system

Abstract

The system according to the embodiment aims to calculate the optimum amount based on past case data. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a calculation unit. The collection unit collects past case data. The analysis unit analyzes the data collected by the collection unit. The calculation unit calculates an amount based on the results of the analysis by the analysis unit.

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 technology does not efficiently calculate the optimal amount based on past case data, and there is room for improvement.

[0005] The system according to the embodiment aims to calculate the optimum amount based on past case data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a calculation unit. The collection unit collects past case data. The analysis unit analyzes the data collected by the collection unit. The calculation unit calculates an amount based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can calculate the optimal amount based on past case data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A price calculation system according to an embodiment of the present invention calculates the optimal price during request aggregation (RA) based on unit prices for past projects from the same vendor, taking current conditions into account. This price calculation system collects past project data, analyzes the collected data, and calculates the optimal price based on the analysis results. For example, the price calculation system collects unit price data for past projects from the same vendor. In doing so, detailed data on past projects is collected to understand fluctuations and trends in unit prices. For example, project data from the past five years is collected and the unit prices for each project are organized chronologically. Next, the price calculation system analyzes the collected unit price data. AI is used for the analysis, and the optimal price is calculated based on the past unit price data, taking into account external data such as current market conditions and economic indicators. For example, the past unit price data is adjusted taking into account current inflation rates, exchange rates, and the balance between supply and demand. Furthermore, the price calculation system calculates the optimal price based on the analysis results. AI comprehensively assesses past unit price data and current market conditions to propose the optimal price. For example, if the past unit price was 1,000 yen, the system calculates that 1,200 yen is the optimal price, taking into account current market conditions. This system makes it possible to set rational prices based on past data. Users can negotiate with vendors using the optimal price calculated by AI as a reference. This enables transactions to be conducted at a fair price, optimizing costs. This allows the price calculation system to calculate the optimal price based on past project data, taking into account the current situation.

[0029] A price calculation system according to an embodiment includes a collection unit, an analysis unit, and a calculation unit. The collection unit collects past project data. Examples of past project data include, but are not limited to, contract data, transaction data, and project data. The collection unit collects project data from the past five years, for example, and organizes the unit prices of each project in chronological order. The collection unit can also collect detailed data on past projects to understand fluctuations and trends in unit prices. For example, the collection unit collects transaction histories, contract details, and project progress statuses of past projects. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, statistical analysis and machine learning algorithms. The analysis unit calculates an optimal price based on the collected unit price data, taking into account external data such as current market conditions and economic indicators. For example, the analysis unit adjusts the past unit price data, taking into account the current inflation rate, exchange rate, and supply-demand balance. The calculation unit calculates a price based on the results of the analysis by the analysis unit. The calculation unit, for example, comprehensively determines past unit price data and current market conditions and proposes the optimal price. For example, if the past unit price was 1,000 yen, the calculation unit calculates that 1,200 yen is the optimal price, taking into account the current market conditions. In this way, the price calculation system according to the embodiment can calculate the optimal price based on past project data and taking into account the current conditions.

[0030] The price calculation system includes a collection unit that collects market conditions and economic indicators. The collection unit collects, for example, current market conditions and economic indicators. Market conditions include, for example, stock prices, the balance between supply and demand, and competitor trends, but are not limited to these examples. Economic indicators include, for example, GDP, unemployment rate, and inflation rate, but are not limited to these examples. The collection unit collects, for example, public databases on the Internet and statistical data from government agencies. The collection unit can also use AI to collect market conditions and economic indicators in real time. For example, the collection unit uses AI to automatically collect the latest stock price data and economic indicators and provide them to the analysis unit. This allows the price calculation system to calculate more accurate prices by taking into account the current market conditions and economic indicators.

[0031] The collection unit collects detailed data on past projects and can grasp fluctuations and trends in unit prices. The collection unit, for example, collects detailed data on past projects. Detailed data includes, but is not limited to, transaction history, contract details, and project progress. The collection unit collects project data from, for example, the past five years and organizes the unit prices of each project in chronological order. The collection unit can also analyze time-series data and calculate moving averages to grasp fluctuations and trends in unit prices. For example, the collection unit grasps fluctuation patterns and trends in unit prices based on past project data and provides the data to the analysis unit. As a result, the collection unit can calculate more accurate amounts by collecting detailed data on past projects and grasping fluctuations and trends in unit prices.

[0032] The analysis unit can analyze the collected unit price data and calculate the amount by taking into account market conditions and economic indicators. The analysis unit, for example, analyzes the collected unit price data. The unit price data includes, for example, past transaction prices and contract unit prices, but is not limited to these examples. The analysis unit can analyze the collected unit price data using, for example, statistical analysis or a machine learning algorithm. The analysis unit can also collect external data and use it for analysis to calculate the amount by taking into account current market conditions and economic indicators. Market conditions include, for example, stock prices, the balance between supply and demand, and competitor trends, but are not limited to these examples. Economic indicators include, for example, GDP, the unemployment rate, and the inflation rate, but are not limited to these examples. The analysis unit adjusts the past unit price data by taking into account, for example, the current inflation rate, exchange rate, and the balance between supply and demand. In this way, the analysis unit can calculate a more accurate amount by taking into account current market conditions and economic indicators in the analysis.

[0033] The calculation unit can comprehensively judge past unit price data and current market conditions and propose a price. The calculation unit, for example, comprehensively judges past unit price data and current market conditions and proposes a price. Past unit price data includes, for example, past transaction prices and contract unit prices, but is not limited to these examples. Market conditions include, for example, stock prices, the balance between supply and demand, and the trends of competitors, but are not limited to these examples. The calculation unit, for example, calculates the optimal price based on past unit price data and taking into account current market conditions. For example, if the past unit price was 1,000 yen, the calculation unit calculates that 1,200 yen is the optimal price taking into account current market conditions. As a result, the calculation unit can comprehensively judge past unit price data and current market conditions and propose the optimal price.

[0034] The collection unit can filter past project data based on the type and size of the project when collecting the data. For example, the collection unit filters past project data based on the type and size of the project when collecting the data. Project types include, but are not limited to, industry and project size. Project size includes, but is not limited to, budget size and team size. For example, the collection unit collects data only for large-scale projects and performs detailed analysis. The collection unit can also prioritize the collection of project data related to a specific industry. Furthermore, the collection unit can filter and collect only past projects that meet specific conditions. In this way, the collection unit can collect more relevant data by filtering based on the type and size of the project.

[0035] When collecting data, the collection unit can select data taking into account the vendor's past performance and reliability. For example, when collecting data, the collection unit selects data taking into account the vendor's past performance and reliability. The vendor's past performance includes, but is not limited to, delivery compliance rate and quality evaluation. Reliability includes, but is not limited to, past transaction history and customer feedback. For example, the collection unit preferentially collects data from highly rated vendors. The collection unit can also select data from vendors who have had few problems in the past. Furthermore, the collection unit can perform detailed analysis based on the data from highly reliable vendors. In this way, the collection unit can collect highly reliable data by selecting data taking into account the vendor's past performance and reliability.

[0036] The collection unit can prioritize collecting highly relevant data by taking geographical factors into consideration when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking geographical factors into consideration when collecting data. Geographical factors include, but are not limited to, regional economic conditions and geographical characteristics. For example, the collection unit prioritizes collecting data related to a specific region. The collection unit can also prioritize collecting data on projects that are geographically close. Furthermore, the collection unit can select data by taking regional market conditions into consideration. In this way, the collection unit can collect more relevant data by collecting data by taking geographical factors into consideration.

[0037] The collection unit may analyze social media and news articles during data collection to collect related data. For example, the collection unit may analyze social media and news articles during data collection to collect related data. Social media includes, but is not limited to, X (formerly Twitter®), Facebook®, Instagram®, etc. News articles include, but are not limited to, online news, newspaper articles, blog articles, etc. For example, the collection unit may analyze social media posts to collect related case data. The collection unit may also analyze news articles to collect data related to current market conditions. Furthermore, the collection unit may integrate both social media and news articles to collect comprehensive data. This allows the collection unit to collect data by analyzing social media and news articles, thereby enabling more comprehensive data collection.

[0038] The analysis unit can evaluate the reliability and accuracy of past data during analysis and reflect the evaluation in the analysis results. For example, the analysis unit can evaluate the reliability and accuracy of past data during analysis and reflect the evaluation in the analysis results. Data reliability includes, for example, the source of the data and the consistency of the data, but is not limited to these examples. Data accuracy includes, for example, the error range of the data and the frequency of data updates, but is not limited to these examples. For example, the analysis unit can evaluate the reliability of past data and prioritize the use of highly reliable data. The analysis unit can also evaluate the accuracy of the data and use highly accurate data for analysis. Furthermore, the analysis unit can select optimal data taking both reliability and accuracy into consideration. In this way, the analysis unit can obtain more accurate analysis results by evaluating the reliability and accuracy of past data.

[0039] The analysis unit can apply different analysis algorithms during analysis and select the optimal result. For example, the analysis unit can apply different analysis algorithms during analysis and select the optimal result. Examples of analysis algorithms include, but are not limited to, regression analysis, clustering, and deep learning. For example, the analysis unit can apply multiple analysis algorithms and select the most accurate result. The analysis unit can also compare results from different algorithms and select the optimal result. Furthermore, the analysis unit can evaluate the performance of the algorithms and select the optimal result. In this way, the analysis unit can obtain the optimal analysis result by applying different analysis algorithms.

[0040] The analysis unit can determine the analysis priority based on the time of data submission during analysis. For example, the analysis unit determines the analysis priority based on the time of data submission during analysis. The time of data submission includes, but is not limited to, for example, a submission deadline and the freshness of the data. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission. This allows the analysis unit to determine the analysis priority based on the time of data submission, thereby enabling efficient analysis.

[0041] The analysis unit can improve the accuracy of the analysis by referring to related market data and economic indicators during analysis. The analysis unit can improve the accuracy of the analysis by, for example, referring to related market data and economic indicators during analysis. Market data includes, for example, stock price data, sales data, consumer trend data, etc., but is not limited to these examples. Economic indicators include, for example, GDP, unemployment rate, inflation rate, etc., but are not limited to these examples. The analysis unit can, for example, refer to market data and reflect it in the analysis results. The analysis unit can also improve the accuracy of the analysis by taking economic indicators into account. Furthermore, the analysis unit can integrate both market data and economic indicators to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to related market data and economic indicators.

[0042] The calculation unit can adjust the amount by taking into account past price fluctuation patterns when calculating. For example, the calculation unit adjusts the amount by taking into account past price fluctuation patterns when calculating. Price fluctuation patterns include, but are not limited to, analysis of time-series data and calculation of moving averages. For example, the calculation unit analyzes past price fluctuation patterns and adjusts the current amount. The calculation unit can also calculate an optimal amount by taking into account price fluctuation trends. Furthermore, the calculation unit can predict future prices based on past price fluctuation data and adjust the amount. In this way, the calculation unit can calculate a more accurate amount by taking into account past price fluctuation patterns.

[0043] The calculation unit can simulate different scenarios during calculation and select the optimal amount. For example, the calculation unit simulates different scenarios during calculation and selects the optimal amount. Scenarios include, but are not limited to, worst-case, best-case, and average-case scenarios. For example, the calculation unit simulates multiple scenarios and selects the optimal amount. The calculation unit can also compare the results of each scenario and select the optimal amount. Furthermore, the calculation unit can propose the optimal amount based on the results of the simulation. In this way, the calculation unit can select the optimal amount by simulating different scenarios.

[0044] The calculation unit can calculate the optimal amount by taking geographical factors into consideration during calculation. For example, the calculation unit calculates the optimal amount by taking geographical factors into consideration during calculation. Geographical factors include, but are not limited to, regional economic conditions and geographical characteristics. For example, the calculation unit calculates the optimal amount for each region by taking geographical factors into consideration. The calculation unit can also adjust the amount by taking regional market conditions into consideration. Furthermore, the calculation unit can propose the optimal amount based on geographical data. In this way, the calculation unit can calculate the optimal amount for each region by taking geographical factors into consideration.

[0045] The calculation unit can adjust the amount by referring to related industry trends and benchmark data when calculating. For example, the calculation unit adjusts the amount by referring to related industry trends and benchmark data when calculating. Industry trends include, but are not limited to, industry reports and market research data. Benchmark data include, but are not limited to, data on competitors and industry standard data. For example, the calculation unit adjusts the amount by referring to industry trends. The calculation unit can also calculate an optimal amount based on the benchmark data. Furthermore, the calculation unit can adjust the amount by taking both industry trends and benchmark data into consideration. In this way, the calculation unit can calculate a more accurate amount by referring to related industry trends and benchmark data.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] When collecting data, the collection unit can also select data taking into account the user's past behavioral history. For example, it can prioritize collection of data sources that have been frequently used in the past. It can also prioritize collection of data sources that the user has given high ratings to in the past. Furthermore, it can analyze the user's past behavioral patterns and collect the most relevant data. In this way, the collection unit can collect more relevant data by selecting data taking into account the user's past behavioral history.

[0048] The analysis department can also integrate data from different data sources during analysis to improve the accuracy of the analysis. For example, internal data and external data can be integrated to perform analysis. Data from different industries can also be integrated to perform a more comprehensive analysis. Furthermore, real-time data and historical data can be integrated to more accurately reflect the current market situation. This allows the analysis department to improve the accuracy of analysis by integrating data from different data sources.

[0049] The calculation unit can also adjust the amount by taking into account data from different time periods during calculation. For example, it can compare data from peak times with data from off-peak times to calculate the optimal amount. It can also calculate an amount that reflects seasonal fluctuations by taking into account seasonal data. Furthermore, it can calculate a special amount by taking into account data from specific events or campaign periods. In this way, the calculation unit can calculate a more accurate amount by taking into account data from different time periods.

[0050] When collecting data, the collection unit can also integrate and collect data from different devices. For example, data can be collected from devices such as smartphones, tablets, and PCs. Data can also be integrated and collected from IoT devices. Furthermore, data can be integrated and collected from cloud services. This allows the collection unit to integrate data from different devices and collect more comprehensive data.

[0051] The calculation unit can also adjust the amount by taking into account data of different currencies when calculating. For example, the calculation unit can calculate the amount in a different currency by taking into account exchange rates. The calculation unit can also calculate an optimal amount by taking into account the economic conditions of different countries. Furthermore, the calculation unit can adjust the amount by taking into account risks in international transactions. In this way, the calculation unit can calculate a more accurate amount by taking into account data of different currencies.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The collection department collects past project data. Past project data includes contract data, transaction data, project data, etc. The collection department collects project data from the past five years, for example, and organizes the unit prices of each project in chronological order. The collection department can also collect detailed data on past projects to understand fluctuations and trends in unit prices. For example, the collection department collects transaction history, contract details, project progress, etc. for past projects. Step 2: The analysis unit analyzes the data collected by the collection unit. Statistical analysis and machine learning algorithms are used for the analysis. The analysis unit calculates the optimal price based on the collected unit price data, taking into account external data such as current market conditions and economic indicators. For example, the analysis unit adjusts past unit price data, taking into account factors such as the current inflation rate, exchange rate, and the balance between supply and demand. Step 3: The calculation unit calculates the price based on the results of the analysis by the analysis unit. The calculation unit comprehensively considers past unit price data and current market conditions and proposes the optimal price. For example, if the past unit price was 1,000 yen, the calculation unit will calculate that 1,200 yen is the optimal price, taking into account the current market conditions.

[0054] (Example 2) A price calculation system according to an embodiment of the present invention calculates the optimal price during request aggregation (RA) based on unit prices for past projects from the same vendor, taking current conditions into account. This price calculation system collects past project data, analyzes the collected data, and calculates the optimal price based on the analysis results. For example, the price calculation system collects unit price data for past projects from the same vendor. In doing so, detailed data on past projects is collected to understand fluctuations and trends in unit prices. For example, project data from the past five years is collected and the unit prices for each project are organized chronologically. Next, the price calculation system analyzes the collected unit price data. AI is used for the analysis, and the optimal price is calculated based on the past unit price data, taking into account external data such as current market conditions and economic indicators. For example, the past unit price data is adjusted taking into account current inflation rates, exchange rates, and the balance between supply and demand. Furthermore, the price calculation system calculates the optimal price based on the analysis results. AI comprehensively assesses past unit price data and current market conditions to propose the optimal price. For example, if the past unit price was 1,000 yen, the system calculates that 1,200 yen is the optimal price, taking into account current market conditions. This system makes it possible to set rational prices based on past data. Users can negotiate with vendors using the optimal price calculated by AI as a reference. This enables transactions to be conducted at a fair price, optimizing costs. This allows the price calculation system to calculate the optimal price based on past project data, taking into account the current situation.

[0055] A price calculation system according to an embodiment includes a collection unit, an analysis unit, and a calculation unit. The collection unit collects past project data. Examples of past project data include, but are not limited to, contract data, transaction data, and project data. The collection unit collects project data from the past five years, for example, and organizes the unit prices of each project in chronological order. The collection unit can also collect detailed data on past projects to understand fluctuations and trends in unit prices. For example, the collection unit collects transaction histories, contract details, and project progress statuses of past projects. The analysis unit analyzes the data collected by the collection unit. Examples of the analysis include, but are not limited to, statistical analysis and machine learning algorithms. The analysis unit calculates an optimal price based on the collected unit price data, taking into account external data such as current market conditions and economic indicators. For example, the analysis unit adjusts the past unit price data, taking into account the current inflation rate, exchange rate, and supply-demand balance. The calculation unit calculates a price based on the results of the analysis by the analysis unit. The calculation unit, for example, comprehensively determines past unit price data and current market conditions and proposes the optimal price. For example, if the past unit price was 1,000 yen, the calculation unit calculates that 1,200 yen is the optimal price, taking into account the current market conditions. In this way, the price calculation system according to the embodiment can calculate the optimal price based on past project data and taking into account the current conditions.

[0056] The price calculation system includes a collection unit that collects market conditions and economic indicators. The collection unit collects, for example, current market conditions and economic indicators. Market conditions include, for example, stock prices, the balance between supply and demand, and competitor trends, but are not limited to these examples. Economic indicators include, for example, GDP, unemployment rate, and inflation rate, but are not limited to these examples. The collection unit collects, for example, public databases on the Internet and statistical data from government agencies. The collection unit can also use AI to collect market conditions and economic indicators in real time. For example, the collection unit uses AI to automatically collect the latest stock price data and economic indicators and provide them to the analysis unit. This allows the price calculation system to calculate more accurate prices by taking into account the current market conditions and economic indicators.

[0057] The collection unit collects detailed data on past projects and can grasp fluctuations and trends in unit prices. The collection unit, for example, collects detailed data on past projects. Detailed data includes, but is not limited to, transaction history, contract details, and project progress. The collection unit collects project data from, for example, the past five years and organizes the unit prices of each project in chronological order. The collection unit can also analyze time-series data and calculate moving averages to grasp fluctuations and trends in unit prices. For example, the collection unit grasps fluctuation patterns and trends in unit prices based on past project data and provides the data to the analysis unit. As a result, the collection unit can calculate more accurate amounts by collecting detailed data on past projects and grasping fluctuations and trends in unit prices.

[0058] The analysis unit can analyze the collected unit price data and calculate the amount by taking into account market conditions and economic indicators. The analysis unit, for example, analyzes the collected unit price data. The unit price data includes, for example, past transaction prices and contract unit prices, but is not limited to these examples. The analysis unit can analyze the collected unit price data using, for example, statistical analysis or a machine learning algorithm. The analysis unit can also collect external data and use it for analysis to calculate the amount by taking into account current market conditions and economic indicators. Market conditions include, for example, stock prices, the balance between supply and demand, and competitor trends, but are not limited to these examples. Economic indicators include, for example, GDP, the unemployment rate, and the inflation rate, but are not limited to these examples. The analysis unit adjusts the past unit price data by taking into account, for example, the current inflation rate, exchange rate, and the balance between supply and demand. In this way, the analysis unit can calculate a more accurate amount by taking into account current market conditions and economic indicators in the analysis.

[0059] The calculation unit can comprehensively judge past unit price data and current market conditions and propose a price. The calculation unit, for example, comprehensively judges past unit price data and current market conditions and proposes a price. Past unit price data includes, for example, past transaction prices and contract unit prices, but is not limited to these examples. Market conditions include, for example, stock prices, the balance between supply and demand, and the trends of competitors, but are not limited to these examples. The calculation unit, for example, calculates the optimal price based on past unit price data and taking into account current market conditions. For example, if the past unit price was 1,000 yen, the calculation unit calculates that 1,200 yen is the optimal price taking into account current market conditions. As a result, the calculation unit can comprehensively judge past unit price data and current market conditions and propose the optimal price.

[0060] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The collection unit estimates the user's emotions using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection and collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can quickly collect data and immediately proceed to analysis. In this way, the collection unit can reduce the burden on the user by adjusting the timing of data collection according to the user's emotions.

[0061] The collection unit can filter past project data based on the type and size of the project when collecting the data. For example, the collection unit filters past project data based on the type and size of the project when collecting the data. Project types include, but are not limited to, industry and project size. Project size includes, but is not limited to, budget size and team size. For example, the collection unit collects data only for large-scale projects and performs detailed analysis. The collection unit can also prioritize the collection of project data related to a specific industry. Furthermore, the collection unit can filter and collect only past projects that meet specific conditions. In this way, the collection unit can collect more relevant data by filtering based on the type and size of the project.

[0062] When collecting data, the collection unit can select data taking into account the vendor's past performance and reliability. For example, when collecting data, the collection unit selects data taking into account the vendor's past performance and reliability. The vendor's past performance includes, but is not limited to, delivery compliance rate and quality evaluation. Reliability includes, but is not limited to, past transaction history and customer feedback. For example, the collection unit preferentially collects data from highly rated vendors. The collection unit can also select data from vendors who have had few problems in the past. Furthermore, the collection unit can perform detailed analysis based on the data from highly reliable vendors. In this way, the collection unit can collect highly reliable data by selecting data taking into account the vendor's past performance and reliability.

[0063] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the emotions. Examples of the user's emotions include, but are not limited to, stress, relaxation, and hurry. The collection unit estimates the user's emotions using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the collection unit can prioritize collecting only important data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize data that can be collected quickly. In this way, the collection unit can reduce the burden on the user by determining the priority of data to be collected according to the user's emotions.

[0064] The collection unit can prioritize collecting highly relevant data by taking geographical factors into consideration when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking geographical factors into consideration when collecting data. Geographical factors include, but are not limited to, regional economic conditions and geographical characteristics. For example, the collection unit prioritizes collecting data related to a specific region. The collection unit can also prioritize collecting data on projects that are geographically close. Furthermore, the collection unit can select data by taking regional market conditions into consideration. In this way, the collection unit can collect more relevant data by collecting data by taking geographical factors into consideration.

[0065] The collection unit may analyze social media and news articles during data collection to collect related data. For example, the collection unit may analyze social media and news articles during data collection to collect related data. Social media includes, but is not limited to, X (formerly Twitter), Facebook, and Instagram. News articles include, but are not limited to, online news, newspaper articles, and blog articles. For example, the collection unit may analyze social media posts to collect related case data. The collection unit may also analyze news articles to collect data related to current market conditions. Furthermore, the collection unit may integrate both social media and news articles to collect comprehensive data. This allows the collection unit to collect data by analyzing social media and news articles, thereby enabling more comprehensive data collection.

[0066] The analysis unit can estimate the user's emotions and adjust the analysis method based on the emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis method based on the emotions. The user's emotions include, but are not limited to, stress, relaxation, and hurry. The analysis unit estimates the user's emotions using, for example, facial expression recognition, voice analysis, survey results, etc. For example, the analysis unit uses a simple analysis method when the user is feeling stressed. The analysis unit can also use a detailed analysis method when the user is relaxed. Furthermore, the analysis unit can also use an analysis method that can quickly obtain results when the user is in a hurry. In this way, the analysis unit can adjust the analysis method according to the user's emotions, thereby reducing the burden on the user.

[0067] The analysis unit can evaluate the reliability and accuracy of past data during analysis and reflect the evaluation in the analysis results. For example, the analysis unit can evaluate the reliability and accuracy of past data during analysis and reflect the evaluation in the analysis results. Data reliability includes, for example, the source of the data and the consistency of the data, but is not limited to these examples. Data accuracy includes, for example, the error range of the data and the frequency of data updates, but is not limited to these examples. For example, the analysis unit can evaluate the reliability of past data and prioritize the use of highly reliable data. The analysis unit can also evaluate the accuracy of the data and use highly accurate data for analysis. Furthermore, the analysis unit can select optimal data taking both reliability and accuracy into consideration. In this way, the analysis unit can obtain more accurate analysis results by evaluating the reliability and accuracy of past data.

[0068] The analysis unit can apply different analysis algorithms during analysis and select the optimal result. For example, the analysis unit can apply different analysis algorithms during analysis and select the optimal result. Examples of analysis algorithms include, but are not limited to, regression analysis, clustering, and deep learning. For example, the analysis unit can apply multiple analysis algorithms and select the most accurate result. The analysis unit can also compare results from different algorithms and select the optimal result. Furthermore, the analysis unit can evaluate the performance of the algorithms and select the optimal result. In this way, the analysis unit can obtain the optimal analysis result by applying different analysis algorithms.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotions. Examples of the user's emotions include, but are not limited to, stress, relaxation, and hurry. The analysis unit estimates the user's emotions using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, the analysis unit can reduce the burden on the user by adjusting the display method of the analysis results according to the user's emotions.

[0070] The analysis unit can determine the analysis priority based on the time of data submission during analysis. For example, the analysis unit determines the analysis priority based on the time of data submission during analysis. The time of data submission includes, but is not limited to, for example, a submission deadline and the freshness of the data. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission. This allows the analysis unit to determine the analysis priority based on the time of data submission, thereby enabling efficient analysis.

[0071] The analysis unit can improve the accuracy of the analysis by referring to related market data and economic indicators during analysis. The analysis unit can improve the accuracy of the analysis by, for example, referring to related market data and economic indicators during analysis. Market data includes, for example, stock price data, sales data, consumer trend data, etc., but is not limited to these examples. Economic indicators include, for example, GDP, unemployment rate, inflation rate, etc., but are not limited to these examples. The analysis unit can, for example, refer to market data and reflect it in the analysis results. The analysis unit can also improve the accuracy of the analysis by taking economic indicators into account. Furthermore, the analysis unit can integrate both market data and economic indicators to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to related market data and economic indicators.

[0072] The calculation unit can estimate the user's emotions and adjust the range of amounts to be calculated based on the emotions. For example, the calculation unit estimates the user's emotions and adjusts the range of amounts to be calculated based on the emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The calculation unit estimates the user's emotions using, for example, facial expression recognition, voice analysis, survey results, etc. For example, the calculation unit presents a conservative range of amounts when the user is feeling stressed. Furthermore, the calculation unit can present a broad range of amounts when the user is relaxed. Furthermore, the calculation unit can present a range of amounts that can be calculated quickly when the user is in a hurry. In this way, the calculation unit can reduce the burden on the user by adjusting the range of amounts to be calculated according to the user's emotions.

[0073] The calculation unit can adjust the amount by taking into account past price fluctuation patterns when calculating. For example, the calculation unit adjusts the amount by taking into account past price fluctuation patterns when calculating. Price fluctuation patterns include, but are not limited to, analysis of time-series data and calculation of moving averages. For example, the calculation unit analyzes past price fluctuation patterns and adjusts the current amount. The calculation unit can also calculate an optimal amount by taking into account price fluctuation trends. Furthermore, the calculation unit can predict future prices based on past price fluctuation data and adjust the amount. In this way, the calculation unit can calculate a more accurate amount by taking into account past price fluctuation patterns.

[0074] The calculation unit can simulate different scenarios during calculation and select the optimal amount. For example, the calculation unit simulates different scenarios during calculation and selects the optimal amount. Scenarios include, but are not limited to, worst-case, best-case, and average-case scenarios. For example, the calculation unit simulates multiple scenarios and selects the optimal amount. The calculation unit can also compare the results of each scenario and select the optimal amount. Furthermore, the calculation unit can propose the optimal amount based on the results of the simulation. In this way, the calculation unit can select the optimal amount by simulating different scenarios.

[0075] The calculation unit can estimate the user's emotion and adjust the display method of the calculation result based on the emotion. For example, the calculation unit can estimate the user's emotion and adjust the display method of the calculation result based on the emotion. The user's emotion includes, but is not limited to, stress, relaxation, and hurry. The calculation unit estimates the user's emotion using, for example, facial expression recognition, voice analysis, survey results, etc. For example, if the user is feeling stressed, the calculation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the calculation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the calculation unit can provide a display method that focuses on the main points. In this way, the calculation unit can reduce the burden on the user by adjusting the display method of the calculation result according to the user's emotion.

[0076] The calculation unit can calculate the optimal amount by taking geographical factors into consideration during calculation. For example, the calculation unit calculates the optimal amount by taking geographical factors into consideration during calculation. Geographical factors include, but are not limited to, regional economic conditions and geographical characteristics. For example, the calculation unit calculates the optimal amount for each region by taking geographical factors into consideration. The calculation unit can also adjust the amount by taking regional market conditions into consideration. Furthermore, the calculation unit can propose the optimal amount based on geographical data. In this way, the calculation unit can calculate the optimal amount for each region by taking geographical factors into consideration.

[0077] The calculation unit can adjust the amount by referring to related industry trends and benchmark data when calculating. For example, the calculation unit adjusts the amount by referring to related industry trends and benchmark data when calculating. Industry trends include, but are not limited to, industry reports and market research data. Benchmark data include, but are not limited to, data on competitors and industry standard data. For example, the calculation unit adjusts the amount by referring to industry trends. The calculation unit can also calculate an optimal amount based on the benchmark data. Furthermore, the calculation unit can adjust the amount by taking both industry trends and benchmark data into consideration. In this way, the calculation unit can calculate a more accurate amount by referring to related industry trends and benchmark data. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and calculation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects past project data using the control unit 46A of the smart device 14, and collects market conditions and economic indicators using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12, and the calculation unit calculates the optimal amount using the specific processing unit 290 of the data processing device 12. Some or all of the collection unit, analysis unit, and calculation unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and calculation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects past case data by the control unit 46A of the smart glasses 214, and collects market conditions and economic indicators by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12, for example, and the calculation unit calculates the optimal amount by the specific processing unit 290 of the data processing device 12, for example. Some or all of the collection unit, analysis unit, and calculation unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and calculation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects past case data by the control unit 46A of the headset type terminal 314, and collects market conditions and economic indicators by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12, for example, and the calculation unit calculates the optimal amount by the specific processing unit 290 of the data processing device 12, for example. Some or all of the collection unit, analysis unit, and calculation unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and calculation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects past case data by the control unit 46A of the robot 414, and collects market conditions and economic indicators by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12, for example, and the calculation unit calculates the optimal amount by the specific processing unit 290 of the data processing device 12, for example. Some or all of the collection unit, analysis unit, and calculation unit may be realized, for example, by the control unit 46A of the robot 414.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The analysis unit can also estimate the user's emotions and adjust the analysis priority based on those emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis of important data and provide results quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and provide more accurate results. Furthermore, if the user is in a hurry, the analysis unit can perform an analysis quickly and provide results immediately. In this way, the analysis unit can reduce the burden on the user by adjusting the analysis priority according to the user's emotions.

[0080] When collecting data, the collection unit can also select data taking into account the user's past behavioral history. For example, it can prioritize collection of data sources that have been frequently used in the past. It can also prioritize collection of data sources that the user has given high ratings to in the past. Furthermore, it can analyze the user's past behavioral patterns and collect the most relevant data. In this way, the collection unit can collect more relevant data by selecting data taking into account the user's past behavioral history.

[0081] The calculation unit can also estimate the user's emotions and adjust the accuracy of the calculated amount based on the emotions. For example, if the user is feeling stressed, the calculation unit can present a conservative amount to minimize risk. If the user is relaxed, the calculation unit can present a wide range of amounts to provide flexible options. Furthermore, if the user is in a hurry, the calculation unit can present an amount that can be calculated quickly to support immediate decision-making. In this way, the calculation unit can reduce the burden on the user by adjusting the accuracy of the calculated amount according to the user's emotions.

[0082] The analysis department can also integrate data from different data sources during analysis to improve the accuracy of the analysis. For example, internal data and external data can be integrated to perform analysis. Data from different industries can also be integrated to perform a more comprehensive analysis. Furthermore, real-time data and historical data can be integrated to more accurately reflect the current market situation. This allows the analysis department to improve the accuracy of analysis by integrating data from different data sources.

[0083] The collection unit can also estimate the user's emotions and adjust the data collection method based on the emotions. For example, if the user is feeling stressed, the collection unit uses an automated data collection method to reduce the burden on the user. Alternatively, if the user is relaxed, the collection unit can prompt manual data collection to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can quickly collect data and immediately move on to analysis. In this way, the collection unit can reduce the burden on the user by adjusting the data collection method according to the user's emotions.

[0084] The calculation unit can also adjust the amount by taking into account data from different time periods during calculation. For example, it can compare data from peak times with data from off-peak times to calculate the optimal amount. It can also calculate an amount that reflects seasonal fluctuations by taking into account seasonal data. Furthermore, it can calculate a special amount by taking into account data from specific events or campaign periods. In this way, the calculation unit can calculate a more accurate amount by taking into account data from different time periods.

[0085] The analysis unit can also estimate the user's emotions and adjust the notification method of the analysis results based on those emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible notification method. If the user is relaxed, the analysis unit can provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a quick notification method that focuses on the main points. In this way, the analysis unit can reduce the burden on the user by adjusting the notification method of the analysis results according to the user's emotions.

[0086] When collecting data, the collection unit can also integrate and collect data from different devices. For example, data can be collected from devices such as smartphones, tablets, and PCs. Data can also be integrated and collected from IoT devices. Furthermore, data can be integrated and collected from cloud services. This allows the collection unit to integrate data from different devices and collect more comprehensive data.

[0087] The calculation unit can also adjust the amount by taking into account data of different currencies when calculating. For example, the calculation unit can calculate the amount in a different currency by taking into account exchange rates. The calculation unit can also calculate an optimal amount by taking into account the economic conditions of different countries. Furthermore, the calculation unit can adjust the amount by taking into account risks in international transactions. In this way, the calculation unit can calculate a more accurate amount by taking into account data of different currencies.

[0088] During analysis, the analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is feeling stressed, the analysis unit can perform a simple analysis and provide results quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and provide more accurate results. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide results immediately. In this way, the analysis unit can reduce the burden on the user by adjusting the depth of analysis according to the user's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The collection department collects past project data. Past project data includes contract data, transaction data, project data, etc. The collection department collects project data from the past five years, for example, and organizes the unit prices of each project in chronological order. The collection department can also collect detailed data on past projects to understand fluctuations and trends in unit prices. For example, the collection department collects transaction history, contract details, project progress, etc. for past projects. Step 2: The analysis unit analyzes the data collected by the collection unit. Statistical analysis and machine learning algorithms are used for the analysis. The analysis unit calculates the optimal price based on the collected unit price data, taking into account external data such as current market conditions and economic indicators. For example, the analysis unit adjusts past unit price data, taking into account factors such as the current inflation rate, exchange rate, and the balance between supply and demand. Step 3: The calculation unit calculates the price based on the results of the analysis by the analysis unit. The calculation unit comprehensively considers past unit price data and current market conditions and proposes the optimal price. For example, if the past unit price was 1,000 yen, the calculation unit will calculate that 1,200 yen is the optimal price, taking into account the current market conditions.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0093] 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] 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.

[0107] 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.

[0108] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects past case data; an analysis unit that analyzes the data collected by the collection unit; a calculation unit that calculates an amount based on the result of the analysis by the analysis unit; Equipped with A system characterized by:

2. Equipping the department with a collection department to collect market conditions and economic indicators The system of claim 1 .

3. The collecting unit Collect detailed data on past projects to understand fluctuations and trends in unit prices The system of claim 1 .

4. The analysis unit Analyze the collected unit price data and calculate the amount taking into account market conditions and economic indicators. The system of claim 1 .

5. The calculation unit We propose prices based on a comprehensive assessment of past unit price data and current market conditions. The system of claim 1 .

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on those emotions The system of claim 1 .

7. The collecting unit When collecting historical case data, filter it based on case type and size. The system of claim 1 .

8. The collecting unit When collecting data, filter the data based on vendors' past performance and reliability. The system of claim 1 .

9. The collecting unit Inferring user sentiment and prioritizing data collection based on that sentiment The system of claim 1 .

Images