system

Abstract

The system according to this embodiment aims to reduce food waste by dynamically optimizing prices based on inventory status and statistical information. [Solution] The system according to the embodiment comprises a collection unit, an optimization unit, and an automation unit. The collection unit monitors inventory status in real time and collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. The optimization unit dynamically optimizes prices based on the data collected by the collection unit. The automation unit automates the price changes optimized by the optimization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that price is not sufficiently dynamically optimized based on information such as inventory status, expiration date, weather, expected number of customers and time of store visit, etc., to reduce food loss.

[0005] The system according to the embodiment aims to dynamically optimize the price based on the inventory status and statistical information and reduce food loss.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an optimization unit, and an automation unit. The data collection unit monitors inventory status in real time and collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. The optimization unit dynamically optimizes prices based on the data collected by the data collection unit. The automation unit automates the price changes optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can dynamically optimize prices based on inventory status and statistical information, thereby reducing food waste. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The price optimization system according to an embodiment of the present invention is a system driven by an AI agent. This price optimization system monitors the store's inventory status in real time and dynamically optimizes prices based on statistical information such as food expiration dates, weather, and the expected number and time of customer visits. The price optimization system aims to reduce food waste by making products nearing their expiration date seem more attractive. Since price changes are automated by the AI ​​agent, the burden on store staff is significantly reduced. For example, the price optimization system includes a collection unit that monitors inventory status in real time. The collection unit collects statistical information such as expiration dates, weather, and the expected number and time of customer visits. Based on the data collected by the collection unit, the optimization unit dynamically optimizes prices. The optimization unit includes AI processing. Furthermore, it includes an automation unit that automates price changes. The automation unit may also include AI processing. As a result, the price optimization system can significantly reduce the burden on store staff by monitoring the store's inventory status in real time, dynamically optimizing prices based on collected data, and automating the process. This allows the price optimization system to monitor store inventory in real time, dynamically optimize prices based on collected data, and automate the process, significantly reducing the burden on store staff.

[0029] The price optimization system according to this embodiment comprises a data collection unit, an optimization unit, and an automation unit. The data collection unit monitors inventory status in real time. For example, the data collection unit monitors inventory status using sensors within the store. The data collection unit also collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. The data collection unit includes a discount unit that sets discounted prices for products nearing their expiration date. For example, it applies a 10% discount to products with an expiration date of less than one week. The data collection unit also includes an adjustment unit that adjusts prices according to weather and customer visit forecasts. The adjustment unit dynamically adjusts prices based on weather and customer visit forecasts. For example, it lowers prices to encourage customer visits when the weather is bad. Furthermore, the data collection unit includes a linking unit that acquires information by linking multiple data sources. The linking unit acquires and integrates information from multiple data sources such as POS data, sensor data, and external APIs. The optimization unit dynamically optimizes prices based on the data collected by the data collection unit. The optimization unit includes AI processing. The optimization unit, for example, analyzes collected data and calculates the optimal price. The optimization unit dynamically optimizes the price using an AI algorithm. For example, the optimization unit forecasts demand based on collected data and adjusts the price. The automation unit automates the price changes optimized by the optimization unit. The automation unit may also include AI processing. The automation unit, for example, automatically executes price changes. The automation unit automates price changes to reduce the burden on store staff. As a result, the price optimization system according to this embodiment can significantly reduce the burden on store staff by monitoring inventory status in real time, dynamically optimizing prices based on collected data, and automating the process.

[0030] The data collection unit monitors inventory status in real time. For example, the data collection unit monitors inventory status using sensors within the store. Specifically, sensors installed on shelves and in warehouses detect the quantity and location of products and transmit this information to a central database. This allows for a constantly up-to-date understanding of inventory status within the store. The data collection unit also collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. For example, it includes a discount unit that sets discounted prices for products nearing their expiration date. Specifically, it applies a 10% discount to products with an expiration date of one week or less. The data collection unit also includes an adjustment unit that adjusts prices according to weather and customer visit forecasts. The adjustment unit dynamically adjusts prices based on weather and customer visit forecasts. For example, if the weather is bad, prices are lowered to encourage customers to visit. Furthermore, the data collection unit includes an integration unit that acquires information by linking multiple data sources. The integration unit acquires and integrates information from multiple data sources such as POS data, sensor data, and external APIs. This allows the data collection unit to centrally manage diverse data from both inside and outside the store, providing foundational data for price optimization. For example, it can obtain sales history and customer purchasing patterns from POS data, grasp inventory movements in real time from sensor data, and obtain weather information and local event information from external APIs. Integrating this data enables more accurate price optimization.

[0031] The optimization unit dynamically optimizes prices based on data collected by the data collection unit. The optimization unit includes AI processing. Specifically, it analyzes the collected data and calculates the optimal price. For example, it uses an AI algorithm to forecast demand and adjust prices. The AI ​​forecasts future demand based on past sales data, current inventory levels, weather information, and customer visit forecasts. For example, if past data shows that a particular product sells well under specific weather conditions, it adjusts the price based on that information. The AI ​​also uses machine learning techniques to learn the effects of pricing and reflects this in future pricing. This allows the optimization unit to always provide the optimal price and maximize sales and profits. Furthermore, the optimization unit can simulate multiple scenarios and select the most effective pricing strategy. For example, it tries different pricing scenarios and simulates sales and inventory movements in each scenario to select the most effective pricing strategy. The optimization unit can also continuously review pricing based on real-time updated data. This allows the optimization unit to always perform price optimization based on the latest information and maximize store sales and profits.

[0032] The automation unit automates price changes optimized by the optimization unit. The automation unit may also include AI processing. Specifically, it automatically executes price changes. For example, it automatically reflects the optimal price calculated by the optimization unit into the store's POS system. This eliminates the need for store staff to manually change prices. The automation unit can also optimize the timing of price changes. For example, it can maximize sales by raising prices during peak hours when the number of customers is expected to be high. It can also lower prices to quickly dispose of excess inventory. Furthermore, the automation unit can monitor the effects of price changes and readjust prices as needed. For example, it can analyze sales data after a price change to evaluate whether the pricing was appropriate. If the pricing was not effective, it can adjust the price again. In this way, the automation unit can always provide the optimal price and maximize store sales and profits. The automation unit also automates price changes to reduce the burden on store staff. This allows store staff to focus on other important tasks, such as customer service and product display. This will improve the overall operational efficiency of the store and increase customer satisfaction.

[0033] The collection unit includes a discount unit that sets discounted prices for products nearing their expiration date. The discount unit sets discounted prices for products nearing their expiration date. For example, it applies a 10% discount to products with an expiration date of one week or less. The discount unit can also apply a 20% discount to products with an expiration date of three days or less. Furthermore, the discount unit can apply a 30% discount to products with an expiration date of one day or less. This aims to reduce food waste by setting discounted prices for products nearing their expiration date. Some or all of the above processing in the discount unit may be performed using AI, for example, or without AI. For example, the discount unit can input products nearing their expiration date into the AI ​​and have the AI ​​set the discounted prices.

[0034] The data collection unit includes an adjustment unit that adjusts prices according to weather and customer traffic forecasts. The adjustment unit dynamically adjusts prices based on weather and customer traffic forecasts. For example, if the weather is bad, it lowers prices to encourage customers to visit. The adjustment unit can also raise prices if the weather is good. Furthermore, the adjustment unit can raise prices if there is a high expectation of customers. In addition, the adjustment unit can lower prices if there is a low expectation of customers. This allows for pricing that responds to demand by adjusting prices according to weather and customer traffic forecasts. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input weather data and customer traffic forecast data into AI and have the AI ​​perform price adjustments.

[0035] The data collection unit includes a linking unit that acquires information by linking multiple data sources. The linking unit acquires and integrates information from multiple data sources such as POS data, sensor data, and external APIs. For example, the linking unit uses POS data to understand the sales status of products. The linking unit can also use sensor data to monitor inventory status in stores. Furthermore, the linking unit can use external APIs to acquire weather data and customer visit forecast data. By linking multiple data sources to acquire information, more accurate data collection becomes possible. Some or all of the above processing in the linking unit may be performed using AI, for example, or without AI. For example, the linking unit can input data acquired from multiple data sources into AI and have the AI ​​perform data integration.

[0036] The data collection unit analyzes historical inventory data and selects the optimal data acquisition method. For example, the data collection unit may concentrate data acquisition during specific time periods based on historical inventory data. The data collection unit can also dynamically adjust the frequency of data acquisition based on historical inventory data. Furthermore, the data collection unit can analyze historical inventory data and prioritize data acquisition for specific products. This allows for the selection of the optimal data acquisition method by analyzing historical inventory data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical inventory data into AI and have the AI ​​select the optimal data acquisition method.

[0037] The data collection unit filters inventory data based on the current store conditions and specific events. For example, the data collection unit adjusts the frequency of inventory data acquisition according to the store's congestion level. The data collection unit can also prioritize the acquisition of inventory data for products related to a specific event, for example, when such an event is taking place. Furthermore, the data collection unit can adjust the timing of inventory data acquisition according to the store's business hours. This allows for efficient acquisition of necessary data by filtering inventory data based on the store's current conditions and specific events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input store congestion data into the AI ​​and have the AI ​​adjust the frequency of inventory data acquisition.

[0038] The data collection unit prioritizes acquiring highly relevant data when obtaining inventory data, taking into account the geographical location of the stores. For example, the data collection unit prioritizes acquiring inventory data that corresponds to local demand based on the store's location. The data collection unit can also, for example, refer to inventory data of nearby competing stores, taking into account the store's location. Furthermore, the data collection unit can prioritize acquiring inventory data for products popular in a particular area, based on the store's location. This enables inventory management that corresponds to local demand by prioritizing the acquisition of highly relevant data while considering the store's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input store location data into AI and have the AI ​​acquire highly relevant data.

[0039] The data collection unit analyzes social media activity and acquires relevant data when acquiring inventory data. For example, the data collection unit prioritizes acquiring inventory data for products that are trending on social media. The data collection unit can also analyze social media trends and acquire inventory data for related products. Furthermore, the data collection unit can adjust the acquisition of inventory data based on user reviews on social media. This enables inventory management that is in line with trends by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into AI and have the AI ​​acquire relevant data.

[0040] The optimization unit adjusts the level of detail of the optimization based on the importance of the products when optimizing prices. For example, the optimization unit performs detailed price optimization for important products. The optimization unit can also simplify price optimization for less important products. Furthermore, the optimization unit can adjust the frequency of optimization according to the importance of the products. This allows for detailed pricing of important products by adjusting the level of detail of the optimization based on the importance of the products. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input product importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the optimization.

[0041] The optimization unit applies different optimization algorithms depending on the product category when optimizing prices. For example, the optimization unit may apply a rapid price optimization algorithm to fresh foods. For example, it may also apply a stable price optimization algorithm to daily necessities. Furthermore, the optimization unit may apply a seasonal price optimization algorithm to seasonal products. By applying different optimization algorithms depending on the product category, it becomes possible to set prices appropriate for each category. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input product category data into the AI ​​and have the AI ​​execute the application of the optimization algorithm.

[0042] The optimization unit determines the optimization priority based on the product's sales period when optimizing prices. For example, the optimization unit prioritizes price optimization for products with upcoming sales periods. The optimization unit may also postpone price optimization for products with later sales periods. Furthermore, the optimization unit can adjust the price optimization priority according to the sales period of seasonal products. This allows for pricing tailored to the sales period by determining the optimization priority based on the product's sales period. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input product sales period data into AI and have the AI ​​determine the optimization priority.

[0043] The optimization unit adjusts the optimization order based on the relevance of products during price optimization. For example, the optimization unit prioritizes the price optimization of highly relevant products. The optimization unit may also postpone the price optimization of less relevant products. Furthermore, the optimization unit can dynamically adjust the order of price optimization according to the relevance of products. This allows for prioritization of pricing for highly relevant products by adjusting the optimization order based on product relevance. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input product relevance data into the AI ​​and have the AI ​​perform the adjustment of the optimization order.

[0044] The automation unit selects the optimal price change method by referring to past price change data when a price is changed. For example, the automation unit selects the optimal price change method based on past price change data. The automation unit can also, for example, analyze past price change data and apply an effective price change method. Furthermore, the automation unit can adjust the timing of price changes by referring to past price change data. In this way, the optimal price change method can be selected by referring to past price change data. Some or all of the above processes in the automation unit may be performed using AI, for example, or without using AI. For example, the automation unit can input past price change data into AI and have the AI ​​perform the selection of the optimal change method.

[0045] The automation unit customizes the method of price change based on the current store situation when a price is changed. For example, the automation unit customizes the method of price change according to the store's congestion level. The automation unit can also adjust the timing of price changes based on the store's business hours, for example. Furthermore, the automation unit can optimize the method of price change according to the store's inventory level. This allows for more appropriate price changes by customizing the method of price change based on the store's current situation. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input store congestion data into the AI ​​and have the AI ​​perform the customization of the method of price change.

[0046] The automated unit selects the optimal price change method when changing prices, taking into account the store's geographical location. For example, the automated unit changes prices to meet local demand based on the store's location. The automated unit can also, for example, refer to the prices of nearby competing stores, taking into account the store's location. Furthermore, the automated unit can prioritize price changes for products popular in a particular area, based on the store's location. This makes it possible to change prices to meet local demand by selecting the optimal price change method while considering the store's geographical location. Some or all of the above processing in the automated unit may be performed using AI, for example, or without AI. For example, the automated unit can input store location data into AI and have the AI ​​select the optimal price change method.

[0047] The automation unit analyzes social media activity and proposes methods for price changes when making adjustments. For example, the automation unit prioritizes price changes for products that are trending on social media. The automation unit can also analyze social media trends and propose price changes for related products. Furthermore, the automation unit can adjust the methods of price changes based on user reviews on social media. This allows for price changes that align with trends by analyzing social media activity. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input social media data into AI and have the AI ​​propose methods for making changes.

[0048] The discount unit selects the optimal discount method by referring to past discount data when setting discounts. For example, the discount unit selects the optimal discount method based on past discount data. The discount unit can also, for example, analyze past discount data and apply an effective discount method. Furthermore, the discount unit can adjust the timing of discounts by referring to past discount data. In this way, the optimal discount method can be selected by referring to past discount data. Some or all of the above processing in the discount unit may be performed using AI, for example, or without using AI. For example, the discount unit can input past discount data into AI and have the AI ​​select the optimal discount method.

[0049] The discounting unit weights discounts based on the product's sales period when setting discounts. For example, the discounting unit prioritizes discounts for products with upcoming sales periods. It can also postpone discounts for products with later sales periods. Furthermore, the discounting unit can adjust the discount weighting according to the sales period of seasonal products. This allows for discount settings tailored to the sales period by weighting discounts based on the product's sales period. Some or all of the above processing in the discounting unit may be performed using AI, for example, or without AI. For example, the discounting unit can input product sales period data into AI and have the AI ​​perform the discount weighting.

[0050] The adjustment unit selects the optimal adjustment method by referring to past adjustment data when adjusting prices. For example, the adjustment unit selects the optimal price adjustment method based on past adjustment data. The adjustment unit can also, for example, analyze past adjustment data and apply an effective price adjustment method. Furthermore, the adjustment unit can adjust the timing of price adjustments by referring to past adjustment data. In this way, the optimal price adjustment method can be selected by referring to past adjustment data. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input past adjustment data into AI and have the AI ​​select the optimal adjustment method.

[0051] The adjustment unit weights the price adjustment based on weather and customer traffic forecasts. For example, if the weather is bad, the adjustment unit will lighten the weight of the price adjustment. For example, if the weather is good, the adjustment unit can also increase the weight of the price adjustment. Furthermore, if there is a high forecast of customer traffic, the adjustment unit can also increase the weight of the price adjustment. This allows for price adjustments that respond to demand by weighting the adjustment based on weather and customer traffic forecasts. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input weather data and customer traffic forecast data into AI and have the AI ​​perform the weighting of the adjustment.

[0052] The integration unit selects the optimal integration method by referring to past integration data when integrating data. For example, the integration unit selects the optimal data integration method based on past integration data. The integration unit can also, for example, analyze past integration data and apply an effective data integration method. Furthermore, the integration unit can adjust the timing of data integration by referring to past integration data. In this way, the optimal data integration method can be selected by referring to past integration data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input past integration data into AI and have the AI ​​select the optimal integration method.

[0053] The integration unit considers multiple data sources when integrating data and assigns weights to the integration. For example, the integration unit integrates information from multiple data sources and assigns weights. The integration unit can also adjust the integration weights based on the reliability of the data sources. Furthermore, the integration unit can dynamically adjust the integration weights according to the importance of the data sources. This enables more accurate data integration by considering multiple data sources when assigning integration weights. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data obtained from multiple data sources into AI and have the AI ​​perform the integration weighting.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze users' purchase history and adjust the timing of inventory data acquisition based on past purchase patterns. For example, if a user tends to purchase a particular product on a specific day or time, the data collection unit can increase the frequency of inventory data acquisition during that time. Similarly, if a user tends to purchase a particular product during a specific season, the data collection unit can increase the frequency of inventory data acquisition during that season. Furthermore, based on user purchase history, the data collection unit can prioritize the acquisition of inventory data for products that tend to remain unsold. This allows for more efficient inventory management by adjusting the timing of inventory data acquisition based on user purchase history.

[0056] The optimization unit can predict product sales and dynamically adjust prices based on those predictions. For example, based on past sales data, if a particular product tends to sell well on a specific day or time, the optimization unit can raise the price during that time. It can also raise the price of a particular product during a specific season if that season is known to sell well. Furthermore, if a particular product tends to remain unsold, the optimization unit can lower its price. This allows for the maximization of sales by dynamically adjusting prices based on sales predictions.

[0057] The automated system can adjust the timing of price changes by taking into account store congestion. For example, if the store is busy, the automated system can delay the price change. Conversely, if the store is not busy, the automated system can speed up the price change. Furthermore, if a specific event is taking place, the automated system can prioritize price changes for products related to that event. This allows for more appropriate price changes by adjusting the timing of price changes based on store congestion and specific events.

[0058] The discount section can weight discounts based on the product's sales period. For example, it can prioritize discounts on products with upcoming sales periods. It can also postpone discounts on products with later sales periods. Furthermore, the discount section can adjust the discount weighting according to the sales period of seasonal products. This allows for discount settings tailored to the sales period by weighting discounts based on the product's sales period.

[0059] The integration unit can weight data integration by considering multiple data sources. For example, it can integrate information from multiple data sources and assign weights to them. Furthermore, the integration unit can adjust the integration weights based on the reliability of the data sources. It can also dynamically adjust the integration weights according to the importance of the data sources. This allows for more accurate data integration by considering multiple data sources when weighting integrations.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit monitors inventory status in real time. For example, it monitors inventory status using sensors within the store. The data collection unit also collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. The data collection unit includes a discount unit that sets discounted prices for products nearing their expiration date, and an adjustment unit that adjusts prices according to weather and customer visit forecasts. Furthermore, the data collection unit includes an integration unit that acquires information by linking multiple data sources. Step 2: The optimization unit dynamically optimizes the price based on the data collected by the data collection unit. The optimization unit includes AI processing to analyze the collected data and calculate the optimal price. For example, it may perform demand forecasting and adjust the price accordingly. Step 3: The automation unit automates the price changes optimized by the optimization unit. The automation unit may include AI processing to automatically execute price changes. This reduces the burden on store staff.

[0062] (Example of form 2) The price optimization system according to an embodiment of the present invention is a system driven by an AI agent. This price optimization system monitors the store's inventory status in real time and dynamically optimizes prices based on statistical information such as food expiration dates, weather, and the expected number and time of customer visits. The price optimization system aims to reduce food waste by making products nearing their expiration date seem more attractive. Since price changes are automated by the AI ​​agent, the burden on store staff is significantly reduced. For example, the price optimization system includes a collection unit that monitors inventory status in real time. The collection unit collects statistical information such as expiration dates, weather, and the expected number and time of customer visits. Based on the data collected by the collection unit, the optimization unit dynamically optimizes prices. The optimization unit includes AI processing. Furthermore, it includes an automation unit that automates price changes. The automation unit may also include AI processing. As a result, the price optimization system can significantly reduce the burden on store staff by monitoring the store's inventory status in real time, dynamically optimizing prices based on collected data, and automating the process. This allows the price optimization system to monitor store inventory in real time, dynamically optimize prices based on collected data, and automate the process, significantly reducing the burden on store staff.

[0063] The price optimization system according to this embodiment comprises a data collection unit, an optimization unit, and an automation unit. The data collection unit monitors inventory status in real time. For example, the data collection unit monitors inventory status using sensors within the store. The data collection unit also collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. The data collection unit includes a discount unit that sets discounted prices for products nearing their expiration date. For example, it applies a 10% discount to products with an expiration date of less than one week. The data collection unit also includes an adjustment unit that adjusts prices according to weather and customer visit forecasts. The adjustment unit dynamically adjusts prices based on weather and customer visit forecasts. For example, it lowers prices to encourage customer visits when the weather is bad. Furthermore, the data collection unit includes a linking unit that acquires information by linking multiple data sources. The linking unit acquires and integrates information from multiple data sources such as POS data, sensor data, and external APIs. The optimization unit dynamically optimizes prices based on the data collected by the data collection unit. The optimization unit includes AI processing. The optimization unit, for example, analyzes collected data and calculates the optimal price. The optimization unit dynamically optimizes the price using an AI algorithm. For example, the optimization unit forecasts demand based on collected data and adjusts the price. The automation unit automates the price changes optimized by the optimization unit. The automation unit may also include AI processing. The automation unit, for example, automatically executes price changes. The automation unit automates price changes to reduce the burden on store staff. As a result, the price optimization system according to this embodiment can significantly reduce the burden on store staff by monitoring inventory status in real time, dynamically optimizing prices based on collected data, and automating the process.

[0064] The data collection unit monitors inventory status in real time. For example, the data collection unit monitors inventory status using sensors within the store. Specifically, sensors installed on shelves and in warehouses detect the quantity and location of products and transmit this information to a central database. This allows for a constantly up-to-date understanding of inventory status within the store. The data collection unit also collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. For example, it includes a discount unit that sets discounted prices for products nearing their expiration date. Specifically, it applies a 10% discount to products with an expiration date of one week or less. The data collection unit also includes an adjustment unit that adjusts prices according to weather and customer visit forecasts. The adjustment unit dynamically adjusts prices based on weather and customer visit forecasts. For example, if the weather is bad, prices are lowered to encourage customers to visit. Furthermore, the data collection unit includes an integration unit that acquires information by linking multiple data sources. The integration unit acquires and integrates information from multiple data sources such as POS data, sensor data, and external APIs. This allows the data collection unit to centrally manage diverse data from both inside and outside the store, providing foundational data for price optimization. For example, it can obtain sales history and customer purchasing patterns from POS data, grasp inventory movements in real time from sensor data, and obtain weather information and local event information from external APIs. Integrating this data enables more accurate price optimization.

[0065] The optimization unit dynamically optimizes prices based on data collected by the data collection unit. The optimization unit includes AI processing. Specifically, it analyzes the collected data and calculates the optimal price. For example, it uses an AI algorithm to forecast demand and adjust prices. The AI ​​forecasts future demand based on past sales data, current inventory levels, weather information, and customer visit forecasts. For example, if past data shows that a particular product sells well under specific weather conditions, it adjusts the price based on that information. The AI ​​also uses machine learning techniques to learn the effects of pricing and reflects this in future pricing. This allows the optimization unit to always provide the optimal price and maximize sales and profits. Furthermore, the optimization unit can simulate multiple scenarios and select the most effective pricing strategy. For example, it tries different pricing scenarios and simulates sales and inventory movements in each scenario to select the most effective pricing strategy. The optimization unit can also continuously review pricing based on real-time updated data. This allows the optimization unit to always perform price optimization based on the latest information and maximize store sales and profits.

[0066] The automation unit automates price changes optimized by the optimization unit. The automation unit may also include AI processing. Specifically, it automatically executes price changes. For example, it automatically reflects the optimal price calculated by the optimization unit into the store's POS system. This eliminates the need for store staff to manually change prices. The automation unit can also optimize the timing of price changes. For example, it can maximize sales by raising prices during peak hours when the number of customers is expected to be high. It can also lower prices to quickly dispose of excess inventory. Furthermore, the automation unit can monitor the effects of price changes and readjust prices as needed. For example, it can analyze sales data after a price change to evaluate whether the pricing was appropriate. If the pricing was not effective, it can adjust the price again. In this way, the automation unit can always provide the optimal price and maximize store sales and profits. The automation unit also automates price changes to reduce the burden on store staff. This allows store staff to focus on other important tasks, such as customer service and product display. This will improve the overall operational efficiency of the store and increase customer satisfaction.

[0067] The collection unit includes a discount unit that sets discounted prices for products nearing their expiration date. The discount unit sets discounted prices for products nearing their expiration date. For example, it applies a 10% discount to products with an expiration date of one week or less. The discount unit can also apply a 20% discount to products with an expiration date of three days or less. Furthermore, the discount unit can apply a 30% discount to products with an expiration date of one day or less. This aims to reduce food waste by setting discounted prices for products nearing their expiration date. Some or all of the above processing in the discount unit may be performed using AI, for example, or without AI. For example, the discount unit can input products nearing their expiration date into the AI ​​and have the AI ​​set the discounted prices.

[0068] The data collection unit includes an adjustment unit that adjusts prices according to weather and customer traffic forecasts. The adjustment unit dynamically adjusts prices based on weather and customer traffic forecasts. For example, if the weather is bad, it lowers prices to encourage customers to visit. The adjustment unit can also raise prices if the weather is good. Furthermore, the adjustment unit can raise prices if there is a high expectation of customers. In addition, the adjustment unit can lower prices if there is a low expectation of customers. This allows for pricing that responds to demand by adjusting prices according to weather and customer traffic forecasts. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input weather data and customer traffic forecast data into AI and have the AI ​​perform price adjustments.

[0069] The data collection unit includes a linking unit that acquires information by linking multiple data sources. The linking unit acquires and integrates information from multiple data sources such as POS data, sensor data, and external APIs. For example, the linking unit uses POS data to understand the sales status of products. The linking unit can also use sensor data to monitor inventory status in stores. Furthermore, the linking unit can use external APIs to acquire weather data and customer visit forecast data. By linking multiple data sources to acquire information, more accurate data collection becomes possible. Some or all of the above processing in the linking unit may be performed using AI, for example, or without AI. For example, the linking unit can input data acquired from multiple data sources into AI and have the AI ​​perform data integration.

[0070] The data collection unit estimates the user's emotions and adjusts the timing of inventory data acquisition based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of inventory data acquisition to alleviate the system load. For example, if the user is relaxed, the data collection unit can increase the frequency of inventory data acquisition and collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize acquiring only important inventory data. This reduces the system load by adjusting the timing of inventory data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit analyzes historical inventory data and selects the optimal data acquisition method. For example, the data collection unit may concentrate data acquisition during specific time periods based on historical inventory data. The data collection unit can also dynamically adjust the frequency of data acquisition based on historical inventory data. Furthermore, the data collection unit can analyze historical inventory data and prioritize data acquisition for specific products. This allows for the selection of the optimal data acquisition method by analyzing historical inventory data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input historical inventory data into AI and have the AI ​​select the optimal data acquisition method.

[0072] The data collection unit filters inventory data based on the current store conditions and specific events. For example, the data collection unit adjusts the frequency of inventory data acquisition according to the store's congestion level. The data collection unit can also prioritize the acquisition of inventory data for products related to a specific event, for example, when such an event is taking place. Furthermore, the data collection unit can adjust the timing of inventory data acquisition according to the store's business hours. This allows for efficient acquisition of necessary data by filtering inventory data based on the store's current conditions and specific events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input store congestion data into the AI ​​and have the AI ​​adjust the frequency of inventory data acquisition.

[0073] The data collection unit estimates the user's emotions and determines the priority of inventory data to acquire based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize acquiring only important inventory data. If the user is relaxed, the data collection unit may also prioritize acquiring detailed inventory data. Furthermore, if the user is in a hurry, the data collection unit may prioritize acquiring inventory data that can be acquired quickly. In this way, by prioritizing inventory data according to the user's emotions, important data can be acquired preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The data collection unit prioritizes acquiring highly relevant data when obtaining inventory data, taking into account the geographical location of the stores. For example, the data collection unit prioritizes acquiring inventory data that corresponds to local demand based on the store's location. The data collection unit can also, for example, refer to inventory data of nearby competing stores, taking into account the store's location. Furthermore, the data collection unit can prioritize acquiring inventory data for products popular in a particular area, based on the store's location. This enables inventory management that corresponds to local demand by prioritizing the acquisition of highly relevant data while considering the store's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input store location data into AI and have the AI ​​acquire highly relevant data.

[0075] The data collection unit analyzes social media activity and acquires relevant data when acquiring inventory data. For example, the data collection unit prioritizes acquiring inventory data for products that are trending on social media. The data collection unit can also analyze social media trends and acquire inventory data for related products. Furthermore, the data collection unit can adjust the acquisition of inventory data based on user reviews on social media. This enables inventory management that is in line with trends by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into AI and have the AI ​​acquire relevant data.

[0076] The optimization unit estimates the user's emotions and adjusts the price optimization algorithm based on the estimated emotions. For example, if the user is stressed, the optimization unit simplifies the price optimization algorithm. If the user is relaxed, for example, the optimization unit can apply a more detailed price optimization algorithm. If the user is in a hurry, the optimization unit can also apply a rapid price optimization algorithm. This allows for more appropriate pricing by adjusting the price optimization algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The optimization unit adjusts the level of detail of the optimization based on the importance of the products when optimizing prices. For example, the optimization unit performs detailed price optimization for important products. The optimization unit can also simplify price optimization for less important products. Furthermore, the optimization unit can adjust the frequency of optimization according to the importance of the products. This allows for detailed pricing of important products by adjusting the level of detail of the optimization based on the importance of the products. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input product importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the optimization.

[0078] The optimization unit applies different optimization algorithms depending on the product category when optimizing prices. For example, the optimization unit may apply a rapid price optimization algorithm to fresh foods. For example, it may also apply a stable price optimization algorithm to daily necessities. Furthermore, the optimization unit may apply a seasonal price optimization algorithm to seasonal products. By applying different optimization algorithms depending on the product category, it becomes possible to set prices appropriate for each category. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input product category data into the AI ​​and have the AI ​​execute the application of the optimization algorithm.

[0079] The optimization unit estimates the user's emotions and adjusts the frequency of price optimization based on the estimated emotions. For example, if the user is stressed, the optimization unit reduces the frequency of price optimization. For example, if the user is relaxed, the optimization unit may increase the frequency of price optimization. The optimization unit may also increase the frequency of rapid price optimization if the user is in a hurry. This reduces the system load by adjusting the frequency of price optimization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not using AI. For example, the optimization unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The optimization unit determines the optimization priority based on the product's sales period when optimizing prices. For example, the optimization unit prioritizes price optimization for products with upcoming sales periods. The optimization unit may also postpone price optimization for products with later sales periods. Furthermore, the optimization unit can adjust the price optimization priority according to the sales period of seasonal products. This allows for pricing tailored to the sales period by determining the optimization priority based on the product's sales period. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input product sales period data into AI and have the AI ​​determine the optimization priority.

[0081] The optimization unit adjusts the optimization order based on the relevance of products during price optimization. For example, the optimization unit prioritizes the price optimization of highly relevant products. The optimization unit may also postpone the price optimization of less relevant products. Furthermore, the optimization unit can dynamically adjust the order of price optimization according to the relevance of products. This allows for prioritization of pricing for highly relevant products by adjusting the optimization order based on product relevance. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input product relevance data into the AI ​​and have the AI ​​perform the adjustment of the optimization order.

[0082] The automated unit estimates the user's emotions and adjusts the timing of price changes based on the estimated emotions. For example, if the user is stressed, the automated unit may delay the timing of the price change. For example, if the user is relaxed, the automated unit may also speed up the timing of the price change. Furthermore, if the user is in a hurry, the automated unit may change the price quickly. This allows for more appropriate timing of price changes by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automated unit may be performed using AI or not using AI. For example, the automated unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The automation unit selects the optimal price change method by referring to past price change data when a price is changed. For example, the automation unit selects the optimal price change method based on past price change data. The automation unit can also, for example, analyze past price change data and apply an effective price change method. Furthermore, the automation unit can adjust the timing of price changes by referring to past price change data. In this way, the optimal price change method can be selected by referring to past price change data. Some or all of the above processes in the automation unit may be performed using AI, for example, or without using AI. For example, the automation unit can input past price change data into AI and have the AI ​​perform the selection of the optimal change method.

[0084] The automation unit customizes the method of price change based on the current store situation when a price is changed. For example, the automation unit customizes the method of price change according to the store's congestion level. The automation unit can also adjust the timing of price changes based on the store's business hours, for example. Furthermore, the automation unit can optimize the method of price change according to the store's inventory level. This allows for more appropriate price changes by customizing the method of price change based on the store's current situation. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input store congestion data into the AI ​​and have the AI ​​perform the customization of the method of price change.

[0085] The automated unit estimates the user's emotions and determines the priority of price changes based on the estimated emotions. For example, if the user is stressed, the automated unit will prioritize only important price changes. For example, if the user is relaxed, the automated unit may also prioritize minor price changes. Furthermore, if the user is in a hurry, the automated unit can make price changes quickly. This allows important price changes to be prioritized by determining the priority of price changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automated unit may be performed using AI or not using AI. For example, the automated unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The automated unit selects the optimal price change method when changing prices, taking into account the store's geographical location. For example, the automated unit changes prices to meet local demand based on the store's location. The automated unit can also, for example, refer to the prices of nearby competing stores, taking into account the store's location. Furthermore, the automated unit can prioritize price changes for products popular in a particular area, based on the store's location. This makes it possible to change prices to meet local demand by selecting the optimal price change method while considering the store's geographical location. Some or all of the above processing in the automated unit may be performed using AI, for example, or without AI. For example, the automated unit can input store location data into AI and have the AI ​​select the optimal price change method.

[0087] The automation unit analyzes social media activity and proposes methods for price changes when making adjustments. For example, the automation unit prioritizes price changes for products that are trending on social media. The automation unit can also analyze social media trends and propose price changes for related products. Furthermore, the automation unit can adjust the methods of price changes based on user reviews on social media. This allows for price changes that align with trends by analyzing social media activity. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input social media data into AI and have the AI ​​propose methods for making changes.

[0088] The discounting unit estimates the user's emotions and adjusts the timing of the discount based on the estimated emotions. For example, if the user is stressed, the discounting unit may delay the discount. For example, if the user is relaxed, the discounting unit may advance the discount. Also, if the user is in a hurry, the discounting unit may apply the discount quickly. In this way, by adjusting the timing of the discount according to the user's emotions, the discount can be applied at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discounting unit may be performed using AI or not using AI. For example, the discounting unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0089] The discount unit selects the optimal discount method by referring to past discount data when setting discounts. For example, the discount unit selects the optimal discount method based on past discount data. The discount unit can also, for example, analyze past discount data and apply an effective discount method. Furthermore, the discount unit can adjust the timing of discounts by referring to past discount data. In this way, the optimal discount method can be selected by referring to past discount data. Some or all of the above processing in the discount unit may be performed using AI, for example, or without using AI. For example, the discount unit can input past discount data into AI and have the AI ​​select the optimal discount method.

[0090] The discounting unit estimates the user's emotions and determines the priority of discounts based on the estimated emotions. For example, if the user is stressed, the discounting unit will prioritize only important discounts. If the user is relaxed, the discounting unit may also prioritize detailed discounts. Furthermore, if the user is in a hurry, the discounting unit can provide discounts quickly. This allows for prioritizing important discounts by determining the priority of discounts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discounting unit may be performed using AI or not. For example, the discounting unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The discounting unit weights discounts based on the product's sales period when setting discounts. For example, the discounting unit prioritizes discounts for products with upcoming sales periods. It can also postpone discounts for products with later sales periods. Furthermore, the discounting unit can adjust the discount weighting according to the sales period of seasonal products. This allows for discount settings tailored to the sales period by weighting discounts based on the product's sales period. Some or all of the above processing in the discounting unit may be performed using AI, for example, or without AI. For example, the discounting unit can input product sales period data into AI and have the AI ​​perform the discount weighting.

[0092] The adjustment unit estimates the user's emotions and adjusts the price adjustment method based on the estimated user emotions. For example, if the user is stressed, the adjustment unit may simplify the price adjustment method. For example, if the user is relaxed, the adjustment unit may apply a more detailed price adjustment method. Furthermore, if the user is in a hurry, the adjustment unit may apply a method for rapid price adjustment. This allows for more appropriate price adjustments by adjusting the price adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit may input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The adjustment unit selects the optimal adjustment method by referring to past adjustment data when adjusting prices. For example, the adjustment unit selects the optimal price adjustment method based on past adjustment data. The adjustment unit can also, for example, analyze past adjustment data and apply an effective price adjustment method. Furthermore, the adjustment unit can adjust the timing of price adjustments by referring to past adjustment data. In this way, the optimal price adjustment method can be selected by referring to past adjustment data. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input past adjustment data into AI and have the AI ​​select the optimal adjustment method.

[0094] The adjustment unit estimates the user's emotions and determines the priority of price adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit will prioritize only important price adjustments. For example, if the user is relaxed, the adjustment unit may also prioritize detailed price adjustments. Furthermore, if the user is in a hurry, the adjustment unit can perform price adjustments quickly. This allows for prioritizing important price adjustments by determining the priority of price adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The adjustment unit weights the price adjustment based on weather and customer traffic forecasts. For example, if the weather is bad, the adjustment unit will lighten the weight of the price adjustment. For example, if the weather is good, the adjustment unit can also increase the weight of the price adjustment. Furthermore, if there is a high forecast of customer traffic, the adjustment unit can also increase the weight of the price adjustment. This allows for price adjustments that respond to demand by weighting the adjustment based on weather and customer traffic forecasts. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input weather data and customer traffic forecast data into AI and have the AI ​​perform the weighting of the adjustment.

[0096] The integration unit estimates the user's emotions and adjusts the data integration method based on the estimated user emotions. For example, if the user is stressed, the integration unit simplifies the data integration method. If the user is relaxed, for example, the integration unit can apply a more detailed data integration method. If the user is in a hurry, the integration unit can also apply a method for rapid data integration. By adjusting the data integration method according to the user's emotions, more appropriate data integration becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0097] The integration unit selects the optimal integration method by referring to past integration data when integrating data. For example, the integration unit selects the optimal data integration method based on past integration data. The integration unit can also, for example, analyze past integration data and apply an effective data integration method. Furthermore, the integration unit can adjust the timing of data integration by referring to past integration data. In this way, the optimal data integration method can be selected by referring to past integration data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without using AI. For example, the integration unit can input past integration data into AI and have the AI ​​select the optimal integration method.

[0098] The integration unit estimates the user's emotions and determines the priority of data integration based on the estimated user emotions. For example, if the user is stressed, the integration unit will prioritize only important data integration. For example, if the user is relaxed, the integration unit may also prioritize detailed data integration. Furthermore, if the user is in a hurry, the integration unit can perform data integration quickly. This allows for prioritizing important data integration by determining the priority of data integration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not using AI. For example, the integration unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The integration unit considers multiple data sources when integrating data and assigns weights to the integration. For example, the integration unit integrates information from multiple data sources and assigns weights. The integration unit can also adjust the integration weights based on the reliability of the data sources. Furthermore, the integration unit can dynamically adjust the integration weights according to the importance of the data sources. This enables more accurate data integration by considering multiple data sources when assigning integration weights. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data obtained from multiple data sources into AI and have the AI ​​perform the integration weighting.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can analyze users' purchase history and adjust the timing of inventory data acquisition based on past purchase patterns. For example, if a user tends to purchase a particular product on a specific day or time, the data collection unit can increase the frequency of inventory data acquisition during that time. Similarly, if a user tends to purchase a particular product during a specific season, the data collection unit can increase the frequency of inventory data acquisition during that season. Furthermore, based on user purchase history, the data collection unit can prioritize the acquisition of inventory data for products that tend to remain unsold. This allows for more efficient inventory management by adjusting the timing of inventory data acquisition based on user purchase history.

[0102] The data collection unit can estimate the user's emotions and adjust how inventory data is retrieved based on those emotions. For example, if the user is stressed, the data collection unit can simplify the inventory data retrieval process. Conversely, if the user is relaxed, the data collection unit can retrieve more detailed inventory data. Furthermore, if the user is in a hurry, the data collection unit can prioritize quickly retrieved inventory data. By adjusting the inventory data retrieval process according to the user's emotions, the system load is reduced, and data retrieval tailored to the user's needs becomes possible.

[0103] The optimization unit can predict product sales and dynamically adjust prices based on those predictions. For example, based on past sales data, if a particular product tends to sell well on a specific day or time, the optimization unit can raise the price during that time. It can also raise the price of a particular product during a specific season if that season is known to sell well. Furthermore, if a particular product tends to remain unsold, the optimization unit can lower its price. This allows for the maximization of sales by dynamically adjusting prices based on sales predictions.

[0104] The optimization unit can estimate the user's emotions and adjust the price optimization algorithm based on those emotions. For example, if the user is stressed, the optimization unit can simplify the price optimization algorithm. Conversely, if the user is relaxed, it can apply a more detailed price optimization algorithm. Furthermore, if the user is in a hurry, the optimization unit can apply a rapid price optimization algorithm. By adjusting the price optimization algorithm according to the user's emotions, more appropriate pricing becomes possible.

[0105] The automated system can adjust the timing of price changes by taking into account store congestion. For example, if the store is busy, the automated system can delay the price change. Conversely, if the store is not busy, the automated system can speed up the price change. Furthermore, if a specific event is taking place, the automated system can prioritize price changes for products related to that event. This allows for more appropriate price changes by adjusting the timing of price changes based on store congestion and specific events.

[0106] The automated system can estimate the user's emotions and prioritize price changes based on those emotions. For example, if the user is stressed, the automated system can prioritize only important price changes. Conversely, if the user is relaxed, it can prioritize minor price changes. Furthermore, if the user is in a hurry, the automated system can quickly implement price changes. This allows for prioritizing important price changes based on the user's emotions.

[0107] The discount section can weight discounts based on the product's sales period. For example, it can prioritize discounts on products with upcoming sales periods. It can also postpone discounts on products with later sales periods. Furthermore, the discount section can adjust the discount weighting according to the sales period of seasonal products. This allows for discount settings tailored to the sales period by weighting discounts based on the product's sales period.

[0108] The adjustment unit can estimate the user's emotions and adjust the price adjustment method based on those emotions. For example, if the user is stressed, the adjustment unit can simplify the price adjustment method. If the user is relaxed, the adjustment unit can apply a more detailed price adjustment method. Furthermore, if the user is in a hurry, the adjustment unit can apply a method for rapid price adjustment. By adjusting the price adjustment method according to the user's emotions, more appropriate price adjustments become possible.

[0109] The integration unit can weight data integration by considering multiple data sources. For example, it can integrate information from multiple data sources and assign weights to them. Furthermore, the integration unit can adjust the integration weights based on the reliability of the data sources. It can also dynamically adjust the integration weights according to the importance of the data sources. This allows for more accurate data integration by considering multiple data sources when weighting integrations.

[0110] The integration unit can estimate the user's emotions and determine the priority of data integration based on those emotions. For example, if the user is stressed, the integration unit can prioritize only important data integration. Conversely, if the user is relaxed, the integration unit can prioritize detailed data integration. Furthermore, if the user is in a hurry, the integration unit can perform data integration quickly. This allows for prioritizing important data integration based on the user's emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit monitors inventory status in real time. For example, it monitors inventory status using sensors within the store. The data collection unit also collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. The data collection unit includes a discount unit that sets discounted prices for products nearing their expiration date, and an adjustment unit that adjusts prices according to weather and customer visit forecasts. Furthermore, the data collection unit includes an integration unit that acquires information by linking multiple data sources. Step 2: The optimization unit dynamically optimizes the price based on the data collected by the data collection unit. The optimization unit includes AI processing to analyze the collected data and calculate the optimal price. For example, it may perform demand forecasting and adjust the price accordingly. Step 3: The automation unit automates the price changes optimized by the optimization unit. The automation unit may include AI processing to automatically execute price changes. This reduces the burden on store staff.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, optimization unit, and automation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit monitors inventory status in real time using sensors and cameras of the smart device 14 and collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and dynamically optimizes prices based on the collected data. The automation unit is implemented, for example, by the control unit 46A of the smart device 14, and automatically executes the optimized price changes. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, optimization unit, and automation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the sensors and cameras of the smart glasses 214 to monitor inventory status in real time and collect statistical information such as expiration dates, weather, and estimated number and time of customer visits. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and dynamically optimizes prices based on the collected data. The automation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and automatically executes the optimized price changes. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, optimization unit, and automation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit monitors inventory status in real time using the sensors and cameras of the headset terminal 314 and collects statistical information such as expiration dates, weather, and estimated number and time of customer visits. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and dynamically optimizes prices based on the collected data. The automation unit is implemented in the control unit 46A of the headset terminal 314 and automatically executes the optimized price changes. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the data collection unit, optimization unit, and automation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit monitors inventory status in real time using the sensors and cameras of the robot 414 and collects statistical information such as expiration dates, weather, and the expected number and time of customer visits. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and dynamically optimizes prices based on the collected data. The automation unit is implemented by, for example, the control unit 46A of the robot 414 and automatically executes the optimized price changes. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The collection department monitors inventory status in real time, A collection unit collects statistical information such as expiration dates, weather, and estimated number and time of customer visits, which have been collected by the aforementioned collection unit. An optimization unit dynamically optimizes the price based on the data collected by the aforementioned collection unit, The system includes an automation unit that automates the price changes optimized by the optimization unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It has a discount section where products nearing their expiration date are offered at a discounted price. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is It features an adjustment unit that adjusts prices according to weather conditions and expected customer traffic. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It includes a linking unit that acquires information by linking multiple data sources. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of inventory data acquisition based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze past inventory data and select the optimal data acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When retrieving inventory data, filter it based on the current store status or specific events. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates user sentiment and determines the priority of inventory data to retrieve based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When acquiring inventory data, the system prioritizes retrieving highly relevant data by considering the geographical location of the stores. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When acquiring inventory data, social media activity is analyzed and relevant data is obtained. The system described in Appendix 1, characterized by the features described herein. (Note 11) The optimization unit, It estimates user sentiment and adjusts the price optimization algorithm based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The optimization unit, When optimizing prices, adjust the level of optimization based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 13) The optimization unit, When optimizing prices, different optimization algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The optimization unit, It estimates user sentiment and adjusts the frequency of price optimization based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The optimization unit, When optimizing prices, prioritize optimization based on the timing of product sales. The system described in Appendix 1, characterized by the features described herein. (Note 16) The optimization unit, When optimizing prices, adjust the optimization order based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned automation unit, It estimates user sentiment and adjusts the timing of price changes based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned automation unit, When changing prices, the system refers to past price change data to select the optimal method of change. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned automation unit, When changing prices, customize the method of change based on the store's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned automation unit, The system estimates user sentiment and determines the priority of price changes based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automation unit, When changing prices, the optimal method of change is selected, taking into account the store's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, When changing prices, we analyze social media activity and suggest ways to implement the changes. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned discount section is, The system estimates the user's emotions and adjusts the timing of discounts based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned discount section is, When setting discounts, the system selects the optimal discount method by referring to past discount data. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned discount section is, It estimates the user's emotions and determines discount priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned discount section is, When setting discounts, weight the discounts based on when the product was sold. The system described in Appendix 2, characterized by the features described herein. (Note 27) The adjustment unit is, We estimate user sentiment and adjust pricing based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 28) The adjustment unit is, When adjusting prices, the optimal adjustment method is selected by referring to past adjustment data. The system described in Appendix 3, characterized by the features described herein. (Note 29) The adjustment unit is, The system estimates user sentiment and determines price adjustment priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The adjustment unit is, When adjusting prices, weighting of the adjustments will be based on weather conditions and customer traffic forecasts. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the data integration method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, When integrating data, the system selects the optimal integration method by referring to past integration data. The system described in Appendix 4, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, It estimates user sentiment and determines the priority of data integration based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, When integrating data, weight the integration based on multiple data sources. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit monitors inventory status in real time, A collection unit collects statistical information such as expiration dates, weather, and estimated number and time of customer visits, which have been collected by the aforementioned collection unit. An optimization unit dynamically optimizes the price based on the data collected by the aforementioned collection unit, The system includes an automation unit that automates the price changes optimized by the optimization unit. A system characterized by the following features.

2. The aforementioned collection unit is It has a discount section where products nearing their expiration date are offered at a discounted price. The system according to feature 1.

3. The aforementioned collection unit is It features an adjustment unit that adjusts prices according to weather conditions and expected customer traffic. The system according to feature 1.

4. The aforementioned collection unit is It includes a linking unit that acquires information by linking multiple data sources. The system according to feature 1.

5. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of inventory data acquisition based on the estimated user sentiment. The system according to feature 1.

6. The aforementioned collection unit is Analyze past inventory data and select the optimal data acquisition method. The system according to feature 1.

7. The aforementioned collection unit is When retrieving inventory data, filter it based on the current store status or specific events. The system according to feature 1.

8. The aforementioned collection unit is It estimates user sentiment and determines the priority of inventory data to retrieve based on the estimated user sentiment. The system according to feature 1.

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