System

Abstract

An object of a system according to an embodiment is to analyze merchandise information and to make and execute an appropriate proposal or distribution strategy.SOLUTION: A system includes a reception unit, an analysis unit, a provision unit, and an execution unit. The reception unit receives commodity information. The analysis unit analyzes the information received by the reception unit. The providing unit makes a proposal based on the analysis result obtained by the analysis unit. The execution unit executes distribution and sales of the product based on the strategy proposed by the provision 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies do not adequately analyze product information efficiently and formulate appropriate proposals and distribution strategies, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze product information and formulate and implement appropriate proposals and distribution strategies. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and an execution unit. The reception unit receives product information. The analysis unit analyzes the information received by the reception unit. The provision unit makes proposals based on the analysis results obtained by the analysis unit. The execution unit executes distribution and sales of the product based on the strategy proposed by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze product information and formulate and implement appropriate proposals and distribution strategies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides services utilizing generative AI. This system enables distribution businesses to enter markets and monetize them with an advantage. For example, a user inputs product information, and a generative AI analyzes the information and proposes optimal distribution channels and sales strategies. Furthermore, the distribution and sales of the products are carried out based on the strategies proposed by the generative AI. This system improves efficiency and monetization in distribution businesses. As a result, the system accurately forecasts product demand and manages inventory, reducing unnecessary inventory and delivery costs. Furthermore, by proposing optimal sales strategies, sales are maximized. For example, sales can be increased by prioritizing the delivery of products with high demand in specific regions. Furthermore, the use of generative AI facilitates market entry for distribution businesses. Because the generative AI proposes optimal distribution channels and sales strategies, even new entrants can efficiently expand their businesses. For example, new entrants can use generative AI to quickly enter the market by optimizing inventory placement and delivery routes.

[0029] A distribution support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and an execution unit. The reception unit receives product information input by a user. The product information includes, but is not limited to, for example, product names, prices, and inventory status. The reception unit can receive detailed information, such as product names, prices, and inventory quantities. The analysis unit uses a generation AI to analyze the product information received by the reception unit. For example, the analysis unit forecasts product demand and proposes optimal inventory allocation and delivery routes. The analysis unit can also use the generation AI to forecast product demand and propose optimal inventory allocation and delivery routes. The provision unit uses the generation AI to make proposals based on the analysis results obtained by the analysis unit. For example, the provision unit proposes a promotion strategy for a specific region or customer segment. The provision unit can also use the generation AI to propose a promotion strategy for a specific region or customer segment. The execution unit distributes and sells the product based on the strategy proposed by the provision unit. The execution unit delivers the product based on the delivery route proposed by the generation AI. The execution unit can also execute advertisements based on the promotion strategies proposed by the generation AI. As a result, the distribution support system according to the embodiment can consistently perform processes from accepting product information to analyzing, proposing, and executing.

[0030] The reception unit can receive detailed information such as product name, price, and stock status. The detailed information includes, but is not limited to, for example, product name, price, and stock status. The reception unit can receive detailed information such as product name, price, and stock quantity. By receiving detailed product information, the accuracy of analysis is improved. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input product information entered by a user to a generation AI, which can then analyze the detailed information.

[0031] The analysis unit can predict product demand and propose inventory placement and delivery routes. The analysis unit, for example, predicts product demand and proposes optimal inventory placement and delivery routes. For example, the analysis unit can predict demand based on past sales data and propose optimal inventory placement. The analysis unit can also propose optimal delivery routes taking current market trends into consideration. The analysis unit can also predict product demand and propose optimal inventory placement and delivery routes using a generation AI. For example, the generation AI can predict demand based on past sales data and market trends and propose optimal inventory placement and delivery routes. This makes it possible to propose optimal inventory placement and delivery routes based on the demand forecast. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a product demand forecast into a generation AI, and the generation AI can perform a demand forecast.

[0032] The provision unit can propose a promotion strategy for a specific region or customer segment. The provision unit, for example, proposes a promotion strategy for a specific region or customer segment. For example, the provision unit can propose a promotion strategy for a product that is in high demand in a specific region. The provision unit can also propose a promotion strategy for a specific customer segment. The provision unit can also use a generation AI to propose a promotion strategy for a specific region or customer segment. For example, the generation AI proposes a promotion strategy based on data on the specific region or customer segment. This makes it possible to propose a promotion strategy for the specific region or customer segment. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input data on the specific region or customer segment into the generation AI, and the generation AI can propose a promotion strategy.

[0033] The execution unit can deliver goods based on the delivery route proposed by the generation AI. The execution unit can deliver goods based on, for example, the delivery route proposed by the generation AI. For example, the execution unit can deliver goods based on the optimal delivery route proposed by the generation AI. The execution unit can also efficiently deliver goods based on the delivery route proposed by the generation AI. This allows goods to be delivered efficiently based on the delivery route proposed by the generation AI. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the execution unit can deliver goods based on the delivery route proposed by the generation AI.

[0034] The execution unit can implement advertising based on the promotion strategy proposed by the generation AI. The execution unit, for example, implements advertising based on the promotion strategy proposed by the generation AI. For example, the execution unit can implement advertising targeted at a specific region or customer demographic proposed by the generation AI. The execution unit can also effectively implement advertising based on the promotion strategy proposed by the generation AI. This makes it possible to effectively implement advertising based on the promotion strategy proposed by the generation AI. Some or all of the above-described processing in the execution unit may be performed using AI, for example, or may be performed without using AI. For example, the execution unit can implement advertising based on the promotion strategy proposed by the generation AI.

[0035] When receiving product information, the reception unit can perform input completion by referring to past input history. For example, the reception unit can automatically display product names and prices previously input by the user as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest product information that will be input during a specific time period based on the user's past input history. This improves input efficiency by referring to the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past input history into a generation AI, which then performs input completion.

[0036] The reception unit can check the consistency of the input content when receiving product information and automatically correct errors. The reception unit can, for example, detect a discrepancy between the product name and price and suggest the correct price. The reception unit can also automatically correct a negative stock quantity. The reception unit can also automatically convert product information in a different format into a consistent format. This maintains the consistency of the input content and automatically corrects errors, making it possible to receive accurate product information. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the consistency of the input content to a generation AI, which can then automatically correct errors.

[0037] When receiving product information, the reception unit can provide an input guide according to the user's industry knowledge level. For example, the reception unit can provide a basic input guide to a novice user. The reception unit can also provide a detailed input guide to an intermediate user. The reception unit can also provide a customizable input guide to an advanced user. This improves input accuracy by providing an input guide according to the user's industry knowledge level. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's industry knowledge level to a generation AI, which can then provide an input guide.

[0038] When receiving product information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize receiving area-limited product information based on the user's current location. The reception unit can also prioritize receiving related product information by referring to the user's past location information. The reception unit can also prioritize receiving deliverable product information based on the user's location information. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and prioritize receiving information that is highly relevant according to the generation AI.

[0039] When receiving product information, the reception unit can analyze the user's social media activity and automatically complete the related information. For example, the reception unit automatically completes product information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and automatically complete the related product information. The reception unit can also automatically complete the related product information by referring to the activity of the user's friends on social media. In this way, the related information can be automatically completed by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can automatically complete the related information.

[0040] The reception unit can customize the input interface by reflecting the user's past feedback when receiving product information. The reception unit can improve the input interface based on, for example, feedback provided by the user in the past. The reception unit can also simplify the input procedure by reflecting the user's past feedback. The reception unit can also adjust the design of the input interface based on the user's past feedback. This customizes the input interface by reflecting the user's past feedback, improving user convenience. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into a generation AI, which can customize the input interface.

[0041] The analysis unit can perform demand forecasting by taking into account the seasonality and trends of products during analysis. The analysis unit can propose optimal inventory allocation, for example, by taking into account seasonal demand fluctuations. The analysis unit can also prioritize analysis of trending products and perform demand forecasting. The analysis unit can also predict demand for highly seasonal products and propose optimal sales strategies. This improves the accuracy of demand forecasting by taking into account the seasonality and trends of products. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input product seasonality and trend data into a generation AI, and the generation AI can perform demand forecasting.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the product's life cycle stage. For example, in the case of a new product, the analysis unit can apply an initial demand forecasting algorithm. In addition, in the case of a mature product, the analysis unit can also apply a stable demand forecasting algorithm. In addition, in the case of a product scheduled for discontinuation, the analysis unit can also apply a demand decline forecasting algorithm. In this way, by applying an analysis algorithm depending on the product's life cycle stage, the accuracy of the analysis is improved. 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 product life cycle stage data into a generation AI, which then applies the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, corrects the current analysis result based on the past analysis result. The analysis unit can also optimize the analysis algorithm by referring to the past analysis result. The analysis unit can also improve the accuracy of the demand forecast based on the past analysis result. In this way, by referring to the past analysis result, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, and the generation AI can improve the analysis accuracy.

[0044] During analysis, the analysis unit can perform demand forecasting by taking into account the market competition situation for the product. The analysis unit performs demand forecasting based on, for example, sales data for competing products. The analysis unit can also propose optimal inventory allocation by taking into account the market competition situation. The analysis unit can also perform demand forecasting by taking into account price fluctuations of competing products. This improves the accuracy of demand forecasting by taking into account the market competition situation for the product. 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 sales data for competing products into a generation AI and use the generation AI to perform demand forecasting.

[0045] During analysis, the analysis unit can improve the analysis accuracy by referring to literature related to the product. The analysis unit can improve the analysis accuracy by referring to, for example, the latest research papers on the product. The analysis unit can also improve the analysis accuracy by referring to reports on market trends for the product. The analysis unit can also improve the analysis accuracy by referring to the technical specifications of the product. In this way, by referring to related literature, the analysis accuracy is improved. 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 related literature data into a generation AI, which can improve the analysis accuracy.

[0046] The analysis unit can perform demand forecasting by taking into account product price fluctuations during analysis. The analysis unit performs demand forecasting based on, for example, past price fluctuation data. The analysis unit can also propose optimal inventory allocation by taking into account the impact of price fluctuations. The analysis unit can also predict price fluctuations and reflect them in the demand forecast. This improves the accuracy of the demand forecast by taking price fluctuations into account. 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 price fluctuation data into a generation AI and use the generation AI to perform demand forecasting.

[0047] When making a proposal, the provision unit can propose an optimal promotion strategy by referring to the product's sales history. The provision unit can propose an optimal promotion strategy based on, for example, past sales data. The provision unit can also propose a promotion strategy for a specific region or customer segment by referring to the sales history. The provision unit can also propose a seasonal promotion strategy based on the sales history. In this way, an optimal promotion strategy can be proposed by referring to the sales history. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input sales history data into a generation AI, which can then propose a promotion strategy.

[0048] When making a proposal, the providing unit can apply different proposal algorithms depending on the target customer demographic of the product. For example, the providing unit can propose a promotion strategy that utilizes social media for products targeted at younger generations. The providing unit can also propose a promotion strategy that utilizes television advertising for products targeted at older generations. The providing unit can also propose a promotion strategy that utilizes business magazines and websites for products targeted at business people. This improves the accuracy of proposals by applying a proposal algorithm depending on the target customer demographic. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input target customer demographic data into a generating AI, which then applies a proposal algorithm.

[0049] When making a proposal, the providing unit can improve the proposal accuracy by referring to past proposal results. The providing unit, for example, corrects the current proposal content based on past proposal results. The providing unit can also optimize the proposal algorithm by referring to past proposal results. The providing unit can also improve the proposal accuracy based on past proposal results. In this way, the proposal accuracy is improved by referring to past proposal results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past proposal result data into a generation AI, which can improve the proposal accuracy.

[0050] When proposing a promotion strategy, the provision unit can propose the promotion strategy taking into consideration market trends for the product. The provision unit, for example, proposes an optimal promotion strategy based on current market trends. The provision unit can also propose a promotion strategy for a specific region or customer segment taking market trends into consideration. The provision unit can also propose a seasonal promotion strategy based on market trends. In this way, an optimal promotion strategy can be proposed by taking market trends into consideration. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input market trend data into a generation AI, and the generation AI can propose a promotion strategy.

[0051] When making a proposal, the providing unit can improve the accuracy of the proposal by referring to market data related to the product. For example, the providing unit can improve the accuracy of the proposal by referring to the latest market data related to the product. The providing unit can also improve the accuracy of the proposal by referring to a report on market trends of the product. The providing unit can also improve the accuracy of the proposal by referring to data on competitors of the product. In this way, the accuracy of the proposal is improved by referring to the related market data. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the related market data into a generation AI, which can improve the accuracy of the proposal.

[0052] When proposing a promotion strategy, the provision unit can propose an optimal promotion strategy taking into account the trends of competitors for the product. For example, the provision unit can propose an optimal promotion strategy based on the promotion strategies of competitors. The provision unit can also propose a promotion strategy for a specific region or customer segment taking into account the trends of competitors. The provision unit can also propose a promotion strategy taking into account price fluctuations of competitors. In this way, an optimal promotion strategy can be proposed by taking into account the trends of competitors. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input competitor trend data into a generation AI, and the generation AI can propose a promotion strategy.

[0053] At the time of execution, the execution unit can select the optimal delivery route by referring to the product delivery history. The execution unit selects the optimal delivery route, for example, based on past delivery history. The execution unit can also select the optimal delivery route for a specific region or customer group by referring to the delivery history. The execution unit can also select the optimal delivery route for each season based on the delivery history. In this way, the optimal delivery route can be selected by referring to the delivery history. Some or all of the above-described processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input delivery history data into a generation AI, which can select the optimal delivery route.

[0054] The execution unit can adjust the delivery schedule according to the inventory status of the product during execution. For example, when inventory is low, the execution unit prioritizes adjusting the delivery schedule. The execution unit can also propose an efficient delivery schedule when inventory is high. The execution unit can also monitor the inventory status in real time and adjust the optimal delivery schedule. This enables efficient delivery by providing a delivery schedule according to the inventory status. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input inventory status data into a generation AI and have the generation AI adjust the delivery schedule.

[0055] The execution unit can improve the execution procedure by reflecting user feedback during execution. The execution unit improves the execution procedure based on, for example, user feedback. The execution unit can also optimize the execution procedure by referring to the user's past feedback. The execution unit can also reflect user feedback in real time and adjust the execution procedure. In this way, by reflecting user feedback, the execution procedure is improved and user convenience is improved. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input user feedback data into a generation AI, which can then improve the execution procedure.

[0056] During execution, the execution unit can select an optimal delivery route taking into account the geographic distribution of products. For example, the execution unit selects an optimal delivery route based on the geographic distribution of products. The execution unit can also select an optimal delivery route for a specific region or customer segment taking into account the geographic distribution. The execution unit can also select an optimal delivery route for each season based on the geographic distribution. In this way, the optimal delivery route can be selected by taking the geographic distribution into account. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input geographic distribution data into a generation AI, which can select an optimal delivery route.

[0057] During execution, the execution unit can improve execution accuracy by referring to literature related to the product. The execution unit can improve execution accuracy by, for example, referring to the latest research papers on the product. The execution unit can also improve execution accuracy by referring to reports on market trends for the product. The execution unit can also improve execution accuracy by referring to technical specifications for the product. In this way, execution accuracy is improved by referring to related literature. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the execution unit can input related literature data into a generation AI, which can improve execution accuracy.

[0058] The execution unit can adjust the execution procedure during execution, taking into account the market value of the product. For example, the execution unit prioritizes processing of products with high market values. The execution unit can also efficiently process products with low market values. The execution unit can also monitor market values ​​in real time and adjust the execution procedure. This makes it possible to provide an efficient execution procedure by taking market values ​​into consideration. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input market value data into a generation AI, and the generation AI can adjust the execution procedure.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can refer to the user's purchase history and suggest new products based on products purchased in the past. For example, it can suggest products similar to products purchased in the past by the user. The reception unit can also suggest related products based on the user's purchase history. Furthermore, the reception unit can analyze the user's purchase history and suggest recommended products for each season. This makes it possible to make more personalized product suggestions by utilizing the user's purchase history.

[0061] The analysis unit can evaluate the eco-friendliness of products and forecast demand for environmentally friendly products. For example, the analysis unit evaluates the eco-friendliness of a product based on the manufacturing process and materials of the product. The analysis unit can also forecast demand for eco-friendly products and propose optimal inventory allocation. Furthermore, the analysis unit can analyze market trends for eco-friendly products and reflect them in the demand forecast. This makes it possible to forecast demand for environmentally friendly products.

[0062] The reception unit can refer to the user's health data and make product suggestions based on their health condition. For example, if the user inputs the results of a health check, health foods and supplements can be suggested based on those results. Fitness-related products can also be suggested based on the user's exercise data. Furthermore, sleep aids can be suggested by referring to the user's sleep data. This makes it possible to make more personalized product suggestions by utilizing the user's health data.

[0063] The providing unit can analyze the user's purchasing history and propose new promotion strategies based on past purchasing patterns. For example, if the user frequently purchases products from a specific brand, new products from that brand can be proposed preferentially. Also, if the user purchases many products from a specific category, related products from that category can be proposed. Furthermore, seasonal promotion strategies can be proposed based on the user's purchasing history. This makes it possible to implement more effective promotion strategies by utilizing the user's purchasing history.

[0064] The analysis unit can propose different marketing strategies depending on the product's life cycle stage. For example, for a new product, it can propose an initial promotion strategy. For a mature product, it can also propose a stable sales strategy. Furthermore, for a product that is scheduled to be discontinued, it can also propose an inventory clearance sale. In this way, by providing a marketing strategy according to the product's life cycle stage, sales can be maximized.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives product information input by the user. The product information includes, but is not limited to, the product name, price, and stock status. The reception unit can receive detailed product information. Step 2: The analysis unit uses the generation AI to analyze the product information received by the reception unit. The analysis unit predicts product demand and proposes optimal inventory allocation and delivery routes. Step 3: The provision department uses the generation AI to make proposals based on the analysis results obtained by the analysis department. The provision department proposes promotion strategies for specific regions and customer segments. Step 4: The execution unit distributes and sells the product based on the strategy proposed by the provision unit. The execution unit delivers the product based on the delivery route proposed by the generation AI and carries out advertising based on the promotion strategy.

[0067] (Example 2) A system according to an embodiment of the present invention provides services utilizing generative AI. This system enables distribution businesses to enter markets and monetize them with an advantage. For example, a user inputs product information, and a generative AI analyzes the information and proposes optimal distribution channels and sales strategies. Furthermore, the distribution and sales of the products are carried out based on the strategies proposed by the generative AI. This system improves efficiency and monetization in distribution businesses. As a result, the system accurately forecasts product demand and manages inventory, reducing unnecessary inventory and delivery costs. Furthermore, by proposing optimal sales strategies, sales are maximized. For example, sales can be increased by prioritizing the delivery of products with high demand in specific regions. Furthermore, the use of generative AI facilitates market entry for distribution businesses. Because the generative AI proposes optimal distribution channels and sales strategies, even new entrants can efficiently expand their businesses. For example, new entrants can use generative AI to quickly enter the market by optimizing inventory placement and delivery routes.

[0068] A distribution support system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and an execution unit. The reception unit receives product information input by a user. The product information includes, but is not limited to, for example, product names, prices, and inventory status. The reception unit can receive detailed information, such as product names, prices, and inventory quantities. The analysis unit uses a generation AI to analyze the product information received by the reception unit. For example, the analysis unit forecasts product demand and proposes optimal inventory allocation and delivery routes. The analysis unit can also use the generation AI to forecast product demand and propose optimal inventory allocation and delivery routes. The provision unit uses the generation AI to make proposals based on the analysis results obtained by the analysis unit. For example, the provision unit proposes a promotion strategy for a specific region or customer segment. The provision unit can also use the generation AI to propose a promotion strategy for a specific region or customer segment. The execution unit distributes and sells the product based on the strategy proposed by the provision unit. The execution unit delivers the product based on the delivery route proposed by the generation AI. The execution unit can also execute advertisements based on the promotion strategies proposed by the generation AI. As a result, the distribution support system according to the embodiment can consistently perform processes from accepting product information to analyzing, proposing, and executing.

[0069] The reception unit can receive detailed information such as product name, price, and stock status. The detailed information includes, but is not limited to, for example, product name, price, and stock status. The reception unit can receive detailed information such as product name, price, and stock quantity. By receiving detailed product information, the accuracy of analysis is improved. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input product information entered by a user to a generation AI, which can then analyze the detailed information.

[0070] The analysis unit can predict product demand and propose inventory placement and delivery routes. The analysis unit, for example, predicts product demand and proposes optimal inventory placement and delivery routes. For example, the analysis unit can predict demand based on past sales data and propose optimal inventory placement. The analysis unit can also propose optimal delivery routes taking current market trends into consideration. The analysis unit can also predict product demand and propose optimal inventory placement and delivery routes using a generation AI. For example, the generation AI can predict demand based on past sales data and market trends and propose optimal inventory placement and delivery routes. This makes it possible to propose optimal inventory placement and delivery routes based on the demand forecast. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a product demand forecast into a generation AI, and the generation AI can perform a demand forecast.

[0071] The provision unit can propose a promotion strategy for a specific region or customer segment. The provision unit, for example, proposes a promotion strategy for a specific region or customer segment. For example, the provision unit can propose a promotion strategy for a product that is in high demand in a specific region. The provision unit can also propose a promotion strategy for a specific customer segment. The provision unit can also use a generation AI to propose a promotion strategy for a specific region or customer segment. For example, the generation AI proposes a promotion strategy based on data on the specific region or customer segment. This makes it possible to propose a promotion strategy for the specific region or customer segment. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input data on the specific region or customer segment into the generation AI, and the generation AI can propose a promotion strategy.

[0072] The execution unit can deliver goods based on the delivery route proposed by the generation AI. The execution unit can deliver goods based on, for example, the delivery route proposed by the generation AI. For example, the execution unit can deliver goods based on the optimal delivery route proposed by the generation AI. The execution unit can also efficiently deliver goods based on the delivery route proposed by the generation AI. This allows goods to be delivered efficiently based on the delivery route proposed by the generation AI. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the execution unit can deliver goods based on the delivery route proposed by the generation AI.

[0073] The execution unit can implement advertising based on the promotion strategy proposed by the generation AI. The execution unit, for example, implements advertising based on the promotion strategy proposed by the generation AI. For example, the execution unit can implement advertising targeted at a specific region or customer demographic proposed by the generation AI. The execution unit can also effectively implement advertising based on the promotion strategy proposed by the generation AI. This makes it possible to effectively implement advertising based on the promotion strategy proposed by the generation AI. Some or all of the above-described processing in the execution unit may be performed using AI, for example, or may be performed without using AI. For example, the execution unit can implement advertising based on the promotion strategy proposed by the generation AI.

[0074] The reception unit can estimate the user's emotions and customize the product information input interface based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick input of product information. This improves user convenience by providing an input interface that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then estimate the emotion.

[0075] When receiving product information, the reception unit can perform input completion by referring to past input history. For example, the reception unit can automatically display product names and prices previously input by the user as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest product information that will be input during a specific time period based on the user's past input history. This improves input efficiency by referring to the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the past input history into a generation AI, which then performs input completion.

[0076] The reception unit can check the consistency of the input content when receiving product information and automatically correct errors. The reception unit can, for example, detect a discrepancy between the product name and price and suggest the correct price. The reception unit can also automatically correct a negative stock quantity. The reception unit can also automatically convert product information in a different format into a consistent format. This maintains the consistency of the input content and automatically corrects errors, making it possible to receive accurate product information. Some or all of the above-described processing in the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input the consistency of the input content to a generation AI, which can then automatically correct errors.

[0077] When receiving product information, the reception unit can provide an input guide according to the user's industry knowledge level. For example, the reception unit can provide a basic input guide to a novice user. The reception unit can also provide a detailed input guide to an intermediate user. The reception unit can also provide a customizable input guide to an advanced user. This improves input accuracy by providing an input guide according to the user's industry knowledge level. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's industry knowledge level to a generation AI, which can then provide an input guide.

[0078] The reception unit can estimate the user's emotions and prioritize the input content based on the estimated emotions. For example, when the user is in a hurry, the reception unit can prioritize input of important items. Furthermore, when the user is relaxed, the reception unit can sequentially input detailed items. Furthermore, when the user is stressed, the reception unit can input only the minimum number of items. This enables efficient input by determining the priority of input content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into a generation AI, which can then estimate the emotion.

[0079] When receiving product information, the reception unit can prioritize receiving highly relevant information taking into account the user's geographical location information. For example, the reception unit can prioritize receiving area-limited product information based on the user's current location. The reception unit can also prioritize receiving related product information by referring to the user's past location information. The reception unit can also prioritize receiving deliverable product information based on the user's location information. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and prioritize receiving information that is highly relevant according to the generation AI.

[0080] When receiving product information, the reception unit can analyze the user's social media activity and automatically complete the related information. For example, the reception unit automatically completes product information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and automatically complete the related product information. The reception unit can also automatically complete the related product information by referring to the activity of the user's friends on social media. In this way, the related information can be automatically completed by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can automatically complete the related information.

[0081] The reception unit can customize the input interface by reflecting the user's past feedback when receiving product information. The reception unit can improve the input interface based on, for example, feedback provided by the user in the past. The reception unit can also simplify the input procedure by reflecting the user's past feedback. The reception unit can also adjust the design of the input interface based on the user's past feedback. This customizes the input interface by reflecting the user's past feedback, improving user convenience. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's past feedback data into a generation AI, which can customize the input interface.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the user to understand the analysis results better by providing a display method that corresponds to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0083] The analysis unit can perform demand forecasting by taking into account the seasonality and trends of products during analysis. The analysis unit can propose optimal inventory allocation, for example, by taking into account seasonal demand fluctuations. The analysis unit can also prioritize analysis of trending products and perform demand forecasting. The analysis unit can also predict demand for highly seasonal products and propose optimal sales strategies. This improves the accuracy of demand forecasting by taking into account the seasonality and trends of products. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input product seasonality and trend data into a generation AI, and the generation AI can perform demand forecasting.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the product's life cycle stage. For example, in the case of a new product, the analysis unit can apply an initial demand forecasting algorithm. In addition, in the case of a mature product, the analysis unit can also apply a stable demand forecasting algorithm. In addition, in the case of a product scheduled for discontinuation, the analysis unit can also apply a demand decline forecasting algorithm. In this way, by applying an analysis algorithm depending on the product's life cycle stage, the accuracy of the analysis is improved. 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 product life cycle stage data into a generation AI, which then applies the analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. The analysis unit, for example, corrects the current analysis result based on the past analysis result. The analysis unit can also optimize the analysis algorithm by referring to the past analysis result. The analysis unit can also improve the accuracy of the demand forecast based on the past analysis result. In this way, by referring to the past analysis result, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, and the generation AI can improve the analysis accuracy.

[0086] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and to-the-point analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. This allows for a deeper understanding of the user by providing a level of detail in the analysis results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into a generation AI, which can then estimate the emotion.

[0087] During analysis, the analysis unit can perform demand forecasting by taking into account the market competition situation for the product. The analysis unit performs demand forecasting based on, for example, sales data for competing products. The analysis unit can also propose optimal inventory allocation by taking into account the market competition situation. The analysis unit can also perform demand forecasting by taking into account price fluctuations of competing products. This improves the accuracy of demand forecasting by taking into account the market competition situation for the product. 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 sales data for competing products into a generation AI and use the generation AI to perform demand forecasting.

[0088] During analysis, the analysis unit can improve the analysis accuracy by referring to literature related to the product. The analysis unit can improve the analysis accuracy by referring to, for example, the latest research papers on the product. The analysis unit can also improve the analysis accuracy by referring to reports on market trends for the product. The analysis unit can also improve the analysis accuracy by referring to the technical specifications of the product. In this way, by referring to related literature, the analysis accuracy is improved. 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 related literature data into a generation AI, which can improve the analysis accuracy.

[0089] The analysis unit can perform demand forecasting by taking into account product price fluctuations during analysis. The analysis unit performs demand forecasting based on, for example, past price fluctuation data. The analysis unit can also propose optimal inventory allocation by taking into account the impact of price fluctuations. The analysis unit can also predict price fluctuations and reflect them in the demand forecast. This improves the accuracy of the demand forecast by taking price fluctuations into account. 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 price fluctuation data into a generation AI and use the generation AI to perform demand forecasting.

[0090] The providing unit can estimate the user's emotions and adjust the way the suggested content is expressed based on the estimated emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible suggested content. Furthermore, if the user is relaxed, the providing unit can also provide suggested content that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide suggested content that focuses on the main points. This allows the user to understand better by providing a way to express suggested content according to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, which then estimates the emotion.

[0091] When making a proposal, the provision unit can propose an optimal promotion strategy by referring to the product's sales history. The provision unit can propose an optimal promotion strategy based on, for example, past sales data. The provision unit can also propose a promotion strategy for a specific region or customer segment by referring to the sales history. The provision unit can also propose a seasonal promotion strategy based on the sales history. In this way, an optimal promotion strategy can be proposed by referring to the sales history. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input sales history data into a generation AI, which can then propose a promotion strategy.

[0092] When making a proposal, the providing unit can apply different proposal algorithms depending on the target customer demographic of the product. For example, the providing unit can propose a promotion strategy that utilizes social media for products targeted at younger generations. The providing unit can also propose a promotion strategy that utilizes television advertising for products targeted at older generations. The providing unit can also propose a promotion strategy that utilizes business magazines and websites for products targeted at business people. This improves the accuracy of proposals by applying a proposal algorithm depending on the target customer demographic. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input target customer demographic data into a generating AI, which then applies a proposal algorithm.

[0093] When making a proposal, the providing unit can improve the proposal accuracy by referring to past proposal results. The providing unit, for example, corrects the current proposal content based on past proposal results. The providing unit can also optimize the proposal algorithm by referring to past proposal results. The providing unit can also improve the proposal accuracy based on past proposal results. In this way, the proposal accuracy is improved by referring to past proposal results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input past proposal result data into a generation AI, which can improve the proposal accuracy.

[0094] The providing unit can estimate the user's emotions and prioritize the suggested content based on the estimated emotions. For example, when the user is in a hurry, the providing unit can prioritize the display of important suggested content. Furthermore, when the user is relaxed, the providing unit can sequentially display detailed suggested content. Furthermore, when the user is stressed, the providing unit can display only minimal suggested content. This enables efficient suggestions by prioritizing the suggested content according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generation AI, which then estimates the emotion.

[0095] When proposing a promotion strategy, the provision unit can propose the promotion strategy taking into consideration market trends for the product. The provision unit, for example, proposes an optimal promotion strategy based on current market trends. The provision unit can also propose a promotion strategy for a specific region or customer segment taking market trends into consideration. The provision unit can also propose a seasonal promotion strategy based on market trends. In this way, an optimal promotion strategy can be proposed by taking market trends into consideration. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input market trend data into a generation AI, and the generation AI can propose a promotion strategy.

[0096] When making a proposal, the providing unit can improve the accuracy of the proposal by referring to market data related to the product. For example, the providing unit can improve the accuracy of the proposal by referring to the latest market data related to the product. The providing unit can also improve the accuracy of the proposal by referring to a report on market trends of the product. The providing unit can also improve the accuracy of the proposal by referring to data on competitors of the product. In this way, the accuracy of the proposal is improved by referring to the related market data. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the related market data into a generation AI, which can improve the accuracy of the proposal.

[0097] When proposing a promotion strategy, the provision unit can propose an optimal promotion strategy taking into account the trends of competitors for the product. For example, the provision unit can propose an optimal promotion strategy based on the promotion strategies of competitors. The provision unit can also propose a promotion strategy for a specific region or customer segment taking into account the trends of competitors. The provision unit can also propose a promotion strategy taking into account price fluctuations of competitors. In this way, an optimal promotion strategy can be proposed by taking into account the trends of competitors. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input competitor trend data into a generation AI, and the generation AI can propose a promotion strategy.

[0098] The execution unit can estimate the user's emotions and adjust the execution procedure based on the estimated emotions. For example, if the user is nervous, the execution unit can provide simple, highly visible execution procedures. Furthermore, if the user is relaxed, the execution unit can provide execution procedures that include detailed information. Furthermore, if the user is in a hurry, the execution unit can provide execution procedures that focus on the main points. This improves user convenience by providing execution procedures that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the execution unit can be performed using, for example, an AI, or without an AI. For example, the execution unit can input the user's emotion data into the generation AI, which can then estimate the emotion.

[0099] At the time of execution, the execution unit can select the optimal delivery route by referring to the product delivery history. The execution unit selects the optimal delivery route, for example, based on past delivery history. The execution unit can also select the optimal delivery route for a specific region or customer group by referring to the delivery history. The execution unit can also select the optimal delivery route for each season based on the delivery history. In this way, the optimal delivery route can be selected by referring to the delivery history. Some or all of the above-described processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input delivery history data into a generation AI, which can select the optimal delivery route.

[0100] The execution unit can adjust the delivery schedule according to the inventory status of the product during execution. For example, when inventory is low, the execution unit prioritizes adjusting the delivery schedule. The execution unit can also propose an efficient delivery schedule when inventory is high. The execution unit can also monitor the inventory status in real time and adjust the optimal delivery schedule. This enables efficient delivery by providing a delivery schedule according to the inventory status. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input inventory status data into a generation AI and have the generation AI adjust the delivery schedule.

[0101] The execution unit can improve the execution procedure by reflecting user feedback during execution. The execution unit improves the execution procedure based on, for example, user feedback. The execution unit can also optimize the execution procedure by referring to the user's past feedback. The execution unit can also reflect user feedback in real time and adjust the execution procedure. In this way, by reflecting user feedback, the execution procedure is improved and user convenience is improved. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input user feedback data into a generation AI, which can then improve the execution procedure.

[0102] The execution unit can estimate the user's emotions and prioritize the execution contents based on the estimated emotions. For example, when the user is in a hurry, the execution unit prioritizes important execution contents. Furthermore, when the user is relaxed, the execution unit can sequentially process detailed execution contents. Furthermore, when the user is stressed, the execution unit can process only the minimum number of execution contents. This enables efficient execution by prioritizing execution contents according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input the user's emotion data into the generation AI, which then estimates the emotion.

[0103] During execution, the execution unit can select an optimal delivery route taking into account the geographic distribution of products. For example, the execution unit selects an optimal delivery route based on the geographic distribution of products. The execution unit can also select an optimal delivery route for a specific region or customer segment taking into account the geographic distribution. The execution unit can also select an optimal delivery route for each season based on the geographic distribution. In this way, the optimal delivery route can be selected by taking the geographic distribution into account. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input geographic distribution data into a generation AI, which can select an optimal delivery route.

[0104] During execution, the execution unit can improve execution accuracy by referring to literature related to the product. The execution unit can improve execution accuracy by, for example, referring to the latest research papers on the product. The execution unit can also improve execution accuracy by referring to reports on market trends for the product. The execution unit can also improve execution accuracy by referring to technical specifications for the product. In this way, execution accuracy is improved by referring to related literature. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the execution unit can input related literature data into a generation AI, which can improve execution accuracy.

[0105] The execution unit can adjust the execution procedure during execution, taking into account the market value of the product. For example, the execution unit prioritizes processing of products with high market values. The execution unit can also efficiently process products with low market values. The execution unit can also monitor market values ​​in real time and adjust the execution procedure. This makes it possible to provide an efficient execution procedure by taking market values ​​into consideration. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input market value data into a generation AI, and the generation AI can adjust the execution procedure. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and execution unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives product information entered by a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the product information using a generation AI. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and makes a proposal based on the analysis result. For example, the execution unit is realized by the control unit 46A of the smart device 14 and executes product distribution and sales based on the proposed strategy. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, and execution unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives product information input by a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the product information using a generative AI. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and makes a proposal based on the analysis results. For example, the execution unit is realized by the control unit 46A of the smart glasses 214 and executes product distribution and sales based on the proposed strategy. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and execution unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives product information input by a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the product information using a generation AI. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and makes a proposal based on the analysis result. For example, the execution unit is realized by the control unit 46A of the headset-type terminal 314 and executes product distribution and sales based on the proposed strategy. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and execution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives product information input by a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the product information using a generative AI. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and makes a proposal based on the analysis result. For example, the execution unit is realized by the control unit 46A of the robot 414 and executes product distribution and sales based on the proposed strategy.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can refer to the user's purchase history and suggest new products based on products purchased in the past. For example, it can suggest products similar to products purchased in the past by the user. The reception unit can also suggest related products based on the user's purchase history. Furthermore, the reception unit can analyze the user's purchase history and suggest recommended products for each season. This makes it possible to make more personalized product suggestions by utilizing the user's purchase history.

[0108] The analysis unit can evaluate the eco-friendliness of products and forecast demand for environmentally friendly products. For example, the analysis unit evaluates the eco-friendliness of a product based on the manufacturing process and materials of the product. The analysis unit can also forecast demand for eco-friendly products and propose optimal inventory allocation. Furthermore, the analysis unit can analyze market trends for eco-friendly products and reflect them in the demand forecast. This makes it possible to forecast demand for environmentally friendly products.

[0109] The provider can estimate the user's emotions and adjust promotion strategies based on the estimated emotions. For example, if the user is excited, the provider can suggest special discounts or limited offers. If the user is calm, the provider can provide detailed product descriptions and reviews. Furthermore, if the user is anxious, the provider can emphasize guarantees and return policies that reassure the user. This enables effective marketing by providing promotion strategies that correspond to the user's emotions.

[0110] The execution unit can estimate the user's emotions when delivering goods and adjust the content of the delivery notification based on the estimated emotions. For example, if the user is excited, the execution unit can provide a detailed notification of the delivery progress. If the user is relaxed, the execution unit can provide a concise notification. Furthermore, if the user is anxious, the execution unit can emphasize countermeasures in case of delivery delays or problems. In this way, by providing a delivery notification that corresponds to the user's emotions, user satisfaction is improved.

[0111] The reception unit can refer to the user's health data and make product suggestions based on their health condition. For example, if the user inputs the results of a health check, health foods and supplements can be suggested based on those results. Fitness-related products can also be suggested based on the user's exercise data. Furthermore, sleep aids can be suggested by referring to the user's sleep data. This makes it possible to make more personalized product suggestions by utilizing the user's health data.

[0112] The analysis unit can estimate the user's emotions and improve the accuracy of demand forecasting based on the estimated emotions. For example, if the user is excited, it can identify products for which demand is likely to increase sharply. If the user is calm, it can also identify products for which stable demand is expected. Furthermore, if the user is anxious, it can also identify products for which demand is likely to decrease. This makes it possible to forecast demand based on the user's emotions, improving the accuracy of inventory management.

[0113] The providing unit can analyze the user's purchasing history and propose new promotion strategies based on past purchasing patterns. For example, if the user frequently purchases products from a specific brand, new products from that brand can be proposed preferentially. Also, if the user purchases many products from a specific category, related products from that category can be proposed. Furthermore, seasonal promotion strategies can be proposed based on the user's purchasing history. This makes it possible to implement more effective promotion strategies by utilizing the user's purchasing history.

[0114] The execution unit can estimate the user's emotions when delivering products and adjust the delivery method based on the estimated emotions. For example, if the user is in a hurry, the execution unit can select the fastest delivery method. If the user is relaxed, the execution unit can suggest a cost-effective delivery method. Furthermore, if the user is feeling anxious, the execution unit can provide a trackable delivery method. In this way, by providing a delivery method that suits the user's emotions, user satisfaction can be improved.

[0115] The analysis unit can propose different marketing strategies depending on the product's life cycle stage. For example, for a new product, it can propose an initial promotion strategy. For a mature product, it can also propose a stable sales strategy. Furthermore, for a product that is scheduled to be discontinued, it can also propose an inventory clearance sale. In this way, by providing a marketing strategy according to the product's life cycle stage, sales can be maximized.

[0116] The providing unit can estimate the user's emotions and adjust the timing of the suggestions based on the estimated emotions. For example, if the user is excited, the suggestion can be made immediately. If the user is relaxed, the suggestion can be made at an appropriate time. Furthermore, if the user is feeling anxious, the suggestion can be made at a time that gives the user a sense of security. This enables effective marketing by providing the timing of the suggestions according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives product information input by the user. The product information includes, but is not limited to, the product name, price, and stock status. The reception unit can receive detailed product information. Step 2: The analysis unit uses the generation AI to analyze the product information received by the reception unit. The analysis unit predicts product demand and proposes optimal inventory allocation and delivery routes. Step 3: The provision department uses the generation AI to make proposals based on the analysis results obtained by the analysis department. The provision department proposes promotion strategies for specific regions and customer segments. Step 4: The execution unit distributes and sells the product based on the strategy proposed by the provision unit. The execution unit delivers the product based on the delivery route proposed by the generation AI and carries out advertising based on the promotion strategy.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] 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.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] 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.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives product information; an analysis unit that analyzes the information received by the reception unit; a providing unit that makes a proposal based on the analysis result obtained by the analyzing unit; an execution unit that executes distribution and sales of products based on the strategy proposed by the provision unit; Equipped with A system characterized by:

2. The reception unit Accept detailed information about product name, price, and availability 2. The system of claim 1.

3. The analysis unit Forecast product demand and propose inventory allocation and delivery routes 2. The system of claim 1.

4. The providing unit Propose promotional strategies for specific regions or customer segments 2. The system of claim 1.

5. The execution unit: Deliver products based on delivery routes proposed by the generated AI 2. The system of claim 1.

6. The execution unit: Implement advertising based on promotion strategies proposed by the generative AI 2. The system of claim 1.

7. The reception unit Estimate user emotions and customize the product information input interface based on the estimated emotions.

2. The system of claim 1.

8. The reception unit When accepting product information, past input history is referenced to complete the input 2. The system of claim 1.

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