system

Abstract

The system according to the embodiment aims to provide the most suitable accommodation price according to demand in real time. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. The analysis unit analyzes the data collected by the collection unit. The prediction unit performs demand forecasting based on the analysis results obtained by the analysis unit. The provision unit provides the accommodation prices calculated by the prediction 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 has the problem that accommodation rates are set roughly, making it difficult to set optimal prices according to demand.

[0005] The system according to the embodiment aims to provide the most suitable accommodation price according to demand in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. The analysis unit analyzes the data collected by the collection unit. The prediction unit performs demand prediction based on the analysis results obtained by the analysis unit. The provision unit provides the accommodation prices calculated by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide the most suitable accommodation price according to demand in real time. [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) An accommodation price optimization system according to an embodiment of the present invention collects accommodation facility past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. AI analyzes this data to predict demand and provide optimal accommodation prices in real time. For example, the accommodation price optimization system collects past reservation data to understand reservation trends for specific days of the week and seasons. Next, it monitors current reservation status in real time to accurately grasp current demand. It also collects and compares prices of competing facilities online. This allows it to understand price trends of competing facilities and reflect them in its own pricing. It also predicts demand fluctuations by taking into account external factors such as seasons and events. Based on this data, AI predicts demand and calculates optimal accommodation prices. This allows accommodation facilities to always offer optimal prices and maximize occupancy rates and sales. The accommodation price optimization system thus optimizes accommodation facility pricing in real time to maximize occupancy rates and sales. For example, lowering prices during off-seasons can attract more reservations and improve occupancy rates. Raising prices during peak seasons can maximize sales and increase revenue.

[0029] An accommodation price optimization system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects past reservation data for accommodation facilities, current reservation status, prices of competing facilities, and external factors such as seasons and events. For example, the collection unit retrieves past reservation data from a database to understand reservation trends for specific days of the week or seasons. The collection unit can also monitor current reservation status in real time and collect data such as the current number of reservations, room availability, and last-minute cancellations. The collection unit can also collect and compare prices of competing facilities from the Internet. For example, the collection unit can collect prices of other accommodation facilities in the same area using web scraping technology. The collection unit can also collect data on local events, holidays, weather, and other external factors such as seasons and events. The analysis unit analyzes the data collected by the collection unit and performs demand forecasting. For example, the analysis unit performs demand forecasting based on past data and current conditions using statistical analysis and machine learning algorithms. For example, the analysis unit can predict future demand using time series analysis. The analysis unit can also predict demand fluctuations using regression analysis. The prediction unit calculates the optimal accommodation price based on the analysis results obtained by the analysis unit. For example, the prediction unit can perform dynamic pricing based on demand forecasts to calculate the optimal accommodation price. The prediction unit can also set fixed prices. The provision unit provides the optimal accommodation price calculated by the prediction unit in real time. For example, the provision unit can instantly update the accommodation price through an online reservation system. The provision unit can also present prices at the front desk. As a result, the accommodation price optimization system according to the embodiment can optimize accommodation price setting in real time and maximize occupancy rates and sales.

[0030] The collection unit can collect past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. For example, the collection unit can retrieve past reservation data from a database to understand reservation trends for specific days of the week or seasons. The collection unit can also monitor current reservation status in real time and collect data such as the current number of reservations, room availability, and last-minute cancellations. Furthermore, the collection unit can collect and compare prices of competing facilities from the Internet. For example, the collection unit can collect prices of other accommodation facilities in the same area using web scraping technology. The collection unit can also consider external factors such as seasons and events and collect data such as local events, holidays, and weather. This allows the collection unit to collect a variety of data, enabling more accurate demand forecasts. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data collected from the Internet into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the data collected by the collection unit and perform demand forecasting. The analysis unit performs demand forecasting based on past data and current conditions, for example, using statistical analysis or machine learning algorithms. For example, the analysis unit can predict future demand using time series analysis. The analysis unit can also predict demand fluctuations using regression analysis. Furthermore, the analysis unit can use clustering technology to extract data patterns and use them for demand forecasting. For example, the analysis unit clusters past reservation data and extracts specific patterns to predict demand fluctuations. This allows the analysis unit to analyze the data and improve the accuracy of the demand forecast. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data collected by the collection unit into a generation AI and have the generation AI perform a demand forecast.

[0032] The prediction unit can calculate accommodation rates based on the analysis results obtained by the analysis unit. The prediction unit can, for example, perform dynamic pricing based on demand forecasts to calculate optimal accommodation rates. The prediction unit can also set fixed prices. For example, the prediction unit can adjust rates in real time based on the results of demand forecasts. Furthermore, the prediction unit can combine past data and current data to predict future demand and set rates based on that. For example, the prediction unit can combine past reservation data and current reservation status to predict demand fluctuations and calculate optimal rates. As a result, the prediction unit calculates optimal accommodation rates, improving the accuracy of pricing. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the analysis results obtained by the analysis unit into a generation AI and have the generation AI calculate the optimal accommodation rate.

[0033] The provision unit can provide the accommodation price calculated by the prediction unit in real time. The provision unit can, for example, instantly update the accommodation price through an online reservation system. The provision unit can also present the price at the front desk. For example, the provision unit can update the accommodation price in real time and reflect it on the accommodation facility's website or reservation system. The provision unit can also customize the way the accommodation price is provided. For example, the provision unit can refer to the user's past reservation history and provide the optimal price. The provision unit can also customize the price based on the user's current situation. This allows the provision unit to provide the optimal accommodation price in real time, thereby maximizing the accommodation facility's occupancy rate and sales. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the accommodation price calculated by the prediction unit into a generation AI and cause the generation AI to provide the price in real time.

[0034] The collection unit can extract specific patterns from past reservation data to improve the accuracy of collection. The collection unit, for example, extracts reservation trends for specific days of the week or seasons from past reservation data. The collection unit can also identify periods with high cancellation rates from past reservation data to improve the accuracy of collection. Furthermore, the collection unit can extract patterns of increased reservations for specific events or holidays from past reservation data. This improves the accuracy of collection by extracting specific patterns from past reservation data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past reservation data into a generation AI and cause the generation AI to extract specific patterns.

[0035] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. For example, the collection unit can verify the accuracy of past data to evaluate the reliability of the data. The collection unit can also prioritize collection of data from highly reliable data sources. Furthermore, the collection unit can compare and verify multiple data sources to evaluate the reliability of the data. This allows highly reliable data to be preferentially collected by evaluating the reliability of the data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input the collected data to a generation AI and have the generation AI perform a reliability evaluation of the data.

[0036] The collection unit can apply different collection methods depending on the type of data during collection. For example, the collection unit can use automatic data acquisition from a reservation system to collect reservation data. The collection unit can also use web scraping to collect pricing data for competing facilities. Furthermore, the collection unit can acquire information from an external database via an API to collect seasonal and event data. This enables efficient data collection by applying a collection method depending on the type of data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a collection method depending on the type of data into the generation AI and have the generation AI apply the collection method.

[0037] The collection unit can collect data taking geographical factors into consideration when collecting data. For example, the collection unit can collect local event information based on the location of the accommodation facility. The collection unit can also collect price data for competing facilities around the accommodation facility. Furthermore, the collection unit can collect weather information for the location of the accommodation facility and reflect this in demand forecasts. This allows for region-specific data collection by collecting data taking geographical factors into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data collection based on geographical factors into a generation AI and have the generation AI perform data collection.

[0038] The collection unit can analyze data from social media during collection and collect relevant information. For example, the collection unit can collect reviews about accommodations on social media. The collection unit can also collect local event information on social media. Furthermore, the collection unit can collect and analyze the reputations of competing facilities on social media. This makes it possible to collect relevant information by analyzing data from social media. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected from social media into a generation AI and have the generation AI analyze the data.

[0039] The collection unit can customize the collection method by reflecting past feedback at the time of collection. For example, the collection unit can adjust the type of data to be collected based on past feedback. The collection unit can also adjust the collection frequency based on past feedback. Furthermore, the collection unit can improve the collection method based on past feedback. In this way, the collection method can be customized by reflecting past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input past feedback into the generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. Furthermore, the analysis unit can perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a time series analysis algorithm to reservation data. The analysis unit can also apply a comparative analysis algorithm to price data for competing facilities. Furthermore, the analysis unit can apply a predictive analysis algorithm to seasonal and event data. This enables appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on past analysis results. The analysis unit can also improve the data preprocessing method based on past analysis results. Furthermore, the analysis unit can optimize analysis parameters based on past analysis results. This improves the accuracy of the analysis by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. Furthermore, the analysis unit can prioritize analysis of data that was submitted recently. In this way, by determining the priority of analysis based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of data with low relevance. Furthermore, the analysis unit can moderately analyze data with medium relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the analysis order.

[0045] During analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, the analysis unit can provide detailed analysis results to users with high levels of expertise. The analysis unit can also provide simplified analysis results to users with low levels of expertise. The analysis unit can also provide analysis results with an appropriate level of detail to users with intermediate levels of expertise. This allows the analysis results suited to the user to be provided by adjusting the level of detail of the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0046] The prediction unit can improve the accuracy of prediction by combining past data and current data when making predictions. The prediction unit, for example, combines past reservation data with current reservation status to make predictions. The prediction unit can also make predictions by combining past price data of competing facilities with current price data. Furthermore, the prediction unit can make predictions by combining past seasonal or event data with current data. This improves the accuracy of predictions by combining past data and current data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past data and current data into the generation AI and cause the generation AI to improve the accuracy of predictions.

[0047] The prediction unit can combine different prediction algorithms to obtain optimal prediction results when making predictions. The prediction unit can, for example, combine a time series prediction algorithm and a regression analysis algorithm to make predictions. The prediction unit can also combine a machine learning algorithm and a statistical prediction algorithm to make predictions. Furthermore, the prediction unit can combine a deep learning algorithm and a Bayesian estimation algorithm to make predictions. This allows optimal prediction results to be obtained by combining different prediction algorithms. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input different prediction algorithms into the generation AI and cause the generation AI to obtain optimal prediction results.

[0048] The prediction unit can improve the accuracy of the prediction by taking into account the influence of external factors when making a prediction. The prediction unit, for example, makes a prediction by taking into account weather data. The prediction unit can also make a prediction by taking into account local event information. Furthermore, the prediction unit can make a prediction by taking into account economic indicators. This improves the accuracy of the prediction by taking into account the influence of external factors. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on external factors into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0049] The prediction unit can make predictions taking geographical factors into consideration. For example, the prediction unit can make predictions taking into consideration local event information based on the location of the accommodation facility. The prediction unit can also make predictions taking into consideration traffic conditions around the accommodation facility. Furthermore, the prediction unit can make predictions taking into consideration the popularity of tourist spots in the location of the accommodation facility. This makes it possible to make predictions that are specialized for a region by taking geographical factors into consideration. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input geographical factors into a generation AI and have the generation AI perform the prediction.

[0050] The prediction unit can improve the accuracy of the prediction by referring to related market data when making a prediction. The prediction unit, for example, makes a prediction by referring to market trend data in the accommodation industry. The prediction unit can also make a prediction by referring to market share data of competing facilities. Furthermore, the prediction unit can make a prediction by referring to market forecast data in the tourism industry. As a result, by referring to related market data, the accuracy of the prediction is improved. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input market data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0051] The prediction unit can make predictions taking into account technological maturity when making predictions. The prediction unit can make predictions taking into account, for example, the maturity of the accommodation facility's technical infrastructure. The prediction unit can also make predictions taking into account the technological maturity of competing facilities. Furthermore, the prediction unit can make predictions taking into account technological advances in the accommodation industry as a whole. This makes it possible to make realistic predictions by taking technological maturity into account. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input technological maturity data into a generation AI and have the generation AI perform the prediction.

[0052] When providing the price, the providing unit can provide the optimal price by referring to the user's past reservation history. The providing unit can propose the optimal price based on, for example, the price plans used by the user in the past. The providing unit can also propose a price suitable for a specific period based on the user's past reservation history. Furthermore, the providing unit can analyze the user's past reservation history and propose the most frequently used price plan. This makes it possible to provide the optimal price by referring to the user's past reservation history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past reservation history into a generation AI and have the generation AI provide the optimal price.

[0053] The providing unit can customize the price based on the user's current situation at the time of providing. The providing unit can propose an optimal price based on, for example, the user's current reservation situation. The providing unit can also customize the price based on the user's current stay period. Furthermore, the providing unit can customize the price based on the user's current purpose of stay. In this way, by customizing the price based on the user's current situation, a price appropriate for the user can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation into the generating AI and cause the generating AI to customize the price.

[0054] The providing unit can improve the method of providing fees by reflecting user feedback at the time of providing the fees. For example, the providing unit can improve the interface for displaying fees based on user feedback. The providing unit can also improve the content of the fee plan based on user feedback. Furthermore, the providing unit can adjust the timing of providing fees based on user feedback. This allows the method of providing fees to be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into the generating AI and cause the generating AI to improve the method of providing fees.

[0055] The providing unit can provide prices taking geographical factors into consideration when providing the prices. For example, the providing unit can provide prices taking into consideration the local price market based on the location of the accommodation facility. The providing unit can also provide prices taking into consideration the prices of competing facilities around the accommodation facility. Furthermore, the providing unit can provide prices taking into consideration the popularity of tourist spots in the location of the accommodation facility. This makes it possible to provide prices that are specialized for the region by taking geographical factors into consideration. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input geographical factors into a generating AI and have the generating AI perform price provision.

[0056] The providing unit can analyze data from social media at the time of providing and provide relevant prices. For example, the providing unit can provide prices based on reviews of accommodations on social media. The providing unit can also provide prices based on local event information on social media. Furthermore, the providing unit can provide prices based on the reputation of competing facilities on social media. In this way, relevant prices can be provided by analyzing data from social media. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data collected from social media into a generating AI and have the generating AI analyze the data.

[0057] The providing unit can customize the method of providing fees by reflecting past feedback when providing the fees. For example, the providing unit customizes the interface for displaying fees based on past feedback. The providing unit can also customize the content of the fee plan based on past feedback. Furthermore, the providing unit can customize the timing of providing fees based on past feedback. This allows the method of providing fees to be customized by reflecting past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past feedback into a generation AI and have the generation AI customize the method of providing fees.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. For example, to evaluate data reliability, the accuracy of past data can be verified. Also, data from highly reliable data sources can be collected preferentially. Furthermore, data reliability can be evaluated by comparing and verifying multiple data sources. In this way, by evaluating the reliability of data, highly reliable data can be collected preferentially.

[0060] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. A simplified analysis can be performed on data with low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0061] The prediction unit can improve the accuracy of prediction by combining past data and current data when making predictions. For example, predictions can be made by combining past reservation data with current reservation status. Also, predictions can be made by combining past price data of competing facilities with current price data. Furthermore, predictions can be made by combining past seasonal or event data with current data. Thus, by combining past data and current data, the accuracy of predictions can be improved.

[0062] When providing the information, the providing unit can refer to the user's past reservation history and provide the optimal price. For example, the optimal price can be proposed based on the price plans the user has used in the past. The providing unit can also propose a price suitable for a specific period based on the user's past reservation history. Furthermore, the providing unit can analyze the user's past reservation history and propose the most frequently used price plan. This makes it possible to provide the optimal price by referring to the user's past reservation history.

[0063] The provider can provide prices taking geographical factors into consideration when providing the prices. For example, the provider can provide prices taking into consideration the local market price based on the location of the accommodation facility. The provider can also provide prices taking into consideration the prices of competing facilities around the accommodation facility. Furthermore, the provider can provide prices taking into consideration the popularity of tourist spots in the location of the accommodation facility. In this way, by taking geographical factors into consideration, it becomes possible to provide prices that are specialized for the area.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects the accommodation facility's past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. For example, the collection unit retrieves past reservation data from a database to understand reservation trends for specific days of the week and seasons. The collection unit can also monitor current reservation status in real time and collect data such as the current number of reservations, room availability, and last-minute cancellations. Furthermore, the collection unit can collect and compare prices of competing facilities from the Internet. For example, the collection unit can collect prices of other accommodation facilities in the same area using web scraping technology. The collection unit can also take into account external factors such as seasons and events and collect data such as local events, holidays, and weather. Step 2: The analysis unit analyzes the data collected by the collection unit and performs demand forecasting. For example, the analysis unit uses statistical analysis or machine learning algorithms to perform demand forecasting based on past data and current conditions. For example, the analysis unit can use time series analysis to predict future demand. The analysis unit can also use regression analysis to predict demand fluctuations. Step 3: The prediction unit calculates the optimal accommodation price based on the analysis results obtained by the analysis unit. For example, the prediction unit can perform dynamic pricing based on demand forecasts to calculate the optimal accommodation price. The prediction unit can also perform fixed pricing. Step 4: The provider provides the optimal accommodation price calculated by the predictor in real time. For example, the provider can instantly update the accommodation price through an online reservation system. The provider can also display the price at the front desk.

[0066] (Example 2) An accommodation price optimization system according to an embodiment of the present invention collects accommodation facility past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. AI analyzes this data to predict demand and provide optimal accommodation prices in real time. For example, the accommodation price optimization system collects past reservation data to understand reservation trends for specific days of the week and seasons. Next, it monitors current reservation status in real time to accurately grasp current demand. It also collects and compares prices of competing facilities online. This allows it to understand price trends of competing facilities and reflect them in its own pricing. It also predicts demand fluctuations by taking into account external factors such as seasons and events. Based on this data, AI predicts demand and calculates optimal accommodation prices. This allows accommodation facilities to always offer optimal prices and maximize occupancy rates and sales. The accommodation price optimization system thus optimizes accommodation facility pricing in real time to maximize occupancy rates and sales. For example, lowering prices during off-seasons can attract more reservations and improve occupancy rates. Raising prices during peak seasons can maximize sales and increase revenue.

[0067] An accommodation price optimization system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects past reservation data for accommodation facilities, current reservation status, prices of competing facilities, and external factors such as seasons and events. For example, the collection unit retrieves past reservation data from a database to understand reservation trends for specific days of the week or seasons. The collection unit can also monitor current reservation status in real time and collect data such as the current number of reservations, room availability, and last-minute cancellations. The collection unit can also collect and compare prices of competing facilities from the Internet. For example, the collection unit can collect prices of other accommodation facilities in the same area using web scraping technology. The collection unit can also collect data on local events, holidays, weather, and other external factors such as seasons and events. The analysis unit analyzes the data collected by the collection unit and performs demand forecasting. For example, the analysis unit performs demand forecasting based on past data and current conditions using statistical analysis and machine learning algorithms. For example, the analysis unit can predict future demand using time series analysis. The analysis unit can also predict demand fluctuations using regression analysis. The prediction unit calculates the optimal accommodation price based on the analysis results obtained by the analysis unit. For example, the prediction unit can perform dynamic pricing based on demand forecasts to calculate the optimal accommodation price. The prediction unit can also set fixed prices. The provision unit provides the optimal accommodation price calculated by the prediction unit in real time. For example, the provision unit can instantly update the accommodation price through an online reservation system. The provision unit can also present prices at the front desk. As a result, the accommodation price optimization system according to the embodiment can optimize accommodation price setting in real time and maximize occupancy rates and sales.

[0068] The collection unit can collect past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. For example, the collection unit can retrieve past reservation data from a database to understand reservation trends for specific days of the week or seasons. The collection unit can also monitor current reservation status in real time and collect data such as the current number of reservations, room availability, and last-minute cancellations. Furthermore, the collection unit can collect and compare prices of competing facilities from the Internet. For example, the collection unit can collect prices of other accommodation facilities in the same area using web scraping technology. The collection unit can also consider external factors such as seasons and events and collect data such as local events, holidays, and weather. This allows the collection unit to collect a variety of data, enabling more accurate demand forecasts. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data collected from the Internet into a generation AI and have the generation AI analyze the data.

[0069] The analysis unit can analyze the data collected by the collection unit and perform demand forecasting. The analysis unit performs demand forecasting based on past data and current conditions, for example, using statistical analysis or machine learning algorithms. For example, the analysis unit can predict future demand using time series analysis. The analysis unit can also predict demand fluctuations using regression analysis. Furthermore, the analysis unit can use clustering technology to extract data patterns and use them for demand forecasting. For example, the analysis unit clusters past reservation data and extracts specific patterns to predict demand fluctuations. This allows the analysis unit to analyze the data and improve the accuracy of the demand forecast. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data collected by the collection unit into a generation AI and have the generation AI perform a demand forecast.

[0070] The prediction unit can calculate accommodation rates based on the analysis results obtained by the analysis unit. The prediction unit can, for example, perform dynamic pricing based on demand forecasts to calculate optimal accommodation rates. The prediction unit can also set fixed prices. For example, the prediction unit can adjust rates in real time based on the results of demand forecasts. Furthermore, the prediction unit can combine past data and current data to predict future demand and set rates based on that. For example, the prediction unit can combine past reservation data and current reservation status to predict demand fluctuations and calculate optimal rates. As a result, the prediction unit calculates optimal accommodation rates, improving the accuracy of pricing. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the analysis results obtained by the analysis unit into a generation AI and have the generation AI calculate the optimal accommodation rate.

[0071] The provision unit can provide the accommodation price calculated by the prediction unit in real time. The provision unit can, for example, instantly update the accommodation price through an online reservation system. The provision unit can also present the price at the front desk. For example, the provision unit can update the accommodation price in real time and reflect it on the accommodation facility's website or reservation system. The provision unit can also customize the way the accommodation price is provided. For example, the provision unit can refer to the user's past reservation history and provide the optimal price. The provision unit can also customize the price based on the user's current situation. This allows the provision unit to provide the optimal accommodation price in real time, thereby maximizing the accommodation facility's occupancy rate and sales. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the accommodation price calculated by the prediction unit into a generation AI and cause the generation AI to provide the price in real time.

[0072] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce 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 to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can shorten the timing of data collection to collect data quickly. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0073] The collection unit can extract specific patterns from past reservation data to improve the accuracy of collection. The collection unit, for example, extracts reservation trends for specific days of the week or seasons from past reservation data. The collection unit can also identify periods with high cancellation rates from past reservation data to improve the accuracy of collection. Furthermore, the collection unit can extract patterns of increased reservations for specific events or holidays from past reservation data. This improves the accuracy of collection by extracting specific patterns from past reservation data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past reservation data into a generation AI and cause the generation AI to extract specific patterns.

[0074] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. For example, the collection unit can verify the accuracy of past data to evaluate the reliability of the data. The collection unit can also prioritize collection of data from highly reliable data sources. Furthermore, the collection unit can compare and verify multiple data sources to evaluate the reliability of the data. This allows highly reliable data to be preferentially collected by evaluating the reliability of the data. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input the collected data to a generation AI and have the generation AI perform a reliability evaluation of the data.

[0075] The collection unit can apply different collection methods depending on the type of data during collection. For example, the collection unit can use automatic data acquisition from a reservation system to collect reservation data. The collection unit can also use web scraping to collect pricing data for competing facilities. Furthermore, the collection unit can acquire information from an external database via an API to collect seasonal and event data. This enables efficient data collection by applying a collection method depending on the type of data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a collection method depending on the type of data into the generation AI and have the generation AI apply the collection method.

[0076] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit can prioritize collecting important data. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. Thus, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0077] The collection unit can collect data taking geographical factors into consideration when collecting data. For example, the collection unit can collect local event information based on the location of the accommodation facility. The collection unit can also collect price data for competing facilities around the accommodation facility. Furthermore, the collection unit can collect weather information for the location of the accommodation facility and reflect this in demand forecasts. This allows for region-specific data collection by collecting data taking geographical factors into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data collection based on geographical factors into a generation AI and have the generation AI perform data collection.

[0078] The collection unit can analyze data from social media during collection and collect relevant information. For example, the collection unit can collect reviews about accommodations on social media. The collection unit can also collect local event information on social media. Furthermore, the collection unit can collect and analyze the reputations of competing facilities on social media. This makes it possible to collect relevant information by analyzing data from social media. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected from social media into a generation AI and have the generation AI analyze the data.

[0079] The collection unit can customize the collection method by reflecting past feedback at the time of collection. For example, the collection unit can adjust the type of data to be collected based on past feedback. The collection unit can also adjust the collection frequency based on past feedback. Furthermore, the collection unit can improve the collection method based on past feedback. In this way, the collection method can be customized by reflecting past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input past feedback into the generation AI and cause the generation AI to customize the collection method.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method. Furthermore, if the user is relaxed, the analysis unit can apply a detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can apply an analysis method that can quickly obtain results. By adjusting the analysis method according to the user's emotions, analysis results suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis method.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. Furthermore, the analysis unit can perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a time series analysis algorithm to reservation data. The analysis unit can also apply a comparative analysis algorithm to price data for competing facilities. Furthermore, the analysis unit can apply a predictive analysis algorithm to seasonal and event data. This enables appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and have the generation AI apply the analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on past analysis results. The analysis unit can also improve the data preprocessing method based on past analysis results. Furthermore, the analysis unit can optimize analysis parameters based on past analysis results. This improves the accuracy of the analysis by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0084] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing data of high importance. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing detailed data. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing data that can be analyzed quickly. Thus, by determining the analysis priority according to the user's emotions, important data can be analyzed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. Furthermore, the analysis unit can prioritize analysis of data that was submitted recently. In this way, by determining the priority of analysis based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the priority of analysis.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of data with low relevance. Furthermore, the analysis unit can moderately analyze data with medium relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the analysis order.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. For example, the analysis unit can provide detailed analysis results to users with high levels of expertise. The analysis unit can also provide simplified analysis results to users with low levels of expertise. The analysis unit can also provide analysis results with an appropriate level of detail to users with intermediate levels of expertise. This allows the analysis results suited to the user to be provided by adjusting the level of detail of the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0088] The prediction unit can estimate the user's emotion and adjust the prediction method based on the estimated user emotion. For example, if the user is stressed, the prediction unit can apply a simple prediction method. Furthermore, if the user is relaxed, the prediction unit can apply a detailed prediction method that can quickly obtain results. By adjusting the prediction method according to the user's emotion, it is possible to provide a prediction result that is suitable for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data into the generation AI and have the generation AI adjust the prediction method.

[0089] The prediction unit can improve the accuracy of prediction by combining past data and current data when making predictions. The prediction unit, for example, combines past reservation data with current reservation status to make predictions. The prediction unit can also make predictions by combining past price data of competing facilities with current price data. Furthermore, the prediction unit can make predictions by combining past seasonal or event data with current data. This improves the accuracy of predictions by combining past data and current data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input past data and current data into the generation AI and cause the generation AI to improve the accuracy of predictions.

[0090] The prediction unit can combine different prediction algorithms to obtain optimal prediction results when making predictions. The prediction unit can, for example, combine a time series prediction algorithm and a regression analysis algorithm to make predictions. The prediction unit can also combine a machine learning algorithm and a statistical prediction algorithm to make predictions. Furthermore, the prediction unit can combine a deep learning algorithm and a Bayesian estimation algorithm to make predictions. This allows optimal prediction results to be obtained by combining different prediction algorithms. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input different prediction algorithms into the generation AI and cause the generation AI to obtain optimal prediction results.

[0091] The prediction unit can improve the accuracy of the prediction by taking into account the influence of external factors when making a prediction. The prediction unit, for example, makes a prediction by taking into account weather data. The prediction unit can also make a prediction by taking into account local event information. Furthermore, the prediction unit can make a prediction by taking into account economic indicators. This improves the accuracy of the prediction by taking into account the influence of external factors. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input data on external factors into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0092] The prediction unit can estimate the user's emotions and determine the priority of predictions based on the estimated user emotions. For example, when the user is stressed, the prediction unit can prioritize predictions with high importance. Furthermore, when the user is relaxed, the prediction unit can prioritize detailed predictions. Furthermore, when the user is in a hurry, the prediction unit can prioritize items that can be predicted quickly. Thus, by determining the priority of predictions according to the user's emotions, important predictions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of predictions.

[0093] The prediction unit can make predictions taking geographical factors into consideration. For example, the prediction unit can make predictions taking into consideration local event information based on the location of the accommodation facility. The prediction unit can also make predictions taking into consideration traffic conditions around the accommodation facility. Furthermore, the prediction unit can make predictions taking into consideration the popularity of tourist spots in the location of the accommodation facility. This makes it possible to make predictions that are specialized for a region by taking geographical factors into consideration. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input geographical factors into a generation AI and have the generation AI perform the prediction.

[0094] The prediction unit can improve the accuracy of the prediction by referring to related market data when making a prediction. The prediction unit, for example, makes a prediction by referring to market trend data in the accommodation industry. The prediction unit can also make a prediction by referring to market share data of competing facilities. Furthermore, the prediction unit can make a prediction by referring to market forecast data in the tourism industry. As a result, by referring to related market data, the accuracy of the prediction is improved. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input market data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0095] The prediction unit can make predictions taking into account technological maturity when making predictions. The prediction unit can make predictions taking into account, for example, the maturity of the accommodation facility's technical infrastructure. The prediction unit can also make predictions taking into account the technological maturity of competing facilities. Furthermore, the prediction unit can make predictions taking into account technological advances in the accommodation industry as a whole. This makes it possible to make realistic predictions by taking technological maturity into account. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input technological maturity data into a generation AI and have the generation AI perform the prediction.

[0096] The providing unit can estimate the user's emotions and adjust the method of displaying the fees based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible fee display. Furthermore, if the user is relaxed, the providing unit can display a detailed fee breakdown. Furthermore, if the user is in a hurry, the providing unit can provide a fee display that focuses on the main points. This enables the fee display method to be adjusted according to the user's emotions, thereby providing a fee display that is appropriate for the user. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the fee display method.

[0097] When providing the price, the providing unit can provide the optimal price by referring to the user's past reservation history. The providing unit can propose the optimal price based on, for example, the price plans used by the user in the past. The providing unit can also propose a price suitable for a specific period based on the user's past reservation history. Furthermore, the providing unit can analyze the user's past reservation history and propose the most frequently used price plan. This makes it possible to provide the optimal price by referring to the user's past reservation history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past reservation history into a generation AI and have the generation AI provide the optimal price.

[0098] The providing unit can customize the price based on the user's current situation at the time of providing. The providing unit can propose an optimal price based on, for example, the user's current reservation situation. The providing unit can also customize the price based on the user's current stay period. Furthermore, the providing unit can customize the price based on the user's current purpose of stay. In this way, by customizing the price based on the user's current situation, a price appropriate for the user can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation into the generating AI and cause the generating AI to customize the price.

[0099] The providing unit can improve the method of providing fees by reflecting user feedback at the time of providing the fees. For example, the providing unit can improve the interface for displaying fees based on user feedback. The providing unit can also improve the content of the fee plan based on user feedback. Furthermore, the providing unit can adjust the timing of providing fees based on user feedback. This allows the method of providing fees to be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback into the generating AI and cause the generating AI to improve the method of providing fees.

[0100] The providing unit can estimate the user's emotions and determine the priority of the fees to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing a fee plan with a high level of importance. Furthermore, if the user is relaxed, the providing unit can prioritize providing a fee plan that can be provided quickly. Thus, by determining the priority of fees according to the user's emotions, important fee plans can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of fees.

[0101] The providing unit can provide prices taking geographical factors into consideration when providing the prices. For example, the providing unit can provide prices taking into consideration the local price market based on the location of the accommodation facility. The providing unit can also provide prices taking into consideration the prices of competing facilities around the accommodation facility. Furthermore, the providing unit can provide prices taking into consideration the popularity of tourist spots in the location of the accommodation facility. This makes it possible to provide prices that are specialized for the region by taking geographical factors into consideration. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input geographical factors into a generating AI and have the generating AI perform price provision.

[0102] The providing unit can analyze data from social media at the time of providing and provide relevant prices. For example, the providing unit can provide prices based on reviews of accommodations on social media. The providing unit can also provide prices based on local event information on social media. Furthermore, the providing unit can provide prices based on the reputation of competing facilities on social media. In this way, relevant prices can be provided by analyzing data from social media. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data collected from social media into a generating AI and have the generating AI analyze the data.

[0103] The providing unit can customize the method of providing fees by reflecting past feedback when providing the fees. For example, the providing unit customizes the interface for displaying fees based on past feedback. The providing unit can also customize the content of the fee plan based on past feedback. Furthermore, the providing unit can customize the timing of providing fees based on past feedback. This allows the method of providing fees to be customized by reflecting past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past feedback into a generation AI and have the generation AI customize the method of providing fees. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and provision 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 uses the camera 42 and communication I / F 44 of the smart device 14 to collect past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs demand forecasting based on the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates optimal accommodation prices based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the calculated accommodation prices in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and provision 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 uses the camera 42 and communication I / F 44 of the smart glasses 214 to collect past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs demand forecasting based on the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates optimal accommodation prices based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the calculated accommodation prices in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the headset terminal 314 to collect past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs demand forecasting based on the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates optimal accommodation prices based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides the calculated accommodation prices in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, and provision 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 uses the camera 42 and communication I / F 44 of the robot 414 to collect past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs demand forecasting based on the collected data. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the optimal accommodation price based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the calculated accommodation price in real time.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, the timing of data collection can be shortened to collect data quickly. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is feeling stressed, a simple analysis method can be applied. If the user is relaxed, a detailed analysis method can be applied. Furthermore, if the user is in a hurry, an analysis method that can obtain results quickly can be applied. In this way, by adjusting the analysis method according to the user's emotions, it is possible to provide analysis results that are suitable for the user.

[0107] The prediction unit can estimate the user's emotions and adjust the prediction method based on the estimated user emotions. For example, if the user is feeling stressed, a simple prediction method can be applied. If the user is relaxed, a detailed prediction method can be applied. Furthermore, if the user is in a hurry, a prediction method that can obtain results quickly can be applied. In this way, by adjusting the prediction method according to the user's emotions, it is possible to provide prediction results that are suitable for the user.

[0108] The providing unit can estimate the user's emotions and adjust the method of displaying the fees to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible fee display can be provided. If the user is relaxed, a detailed fee breakdown can be displayed. Furthermore, if the user is in a hurry, a fee display that focuses on the main points can be provided. In this way, by adjusting the method of displaying fees according to the user's emotions, it is possible to display fees that are appropriate for the user.

[0109] The provision unit can estimate the user's emotions and determine the priority of the fees to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, a fee plan with a high level of importance can be provided preferentially. Also, if the user is relaxed, a fee plan with a high level of detail can be provided preferentially. Furthermore, if the user is in a hurry, a fee plan that can be provided quickly can be provided preferentially. In this way, by determining the priority of fees according to the user's emotions, important fee plans can be provided preferentially.

[0110] The collection unit can evaluate the reliability of data at the time of collection and prioritize collection of highly reliable data. For example, to evaluate data reliability, the accuracy of past data can be verified. Also, data from highly reliable data sources can be collected preferentially. Furthermore, data reliability can be evaluated by comparing and verifying multiple data sources. In this way, by evaluating the reliability of data, highly reliable data can be collected preferentially.

[0111] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. A simplified analysis can be performed on data with low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0112] The prediction unit can improve the accuracy of prediction by combining past data and current data when making predictions. For example, predictions can be made by combining past reservation data with current reservation status. Also, predictions can be made by combining past price data of competing facilities with current price data. Furthermore, predictions can be made by combining past seasonal or event data with current data. Thus, by combining past data and current data, the accuracy of predictions can be improved.

[0113] When providing the information, the providing unit can refer to the user's past reservation history and provide the optimal price. For example, the optimal price can be proposed based on the price plans the user has used in the past. The providing unit can also propose a price suitable for a specific period based on the user's past reservation history. Furthermore, the providing unit can analyze the user's past reservation history and propose the most frequently used price plan. This makes it possible to provide the optimal price by referring to the user's past reservation history.

[0114] The provider can provide prices taking geographical factors into consideration when providing the prices. For example, the provider can provide prices taking into consideration the local market price based on the location of the accommodation facility. The provider can also provide prices taking into consideration the prices of competing facilities around the accommodation facility. Furthermore, the provider can provide prices taking into consideration the popularity of tourist spots in the location of the accommodation facility. In this way, by taking geographical factors into consideration, it becomes possible to provide prices that are specialized for the area.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects the accommodation facility's past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events. For example, the collection unit retrieves past reservation data from a database to understand reservation trends for specific days of the week and seasons. The collection unit can also monitor current reservation status in real time and collect data such as the current number of reservations, room availability, and last-minute cancellations. Furthermore, the collection unit can collect and compare prices of competing facilities from the Internet. For example, the collection unit can collect prices of other accommodation facilities in the same area using web scraping technology. The collection unit can also take into account external factors such as seasons and events and collect data such as local events, holidays, and weather. Step 2: The analysis unit analyzes the data collected by the collection unit and performs demand forecasting. For example, the analysis unit uses statistical analysis or machine learning algorithms to perform demand forecasting based on past data and current conditions. For example, the analysis unit can use time series analysis to predict future demand. The analysis unit can also use regression analysis to predict demand fluctuations. Step 3: The prediction unit calculates the optimal accommodation price based on the analysis results obtained by the analysis unit. For example, the prediction unit can perform dynamic pricing based on demand forecasts to calculate the optimal accommodation price. The prediction unit can also perform fixed pricing. Step 4: The provider provides the optimal accommodation price calculated by the predictor in real time. For example, the provider can instantly update the accommodation price through an online reservation system. The provider can also display the price at the front desk.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 unit that collects past reservation data, current reservation status, prices of competing facilities, and external factors such as seasons and events; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that performs demand prediction based on the analysis results obtained by the analysis unit; a provider unit that provides the accommodation price calculated by the prediction unit. A system characterized by:

2. The collecting unit Collect past booking data, current bookings, competitor prices, and external factors such as seasons and events 2. The system of claim 1.

3. The analysis unit Analyze the data collected by the collection unit and make a demand forecast.

2. The system of claim 1.

4. The prediction unit Calculating accommodation fees based on the analysis results obtained by the analysis unit 2. The system of claim 1.

5. The providing unit Providing the accommodation price calculated by the prediction unit in real time 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Extract specific patterns from past reservation data to improve collection accuracy 2. The system of claim 1.

8. The collecting unit When collecting data, evaluate its reliability and prioritize collection of reliable data.

2. The system of claim 1.

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