System

Abstract

An object of a system according to an embodiment is to evaluate a disposal risk of stock and to make an effective recipe proposal.SOLUTION: A system includes a demand prediction part, a stock evaluation part, a recipe proposal part, and a display part. The demand prediction unit predicts demand. The stock evaluation part evaluates the disposal risk of the stock on the basis of the demand data predicted by the demand prediction part. The recipe proposal unit proposes a recipe based on a result evaluated by the stock evaluation unit. The display unit displays the recipe proposed by the recipe proposal unit on a display device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have posed a challenge in the food retail industry, making it difficult to properly assess the risk of inventory disposal and to carry out effective sales promotion activities.

[0005] The system according to the embodiment aims to evaluate the risk of discarding inventory and propose effective recipes. [Means for solving the problem]

[0006] The system according to the embodiment includes a demand forecasting unit, an inventory evaluation unit, a recipe suggestion unit, and a display unit. The demand forecasting unit predicts demand. The inventory evaluation unit evaluates the risk of inventory disposal based on the demand data predicted by the demand forecasting unit. The recipe suggestion unit proposes recipes based on the results of the evaluation by the inventory evaluation unit. The display unit displays the recipes proposed by the recipe suggestion unit on a display device. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the risk of wasting inventory and propose effective recipes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A management support system according to an embodiment of the present invention utilizes AI to support the management of a grocery retail business. This system achieves efficient operations by coordinating demand forecasting, inventory evaluation, recipe proposals, and display. For example, the management support system analyzes past sales data and seasonal demand to predict the amount of inventory needed for the next purchase. This minimizes waste and loss. The management support system not only identifies products that are about to be discarded, but also evaluates the waste risk of in-store inventory and proposes combined recipes at low prices on an in-store display device. For example, by proposing recipes that combine vegetables that are about to be discarded with other ingredients and offering the ingredients required for those recipes at special prices, inventory turnover can be increased. This allows the management support system to support the management of a grocery retail business in both purchasing and sales promotion activities, thereby achieving efficient operations. For example, waste and loss can be minimized and inventory turnover can be increased. Furthermore, special price proposals can improve customer satisfaction.

[0029] A management support system according to an embodiment includes a demand forecasting unit, an inventory evaluation unit, a recipe suggestion unit, and a display unit. The demand forecasting unit acquires past sales data and predicts demand. For example, the demand forecasting unit predicts the amount of goods needed for the next purchase based on past sales history and sales quantities. The demand forecasting unit can also analyze seasonal demand patterns to improve prediction accuracy. The demand forecasting unit can also predict demand taking into account weather data and local event information. For example, the demand for umbrellas and raincoats on rainy days can be predicted based on weather data. The inventory evaluation unit assesses the risk of inventory waste based on the demand data predicted by the demand forecasting unit. For example, the inventory evaluation unit assesses the risk of waste based on product shelf life and quality information. The inventory evaluation unit can also improve evaluation accuracy by referring to past waste data. The inventory evaluation unit can also perform evaluations taking into account product supply chain information. The recipe suggestion unit proposes recipes based on the evaluation results of the inventory evaluation unit. For example, the recipe suggestion unit proposes recipes that combine products that are close to being disposed of with other ingredients. The recipe suggestion unit can also propose recipes using seasonal ingredients. Furthermore, the recipe suggestion unit can make suggestions taking into consideration the user's dietary restrictions and allergy information. The display unit displays the recipes suggested by the recipe suggestion unit on a display device. For example, the display unit displays the recipes on digital signage within the store. The display unit can also display the recipes on smartphones and tablets. As a result, the management support system according to the embodiment enables efficient management by linking demand forecasting, inventory evaluation, recipe suggestion, and display.

[0030] The management support system includes a special price proposal unit that proposes special prices. The special price proposal unit proposes special prices based on recipes proposed by the recipe proposal unit. For example, the special price proposal unit offers products that are close to being discarded at a special price. The special price proposal unit can also propose special prices taking into account inventory status and the risk of disposal. Furthermore, the special price proposal unit can improve the accuracy of proposals by referring to past special price proposal results. Thus, by including the special price proposal unit, it becomes possible to propose special prices.

[0031] The demand forecasting unit can obtain past sales data and forecast demand. The demand forecasting unit can forecast the amount of goods required for the next purchase based on, for example, past sales history and sales quantities. The demand forecasting unit can also analyze seasonal demand patterns to improve forecast accuracy. Furthermore, the demand forecasting unit can forecast demand taking into account weather data and local event information. For example, the demand for umbrellas and raincoats on rainy days can be predicted based on weather data. In this way, forecasting demand based on past sales data improves forecast accuracy.

[0032] The inventory evaluation unit can evaluate the risk of inventory disposal based on the demand data predicted by the demand forecasting unit. The inventory evaluation unit evaluates the risk of disposal based on, for example, the shelf life and quality information of the product. The inventory evaluation unit can also improve the accuracy of the evaluation by referring to past disposal data. Furthermore, the inventory evaluation unit can also perform the evaluation taking into account product supply chain information. As a result, the accuracy of the disposal risk is improved by evaluating the risk of inventory disposal based on demand data.

[0033] The recipe suggestion unit can suggest recipes based on the evaluation results of the inventory evaluation unit. For example, the recipe suggestion unit can suggest recipes that combine products that are about to be discarded with other ingredients. The recipe suggestion unit can also suggest recipes that use seasonal ingredients. Furthermore, the recipe suggestion unit can make suggestions taking into account the user's dietary restrictions and allergy information. This improves the accuracy of suggestions by suggesting recipes based on the inventory evaluation results.

[0034] The display unit can display the recipe suggested by the recipe suggestion unit on a display device. The display unit can display the recipe on digital signage in a store, for example. The display unit can also display the recipe on a smartphone or tablet. This allows the suggested recipe to be displayed on a display device, thereby providing information visually to the user.

[0035] The demand forecasting unit can forecast demand based on weather data and local event information in addition to past sales data. For example, the demand forecasting unit can forecast demand for umbrellas and raincoats on rainy days based on weather data. The demand forecasting unit can also forecast that specific products will sell well on festival or event days based on local event information. Furthermore, the demand forecasting unit can combine past sales data and weather data to forecast seasonal demand patterns. In this way, the accuracy of demand forecasting can be improved by taking weather data and local event information into consideration.

[0036] The demand forecasting unit can analyze demand patterns for specific days of the week and time periods. For example, the demand forecasting unit can predict that there will be high demand for bread and milk on weekday mornings. The demand forecasting unit can also predict that there will be high demand for alcoholic beverages on weekend evenings. Furthermore, the demand forecasting unit can analyze sales patterns for specific products during specific time periods and reflect this in the demand forecast. In this way, the accuracy of the demand forecast can be improved by analyzing demand patterns for specific days of the week and time periods.

[0037] The demand forecasting unit can improve the accuracy of the forecast based on the sales data of competing stores. For example, the demand forecasting unit acquires sales data of competing stores and predicts demand in the same area. The demand forecasting unit can also predict when a particular product will sell based on the sales data of competing stores. Furthermore, the demand forecasting unit can also improve the accuracy of the demand forecast by referring to the sales data of competing stores. In this way, by referring to the sales data of competing stores, the accuracy of the demand forecast is improved.

[0038] The demand forecasting unit can make predictions based on social media trend information. For example, the demand forecasting unit predicts demand based on products that are trending on social media. The demand forecasting unit can also analyze social media trend information and predict when a particular product will sell. Furthermore, the demand forecasting unit can incorporate social media trend information to improve the accuracy of the demand forecast. In this way, incorporating social media trend information improves the accuracy of the demand forecast.

[0039] The demand forecasting unit can analyze the user's purchasing history and perform individual demand forecasts. The demand forecasting unit can predict individual demand based on, for example, the user's past purchasing history. The demand forecasting unit can also analyze the user's purchasing patterns and predict when a particular product will sell. Furthermore, the demand forecasting unit can analyze the user's purchasing history and perform individual demand forecasts to improve accuracy. In this way, by analyzing the user's purchasing history, the accuracy of individual demand forecasts can be improved.

[0040] The demand forecasting unit can make a forecast based on regional demographic data. The demand forecasting unit, for example, forecasts demand based on regional demographic data. The demand forecasting unit can also analyze regional demographic data and predict when a particular product will sell. Furthermore, the demand forecasting unit can also improve the accuracy of the demand forecast by taking regional demographic data into account. As a result, the accuracy of the demand forecast is improved by taking regional demographic data into account.

[0041] The inventory evaluation unit can evaluate the risk of disposal based on the shelf life and quality information of the product. The inventory evaluation unit evaluates the risk of disposal based on, for example, the shelf life of the product. The inventory evaluation unit can also evaluate the risk of disposal by taking into account the quality information of the product. Furthermore, the inventory evaluation unit can evaluate the risk of disposal by combining the shelf life and quality information. In this way, by taking into account the shelf life and quality information of the product, the accuracy of the evaluation of the risk of disposal is improved.

[0042] The inventory evaluation unit can improve the accuracy of evaluation based on past disposal data. The inventory evaluation unit performs inventory evaluation based on, for example, past disposal data. The inventory evaluation unit can also evaluate disposal risk by referring to past disposal data. Furthermore, the inventory evaluation unit can analyze past disposal data and improve the accuracy of inventory evaluation. As a result, the accuracy of inventory evaluation is improved by referring to past disposal data.

[0043] The inventory evaluation unit can perform evaluation based on product supply chain information. The inventory evaluation unit performs inventory evaluation based on, for example, product supply chain information. The inventory evaluation unit can also evaluate waste risk by taking supply chain information into consideration. Furthermore, the inventory evaluation unit can also improve the accuracy of inventory evaluation by referring to supply chain information. As a result, the accuracy of inventory evaluation is improved by taking product supply chain information into consideration.

[0044] The inventory evaluation unit can perform evaluation based on product price fluctuation data. The inventory evaluation unit performs inventory evaluation based on, for example, product price fluctuation data. The inventory evaluation unit can also evaluate waste risk by taking price fluctuation data into consideration. Furthermore, the inventory evaluation unit can also improve the accuracy of inventory evaluation by referring to price fluctuation data. As a result, the accuracy of inventory evaluation is improved by taking product price fluctuation data into consideration.

[0045] The inventory evaluation unit can perform evaluation by referring to sales promotion campaign information for the product. The inventory evaluation unit performs inventory evaluation based on, for example, the sales promotion campaign information. The inventory evaluation unit can also evaluate waste risk by taking the sales promotion campaign information into consideration. Furthermore, the inventory evaluation unit can also improve the accuracy of inventory evaluation by referring to the sales promotion campaign information. As a result, the accuracy of inventory evaluation is improved by referring to the sales promotion campaign information.

[0046] The inventory evaluation unit can apply different evaluation criteria to each product category. For example, the inventory evaluation unit applies different evaluation criteria to fresh foods and processed foods. The inventory evaluation unit can also set evaluation criteria for each product category, taking into account shelf life and quality information. Furthermore, the inventory evaluation unit can also apply evaluation criteria for each category to improve the accuracy of inventory evaluation. In this way, applying evaluation criteria for each category improves the accuracy of inventory evaluation.

[0047] The recipe suggestion unit can make suggestions based on the use of seasonal ingredients. For example, the recipe suggestion unit can suggest recipes that use seasonal ingredients. The recipe suggestion unit can also suggest recipes taking into account the use of seasonal ingredients. Furthermore, the recipe suggestion unit can also suggest recipes based on seasonal ingredients. This improves the accuracy of recipe suggestions by taking into account seasonal ingredients.

[0048] The recipe suggestion unit can improve suggestion accuracy based on past recipe suggestion results. The recipe suggestion unit suggests recipes based on past recipe suggestion results, for example. The recipe suggestion unit can also improve suggestion accuracy by referring to past recipe suggestion results. Furthermore, the recipe suggestion unit can analyze past recipe suggestion results and suggest optimal recipes. In this way, suggestion accuracy is improved by referring to past recipe suggestion results.

[0049] The recipe suggestion unit can make suggestions based on the user's dietary restrictions and allergy information. For example, the recipe suggestion unit can suggest recipes taking into account the user's dietary restrictions. The recipe suggestion unit can also suggest recipes based on the user's allergy information. Furthermore, the recipe suggestion unit can also suggest optimal recipes taking into account the user's dietary restrictions and allergy information. This allows appropriate recipes to be suggested by taking into account the user's dietary restrictions and allergy information.

[0050] The recipe suggestion unit can make suggestions based on the food culture and preferences of the region. For example, the recipe suggestion unit can suggest recipes using local ingredients, taking into account the food culture of the region. The recipe suggestion unit can also suggest popular recipes based on the preferences of the region. Furthermore, the recipe suggestion unit can also suggest optimal recipes, taking into account the food culture and preferences of the region. This improves the accuracy of recipe suggestions by taking into account the food culture and preferences of the region.

[0051] The recipe suggestion unit can suggest individual recipes by referring to the user's past purchase history. The recipe suggestion unit can suggest individual recipes based on, for example, the user's past purchase history. The recipe suggestion unit can also analyze the user's purchasing patterns and suggest recipes using specific ingredients. Furthermore, the recipe suggestion unit can suggest individual recipes by referring to the user's purchase history, thereby improving accuracy. In this way, by referring to the user's past purchase history, the accuracy of individual recipe suggestions is improved.

[0052] The recipe suggestion unit can make suggestions based on trending information on social media. For example, the recipe suggestion unit makes suggestions based on popular recipes on social media. The recipe suggestion unit can also analyze trending information on social media and suggest popular recipes. Furthermore, the recipe suggestion unit can incorporate trending information on social media to improve the accuracy of recipe suggestions. In this way, incorporating trending information on social media improves the accuracy of recipe suggestions.

[0053] The display unit can select the optimal display method by referring to the user's past operation history. The display unit selects the optimal display method based on, for example, the user's past operation history. The display unit can also analyze the user's operation patterns and suggest a specific display method. Furthermore, the display unit can select the optimal display method by referring to the user's operation history and improve accuracy. In this way, the optimal display method can be selected by referring to the user's past operation history.

[0054] The display unit can customize the display content according to the user's current task. For example, if the user is shopping, the display unit can display information about related products. Also, if the user is searching for a recipe, the display unit can display related recipes. Furthermore, the display unit can customize the display content according to the user's current task and provide optimal information. This makes it possible to provide optimal information by customizing the display content according to the user's current task.

[0055] The display unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information.

[0056] The display unit can make the display content multilingual according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can provide the display content in that language. This makes it possible to provide appropriate information to the user by making the display content multilingual according to the user's language setting.

[0057] The display unit can analyze the user's social media activity and provide related information. For example, the display unit can provide information about places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the display unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity.

[0058] The special price proposal unit can make proposals based on the inventory status and waste risk of the product. The special price proposal unit can propose a special price based on, for example, the inventory status of the product. The special price proposal unit can also propose a special price taking into account the waste risk. Furthermore, the special price proposal unit can propose a special price by combining the inventory status and the waste risk. In this way, by taking into account the inventory status and the waste risk, the accuracy of the special price proposal is improved.

[0059] The special price proposal unit can improve proposal accuracy based on past special price proposal results. The special price proposal unit proposes a special price based on, for example, past special price proposal results. The special price proposal unit can also improve proposal accuracy by referring to past special price proposal results. Furthermore, the special price proposal unit can analyze past special price proposal results and propose the optimal special price. In this way, proposal accuracy is improved by referring to past special price proposal results.

[0060] The special price proposal unit can make proposals based on the price competition situation in the region. The special price proposal unit can propose a special price based on, for example, the price competition situation in the region. The special price proposal unit can also propose a special price taking the price competition situation into consideration. Furthermore, the special price proposal unit can also improve the accuracy of special price proposals by referring to the price competition situation in the region. In this way, the accuracy of special price proposals can be improved by taking the price competition situation in the region into consideration.

[0061] The special price proposal unit can propose an individual special price based on the user's purchase history. The special price proposal unit proposes an individual special price based on, for example, the user's past purchase history. The special price proposal unit can also analyze the user's purchase patterns and propose a special price for a specific product. Furthermore, the special price proposal unit can also refer to the user's purchase history to propose an individual special price and improve accuracy. In this way, by referring to the user's purchase history, the accuracy of the individual special price proposal is improved.

[0062] The special price proposal unit can propose an individual special price based on the user's purchase history. The special price proposal unit proposes an individual special price based on, for example, the user's past purchase history. The special price proposal unit can also analyze the user's purchase patterns and propose a special price for a specific product. Furthermore, the special price proposal unit can also refer to the user's purchase history to propose an individual special price and improve accuracy. In this way, by referring to the user's purchase history, the accuracy of the individual special price proposal is improved.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The management support system can further include a customer feedback collection unit. The customer feedback collection unit can collect feedback from customers and use it to improve the entire system. For example, reviews and ratings provided by customers after purchases can be collected and used to improve the accuracy of the demand forecasting unit and inventory evaluation unit. The customer feedback collection unit can also propose new recipes or special prices based on customer opinions. Furthermore, the customer feedback collection unit can monitor customer satisfaction in real time and take immediate countermeasures. This enables flexible management support that reflects customer feedback.

[0065] The management support system can further include an energy consumption optimization unit. The energy consumption optimization unit monitors energy consumption within the store and proposes efficient energy usage. For example, it analyzes the usage of refrigerators and lights and proposes optimal energy consumption patterns. The energy consumption optimization unit can also adjust energy consumption based on weather data and business hours. Furthermore, the energy consumption optimization unit aims to reduce energy costs and contributes to more efficient store operations. This makes it possible to reduce management costs through the optimization of energy consumption.

[0066] The management support system can further include a customer behavior analysis unit. The customer behavior analysis unit analyzes customer behavior within the store and uses this information to help formulate marketing strategies. For example, it analyzes customer movement patterns and length of stay to optimize product placement and promotions. The customer behavior analysis unit can also make personalized suggestions based on customers' purchasing history and preferences. Furthermore, the customer behavior analysis unit can collect customer behavior data in real time and instantly adjust marketing measures. This enables effective marketing based on customer behavior.

[0067] The management support system can further be equipped with a supply chain optimization unit. The supply chain optimization unit monitors the entire product supply chain to ensure efficient supply. For example, it analyzes logistics data from the supplier to the store and proposes the optimal supply route. The supply chain optimization unit can also adjust the supply schedule based on inventory status and demand forecast data. Furthermore, the supply chain optimization unit can identify bottlenecks in the supply chain and propose improvement measures. This improves the efficiency of the entire supply chain and contributes to stabilizing management.

[0068] The management support system can further include an environmental impact assessment section. The environmental impact assessment section evaluates the impact that store operations have on the environment and supports sustainable management. For example, it monitors the amount of waste and energy consumption and makes suggestions to minimize environmental impact. The environmental impact assessment section can also suggest the use of recyclable materials and eco-friendly products. Furthermore, the environmental impact assessment section can formulate environmentally conscious management policies and raise environmental awareness throughout the store. This will help achieve environmentally friendly, sustainable management.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The demand forecasting unit obtains past sales data and forecasts demand. For example, it predicts the next required quantity of stock based on past sales history and sales volume. It can also analyze seasonal demand patterns to improve forecast accuracy. It can also forecast demand taking into account weather data and local event information. Step 2: The inventory evaluation unit evaluates the risk of inventory disposal based on the demand data predicted by the demand forecasting unit. For example, the disposal risk is evaluated based on the product's shelf life and quality information. It is also possible to improve the accuracy of the evaluation by referring to past disposal data. Furthermore, the evaluation can also take into account product supply chain information. Step 3: The recipe suggestion unit suggests recipes based on the evaluation results of the inventory evaluation unit. For example, it suggests recipes that combine products that are about to be discarded with other ingredients. It can also suggest recipes that use seasonal ingredients. It can also make suggestions that take into account the user's dietary restrictions and allergy information. Step 4: The display unit displays the recipe suggested by the recipe suggestion unit on a display device. For example, the recipe is displayed on digital signage in the store. The recipe can also be displayed on a smartphone or tablet.

[0071] (Example 2) A management support system according to an embodiment of the present invention utilizes AI to support the management of a grocery retail business. This system achieves efficient operations by coordinating demand forecasting, inventory evaluation, recipe proposals, and display. For example, the management support system analyzes past sales data and seasonal demand to predict the amount of inventory needed for the next purchase. This minimizes waste and loss. The management support system not only identifies products that are about to be discarded, but also evaluates the waste risk of in-store inventory and proposes combined recipes at low prices on an in-store display device. For example, by proposing recipes that combine vegetables that are about to be discarded with other ingredients and offering the ingredients required for those recipes at special prices, inventory turnover can be increased. This allows the management support system to support the management of a grocery retail business in both purchasing and sales promotion activities, thereby achieving efficient operations. For example, waste and loss can be minimized and inventory turnover can be increased. Furthermore, special price proposals can improve customer satisfaction.

[0072] A management support system according to an embodiment includes a demand forecasting unit, an inventory evaluation unit, a recipe suggestion unit, and a display unit. The demand forecasting unit acquires past sales data and predicts demand. For example, the demand forecasting unit predicts the amount of goods needed for the next purchase based on past sales history and sales quantities. The demand forecasting unit can also analyze seasonal demand patterns to improve prediction accuracy. The demand forecasting unit can also predict demand taking into account weather data and local event information. For example, the demand for umbrellas and raincoats on rainy days can be predicted based on weather data. The inventory evaluation unit assesses the risk of inventory waste based on the demand data predicted by the demand forecasting unit. For example, the inventory evaluation unit assesses the risk of waste based on product shelf life and quality information. The inventory evaluation unit can also improve evaluation accuracy by referring to past waste data. The inventory evaluation unit can also perform evaluations taking into account product supply chain information. The recipe suggestion unit proposes recipes based on the evaluation results of the inventory evaluation unit. For example, the recipe suggestion unit proposes recipes that combine products that are close to being disposed of with other ingredients. The recipe suggestion unit can also propose recipes using seasonal ingredients. Furthermore, the recipe suggestion unit can make suggestions taking into consideration the user's dietary restrictions and allergy information. The display unit displays the recipes suggested by the recipe suggestion unit on a display device. For example, the display unit displays the recipes on digital signage within the store. The display unit can also display the recipes on smartphones and tablets. As a result, the management support system according to the embodiment enables efficient management by linking demand forecasting, inventory evaluation, recipe suggestion, and display.

[0073] The management support system includes a special price proposal unit that proposes special prices. The special price proposal unit proposes special prices based on recipes proposed by the recipe proposal unit. For example, the special price proposal unit offers products that are close to being discarded at a special price. The special price proposal unit can also propose special prices taking into account inventory status and the risk of disposal. Furthermore, the special price proposal unit can improve the accuracy of proposals by referring to past special price proposal results. Thus, by including the special price proposal unit, it becomes possible to propose special prices.

[0074] The demand forecasting unit can obtain past sales data and forecast demand. The demand forecasting unit can forecast the amount of goods required for the next purchase based on, for example, past sales history and sales quantities. The demand forecasting unit can also analyze seasonal demand patterns to improve forecast accuracy. Furthermore, the demand forecasting unit can forecast demand taking into account weather data and local event information. For example, the demand for umbrellas and raincoats on rainy days can be predicted based on weather data. In this way, forecasting demand based on past sales data improves forecast accuracy.

[0075] The inventory evaluation unit can evaluate the risk of inventory disposal based on the demand data predicted by the demand forecasting unit. The inventory evaluation unit evaluates the risk of disposal based on, for example, the shelf life and quality information of the product. The inventory evaluation unit can also improve the accuracy of the evaluation by referring to past disposal data. Furthermore, the inventory evaluation unit can also perform the evaluation taking into account product supply chain information. As a result, the accuracy of the disposal risk is improved by evaluating the risk of inventory disposal based on demand data.

[0076] The recipe suggestion unit can suggest recipes based on the evaluation results of the inventory evaluation unit. For example, the recipe suggestion unit can suggest recipes that combine products that are about to be discarded with other ingredients. The recipe suggestion unit can also suggest recipes that use seasonal ingredients. Furthermore, the recipe suggestion unit can make suggestions taking into account the user's dietary restrictions and allergy information. This improves the accuracy of suggestions by suggesting recipes based on the inventory evaluation results.

[0077] The display unit can display the recipe suggested by the recipe suggestion unit on a display device. The display unit can display the recipe on digital signage in a store, for example. The display unit can also display the recipe on a smartphone or tablet. This allows the suggested recipe to be displayed on a display device, thereby providing information visually to the user.

[0078] The demand prediction unit can estimate the user's emotions and adjust the timing of demand prediction based on the estimated user emotions. For example, if the user is feeling stressed, the demand prediction unit can reduce the frequency of demand prediction to reduce the burden on the user. Furthermore, if the user is relaxed, the demand prediction unit can increase the frequency of demand prediction and provide detailed data. Furthermore, if the user is in a hurry, the demand prediction unit can quickly perform demand prediction and provide the results immediately. This reduces the burden on the user by adjusting the timing of demand prediction according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The demand forecasting unit can forecast demand based on weather data and local event information in addition to past sales data. For example, the demand forecasting unit can forecast demand for umbrellas and raincoats on rainy days based on weather data. The demand forecasting unit can also forecast that specific products will sell well on festival or event days based on local event information. Furthermore, the demand forecasting unit can combine past sales data and weather data to forecast seasonal demand patterns. In this way, the accuracy of demand forecasting can be improved by taking weather data and local event information into consideration.

[0080] The demand forecasting unit can analyze demand patterns for specific days of the week and time periods. For example, the demand forecasting unit can predict that there will be high demand for bread and milk on weekday mornings. The demand forecasting unit can also predict that there will be high demand for alcoholic beverages on weekend evenings. Furthermore, the demand forecasting unit can analyze sales patterns for specific products during specific time periods and reflect this in the demand forecast. In this way, the accuracy of the demand forecast can be improved by analyzing demand patterns for specific days of the week and time periods.

[0081] The demand forecasting unit can improve the accuracy of the forecast based on the sales data of competing stores. For example, the demand forecasting unit acquires sales data of competing stores and predicts demand in the same area. The demand forecasting unit can also predict when a particular product will sell based on the sales data of competing stores. Furthermore, the demand forecasting unit can also improve the accuracy of the demand forecast by referring to the sales data of competing stores. In this way, by referring to the sales data of competing stores, the accuracy of the demand forecast is improved.

[0082] The demand forecasting unit can estimate the user's emotions and determine the priority of demand forecasts based on the estimated user emotions. For example, when the user is feeling stressed, the demand forecasting unit prioritizes important demand forecasts. Furthermore, when the user is relaxed, the demand forecasting unit can perform detailed demand forecasts and adjust the priority. Furthermore, when the user is in a hurry, the demand forecasting unit can perform demand forecasts immediately and determine the priority. This reduces the burden on the user by determining the priority of demand forecasts according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The demand forecasting unit can make predictions based on social media trend information. For example, the demand forecasting unit predicts demand based on products that are trending on social media. The demand forecasting unit can also analyze social media trend information and predict when a particular product will sell. Furthermore, the demand forecasting unit can incorporate social media trend information to improve the accuracy of the demand forecast. In this way, incorporating social media trend information improves the accuracy of the demand forecast.

[0084] The demand forecasting unit can analyze the user's purchasing history and perform individual demand forecasts. The demand forecasting unit can predict individual demand based on, for example, the user's past purchasing history. The demand forecasting unit can also analyze the user's purchasing patterns and predict when a particular product will sell. Furthermore, the demand forecasting unit can analyze the user's purchasing history and perform individual demand forecasts to improve accuracy. In this way, by analyzing the user's purchasing history, the accuracy of individual demand forecasts can be improved.

[0085] The demand forecasting unit can make a forecast based on regional demographic data. The demand forecasting unit, for example, forecasts demand based on regional demographic data. The demand forecasting unit can also analyze regional demographic data and predict when a particular product will sell. Furthermore, the demand forecasting unit can also improve the accuracy of the demand forecast by taking regional demographic data into account. As a result, the accuracy of the demand forecast is improved by taking regional demographic data into account.

[0086] The inventory assessment unit can estimate the user's emotions and adjust the inventory assessment criteria based on the estimated user emotions. For example, if the user is feeling stressed, the inventory assessment unit can relax the inventory assessment criteria to reduce the burden on the user. Furthermore, if the user is relaxed, the inventory assessment unit can perform a detailed inventory assessment and tighten the criteria. Furthermore, if the user is in a hurry, the inventory assessment unit can quickly perform an inventory assessment and adjust the criteria. This reduces the burden on the user by adjusting the inventory assessment criteria 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The inventory evaluation unit can evaluate the risk of disposal based on the shelf life and quality information of the product. The inventory evaluation unit evaluates the risk of disposal based on, for example, the shelf life of the product. The inventory evaluation unit can also evaluate the risk of disposal by taking into account the quality information of the product. Furthermore, the inventory evaluation unit can evaluate the risk of disposal by combining the shelf life and quality information. In this way, by taking into account the shelf life and quality information of the product, the accuracy of the evaluation of the risk of disposal is improved.

[0088] The inventory evaluation unit can improve the accuracy of evaluation based on past disposal data. The inventory evaluation unit performs inventory evaluation based on, for example, past disposal data. The inventory evaluation unit can also evaluate disposal risk by referring to past disposal data. Furthermore, the inventory evaluation unit can analyze past disposal data and improve the accuracy of inventory evaluation. As a result, the accuracy of inventory evaluation is improved by referring to past disposal data.

[0089] The inventory evaluation unit can perform evaluation based on product supply chain information. The inventory evaluation unit performs inventory evaluation based on, for example, product supply chain information. The inventory evaluation unit can also evaluate waste risk by taking supply chain information into consideration. Furthermore, the inventory evaluation unit can also improve the accuracy of inventory evaluation by referring to supply chain information. As a result, the accuracy of inventory evaluation is improved by taking product supply chain information into consideration.

[0090] The inventory assessment unit can estimate the user's emotions and adjust the order in which inventory assessment results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the inventory assessment unit can prioritize and display important inventory assessment results. Furthermore, if the user is relaxed, the inventory assessment unit can also display detailed inventory assessment results. Furthermore, if the user is in a hurry, the inventory assessment unit can instantly display inventory assessment results. This reduces the burden on the user by adjusting the order in which inventory assessment results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The inventory evaluation unit can perform evaluation based on product price fluctuation data. The inventory evaluation unit performs inventory evaluation based on, for example, product price fluctuation data. The inventory evaluation unit can also evaluate waste risk by taking price fluctuation data into consideration. Furthermore, the inventory evaluation unit can also improve the accuracy of inventory evaluation by referring to price fluctuation data. As a result, the accuracy of inventory evaluation is improved by taking product price fluctuation data into consideration.

[0092] The inventory evaluation unit can perform evaluation by referring to sales promotion campaign information for the product. The inventory evaluation unit performs inventory evaluation based on, for example, the sales promotion campaign information. The inventory evaluation unit can also evaluate waste risk by taking the sales promotion campaign information into consideration. Furthermore, the inventory evaluation unit can also improve the accuracy of inventory evaluation by referring to the sales promotion campaign information. As a result, the accuracy of inventory evaluation is improved by referring to the sales promotion campaign information.

[0093] The inventory evaluation unit can apply different evaluation criteria to each product category. For example, the inventory evaluation unit applies different evaluation criteria to fresh foods and processed foods. The inventory evaluation unit can also set evaluation criteria for each product category, taking into account shelf life and quality information. Furthermore, the inventory evaluation unit can also apply evaluation criteria for each category to improve the accuracy of inventory evaluation. In this way, applying evaluation criteria for each category improves the accuracy of inventory evaluation.

[0094] The recipe suggestion unit can estimate the user's emotions and adjust the recipe suggestion method based on the estimated user emotions. For example, if the user is feeling stressed, the recipe suggestion unit can suggest simple and easy recipes. Furthermore, if the user is relaxed, the recipe suggestion unit can also suggest recipes that take time to make. Furthermore, if the user is in a hurry, the recipe suggestion unit can also suggest recipes that can be made in a short time. This reduces the burden on the user by adjusting the recipe suggestion 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. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] The recipe suggestion unit can make suggestions based on the use of seasonal ingredients. For example, the recipe suggestion unit can suggest recipes that use seasonal ingredients. The recipe suggestion unit can also suggest recipes taking into account the use of seasonal ingredients. Furthermore, the recipe suggestion unit can also suggest recipes based on seasonal ingredients. This improves the accuracy of recipe suggestions by taking into account seasonal ingredients.

[0096] The recipe suggestion unit can improve suggestion accuracy based on past recipe suggestion results. The recipe suggestion unit suggests recipes based on past recipe suggestion results, for example. The recipe suggestion unit can also improve suggestion accuracy by referring to past recipe suggestion results. Furthermore, the recipe suggestion unit can analyze past recipe suggestion results and suggest optimal recipes. In this way, suggestion accuracy is improved by referring to past recipe suggestion results.

[0097] The recipe suggestion unit can make suggestions based on the user's dietary restrictions and allergy information. For example, the recipe suggestion unit can suggest recipes taking into account the user's dietary restrictions. The recipe suggestion unit can also suggest recipes based on the user's allergy information. Furthermore, the recipe suggestion unit can also suggest optimal recipes taking into account the user's dietary restrictions and allergy information. This allows appropriate recipes to be suggested by taking into account the user's dietary restrictions and allergy information.

[0098] The recipe suggestion unit can estimate the user's emotions and prioritize recipes based on the estimated user emotions. For example, if the user is feeling stressed, the recipe suggestion unit can prioritize and suggest recipes that are easy and quick to make. Furthermore, if the user is relaxed, the recipe suggestion unit can prioritize and suggest recipes that take time to make. Furthermore, if the user is in a hurry, the recipe suggestion unit can prioritize and suggest recipes that can be made in a short time. This reduces the burden on the user by prioritizing recipes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The recipe suggestion unit can make suggestions based on the food culture and preferences of the region. For example, the recipe suggestion unit can suggest recipes using local ingredients, taking into account the food culture of the region. The recipe suggestion unit can also suggest popular recipes based on the preferences of the region. Furthermore, the recipe suggestion unit can also suggest optimal recipes, taking into account the food culture and preferences of the region. This improves the accuracy of recipe suggestions by taking into account the food culture and preferences of the region.

[0100] The recipe suggestion unit can suggest individual recipes by referring to the user's past purchase history. The recipe suggestion unit can suggest individual recipes based on, for example, the user's past purchase history. The recipe suggestion unit can also analyze the user's purchasing patterns and suggest recipes using specific ingredients. Furthermore, the recipe suggestion unit can suggest individual recipes by referring to the user's purchase history, thereby improving accuracy. In this way, by referring to the user's past purchase history, the accuracy of individual recipe suggestions is improved.

[0101] The recipe suggestion unit can make suggestions based on trending information on social media. For example, the recipe suggestion unit makes suggestions based on popular recipes on social media. The recipe suggestion unit can also analyze trending information on social media and suggest popular recipes. Furthermore, the recipe suggestion unit can incorporate trending information on social media to improve the accuracy of recipe suggestions. In this way, incorporating trending information on social media improves the accuracy of recipe suggestions.

[0102] The display unit can estimate the user's emotions and adjust the display content based on the estimated user emotions. For example, when the user is feeling stressed, the display unit provides simple, highly visible display content. When the user is relaxed, the display unit can also provide display content including detailed information. When the user is in a hurry, the display unit can also provide display content that focuses on the main points. This reduces the burden on the user by adjusting the display content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The display unit can select the optimal display method by referring to the user's past operation history. The display unit selects the optimal display method based on, for example, the user's past operation history. The display unit can also analyze the user's operation patterns and suggest a specific display method. Furthermore, the display unit can select the optimal display method by referring to the user's operation history and improve accuracy. In this way, the optimal display method can be selected by referring to the user's past operation history.

[0104] The display unit can customize the display content according to the user's current task. For example, if the user is shopping, the display unit can display information about related products. Also, if the user is searching for a recipe, the display unit can display related recipes. Furthermore, the display unit can customize the display content according to the user's current task and provide optimal information. This makes it possible to provide optimal information by customizing the display content according to the user's current task.

[0105] The display unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information.

[0106] The display unit can estimate the user's emotions and determine display priorities based on the estimated user emotions. For example, when the user is feeling stressed, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can prioritize displaying detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying information that is immediately required. This reduces the burden on the user by determining display priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The display unit can make the display content multilingual according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can provide the display content in that language. This makes it possible to provide appropriate information to the user by making the display content multilingual according to the user's language setting.

[0108] The display unit can analyze the user's social media activity and provide related information. For example, the display unit can provide information about places where the user has checked in on social media. The display unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the display unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity.

[0109] The special price proposal unit can estimate the user's emotions and adjust the special price proposal method based on the estimated user emotions. For example, if the user is feeling stressed, the special price proposal unit can propose a simple and easy-to-understand special price. Furthermore, if the user is relaxed, the special price proposal unit can also propose a detailed special price. Furthermore, if the user is in a hurry, the special price proposal unit can also propose a quick special price. This reduces the burden on the user by adjusting the special price proposal method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The special price proposal unit can make proposals based on the inventory status and waste risk of the product. The special price proposal unit can propose a special price based on, for example, the inventory status of the product. The special price proposal unit can also propose a special price taking into account the waste risk. Furthermore, the special price proposal unit can propose a special price by combining the inventory status and the waste risk. In this way, by taking into account the inventory status and the waste risk, the accuracy of the special price proposal is improved.

[0111] The special price proposal unit can improve proposal accuracy based on past special price proposal results. The special price proposal unit proposes a special price based on, for example, past special price proposal results. The special price proposal unit can also improve proposal accuracy by referring to past special price proposal results. Furthermore, the special price proposal unit can analyze past special price proposal results and propose the optimal special price. In this way, proposal accuracy is improved by referring to past special price proposal results.

[0112] The special price proposal unit can make proposals based on the price competition situation in the region. The special price proposal unit can propose a special price based on, for example, the price competition situation in the region. The special price proposal unit can also propose a special price taking the price competition situation into consideration. Furthermore, the special price proposal unit can also improve the accuracy of special price proposals by referring to the price competition situation in the region. In this way, the accuracy of special price proposals can be improved by taking the price competition situation in the region into consideration.

[0113] The special price proposal unit can estimate the user's emotions and determine the priority of special prices based on the estimated user emotions. For example, if the user is feeling stressed, the special price proposal unit can prioritize proposing important special prices. The special price proposal unit can also prioritize proposing detailed special prices if the user is relaxed. Furthermore, if the user is in a hurry, the special price proposal unit can also prioritize proposing special prices immediately. This reduces the burden on the user by determining the priority of special prices according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The special price proposal unit can propose an individual special price based on the user's purchase history. The special price proposal unit proposes an individual special price based on, for example, the user's past purchase history. The special price proposal unit can also analyze the user's purchase patterns and propose a special price for a specific product. Furthermore, the special price proposal unit can also refer to the user's purchase history to propose an individual special price and improve accuracy. In this way, by referring to the user's purchase history, the accuracy of the individual special price proposal is improved.

[0115] The special price proposal unit can propose an individual special price based on the user's purchase history. The special price proposal unit proposes an individual special price based on, for example, the user's past purchase history. The special price proposal unit can also analyze the user's purchase patterns and propose a special price for a specific product. Furthermore, the special price proposal unit can also refer to the user's purchase history to propose an individual special price and improve accuracy. In this way, by referring to the user's purchase history, the accuracy of the individual special price proposal is improved. === Hard Collateral 1-1 === Each of the multiple elements, including the demand forecasting unit, inventory assessment unit, recipe suggestion unit, display unit, and special price proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the demand forecasting unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes past sales data and seasonal demand. The inventory assessment unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the risk of waste based on product shelf life and quality information. The recipe suggestion unit is implemented, for example, by the control unit 46A of the smart device 14 and proposes recipes combining products that are about to be discarded with other ingredients. The display unit is implemented, for example, by the output device 40 of the smart device 14 and displays recipes on digital signage in the store. The special price proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and offers products that are about to be discarded at special prices. === Hard Collateral 1-2 === Each of the multiple elements, including the demand forecasting unit, inventory assessment unit, recipe suggestion unit, display unit, and special price proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the demand forecasting unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes past sales data and seasonal demand. The inventory assessment unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the risk of waste based on product shelf life and quality information. The recipe suggestion unit is implemented, for example, by the control unit 46A of the smart glasses 214 and proposes recipes combining products that are about to be discarded with other ingredients. The display unit is implemented, for example, by the output device 40 of the smart glasses 214 and displays recipes on digital signage in the store. The special price proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and offers products that are about to be discarded at a special price. === Hard Collateral 1-3 === Each of the multiple elements, including the demand forecasting unit, inventory assessment unit, recipe suggestion unit, display unit, and special price proposal unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the demand forecasting unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes past sales data and seasonal demand. The inventory assessment unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the risk of waste based on product shelf life and quality information. The recipe suggestion unit is implemented, for example, by the control unit 46A of the headset terminal 314 and proposes recipes combining products that are about to be discarded with other ingredients. The display unit is implemented, for example, by the output device 40 of the headset terminal 314 and displays recipes on digital signage in the store. The special price proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and offers products that are about to be discarded at special prices. === Hard Collateral 1-4 === Each of the multiple elements, including the demand forecasting unit, inventory assessment unit, recipe suggestion unit, display unit, and special price proposal unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the demand forecasting unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes past sales data and seasonal demand. The inventory assessment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the risk of waste based on product shelf life and quality information. The recipe suggestion unit is realized, for example, by the control unit 46A of the robot 414 and proposes recipes combining products that are about to be discarded with other ingredients. The display unit is realized, for example, by the output device 40 of the robot 414 and displays recipes on digital signage in the store. The special price proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and offers products that are about to be discarded at a special price.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The management support system can further include a customer feedback collection unit. The customer feedback collection unit can collect feedback from customers and use it to improve the entire system. For example, reviews and ratings provided by customers after purchases can be collected and used to improve the accuracy of the demand forecasting unit and inventory evaluation unit. The customer feedback collection unit can also propose new recipes or special prices based on customer opinions. Furthermore, the customer feedback collection unit can monitor customer satisfaction in real time and take immediate countermeasures. This enables flexible management support that reflects customer feedback.

[0118] The management support system can further include an energy consumption optimization unit. The energy consumption optimization unit monitors energy consumption within the store and proposes efficient energy usage. For example, it analyzes the usage of refrigerators and lights and proposes optimal energy consumption patterns. The energy consumption optimization unit can also adjust energy consumption based on weather data and business hours. Furthermore, the energy consumption optimization unit aims to reduce energy costs and contributes to more efficient store operations. This makes it possible to reduce management costs through the optimization of energy consumption.

[0119] The management support system can further include a customer behavior analysis unit. The customer behavior analysis unit analyzes customer behavior within the store and uses this information to help formulate marketing strategies. For example, it analyzes customer movement patterns and length of stay to optimize product placement and promotions. The customer behavior analysis unit can also make personalized suggestions based on customers' purchasing history and preferences. Furthermore, the customer behavior analysis unit can collect customer behavior data in real time and instantly adjust marketing measures. This enables effective marketing based on customer behavior.

[0120] The management support system can further be equipped with a supply chain optimization unit. The supply chain optimization unit monitors the entire product supply chain to ensure efficient supply. For example, it analyzes logistics data from the supplier to the store and proposes the optimal supply route. The supply chain optimization unit can also adjust the supply schedule based on inventory status and demand forecast data. Furthermore, the supply chain optimization unit can identify bottlenecks in the supply chain and propose improvement measures. This improves the efficiency of the entire supply chain and contributes to stabilizing management.

[0121] The management support system can further include an environmental impact assessment section. The environmental impact assessment section evaluates the impact that store operations have on the environment and supports sustainable management. For example, it monitors the amount of waste and energy consumption and makes suggestions to minimize environmental impact. The environmental impact assessment section can also suggest the use of recyclable materials and eco-friendly products. Furthermore, the environmental impact assessment section can formulate environmentally conscious management policies and raise environmental awareness throughout the store. This will help achieve environmentally friendly, sustainable management.

[0122] The demand forecasting unit can estimate the user's emotions and adjust the accuracy of the demand forecast based on the estimated user emotions. For example, if the user is feeling stressed, the accuracy of the demand forecast can be increased and detailed data can be provided. Also, if the user is relaxed, the accuracy of the demand forecast can be adjusted and concise data can be provided. Furthermore, if the user is in a hurry, the demand forecast can be performed quickly and the results can be provided immediately. This reduces the burden on the user by adjusting the accuracy of the demand forecast according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The inventory assessment unit can estimate the user's emotions and adjust the frequency of inventory assessments based on the estimated user emotions. For example, if the user is feeling stressed, the inventory assessment frequency can be reduced to reduce the burden. Also, if the user is relaxed, the inventory assessment frequency can be increased and detailed data can be provided. Furthermore, if the user is in a hurry, the inventory assessment can be performed quickly and the results can be provided immediately. This reduces the burden on the user by adjusting the inventory assessment frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The recipe suggestion unit can estimate the user's emotions and adjust the recipe suggestions based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest simple and easy recipes. If the user is relaxed, it can suggest recipes that take time to make. Furthermore, if the user is in a hurry, it can suggest recipes that can be made in a short time. This reduces the burden on the user by adjusting the recipe suggestions 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 can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The display unit can estimate the user's emotions and adjust the priority of the display content based on the estimated user's emotions. For example, if the user is feeling stressed, important information can be displayed with priority. Also, if the user is relaxed, detailed information can be displayed with priority. Furthermore, if the user is in a hurry, information that is immediately required can be displayed with priority. This allows the priority of the display content to be adjusted according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0126] The special price proposal unit can estimate the user's emotions and adjust the content of the special price proposal based on the estimated user emotions. For example, if the user is feeling stressed, a simple and easy-to-understand special price proposal can be made. If the user is relaxed, a detailed special price proposal can be made. Furthermore, if the user is in a hurry, a quick special price proposal can be made. This reduces the burden on the user by adjusting the content of the special price proposal according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The demand forecasting unit obtains past sales data and forecasts demand. For example, it predicts the next required quantity of stock based on past sales history and sales volume. It can also analyze seasonal demand patterns to improve forecast accuracy. It can also forecast demand taking into account weather data and local event information. Step 2: The inventory evaluation unit evaluates the risk of inventory disposal based on the demand data predicted by the demand forecasting unit. For example, the disposal risk is evaluated based on the product's shelf life and quality information. It is also possible to improve the accuracy of the evaluation by referring to past disposal data. Furthermore, the evaluation can also take into account product supply chain information. Step 3: The recipe suggestion unit suggests recipes based on the evaluation results of the inventory evaluation unit. For example, it suggests recipes that combine products that are about to be discarded with other ingredients. It can also suggest recipes that use seasonal ingredients. It can also make suggestions that take into account the user's dietary restrictions and allergy information. Step 4: The display unit displays the recipe suggested by the recipe suggestion unit on a display device. For example, the recipe is displayed on digital signage in the store. The recipe can also be displayed on a smartphone or tablet.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0152] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0174] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] 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.

[0192] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a demand forecasting unit that forecasts demand; an inventory evaluation unit that evaluates a risk of disposal of inventory based on the demand data predicted by the demand forecasting unit; a recipe suggestion unit that suggests recipes based on the results of the evaluation by the inventory evaluation unit; a display unit that displays the recipes proposed by the recipe suggestion unit on a display device; Equipped with A system characterized by:

2. Equipped with a special price proposal department that proposes special prices 2. The system of claim 1.

3. The demand forecasting unit Obtain past sales data and forecast demand 2. The system of claim 1.

4. The inventory evaluation unit Evaluating the risk of discarding inventory based on the demand data predicted by the demand forecasting unit 2. The system of claim 1.

5. The recipe suggestion unit Proposes recipes based on the evaluation results of the inventory evaluation unit 2. The system of claim 1.

6. The display unit The recipe proposed by the recipe suggestion unit is displayed on a display device.

2. The system of claim 1.

7. The demand forecasting unit Estimate user emotions and adjust the timing of demand forecasts based on the estimated user emotions 2. The system of claim 1.

8. The demand forecasting unit Forecast demand based on past sales data, weather data, and local event information 2. The system of claim 1.

9. The demand forecasting unit Analyze demand patterns by specific days of the week or time of day 2. The system of claim 1.

10. The demand forecasting unit Improve forecast accuracy based on competitor sales data 2. The system of claim 1.

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