System

Abstract

To provide a system that optimizes the discount rates for unsold food items and efficiently provides them to consumers.SOLUTION: A system comprises: an input unit; an analysis unit; a calculation unit; and a provision unit. The input unit inputs information about unsold food items. The analysis unit analyzes past sales data or discount plans on the basis of the information input by the input unit. The calculation unit calculates an appropriate discount rate on the basis of the data analyzed by the analysis unit. The provision unit provides time-sale information to consumers on the basis of the discount rate calculated by the calculation unit.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately optimized the discount rates of unsold food products and efficiently provided them to consumers, and there is room for improvement.

[0005] The system according to the embodiment aims to optimize the discount rate of unsold food products and provide them to consumers efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a calculation unit, and a provision unit. The input unit inputs information on unsold food. The analysis unit analyzes past sales data or discount plans based on the information input by the input unit. The calculation unit calculates an appropriate discount rate based on the data analyzed by the analysis unit. The provision unit provides limited-time sale information to consumers based on the discount rate calculated by the calculation unit. Effect of the Invention

[0007] The system according to the embodiment can optimize the discount rate of unsold food products and efficiently provide them to consumers. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A delivery service system according to an embodiment of the present invention is a system that applies a time sale to unsold food at a restaurant, analyzes past sales data and discount plans using a generation AI (generation AI), and calculates an optimal discount rate. In this system, the restaurant inputs information on the unsold food, and the generation AI analyzes past sales data and discount plans to calculate an optimal discount rate. Based on the calculated discount rate, the system provides time sale information to consumers. Consumers can order meals at low cost, and the restaurant can reduce food waste and acquire new customers. For example, if a restaurant has 10 unsold pizzas, the information is input into the system. The generation AI analyzes past sales data and discount plans to calculate an optimal discount rate. For example, if it is known from past data that if the discount rate for pizza is set to 30%, the unsold pizza will sell quickly, the generation AI proposes a discount rate of 30%. Based on this discount rate, the system provides consumers with time sale information such as "30% off pizza." Consumers can order pizza at low cost, and the restaurant can sell the unsold pizza efficiently. This system has the benefit of allowing consumers to order meals at a lower price, and restaurants can reduce food waste and increase sales by acquiring new customers. As a result, the delivery service system can efficiently sell unsold food at restaurants, reducing food waste and increasing sales.

[0029] The delivery service system according to the embodiment includes an input unit, an analysis unit, a calculation unit, and a provision unit. The input unit inputs information on unsold food from the restaurant. For example, the input unit can input the type and quantity of unsold food from the restaurant to the system. The input unit provides an interface for manually inputting information, for example. The input unit can also automatically acquire information in cooperation with an inventory management system of the restaurant. For example, the input unit automatically acquires information on unsold food from the inventory management system and inputs it to the system. The analysis unit uses a generation AI to analyze past sales data and discount plans based on the information input by the input unit. For example, the analysis unit analyzes past sales data to grasp the sales trend of unsold food. The analysis unit also provides data for evaluating the effectiveness of past discount plans and calculating an optimal discount rate. For example, the analysis unit analyzes the sales of a specific food and the effectiveness of a discount plan based on past sales data. The calculation unit uses a generation AI to calculate an optimal discount rate based on the data analyzed by the analysis unit. For example, the calculation unit calculates a discount rate taking into account the type and quantity of unsold food, past sales data, and the effectiveness of a discount plan. The calculation unit sets different discount rates according to the type and quantity of unsold food, for example. The calculation unit also calculates an optimal discount rate based on past sales data and the effect of a discount plan. For example, the calculation unit calculates a discount rate for a specific food based on past data. The provision unit provides time sale information to consumers based on the discount rate calculated by the calculation unit. For example, the provision unit notifies consumers of the time sale information. The provision unit provides the time sale information through, for example, a mobile app or a website. The provision unit can also provide time sale information customized based on the consumer's purchase history and preferences. For example, the provision unit provides related time sale information based on the consumer's past purchase history. As a result, the delivery service system according to the embodiment can efficiently sell unsold food at restaurants, reduce food waste, and increase sales. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI.For example, the provision unit can input a consumer's purchasing history into the AI ​​and have the AI ​​provide customized limited-time sale information.

[0030] The input unit inputs information on unsold food items from the restaurant. For example, the input unit can input the type and quantity of unsold food items from the restaurant into the system. The input unit provides, for example, an interface for manually inputting information. The input unit can also automatically acquire information by linking with the restaurant's inventory management system. For example, the input unit automatically acquires information on unsold food items from the inventory management system and inputs it into the system. Specifically, the input unit can acquire data on unsold food items in real time by linking with the restaurant's POS system and inventory management software via an API. This allows the restaurant to omit manual input work and efficiently reflect information in the system. The input unit can also quickly and accurately input food information using a barcode scanner or a two-dimensional code (e.g., QR Code (registered trademark)) reader. For example, the restaurant staff simply scans the barcode of the unsold food item, and the necessary information is automatically entered into the system. In this way, the input unit provides both manual input and automatic acquisition methods, enabling flexible operation according to the needs of the restaurant. Furthermore, the input unit also has a function for checking the consistency of the input data and preventing erroneous input and duplicate input. For example, if the entered data contradicts existing data or an abnormal value is entered, a warning is displayed and the user is prompted to make corrections. This allows the input unit to provide accurate and reliable data to the system, helping to ensure that subsequent processing is carried out smoothly.

[0031] The analysis unit uses the generation AI to analyze past sales data and discount plans based on the information input by the input unit. For example, the analysis unit analyzes past sales data to grasp the sales trends of unsold food. In addition, the analysis unit evaluates the effectiveness of past discount plans and provides data for calculating the optimal discount rate. Specifically, the generation AI uses a machine learning algorithm to analyze past sales data and identify which time of day or day of the week a particular food is likely to sell or remain unsold. In addition, the generation AI evaluates the effectiveness of past discount plans and analyzes which discount rate was most effective. For example, it can evaluate the sales increase rate and consumer response when a particular discount rate is applied. This allows the analysis unit to grasp the sales trends and optimal discount rate of unsold food and provide useful data to the calculation unit. In addition, the analysis unit can perform more accurate analysis by incorporating external market data and trend information. For example, by taking into account seasonal consumer preferences and competitors' price trends, a more realistic sales strategy can be developed. This enables the analysis department to go beyond simply analyzing past data to predict future market trends and propose more effective sales strategies to restaurants.

[0032] The calculation unit uses the generation AI to calculate the optimal discount rate based on the data analyzed by the analysis unit. For example, the calculation unit calculates the discount rate by taking into account the type and quantity of unsold food, past sales data, and the effect of the discount plan. Specifically, the generation AI uses a complex algorithm that takes into account multiple variables to derive the optimal discount rate. For example, if the expiration date of the unsold food is close or if a large amount of inventory remains, a higher discount rate can be set. In addition, the calculation unit can calculate the optimal discount rate for a specific time period or day of the week based on past sales data and the effect of the discount plan. This allows the calculation unit to provide an optimal discount strategy for efficiently selling unsold food and reducing food waste. Furthermore, the calculation unit can update data in real time and dynamically adjust the discount rate according to the situation. For example, the discount rate can be instantly changed according to the inventory status of unsold food and the purchasing behavior of consumers to provide optimal sales conditions. This allows the calculation unit to realize a flexible discount strategy based on the latest information at all times and maximize the sales of restaurants.

[0033] The providing unit provides the time sale information to the consumer based on the discount rate calculated by the calculation unit. For example, the providing unit notifies the consumer of the time sale information. The providing unit provides the time sale information, for example, through a mobile app or a website. The providing unit can also provide customized time sale information based on the consumer's purchase history and preferences. Specifically, the providing unit inputs the consumer's past purchase history and preference data into the generation AI to generate time sale information optimal for each consumer. For example, customized time sale information can be provided taking into account foods that a particular consumer has purchased in the past and their preferred discount rate. This allows the providing unit to provide more attractive offers to consumers and increase their desire to purchase. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, the time sale information can be quickly and reliably delivered to consumers through push notifications of mobile apps, SMS, email, social media, etc. This allows the providing unit to provide information to consumers in a timely and effective manner and promote the sale of unsold foods.

[0034] The analysis unit can analyze past sales data and discount plans. For example, the analysis unit analyzes past sales data to grasp the sales trend of unsold food. For example, the analysis unit analyzes the sales trend of unsold food based on sales data for a specific period. The analysis unit also provides data for evaluating the effectiveness of past discount plans and calculating an optimal discount rate. For example, the analysis unit provides data for calculating the discount rate of a specific food based on the effectiveness of past discount plans. This allows for more accurate analysis by analyzing past sales data and discount plans. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input past sales data into AI and cause AI to perform an analysis of the sales trend of unsold food.

[0035] The calculation unit can calculate an appropriate discount rate based on the type and quantity of unsold food, past sales data, and the effect of the discount plan. The calculation unit, for example, sets different discount rates depending on the type and quantity of unsold food. For example, the calculation unit calculates an optimal discount rate based on the type and quantity of unsold food. The calculation unit also calculates an optimal discount rate based on past sales data and the effect of the discount plan. For example, the calculation unit calculates a discount rate for a specific food based on past data. This allows for efficient discounts by calculating an optimal discount rate taking into account the type and quantity of unsold food, past sales data, and the effect of the discount plan. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the type and quantity of unsold food, past sales data, and the effect of the discount plan into the AI, and cause the AI ​​to calculate the optimal discount rate.

[0036] The providing unit can provide time sale information to consumers. The providing unit, for example, notifies consumers of the time sale information. For example, the providing unit provides the time sale information through a mobile app or a website. The providing unit can also provide time sale information customized based on the consumer's purchase history and preferences. For example, the providing unit provides related time sale information based on the consumer's past purchase history. In this way, the sale information is provided to consumers, thereby promoting the sale of unsold food products. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the consumer's purchase history into AI and cause AI to provide customized time sale information.

[0037] The input unit allows the restaurant to input information on unsold food. The input unit allows the restaurant to input the type and quantity of unsold food into the system, for example. The input unit provides an interface for manually inputting information, for example. The input unit can also automatically acquire information in cooperation with an inventory management system of the restaurant. For example, the input unit automatically acquires information on unsold food from the inventory management system and inputs it into the system. In this way, the restaurant can input information on unsold food, and the system can perform processing based on accurate information. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input information on unsold food acquired from the inventory management system of the restaurant into AI, and cause the AI ​​to execute the input of information.

[0038] The input unit can analyze the restaurant's past input history and select the optimal input method. For example, the input unit preferentially suggests input methods that have been frequently used in the past. For example, the input unit suggests an optimal input method for a specific time period from the past input history. The input unit can also select a method with fewer input errors based on the past input history. For example, the input unit analyzes the past input history and selects a method with fewer input errors. In this way, the optimal input method can be selected by analyzing the past input history. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the past input history to AI and cause the AI ​​to select the optimal input method.

[0039] The input unit can filter the information on unsold food based on the current inventory status and business status of the restaurant when the information is input. The input unit, for example, filters the food information to be input, taking into account the current inventory status. For example, the input unit preferentially displays the food information to be input when the inventory is low. The input unit can also adjust the food information to be input based on the business status. For example, the input unit adjusts the food information to be input, taking into account whether the restaurant is open for business. This allows more accurate information to be input by filtering the information based on the current inventory status and business status. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input data on the current inventory status and business status to AI, and have the AI ​​execute the filtering of the information.

[0040] When inputting information on unsold food, the input unit can preferentially input highly relevant information in consideration of the geographical location information of the restaurant. For example, the input unit preferentially inputs food information related to nearby consumers based on the geographical location information. The input unit can also preferentially input food information that is popular in a specific area in consideration of the geographical location information. Furthermore, the input unit can also preferentially input food information that has a large number of unsold items in a specific area based on the geographical location information. In this way, highly relevant information can be preferentially input by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the geographical location information of the restaurant to AI, and cause the AI ​​to execute the preferential input of highly relevant information.

[0041] The input unit can analyze the social media activity of the restaurant and input related information when inputting information on unsold food. The input unit preferentially inputs related food information based on, for example, popularity on social media. The input unit can also analyze trends on social media and input related food information. Furthermore, the input unit can also input related food information based on consumer feedback on social media. In this way, related information can be input by analyzing social media activity. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input data on the social media activity of the restaurant to AI and cause AI to input related information.

[0042] During analysis, the analysis unit can predict current sales data by referring to trends in past sales data. The analysis unit can predict current sales data based on, for example, trends in past sales data. The analysis unit can also predict current sales data taking into account seasonal fluctuations in past sales data. Furthermore, the analysis unit can predict current sales data based on fluctuations in past sales data by day of the week. This makes it possible to predict current sales data by referring to trends in past sales data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without using AI. For example, the analysis unit can input trends in past sales data into AI and cause AI to execute a prediction of current sales data.

[0043] The analysis unit can apply different analysis algorithms to each food category during analysis. The analysis unit applies different analysis algorithms to each category, such as pizza, sushi, and salad. The analysis unit can also select an optimal analysis algorithm based on sales data for each category. Furthermore, the analysis unit can apply different analysis algorithms taking into account the effect of discount plans for each category. This allows for more accurate analysis by applying different analysis algorithms to each food category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data for each food category into AI and cause the AI ​​to apply different analysis algorithms.

[0044] During analysis, the analysis unit can determine the priority of the analysis based on the time of submission of the sales data. For example, the analysis unit prioritizes the analysis of the latest sales data. The analysis unit can also postpone data submitted earlier. Furthermore, the analysis unit can automatically adjust the priority of the analysis based on the time of submission. This enables efficient analysis by determining the priority of the analysis based on the time of submission of the sales data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the time of submission of the sales data to an AI and have the AI ​​determine the priority of the analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the sales data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the sales data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the sales data to AI and cause the AI ​​to adjust the order of analysis.

[0046] When calculating the discount rate, the calculation unit can calculate the optimal discount rate by referring to the effects of past discount plans. The calculation unit calculates the optimal discount rate based on, for example, the effects of past discount plans. The calculation unit can also determine the discount rate by referring to successful examples of past discount plans. Furthermore, the calculation unit can adjust the discount rate to avoid failed examples of past discount plans. In this way, the optimal discount rate can be calculated by referring to the effects of past discount plans. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the effects of past discount plans into AI and cause AI to calculate the optimal discount rate.

[0047] The calculation unit can apply different calculation algorithms depending on the type and quantity of food when calculating the discount rate. The calculation unit applies different calculation algorithms for each type of food, such as pizza, sushi, and salad. The calculation unit can also apply a calculation algorithm that adjusts the discount rate depending on the quantity of food. Furthermore, the calculation unit can combine the type and quantity of food to apply an optimal calculation algorithm. In this way, a more accurate discount rate can be calculated by applying different calculation algorithms depending on the type and quantity of food. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the type and quantity of food into AI and cause AI to apply different calculation algorithms.

[0048] The calculation unit can determine the priority of calculation based on the submission time of the unsold food when calculating the discount rate. For example, the calculation unit gives priority to the most recent unsold food in the calculation. The calculation unit can also postpone the food that was submitted earlier. Furthermore, the calculation unit can automatically adjust the priority of calculation based on the submission time. This enables efficient calculation by determining the priority of calculation based on the submission time of the unsold food. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the submission time of the unsold food to AI and have the AI ​​execute the determination of the priority of calculation.

[0049] The calculation unit can adjust the order of calculation based on the relevance of the unsold food when calculating the discount rate. For example, the calculation unit prioritizes calculation of highly related foods. The calculation unit can also postpone calculation of less related foods. Furthermore, the calculation unit can automatically adjust the order of calculation based on the relevance of the foods. This allows efficient calculation by adjusting the order of calculation based on the relevance of the unsold foods. A part or all of the above-mentioned processing in the calculation unit can be performed, for example, using AI or can be performed without using AI. For example, the calculation unit can input the relevance of the unsold foods to AI and cause AI to adjust the order of calculation.

[0050] The providing unit can select the optimal providing method by referring to the past providing history when providing the time sale information. The providing unit selects the optimal providing method based on the past providing history, for example. The providing unit can also select the optimal providing method for a specific time period from the past providing history. Furthermore, the providing unit can analyze the past providing history and select a method with fewer providing errors. In this way, the optimal providing method can be selected by referring to the past providing history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past providing history into AI and cause the AI ​​to select the optimal providing method.

[0051] The providing unit can customize the content of the sale based on the consumer's current purchase history when providing the sale information. The providing unit can provide related sale information based on the consumer's past purchase history, for example. The providing unit can also provide sale information related to a specific food from the consumer's current purchase history. Furthermore, the providing unit can analyze the consumer's purchase history and provide the most interesting sale information. This allows the provision of more relevant information by customizing the content of the sale based on the consumer's current purchase history. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the consumer's purchase history into AI and cause AI to customize the content of the sale.

[0052] The providing unit can select the optimal providing method in consideration of the geographical location information of the consumer when providing the time sale information. For example, the providing unit can provide time sale information related to nearby consumers based on the geographical location information. The providing unit can also provide time sale information that is popular in a specific area based on the geographical location information. Furthermore, the providing unit can provide time sale information of food that is largely unsold in a specific area based on the geographical location information. In this way, by considering the geographical location information of the consumer, more relevant information can be provided. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the geographical location information of the consumer to AI and cause the AI ​​to select the optimal providing method.

[0053] The providing unit can adjust the content of the sale information provided by analyzing the social media activity of the consumer when providing the sale information. The providing unit can provide related sale information based on, for example, popularity on social media. The providing unit can also analyze trends on social media and provide related sale information. Furthermore, the providing unit can provide related sale information based on consumer feedback on social media. This makes it possible to provide more relevant information by analyzing the consumer's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the consumer's social media activity into AI and cause AI to adjust the content of the sale.

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

[0055] The providing unit can individually adjust the discount rate for a particular food product based on the consumer's purchasing history. For example, a higher discount rate can be provided for food products that the consumer has frequently purchased in the past. Also, a special discount can be provided for food products that the consumer has never purchased in the past to encourage tasting. Furthermore, the purchasing history of the consumer can be analyzed to provide discounts for food products related to a particular season or event. This allows for a more personalized service to be provided by individually adjusting the discount rate based on the consumer's purchasing history.

[0056] The analytics department can analyze consumers' social media activities and adjust discount plans based on trends. For example, if a particular food item is trending on social media, a special discount can be offered for that food item. Also, based on posts that consumers have shared on social media, discounts on related foods can be suggested. In addition, the analytics department can analyze consumers' feedback on social media to evaluate the effectiveness of discount plans. This allows the analytics department to provide more effective discount plans by analyzing social media activities.

[0057] The calculation unit can set different discount rates for each region, taking into account the geographic location information of the consumer. For example, a higher discount rate can be offered for food that is popular in a particular region. Also, a special discount can be offered for food that is largely unsold in a particular region. Furthermore, the calculation unit can analyze the purchasing trends of consumers in each region and set an optimal discount rate. In this way, the calculation unit can provide an optimal discount rate for each region, taking into account the geographic location information.

[0058] The providing unit can provide optimal time sale information for a specific time period based on the consumer's purchasing history. For example, a special discount can be provided for a specific time period for food that the consumer frequently purchased during that time period in the past. Also, a special discount can be provided on a specific day of the week for food that the consumer purchased on that day in the past. Furthermore, the providing unit can analyze the consumer's purchasing history and provide time sale information related to a specific event or season. This makes it possible to provide optimal time sale information based on the consumer's purchasing history.

[0059] The input unit can work with the restaurant's inventory management system to update information about unsold food in real time. For example, it can automatically obtain information about unsold food from the inventory management system and input it into the system. It can also work with the inventory management system to update the quantity and type of unsold food in real time. Furthermore, it can automatically filter information about unsold food based on data from the inventory management system. In this way, by working with the inventory management system, more accurate information can be input in real time.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The input unit inputs information about unsold food items from a restaurant. For example, the input unit can input the type and quantity of unsold food items from a restaurant into the system. The input unit provides an interface for manually inputting information. The input unit can also automatically obtain information by linking with an inventory management system of the restaurant. Step 2: The analysis unit uses the generation AI to analyze past sales data and discount plans based on the information input by the input unit. For example, the analysis unit analyzes past sales data to understand sales trends of unsold food products. The analysis unit also evaluates the effectiveness of past discount plans and provides data for calculating optimal discount rates. Step 3: The calculation unit uses the generation AI to calculate the optimal discount rate based on the data analyzed by the analysis unit. For example, the calculation unit calculates the discount rate taking into account the type and quantity of unsold food, past sales data, and the effect of the discount plan. The calculation unit sets different discount rates depending on the type and quantity of unsold food. Step 4: The providing unit provides the limited-time sale information to the consumer based on the discount rate calculated by the calculation unit. For example, the providing unit notifies the consumer of the limited-time sale information. The providing unit provides the limited-time sale information through a mobile app or a website. The providing unit can also provide limited-time sale information customized based on the consumer's purchase history and preferences.

[0062] (Example 2) A delivery service system according to an embodiment of the present invention is a system that applies a time sale to unsold food at a restaurant, analyzes past sales data and discount plans using a generation AI (generation AI), and calculates an optimal discount rate. In this system, the restaurant inputs information on the unsold food, and the generation AI analyzes past sales data and discount plans to calculate an optimal discount rate. Based on the calculated discount rate, the system provides time sale information to consumers. Consumers can order meals at low cost, and the restaurant can reduce food waste and acquire new customers. For example, if a restaurant has 10 unsold pizzas, the information is input into the system. The generation AI analyzes past sales data and discount plans to calculate an optimal discount rate. For example, if it is known from past data that if the discount rate for pizza is set to 30%, the unsold pizza will sell quickly, the generation AI proposes a discount rate of 30%. Based on this discount rate, the system provides consumers with time sale information such as "30% off pizza." Consumers can order pizza at low cost, and the restaurant can sell the unsold pizza efficiently. This system has the benefit of allowing consumers to order meals at a lower price, and restaurants can reduce food waste and increase sales by acquiring new customers. As a result, the delivery service system can efficiently sell unsold food at restaurants, reducing food waste and increasing sales.

[0063] The delivery service system according to the embodiment includes an input unit, an analysis unit, a calculation unit, and a provision unit. The input unit inputs information on unsold food from the restaurant. For example, the input unit can input the type and quantity of unsold food from the restaurant to the system. The input unit provides an interface for manually inputting information, for example. The input unit can also automatically acquire information in cooperation with an inventory management system of the restaurant. For example, the input unit automatically acquires information on unsold food from the inventory management system and inputs it to the system. The analysis unit uses a generation AI to analyze past sales data and discount plans based on the information input by the input unit. For example, the analysis unit analyzes past sales data to grasp the sales trend of unsold food. The analysis unit also provides data for evaluating the effectiveness of past discount plans and calculating an optimal discount rate. For example, the analysis unit analyzes the sales of a specific food and the effectiveness of a discount plan based on past sales data. The calculation unit uses a generation AI to calculate an optimal discount rate based on the data analyzed by the analysis unit. For example, the calculation unit calculates a discount rate taking into account the type and quantity of unsold food, past sales data, and the effectiveness of a discount plan. The calculation unit sets different discount rates according to the type and quantity of unsold food, for example. The calculation unit also calculates an optimal discount rate based on past sales data and the effect of a discount plan. For example, the calculation unit calculates a discount rate for a specific food based on past data. The provision unit provides time sale information to consumers based on the discount rate calculated by the calculation unit. For example, the provision unit notifies consumers of the time sale information. The provision unit provides the time sale information through, for example, a mobile app or a website. The provision unit can also provide time sale information customized based on the consumer's purchase history and preferences. For example, the provision unit provides related time sale information based on the consumer's past purchase history. As a result, the delivery service system according to the embodiment can efficiently sell unsold food at restaurants, reduce food waste, and increase sales. A part or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI.For example, the provision unit can input a consumer's purchasing history into the AI ​​and have the AI ​​provide customized limited-time sale information.

[0064] The input unit inputs information on unsold food items from the restaurant. For example, the input unit can input the type and quantity of unsold food items from the restaurant into the system. The input unit provides, for example, an interface for manually inputting information. The input unit can also automatically acquire information by linking with the restaurant's inventory management system. For example, the input unit automatically acquires information on unsold food items from the inventory management system and inputs it into the system. Specifically, the input unit can acquire data on unsold food items in real time by linking with the restaurant's POS system and inventory management software via an API. This allows the restaurant to omit manual input work and efficiently reflect information in the system. The input unit can also quickly and accurately input food information using a barcode scanner or a two-dimensional code (e.g., QR code) reader. For example, the restaurant staff simply scans the barcode of the unsold food item, and the necessary information is automatically entered into the system. In this way, the input unit provides both manual input and automatic acquisition methods, enabling flexible operation according to the needs of the restaurant. Furthermore, the input unit also has a function for checking the consistency of the input data and preventing erroneous input and duplicate input. For example, if the entered data contradicts existing data or an abnormal value is entered, a warning is displayed and the user is prompted to make corrections. This allows the input unit to provide accurate and reliable data to the system, helping to ensure that subsequent processing is carried out smoothly.

[0065] The analysis unit uses the generation AI to analyze past sales data and discount plans based on the information input by the input unit. For example, the analysis unit analyzes past sales data to grasp the sales trends of unsold food. In addition, the analysis unit evaluates the effectiveness of past discount plans and provides data for calculating the optimal discount rate. Specifically, the generation AI uses a machine learning algorithm to analyze past sales data and identify which time of day or day of the week a particular food is likely to sell or remain unsold. In addition, the generation AI evaluates the effectiveness of past discount plans and analyzes which discount rate was most effective. For example, it can evaluate the sales increase rate and consumer response when a particular discount rate is applied. This allows the analysis unit to grasp the sales trends and optimal discount rate of unsold food and provide useful data to the calculation unit. In addition, the analysis unit can perform more accurate analysis by incorporating external market data and trend information. For example, by taking into account seasonal consumer preferences and competitors' price trends, a more realistic sales strategy can be developed. This enables the analysis department to go beyond simply analyzing past data to predict future market trends and propose more effective sales strategies to restaurants.

[0066] The calculation unit uses the generation AI to calculate the optimal discount rate based on the data analyzed by the analysis unit. For example, the calculation unit calculates the discount rate by taking into account the type and quantity of unsold food, past sales data, and the effect of the discount plan. Specifically, the generation AI uses a complex algorithm that takes into account multiple variables to derive the optimal discount rate. For example, if the expiration date of the unsold food is close or if a large amount of inventory remains, a higher discount rate can be set. In addition, the calculation unit can calculate the optimal discount rate for a specific time period or day of the week based on past sales data and the effect of the discount plan. This allows the calculation unit to provide an optimal discount strategy for efficiently selling unsold food and reducing food waste. Furthermore, the calculation unit can update data in real time and dynamically adjust the discount rate according to the situation. For example, the discount rate can be instantly changed according to the inventory status of unsold food and the purchasing behavior of consumers to provide optimal sales conditions. This allows the calculation unit to realize a flexible discount strategy based on the latest information at all times and maximize the sales of restaurants.

[0067] The providing unit provides the time sale information to the consumer based on the discount rate calculated by the calculation unit. For example, the providing unit notifies the consumer of the time sale information. The providing unit provides the time sale information, for example, through a mobile app or a website. The providing unit can also provide customized time sale information based on the consumer's purchase history and preferences. Specifically, the providing unit inputs the consumer's past purchase history and preference data into the generation AI to generate time sale information optimal for each consumer. For example, customized time sale information can be provided taking into account foods that a particular consumer has purchased in the past and their preferred discount rate. This allows the providing unit to provide more attractive offers to consumers and increase their desire to purchase. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, the time sale information can be quickly and reliably delivered to consumers through push notifications of mobile apps, SMS, email, social media, etc. This allows the providing unit to provide information to consumers in a timely and effective manner and promote the sale of unsold foods.

[0068] The analysis unit can analyze past sales data and discount plans. For example, the analysis unit analyzes past sales data to grasp the sales trend of unsold food. For example, the analysis unit analyzes the sales trend of unsold food based on sales data for a specific period. The analysis unit also provides data for evaluating the effectiveness of past discount plans and calculating an optimal discount rate. For example, the analysis unit provides data for calculating the discount rate of a specific food based on the effectiveness of past discount plans. This allows for more accurate analysis by analyzing past sales data and discount plans. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input past sales data into AI and cause AI to perform an analysis of the sales trend of unsold food.

[0069] The calculation unit can calculate an appropriate discount rate based on the type and quantity of unsold food, past sales data, and the effect of the discount plan. The calculation unit, for example, sets different discount rates depending on the type and quantity of unsold food. For example, the calculation unit calculates an optimal discount rate based on the type and quantity of unsold food. The calculation unit also calculates an optimal discount rate based on past sales data and the effect of the discount plan. For example, the calculation unit calculates a discount rate for a specific food based on past data. This allows for efficient discounts by calculating an optimal discount rate taking into account the type and quantity of unsold food, past sales data, and the effect of the discount plan. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the type and quantity of unsold food, past sales data, and the effect of the discount plan into the AI, and cause the AI ​​to calculate the optimal discount rate.

[0070] The providing unit can provide time sale information to consumers. The providing unit, for example, notifies consumers of the time sale information. For example, the providing unit provides the time sale information through a mobile app or a website. The providing unit can also provide time sale information customized based on the consumer's purchase history and preferences. For example, the providing unit provides related time sale information based on the consumer's past purchase history. In this way, the sale information is provided to consumers, thereby promoting the sale of unsold food products. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the consumer's purchase history into AI and cause AI to provide customized time sale information.

[0071] The input unit allows the restaurant to input information on unsold food. The input unit allows the restaurant to input the type and quantity of unsold food into the system, for example. The input unit provides an interface for manually inputting information, for example. The input unit can also automatically acquire information in cooperation with an inventory management system of the restaurant. For example, the input unit automatically acquires information on unsold food from the inventory management system and inputs it into the system. In this way, the restaurant can input information on unsold food, and the system can perform processing based on accurate information. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input information on unsold food acquired from the inventory management system of the restaurant into AI, and cause the AI ​​to execute the input of information.

[0072] The input unit can estimate the user's emotion and adjust the timing of inputting information on unsold food based on the estimated emotion of the user. For example, when the user is feeling stressed, the input unit delays the input timing so that the user can input in a relaxed state. Also, when the user is in a hurry, the input unit can prompt the user to input immediately so that the user can input information quickly. Furthermore, when the user is relaxed, the input unit can cause the user to input information at a normal input timing. This allows the user to input information at a more appropriate timing by adjusting the input timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the input timing based on the emotion.

[0073] The input unit can analyze the restaurant's past input history and select the optimal input method. For example, the input unit preferentially suggests input methods that have been frequently used in the past. For example, the input unit suggests an optimal input method for a specific time period from the past input history. The input unit can also select a method with fewer input errors based on the past input history. For example, the input unit analyzes the past input history and selects a method with fewer input errors. In this way, the optimal input method can be selected by analyzing the past input history. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the past input history to AI and cause the AI ​​to select the optimal input method.

[0074] The input unit can filter the information on unsold food based on the current inventory status and business status of the restaurant when the information is input. The input unit, for example, filters the food information to be input, taking into account the current inventory status. For example, the input unit preferentially displays the food information to be input when the inventory is low. The input unit can also adjust the food information to be input based on the business status. For example, the input unit adjusts the food information to be input, taking into account whether the restaurant is open for business. This allows more accurate information to be input by filtering the information based on the current inventory status and business status. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input data on the current inventory status and business status to AI, and have the AI ​​execute the filtering of the information.

[0075] The input unit can estimate the user's emotion and determine the priority of food information to be input based on the estimated user's emotion. For example, when the user is feeling stressed, the input unit allows important food information to be input preferentially. Furthermore, when the user is relaxed, the input unit can also allow detailed food information to be input preferentially. Furthermore, when the user is in a hurry, the input unit can also allow simple food information to be input preferentially. This allows more appropriate information to be input by determining the priority of food information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of food information based on the emotion.

[0076] When inputting information on unsold food, the input unit can preferentially input highly relevant information in consideration of the geographical location information of the restaurant. For example, the input unit preferentially inputs food information related to nearby consumers based on the geographical location information. The input unit can also preferentially input food information that is popular in a specific area in consideration of the geographical location information. Furthermore, the input unit can also preferentially input food information that has a large number of unsold items in a specific area based on the geographical location information. In this way, highly relevant information can be preferentially input by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the geographical location information of the restaurant to AI, and cause the AI ​​to execute the preferential input of highly relevant information.

[0077] The input unit can analyze the social media activity of the restaurant and input related information when inputting information on unsold food. The input unit preferentially inputs related food information based on, for example, popularity on social media. The input unit can also analyze trends on social media and input related food information. Furthermore, the input unit can also input related food information based on consumer feedback on social media. In this way, related information can be input by analyzing social media activity. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input data on the social media activity of the restaurant to AI and cause AI to input related information.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method of the sales data and the discount plan based on the estimated user's emotions. For example, the analysis unit provides a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can also provide a concise analysis result when the user is in a hurry. Furthermore, the analysis unit can provide a visually easy-to-understand analysis result when the user is stressed. This makes it possible to provide a more appropriate analysis result by adjusting the analysis method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the analysis method based on the emotion.

[0079] During analysis, the analysis unit can predict current sales data by referring to trends in past sales data. The analysis unit can predict current sales data based on, for example, trends in past sales data. The analysis unit can also predict current sales data taking into account seasonal fluctuations in past sales data. Furthermore, the analysis unit can predict current sales data based on fluctuations in past sales data by day of the week. This makes it possible to predict current sales data by referring to trends in past sales data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without using AI. For example, the analysis unit can input trends in past sales data into AI and cause AI to execute a prediction of current sales data.

[0080] The analysis unit can apply different analysis algorithms to each food category during analysis. The analysis unit applies different analysis algorithms to each category, such as pizza, sushi, and salad. The analysis unit can also select an optimal analysis algorithm based on sales data for each category. Furthermore, the analysis unit can apply different analysis algorithms taking into account the effect of discount plans for each category. This allows for more accurate analysis by applying different analysis algorithms to each food category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data for each food category into AI and cause the AI ​​to apply different analysis algorithms.

[0081] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI and cause the AI ​​to adjust the display method based on the emotion.

[0082] During analysis, the analysis unit can determine the priority of the analysis based on the time of submission of the sales data. For example, the analysis unit prioritizes the analysis of the latest sales data. The analysis unit can also postpone data submitted earlier. Furthermore, the analysis unit can automatically adjust the priority of the analysis based on the time of submission. This enables efficient analysis by determining the priority of the analysis based on the time of submission of the sales data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the time of submission of the sales data to an AI and have the AI ​​determine the priority of the analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the sales data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the sales data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the sales data to AI and cause the AI ​​to adjust the order of analysis.

[0084] The calculation unit can estimate the user's emotion and adjust the calculation method of the discount rate based on the estimated user's emotion. For example, when the user is relaxed, the calculation unit applies a detailed calculation method. Furthermore, when the user is in a hurry, the calculation unit can also apply a simple calculation method. Furthermore, when the user is stressed, the calculation unit can also apply a visually easy-to-understand calculation method. In this way, by adjusting the calculation method according to the user's emotion, a more appropriate discount rate can be calculated. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the calculation method based on the emotion.

[0085] When calculating the discount rate, the calculation unit can calculate the optimal discount rate by referring to the effects of past discount plans. The calculation unit calculates the optimal discount rate based on, for example, the effects of past discount plans. The calculation unit can also determine the discount rate by referring to successful examples of past discount plans. Furthermore, the calculation unit can adjust the discount rate to avoid failed examples of past discount plans. In this way, the optimal discount rate can be calculated by referring to the effects of past discount plans. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the effects of past discount plans into AI and cause AI to calculate the optimal discount rate.

[0086] The calculation unit can apply different calculation algorithms depending on the type and quantity of food when calculating the discount rate. The calculation unit applies different calculation algorithms for each type of food, such as pizza, sushi, and salad. The calculation unit can also apply a calculation algorithm that adjusts the discount rate depending on the quantity of food. Furthermore, the calculation unit can combine the type and quantity of food to apply an optimal calculation algorithm. In this way, a more accurate discount rate can be calculated by applying different calculation algorithms depending on the type and quantity of food. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the type and quantity of food into AI and cause AI to apply different calculation algorithms.

[0087] The calculation unit can estimate the user's emotion and adjust the display method of the discount rate based on the estimated user's emotion. For example, when the user is nervous, the calculation unit provides a simple and highly visible display method. In addition, when the user is relaxed, the calculation unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the calculation unit can also provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the user's emotion data into the AI ​​and cause the AI ​​to adjust the display method based on the emotion.

[0088] The calculation unit can determine the priority of calculation based on the submission time of the unsold food when calculating the discount rate. For example, the calculation unit gives priority to the most recent unsold food in the calculation. The calculation unit can also postpone the food that was submitted earlier. Furthermore, the calculation unit can automatically adjust the priority of calculation based on the submission time. This enables efficient calculation by determining the priority of calculation based on the submission time of the unsold food. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the submission time of the unsold food to AI and have the AI ​​execute the determination of the priority of calculation.

[0089] The calculation unit can adjust the order of calculation based on the relevance of the unsold food when calculating the discount rate. For example, the calculation unit prioritizes calculation of highly related foods. The calculation unit can also postpone calculation of less related foods. Furthermore, the calculation unit can automatically adjust the order of calculation based on the relevance of the foods. This allows efficient calculation by adjusting the order of calculation based on the relevance of the unsold foods. A part or all of the above-mentioned processing in the calculation unit can be performed, for example, using AI or can be performed without using AI. For example, the calculation unit can input the relevance of the unsold foods to AI and cause AI to adjust the order of calculation.

[0090] The providing unit can estimate the user's emotion and adjust the method of providing the time sale information based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide detailed time sale information. Furthermore, when the user is in a hurry, the providing unit can also provide brief time sale information. Furthermore, when the user is stressed, the providing unit can also provide visually easy-to-understand time sale information. This allows more appropriate information to be provided by adjusting the providing method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the providing method based on the emotion.

[0091] The providing unit can select the optimal providing method by referring to the past providing history when providing the time sale information. The providing unit selects the optimal providing method based on the past providing history, for example. The providing unit can also select the optimal providing method for a specific time period from the past providing history. Furthermore, the providing unit can analyze the past providing history and select a method with fewer providing errors. In this way, the optimal providing method can be selected by referring to the past providing history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past providing history into AI and cause the AI ​​to select the optimal providing method.

[0092] The providing unit can customize the content of the sale based on the consumer's current purchase history when providing the sale information. The providing unit can provide related sale information based on the consumer's past purchase history, for example. The providing unit can also provide sale information related to a specific food from the consumer's current purchase history. Furthermore, the providing unit can analyze the consumer's purchase history and provide the most interesting sale information. This allows the provision of more relevant information by customizing the content of the sale based on the consumer's current purchase history. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the consumer's purchase history into AI and cause AI to customize the content of the sale.

[0093] The providing unit can estimate the user's emotion and determine the priority of the time sale information based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide important time sale information with priority. In addition, when the user is relaxed, the providing unit can also provide detailed time sale information with priority. Furthermore, when the user is in a hurry, the providing unit can also provide concise time sale information with priority. This enables more appropriate information provision by determining the priority of information according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to an AI and cause the AI ​​to execute the determination of the priority of information based on the emotion.

[0094] The providing unit can select the optimal providing method in consideration of the geographical location information of the consumer when providing the time sale information. For example, the providing unit can provide time sale information related to nearby consumers based on the geographical location information. The providing unit can also provide time sale information that is popular in a specific area based on the geographical location information. Furthermore, the providing unit can provide time sale information of food that is largely unsold in a specific area based on the geographical location information. In this way, by considering the geographical location information of the consumer, more relevant information can be provided. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the geographical location information of the consumer to AI and cause the AI ​​to select the optimal providing method.

[0095] The providing unit can adjust the content of the sale information provided by analyzing the social media activity of the consumer when providing the sale information. The providing unit can provide related sale information based on, for example, popularity on social media. The providing unit can also analyze trends on social media and provide related sale information. Furthermore, the providing unit can provide related sale information based on consumer feedback on social media. This makes it possible to provide more relevant information by analyzing the consumer's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the consumer's social media activity into AI and cause AI to adjust the content of the sale.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The providing unit can individually adjust the discount rate for a particular food product based on the consumer's purchasing history. For example, a higher discount rate can be provided for food products that the consumer has frequently purchased in the past. Also, a special discount can be provided for food products that the consumer has never purchased in the past to encourage tasting. Furthermore, the purchasing history of the consumer can be analyzed to provide discounts for food products related to a particular season or event. This allows for a more personalized service to be provided by individually adjusting the discount rate based on the consumer's purchasing history.

[0098] The analytics department can analyze consumers' social media activities and adjust discount plans based on trends. For example, if a particular food item is trending on social media, a special discount can be offered for that food item. Also, based on posts that consumers have shared on social media, discounts on related foods can be suggested. In addition, the analytics department can analyze consumers' feedback on social media to evaluate the effectiveness of discount plans. This allows the analytics department to provide more effective discount plans by analyzing social media activities.

[0099] The calculation unit can set different discount rates for each region, taking into account the geographic location information of the consumer. For example, a higher discount rate can be offered for food that is popular in a particular region. Also, a special discount can be offered for food that is largely unsold in a particular region. Furthermore, the calculation unit can analyze the purchasing trends of consumers in each region and set an optimal discount rate. In this way, the calculation unit can provide an optimal discount rate for each region, taking into account the geographic location information.

[0100] The providing unit can provide optimal time sale information for a specific time period based on the consumer's purchasing history. For example, a special discount can be provided for a specific time period for food that the consumer frequently purchased during that time period in the past. Also, a special discount can be provided on a specific day of the week for food that the consumer purchased on that day in the past. Furthermore, the providing unit can analyze the consumer's purchasing history and provide time sale information related to a specific event or season. This makes it possible to provide optimal time sale information based on the consumer's purchasing history.

[0101] The input unit can work with the restaurant's inventory management system to update information about unsold food in real time. For example, it can automatically obtain information about unsold food from the inventory management system and input it into the system. It can also work with the inventory management system to update the quantity and type of unsold food in real time. Furthermore, it can automatically filter information about unsold food based on data from the inventory management system. In this way, by working with the inventory management system, more accurate information can be input in real time.

[0102] The providing unit can estimate the user's emotions and adjust the notification method of the limited-time sale information based on the estimated user's emotions. For example, if the user is relaxed, detailed limited-time sale information can be notified. If the user is in a hurry, brief limited-time sale information can be notified. Furthermore, if the user is stressed, visually easy-to-understand limited-time sale information can be notified. This makes it possible to provide more appropriate information by adjusting the notification method according to the user's emotions.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to provide a more appropriate display by adjusting the display method according to the user's emotions.

[0104] The calculation unit can estimate the user's emotion and adjust the calculation method of the discount rate based on the estimated user's emotion. For example, if the user is relaxed, a detailed calculation method can be applied. If the user is in a hurry, a simple calculation method can be applied. Furthermore, if the user is stressed, a visually easy-to-understand calculation method can be applied. In this way, a more appropriate discount rate can be calculated by adjusting the calculation method according to the user's emotion.

[0105] The providing unit can estimate the user's emotions and determine the priority of time sale information based on the estimated user's emotions. For example, if the user is feeling stressed, important time sale information can be provided with priority. Also, if the user is relaxed, detailed time sale information can be provided with priority. Furthermore, if the user is in a hurry, concise time sale information can be provided with priority. In this way, by determining the priority of information according to the user's emotions, more appropriate information can be provided.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method of sales data and discount plans based on the estimated user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results can be provided. Furthermore, if the user is stressed, visually easy-to-understand analysis results can be provided. In this way, more appropriate analysis results can be provided by adjusting the analysis method according to the user's emotions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The input unit inputs information about unsold food items from a restaurant. For example, the input unit can input the type and quantity of unsold food items from a restaurant into the system. The input unit provides an interface for manually inputting information. The input unit can also automatically obtain information by linking with an inventory management system of the restaurant. Step 2: The analysis unit uses the generation AI to analyze past sales data and discount plans based on the information input by the input unit. For example, the analysis unit analyzes past sales data to understand sales trends of unsold food products. The analysis unit also evaluates the effectiveness of past discount plans and provides data for calculating optimal discount rates. Step 3: The calculation unit uses the generation AI to calculate the optimal discount rate based on the data analyzed by the analysis unit. For example, the calculation unit calculates the discount rate taking into account the type and quantity of unsold food, past sales data, and the effect of the discount plan. The calculation unit sets different discount rates depending on the type and quantity of unsold food. Step 4: The providing unit provides the limited-time sale information to the consumer based on the discount rate calculated by the calculation unit. For example, the providing unit notifies the consumer of the limited-time sale information. The providing unit provides the limited-time sale information through a mobile app or a website. The providing unit can also provide limited-time sale information customized based on the consumer's purchase history and preferences.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned input unit, analysis unit, calculation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart device 14, and provides an interface for the restaurant to manually input information on unsold food. The input unit is also realized by the specific processing unit 290 of the data processing device 12, and can automatically acquire information in cooperation with an inventory management system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past sales data and discount plans using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an optimal discount rate based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides time sale information to consumers based on the calculated discount rate. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned input unit, analysis unit, calculation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214, and provides an interface for a restaurant to manually input information on unsold food. The input unit is also realized by the specific processing unit 290 of the data processing device 12, and can automatically acquire information in cooperation with an inventory management system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past sales data and discount plans using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an optimal discount rate based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides limited-time sale information to consumers based on the calculated discount rate. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the input unit, analysis unit, calculation unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the headset type terminal 314, and provides an interface for the restaurant to manually input information on unsold food. The input unit is also realized by the specific processing unit 290 of the data processing device 12, and can automatically acquire information in cooperation with an inventory management system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past sales data and discount plans using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an optimal discount rate based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides limited-time sale information to consumers based on the calculated discount rate. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] Each of the multiple elements including the above-mentioned input unit, analysis unit, calculation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414, and provides an interface for the restaurant to manually input information on unsold food. The input unit is also realized by the specific processing unit 290 of the data processing device 12, and can automatically acquire information in cooperation with an inventory management system. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes past sales data and discount plans using a generation AI. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates an optimal discount rate based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides time sale information to consumers based on the calculated discount rate. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) an input unit for inputting information on unsold food; an analysis unit that analyzes past sales data or discount plans based on the information input by the input unit; a calculation unit for calculating an appropriate discount rate based on the data analyzed by the analysis unit; A providing unit that provides limited-time sale information to consumers based on the discount rate calculated by the calculation unit. A system characterized by: (Appendix 2) The analysis unit includes: Analyze past sales data and discount plans 2. The system of claim 1, (Appendix 3) The calculation unit is Calculate the appropriate discount rate based on the type and quantity of unsold food, past sales data, and the effectiveness of discount plans 2. The system of claim 1, (Appendix 4) The providing unit is Providing time-limited sale information to consumers 2. The system of claim 1, (Appendix 5) The input unit is The restaurant enters information about unsold food 2. The system of claim 1, (Appendix 6) The input unit is The system estimates the user's emotions and adjusts the timing of inputting information about unsold food based on the estimated user emotions. 2. The system of claim 1, (Appendix 7) The input unit is Analyze the restaurant's past data entry history and select the optimal data entry method 2. The system of claim 1, (Appendix 8) The input unit is When entering unsold food information, filtering is performed based on the restaurant's current inventory and business status. 2. The system of claim 1, (Appendix 9) The input unit includes: Estimate the user's emotions and prioritize the food information to be input based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The input unit includes: When entering information about unsold food, the system takes into account the geographic location of the restaurant and prioritizes entering information that is more relevant. 2. The system of claim 1, (Appendix 11) The input unit includes: When entering unsold food information, analyze the restaurant's social media activity and enter related information. 2. The system of claim 1, (Appendix 12) The analysis unit includes: Infer user sentiment and adjust how sales data or discount offers are analyzed based on the inferred user sentiment 2. The system of claim 1, (Appendix 13) The analysis unit includes: During analysis, look at trends in past sales data to forecast current sales data 2. The system of claim 1, (Appendix 14) The analysis unit includes: During analysis, different analysis algorithms are applied to different food categories. 2. The system of claim 1, (Appendix 15) The analysis unit includes: Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit includes: During analysis, prioritize your analysis based on when sales data is submitted 2. The system of claim 1, (Appendix 17) The analysis unit includes: During analysis, adjust the order of analysis based on the relevance of your sales data 2. The system of claim 1, (Appendix 18) The calculation unit is Infer user sentiment and adjust the discount rate calculation method based on the inferred user sentiment 2. The system of claim 1, (Appendix 19) The calculation unit is When calculating the discount rate, the optimal discount rate is calculated by referring to the effect of past discount plans. 2. The system of claim 1, (Appendix 20) The calculation unit is When calculating the discount rate, different calculation algorithms are applied depending on the type and quantity of food. 2. The system of claim 1, (Appendix 21) The calculation unit is Infer user sentiment and adjust how discount rates are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 22) The calculation unit is When calculating discount rates, the priority of calculations is determined based on the time of submission of unsold food. 2. The system of claim 1, (Appendix 23) The calculation unit is When calculating discount rates, adjust the order of calculation based on the relevance of unsold food items 2. The system of claim 1, (Appendix 24) The providing unit is To estimate a user's emotions and adjust a method of providing time sale information based on the estimated user's emotions. 2. The system of claim 1, (Appendix 25) The providing unit is When providing time sale information, select the optimal method of providing it by referring to past provision history 2. The system of claim 1, (Appendix 26) The providing unit is When providing limited-time deals, tailor offers based on a consumer's current purchasing history 2. The system of claim 1, (Appendix 27) The providing unit is Estimate user sentiment and prioritize time sale information based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The providing unit is When providing limited-time sale information, the optimal method of providing it is selected taking into account the consumer's geographic location information. 2. The system of claim 1, (Appendix 29) The providing unit is Analyze consumer social media activity to tailor time-limited sales offers 2. The system of claim 1, [Explanation of symbols]

[0181] 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. an input unit for inputting information on unsold food; an analysis unit that analyzes past sales data or discount plans based on the information input by the input unit; a calculation unit for calculating an appropriate discount rate based on the data analyzed by the analysis unit; A providing unit that provides limited-time sale information to consumers based on the discount rate calculated by the calculation unit. A system characterized by:

2. The analysis unit includes: Analyze past sales data and discount plans 2. The system of claim 1 .

3. The calculation unit is Calculate the appropriate discount rate based on the type and quantity of unsold food, past sales data, and the effectiveness of discount plans 2. The system of claim 1 .

4. The providing unit is Providing time-limited sale information to consumers 2. The system of claim 1 .

5. The input unit includes: The restaurant enters information about unsold food 2. The system of claim 1 .

6. The input unit includes: The system estimates the user's emotions and adjusts the timing of inputting information about unsold food based on the estimated user emotions.

2. The system of claim 1 .

7. The input unit includes: Analyze the restaurant's past data entry history and select the optimal data entry method 2. The system of claim 1 .

8. The input unit includes: When entering unsold food information, filtering is performed based on the restaurant's current inventory and business status.

2. The system of claim 1 .

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