example, it analyzes the price change history of major competitors to predict future price trends. The prediction unit can also take market share into account. For example, if a particular product has a high market share, it analyzes the tendency for that product's price to rise. This allows for more accurate price increase predictions by taking into account the pricing strategies and market share of competitors.

[0079] The price fluctuation analysis unit can use the emotion estimation function to analyze consumer purchasing willingness and reflect the impact of that willingness on product price increases in the prediction. For example, the price fluctuation analysis unit can use the emotion estimation function to analyze consumer purchasing willingness and reflect the impact of that willingness on product price increases in the prediction. For example, it can analyze the tendency for the price of a specific product to rise when consumer purchasing willingness increases. The price fluctuation analysis unit can also analyze the purchasing willingness of companies and reflect the impact of that willingness on product price increases in the prediction. For example, it can analyze the tendency for raw material prices to rise when corporate purchasing willingness increases. In this way, by taking consumer purchasing willingness into consideration, more accurate price increase predictions are possible.

[0080] The provision unit can take into account the consumer's purchasing history and preferences when providing a predicted price to the consumer. For example, the provision unit takes into account the consumer's purchasing history when the generation AI provides a predicted price to the consumer. For example, the provision unit predicts future prices based on price fluctuations of products purchased in the past. The provision unit can also take into account the consumer's preferences. For example, the provision unit provides a predicted price based on preferences for specific brands or product categories. In this way, by taking into account the consumer's purchasing history and preferences, it is possible to provide a more personalized predicted price.

[0081] The provision unit can take into account regional price differences when providing predicted prices to consumers. For example, the provision unit takes into account regional price differences when the generation AI provides predicted prices to consumers. For example, the provision unit makes predictions based on price differences between urban and rural areas. The provision unit can also take into account logistics costs. For example, the provision unit provides predicted prices based on regional logistics costs. This allows for more accurate predicted prices to be provided by taking into account regional price differences.

[0082] The providing unit can use the emotion estimation function to analyze consumer emotions and provide a price forecast based on the emotions. For example, the providing unit can use the emotion estimation function to analyze consumer emotions and provide a price forecast based on the emotions. For example, the providing unit can analyze the tendency for the price of a specific product to rise as consumer anxiety increases. The providing unit can also provide a price forecast based on consumer emotions. For example, the providing unit can analyze the tendency for prices to stabilize as consumer optimism increases. In this way, by taking consumer emotions into consideration, a more accurate price forecast can be provided.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The price data collection unit collects price data for various raw materials. For example, it collects price data for oil, metals, agricultural products, etc. The price data collection unit can also collect data from public databases on the Internet and market research reports. Furthermore, the price data collection unit obtains data in real time through APIs. Step 2: The price fluctuation analysis unit analyzes the collected price data. For example, the price fluctuation analysis unit extracts price fluctuation patterns using statistical analysis. The price fluctuation analysis unit can also build a price fluctuation prediction model using a machine learning algorithm. For example, the price fluctuation analysis unit performs regression analysis based on past price data to predict future prices. Step 3: The forecasting unit predicts price fluctuations based on the analyzed data. For example, the forecasting unit predicts future price fluctuations using time series forecasting. The forecasting unit can also generate price fluctuation scenarios using a generation AI. For example, the forecasting unit uses a generation AI to analyze market trends and make price predictions based on multiple scenarios. Step 4: The provider provides the predicted price fluctuation information to consumers and businesses. For example, the provider provides the forecast information through a website or mobile app. The provider can also link the forecast information to a company's system via an API. For example, the provider displays the forecast information in a dashboard format so that users can understand it intuitively.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 price data collection unit that collects price data of various raw materials; a price fluctuation analysis unit that analyzes the price data collected by the price data collection unit; a prediction unit that predicts price fluctuations based on the data analyzed by the price fluctuation analysis unit; a providing unit that provides consumers and businesses with the price fluctuation information predicted by the prediction unit. A system characterized by:

2. The price fluctuation analysis unit Taking external factors such as weather data and political events into account when forecasting price movements 2. The system of claim 1.

3. The prediction unit When forecasting product price increases, consider product life cycles and seasonality.

2. The system of claim 1.

4. The providing unit Considering a consumer's purchasing history and preferences when providing a predicted price to that consumer 2. The system of claim 1.

5. The price fluctuation analysis unit Using a sentiment estimation function, the sentiment of the consumer or the company is analyzed, and the impact of fluctuations in sentiment on prices is reflected in the forecast.

2. The system of claim 1.

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