system

Abstract

The system according to the embodiment aims to propose optimal products based on the customer's preferences and purchase history. [Solution] A system according to an embodiment includes a registration unit, an analysis unit, a proposal unit, a calculation unit, and a purchase unit. The registration unit registers product information. The analysis unit analyzes customer preferences and purchase history based on the product information registered by the registration unit. The proposal unit proposes products based on the results of the analysis by the analysis unit. The calculation unit calculates sales commissions and profit margins. The purchase unit offers products at the prices calculated by the calculation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest optimal products based on customer preferences and purchasing history, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal products based on the customer's preferences and purchase history. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, an analysis unit, a proposal unit, a calculation unit, and a purchase unit. The registration unit registers product information. The analysis unit analyzes customer preferences and purchase history based on the product information registered by the registration unit. The proposal unit proposes products based on the results of the analysis by the analysis unit. The calculation unit calculates sales commissions and profit margins. The purchase unit offers products at the prices calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal products based on the customer's preferences and purchasing history. [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 subscription sales platform according to an embodiment of the present invention is a system that allows retailers and manufacturers to regularly provide their products to customers. This system registers product information, analyzes customer preferences and purchase history, recommends optimal products, calculates sales commissions and profit margins, and provides the products at the calculated prices. For example, a subscription sales platform allows retailers and manufacturers to register product information. For example, the product information includes product names, descriptions, prices, sales commissions, and profit margins. Next, customers request periodic product delivery. For example, customers can select products that suit their preferences and needs and set up periodic delivery. Furthermore, the subscription sales platform automatically calculates and presents the sales commissions and profit margins to customers. This allows customers to purchase products at transparent prices. Furthermore, the subscription sales platform utilizes human augmentation and generative AI to recommend optimal products based on customer preferences and purchase history. For example, generative AI analyzes a customer's past purchase history and preferences and suggests new products based on the results. This allows retailers and manufacturers to regularly provide their products to customers, allowing customers to regularly receive products that suit their preferences and needs. Customer satisfaction is also improved as products can be purchased at transparent prices that take into account sales commissions and profit margins. Furthermore, by utilizing human augmentation and generative AI, the most suitable products can be suggested to customers, improving the purchasing experience. This allows subscription sales platforms to enable retailers and manufacturers to regularly provide their products to customers, and customers can regularly receive products that suit their tastes and needs. Customer satisfaction is also improved as products can be purchased at transparent prices that take into account sales commissions and profit margins. Furthermore, by utilizing human augmentation and generative AI, the most suitable products can be suggested to customers, improving the purchasing experience.

[0029] A subscription sales platform according to an embodiment includes a registration unit, an analysis unit, a proposal unit, a calculation unit, and a purchase unit. The registration unit registers product information. The product information includes, for example, a product name, description, price, category, sales commission, and profit margin, but is not limited to these examples. For example, when a cosmetics manufacturer registers a new skin care product, the registration unit inputs information such as the product's ingredients, usage instructions, and price. The analysis unit analyzes customer preferences and purchase history based on the product information registered by the registration unit. The analysis is performed using, for example, data mining or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the customer's past purchase history and preferences to identify products that the customer prefers. The proposal unit proposes products based on the results of the analysis by the analysis unit. The proposal is performed using, for example, a recommendation engine or a filtering algorithm, but is not limited to these examples. For example, the proposal unit proposes new skin care products based on the analysis results. The calculation unit calculates the sales commission and profit margin. The calculation is performed using, for example, a fixed commission or a percentage commission, but is not limited to these examples. For example, the calculation unit automatically calculates sales commissions and profit margins and presents them to customers. The purchase unit offers products at the prices calculated by the calculation unit. The offering may be, for example, through online sales or in-store sales, but is not limited to these examples. For example, the purchase unit offers products at the calculated prices, allowing customers to purchase products at transparent prices. As a result, the subscription sales platform according to the embodiment can efficiently perform a series of processes from registering product information to analysis, proposals, calculations, and purchases.

[0030] The registration unit can input detailed information about the product, such as the price, sales commission, and profit margin. Detailed information includes, but is not limited to, product specifications, manufacturer, and warranty information, for example. The registration unit can input detailed product specifications, for example, to provide customers with transparent information. The registration unit can also input product manufacturer information to provide customers with reliable information. Furthermore, the registration unit can input product warranty information to provide customers with information that allows them to purchase with confidence. Thus, by inputting detailed product information, transparent information can be provided to customers.

[0031] The analysis unit can analyze the customer's past purchase history and preferences. The past purchase history includes, for example, purchase date and time, purchased items, and purchase amount, but is not limited to these examples. The analysis unit, for example, analyzes the customer's purchase date and time to identify purchasing patterns. The analysis unit can also analyze purchased items to identify the customer's preferences. The analysis unit can also analyze purchase amounts to identify the customer's purchasing power. Preferences include, for example, past purchase history, browsing history, survey results, etc., but are not limited to these examples. The analysis unit, for example, analyzes the customer's browsing history to identify items in which the customer is interested. The analysis unit can also analyze survey results to identify the customer's preferences. By analyzing the customer's past purchase history and preferences, more appropriate products can be suggested.

[0032] The suggestion unit can suggest new products based on the analyzed results. New products include, but are not limited to, recently released products and products that the customer has not yet purchased. The suggestion unit can suggest, for example, recently released products to provide the customer with new options. The suggestion unit can also suggest products that the customer has not yet purchased to provide a new purchasing experience. Furthermore, the suggestion unit can suggest new products based on the customer's preferences. In this way, customer satisfaction can be improved by suggesting new products based on the analyzed results.

[0033] The calculation unit can automatically calculate the sales commission and profit margin. Examples of automatic calculation include, but are not limited to, the algorithm to be used and the timing of calculation. For example, the calculation unit can calculate the sales commission using a fixed commission. The calculation unit can also calculate the sales commission using a percentage commission. Furthermore, the calculation unit can automatically calculate the profit margin and present it to the customer. This ensures transparency in pricing by automatically calculating the sales commission and profit margin.

[0034] The purchasing department can provide products at a calculated price. Examples of calculated prices include, but are not limited to, cost, sales commission, and profit margin. For example, the purchasing department sets a price based on cost. The purchasing department can also set a price based on sales commission. Furthermore, the purchasing department can set a price based on a profit margin and present it to the customer. By providing products at calculated prices, it is possible to provide products at transparent prices to customers.

[0035] When registering product information, the registration unit can adjust the registered content taking into account the seasonality or trends of the product. Seasonality includes, but is not limited to, seasonal demand fluctuations and seasonal limited-edition products, for example. The registration unit, for example, automatically suggests seasonal trend products and optimizes the registered content. The registration unit can also perform seasonal demand forecasts based on past sales data and adjust the registered content. Furthermore, the registration unit can perform trend analysis and prioritize the registration of popular product categories. This allows the provision of optimal product information to customers by taking into account the seasonality and trends of the product.

[0036] The registration unit can include product manufacturer and supply chain information when registering product information. Examples of manufacturer include, but are not limited to, the name, address, and manufacturing process of the manufacturer. For example, the registration unit inputs detailed product manufacturer information to provide transparency to customers. The registration unit can also track each stage of the supply chain and reflect this in the registration content. Furthermore, the registration unit can include manufacturer certification information and quality assurance information in the registration content. In this way, by including product manufacturer and supply chain information, transparent information can be provided to customers.

[0037] When registering product information, the registration unit can describe in detail how to use the product and the recommended frequency of use. Examples of usage methods include, but are not limited to, usage procedures, precautions, and recommended usage environments. For example, the registration unit can describe how to use the product step by step to provide an easy-to-understand explanation to the customer. The registration unit can also specifically describe the recommended frequency of use to suggest an appropriate usage method to the customer. Furthermore, the registration unit can include videos and images about the usage method in the registered content to provide a visual explanation. By providing detailed information about how to use the product and the recommended frequency of use, the registration unit can provide an appropriate usage method to the customer.

[0038] The registration unit can emphasize the eco-friendly characteristics of the product when registering the product information. Eco-friendly characteristics include, but are not limited to, the use of renewable energy and the use of recyclable materials. For example, the registration unit can describe the eco-friendly characteristics of the product in detail to appeal to customers. The registration unit can also emphasize the use of environmentally friendly manufacturing processes and materials. Furthermore, the registration unit can include eco-friendly certifications and labels in the registration content. This allows the eco-friendly characteristics of the product to be emphasized, making it possible to provide environmentally conscious products to customers.

[0039] The registration unit can include product reviews and ratings when registering product information. Reviews include, for example, customer ratings, comments, and star ratings, but are not limited to these examples. The registration unit, for example, automatically collects customer reviews and ratings and reflects them in the registered content. The registration unit can also display product ratings in the form of star ratings or comments to provide reference information to customers. Furthermore, the registration unit can analyze past reviews and ratings and reflect improvements to the product in the registered content. In this way, by including product reviews and ratings, reference information can be provided to customers.

[0040] The registration unit can suggest related accessories and complementary products for a product when registering product information. Related accessories include, but are not limited to, attachments and optional parts for the product. For example, the registration unit can automatically suggest related accessories for the product to encourage the customer to purchase additional items. The registration unit can also suggest complementary products to improve the customer's purchasing experience. Furthermore, the registration unit can suggest related products based on the customer's past purchase history. This improves the customer's purchasing experience by suggesting related accessories and complementary products for the product.

[0041] During analysis, the analysis unit can take into account not only the customer's purchase history but also their browsing history and products added to their cart. The browsing history includes, for example, a list of viewed products, the date and time of viewing, and the number of views, but is not limited to these examples. For example, the analysis unit analyzes the customer's browsing history to identify products in which the customer is interested. The analysis unit can also analyze products added to the cart to identify products that the customer is highly interested in purchasing. Furthermore, the analysis unit can combine the purchase history and browsing history to more accurately grasp the customer's preferences. This allows for more accurate analysis results to be provided by taking into account not only the customer's purchase history but also their browsing history and products added to their cart.

[0042] The analysis unit can reflect the customer's lifestyle and hobbies and preferences during analysis. Lifestyle includes, but is not limited to, daily behavior patterns, hobbies, and living habits, for example. The analysis unit can, for example, suggest appropriate products based on the customer's lifestyle. The analysis unit can also analyze the customer's hobbies and preferences and suggest related products. Furthermore, the analysis unit can analyze the customer's past behavior patterns and suggest products that suit the customer's lifestyle. In this way, more appropriate products can be suggested by reflecting the customer's lifestyle and hobbies and preferences.

[0043] During the analysis, the analysis unit can analyze the customer's social media activity to identify preferences. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the analysis unit can analyze the content of the customer's social media posts to identify products in which the customer is interested. The analysis unit can also suggest related products based on the activities of the customer's friends on social media. Furthermore, the analysis unit can analyze social media trends to suggest products that suit the customer. In this way, by analyzing the customer's social media activity, it is possible to identify more accurate preferences.

[0044] The analysis unit can take into account the customer's geographical location information during analysis. Geographical location information includes, but is not limited to, GPS data, address information, and local characteristics. For example, the analysis unit can suggest nearby stores and services based on the customer's current location. The analysis unit can also analyze the customer's past location information and suggest products related to frequently visited places. Furthermore, the analysis unit can suggest optimal delivery options by taking the customer's geographical location information into account. This allows for more appropriate product suggestions by taking the customer's geographical location information into account.

[0045] The analysis unit can take into account demographic information such as the customer's age and gender during analysis. Demographic information includes, but is not limited to, age, gender, occupation, and income. For example, the analysis unit can suggest appropriate products based on the customer's age. The analysis unit can also suggest related products based on the customer's gender. Furthermore, the analysis unit can combine customer demographic information to perform more accurate analysis. This allows for more appropriate product suggestions by taking into account demographic information such as the customer's age and gender.

[0046] During analysis, the analysis unit can improve the analysis algorithm by reflecting past customer feedback. Examples of feedback include, but are not limited to, customer comments, ratings, and survey results. For example, the analysis unit collects past customer feedback and reflects it in the analysis algorithm. The analysis unit can also improve the accuracy of the analysis results based on the feedback. Furthermore, the analysis unit can analyze customer feedback and identify areas for improvement in the algorithm. Thus, by reflecting past customer feedback, the accuracy of the analysis algorithm is improved.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the customer's past purchase history. The level of detail includes, but is not limited to, for example, the depth of information and the length of explanation. For example, the suggestion unit suggests related products in detail based on the customer's past purchase history. Furthermore, the suggestion unit can make simple suggestions when the customer has a small purchase history. Furthermore, the suggestion unit can make detailed suggestions to attract the customer's interest when the customer has a large purchase history. This allows for more appropriate suggestions to be made by adjusting the level of detail of the proposal based on the customer's past purchase history.

[0048] When making a proposal, the proposal unit can customize the proposal content based on the customer's current needs and life events. Current needs include, but are not limited to, products and services that the customer currently needs. For example, the proposal unit analyzes the customer's current needs and proposes appropriate products. The proposal unit can also propose related products based on the customer's life events (marriage, childbirth, etc.). Furthermore, the proposal unit can propose products that match the customer's current needs based on the customer's past behavioral patterns. This allows for more appropriate proposals by customizing the proposal content based on the customer's current needs and life events.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the customer's past proposal results. Examples of the accuracy of the proposal include, but are not limited to, the success rate of the proposal and the customer's satisfaction. For example, the suggestion unit analyzes the customer's past proposal results to improve the accuracy of the proposal. The suggestion unit can also grasp the customer's preferences more accurately based on the past proposal results. Furthermore, the suggestion unit can collect feedback on the proposal results and improve the proposal algorithm. In this way, the accuracy of the proposal is improved by referring to the customer's past proposal results.

[0050] When making a proposal, the proposal unit can adjust the proposal content taking into account the seasonality and trends of the product. Seasonality includes, but is not limited to, seasonal demand fluctuations and seasonal limited products, for example. The proposal unit, for example, proposes seasonal trend products to attract customer interest. The proposal unit can also predict seasonal demand based on past sales data and adjust the proposal content. Furthermore, the proposal unit can perform trend analysis and prioritize the proposal of popular product categories. In this way, the proposal unit can propose the most suitable product to the customer by taking into account the seasonality and trends of the product.

[0051] When making a suggestion, the suggestion unit can analyze the customer's social media activity to suggest related products. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the suggestion unit can analyze the content of the customer's posts on social media to identify products in which the customer is interested. The suggestion unit can also suggest related products by taking into account the activities of the customer's friends on social media. Furthermore, the suggestion unit can analyze social media trends to suggest products that suit the customer. In this way, more appropriate products can be suggested by analyzing the customer's social media activity.

[0052] When making a proposal, the suggestion unit can customize the proposal content by reflecting past customer feedback. Feedback includes, but is not limited to, customer comments, ratings, survey results, and the like. For example, the suggestion unit collects past customer feedback and reflects it in the proposal content. The suggestion unit can also improve the accuracy of the proposal content based on the feedback. Furthermore, the suggestion unit can analyze customer feedback and identify areas for improvement in the proposal content. In this way, the accuracy of the proposal content is improved by reflecting past customer feedback.

[0053] The calculation unit can take into account the product supply chain costs and logistics costs when making calculations. Supply chain costs include, but are not limited to, raw material costs, manufacturing costs, and logistics costs. For example, the calculation unit calculates the costs of each stage of the supply chain in detail and reflects them in the price. The calculation unit can also set an optimal price by taking logistics costs into account. Furthermore, the calculation unit can improve the efficiency of the supply chain and reflect cost reductions in the price. This allows for more accurate pricing by taking into account the product supply chain costs and logistics costs.

[0054] The calculation unit can reflect the seasonality and trends of the product when calculating the price. Seasonality includes, but is not limited to, seasonal demand fluctuations and seasonal limited-edition products. The calculation unit adjusts the price based on, for example, seasonal demand forecasts. The calculation unit can also perform trend analysis to optimize the prices of popular products. Furthermore, the calculation unit can fluctuate the prices of seasonal products according to demand. This allows for more appropriate pricing by reflecting the seasonality and trends of the product.

[0055] When calculating, the calculation unit can apply discounts and benefits based on the customer's past purchase history. Discounts include, but are not limited to, discount rates and application conditions. For example, the calculation unit can apply a repeat customer discount based on the customer's past purchase history. The calculation unit can also provide benefits to customers with a high purchase history. Furthermore, the calculation unit can analyze past purchase history and suggest optimal discounts. In this way, customer satisfaction can be improved by applying discounts and benefits based on the customer's past purchase history.

[0056] The calculation unit can adjust the price by taking into account the eco-friendly characteristics of the product during calculation. Eco-friendly characteristics include, but are not limited to, the use of renewable energy and the use of recyclable materials. For example, the calculation unit can give preferential treatment to the price of eco-friendly products. The calculation unit can also adjust the price of products with eco-friendly manufacturing processes. Furthermore, the calculation unit can optimize the price of products with eco-friendly certifications. This enables environmentally friendly pricing by taking into account the eco-friendly characteristics of the product.

[0057] The calculation unit can adjust the price by taking into account the customer's geographical location information during calculation. Geographical location information includes, but is not limited to, GPS data, address information, and regional characteristics. For example, the calculation unit calculates the optimal delivery cost based on the customer's current location. The calculation unit can also set prices for each region by taking into account the geographical location information. Furthermore, the calculation unit can propose the optimal price based on the customer's location information. This allows for more appropriate pricing by taking into account the customer's geographical location information.

[0058] The calculation unit can improve the price calculation algorithm by reflecting past customer feedback during calculation. Examples of feedback include, but are not limited to, customer comments, ratings, and survey results. For example, the calculation unit collects past customer feedback and reflects it in the price calculation algorithm. The calculation unit can also improve the accuracy of price calculation based on the feedback. Furthermore, the calculation unit can analyze customer feedback and identify areas for improvement in the algorithm. In this way, the accuracy of price calculation is improved by reflecting past customer feedback.

[0059] The purchasing unit can simplify the purchasing procedure based on the customer's past purchase history at the time of purchase. The purchasing procedure includes, but is not limited to, adding items to a cart, selecting a payment method, and inputting a shipping address. For example, the purchasing unit automatically simplifies the purchasing procedure based on the customer's past purchase history. The purchasing unit can also provide a one-click purchasing option to customers with a large purchase history. Furthermore, the purchasing unit can analyze the customer's past purchase history and suggest the optimal purchasing procedure. This simplifies the purchasing procedure based on the customer's past purchase history, thereby streamlining the purchasing process.

[0060] The purchasing department can provide purchasing options based on the customer's current needs and life events at the time of purchase. Current needs include, but are not limited to, products and services that the customer currently needs. For example, the purchasing department analyzes the customer's current needs and provides appropriate purchasing options. The purchasing department can also provide related purchasing options based on the customer's life events (marriage, childbirth, etc.). Furthermore, the purchasing department can provide purchasing options that match the customer's current needs based on the customer's past behavioral patterns. This allows for more appropriate purchases by providing purchasing options based on the customer's current needs and life events.

[0061] The purchasing department can improve the purchasing process by reflecting customer feedback at the time of purchase. Feedback includes, but is not limited to, customer comments, ratings, survey results, and the like. For example, the purchasing department collects past customer feedback and reflects it in the purchasing process. The purchasing department can also improve the accuracy of the purchasing process based on the feedback. Furthermore, the purchasing department can analyze customer feedback and identify areas for improvement in the purchasing process. In this way, the accuracy of the purchasing process can be improved by reflecting customer feedback.

[0062] The purchasing department can provide optimal delivery options by taking into account the customer's geographic location information at the time of purchase. Geographic location information includes, but is not limited to, GPS data, address information, and local characteristics. The purchasing department can, for example, propose optimal delivery options based on the customer's current location. The purchasing department can also propose optimal delivery times by taking into account the geographic location information. Furthermore, the purchasing department can propose optimal delivery methods based on the customer's location information. This allows for more appropriate delivery options to be provided by taking into account the customer's geographic location information.

[0063] The purchasing department can analyze a customer's social media activity at the time of purchase to provide relevant purchasing options. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the purchasing department can analyze the content of a customer's posts on social media to provide relevant purchasing options. The purchasing department can also provide relevant purchasing options based on the activities of the customer's friends on social media. Furthermore, the purchasing department can analyze social media trends to provide purchasing options that suit the customer. In this way, by analyzing a customer's social media activity, more appropriate purchasing options can be provided.

[0064] The purchasing department can customize the purchasing process by reflecting past customer feedback at the time of purchase. Feedback includes, but is not limited to, customer comments, ratings, survey results, and the like. For example, the purchasing department collects past customer feedback and reflects it in the purchasing process. The purchasing department can also improve the accuracy of the purchasing process based on the feedback. Furthermore, the purchasing department can analyze customer feedback and identify areas for improvement in the purchasing process. In this way, the accuracy of the purchasing process can be improved by reflecting past customer feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can analyze not only a customer's purchase history but also their social media activity. For example, it can analyze the products that the customer has "liked" on social media and the posts that the customer has shared to identify the customer's interests. It can also analyze the number of followers and the content of comments that the customer has made to evaluate the customer's influence. It can also analyze social media trends and suggest products that suit the customer. This allows the analysis results to be more accurate by taking into account the customer's social media activity.

[0067] The calculation unit can adjust prices by taking into account the eco-friendly characteristics of the product. For example, it can give preferential treatment to the prices of products manufactured using renewable energy or products made from recyclable materials. It can also adjust prices of products with eco-friendly manufacturing processes. Furthermore, it can optimize prices of products with eco-friendly certifications. This enables environmentally friendly pricing by taking into account the eco-friendly characteristics of the product.

[0068] The analysis unit can perform analysis taking into account the customer's geographical location information. For example, it can suggest nearby stores and services based on the customer's current location. It can also analyze the customer's past location information and suggest products related to frequently visited places. It can also suggest optimal delivery options taking into account the customer's geographical location information. This allows it to suggest more appropriate products by taking into account the customer's geographical location information.

[0069] The calculation unit can apply discounts and special offers based on the customer's past purchase history. For example, a repeat customer discount can be applied based on the customer's past purchase history. It can also provide special offers to customers with a high purchase history. It can also analyze past purchase history and propose optimal discounts. This improves customer satisfaction by applying discounts and special offers based on the customer's past purchase history.

[0070] The proposal unit can customize the proposal content based on the customer's current needs and life events. For example, it can analyze the customer's current needs and propose appropriate products. It can also propose related products based on the customer's life events (marriage, childbirth, etc.). It can also propose products that match the customer's current needs based on the customer's past behavioral patterns. This allows for more appropriate proposals by customizing the proposal content based on the customer's current needs and life events.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The registration unit registers product information. Product information includes, for example, the product name, description, price, category, sales commission, profit margin, etc. For example, when a cosmetics manufacturer registers a new skin care product, they enter information such as the product's ingredients, usage instructions, and price. Step 2: The analysis unit analyzes the customer's preferences and purchase history based on the product information registered by the registration unit. The analysis is performed using, for example, data mining or machine learning algorithms. For example, the analysis analyzes the customer's past purchase history and preferences to identify the products that the customer prefers. Step 3: The suggestion unit suggests products based on the results of the analysis by the analysis unit. The suggestion is performed, for example, using a recommendation engine or a filtering algorithm. For example, a new skin care product is suggested based on the results of the analysis. Step 4: The calculation unit calculates the sales commission and profit margin. The calculation is performed using, for example, a fixed commission or a percentage commission. For example, the sales commission and profit margin are automatically calculated and presented to the client. Step 5: The purchasing unit offers the product at the price calculated by the calculation unit. The offer may be made, for example, through online sales or in-store sales. For example, the product may be offered at the calculated price, allowing customers to purchase the product at a transparent price.

[0073] (Example 2) A subscription sales platform according to an embodiment of the present invention is a system that allows retailers and manufacturers to regularly provide their products to customers. This system registers product information, analyzes customer preferences and purchase history, recommends optimal products, calculates sales commissions and profit margins, and provides the products at the calculated prices. For example, a subscription sales platform allows retailers and manufacturers to register product information. For example, the product information includes product names, descriptions, prices, sales commissions, and profit margins. Next, customers request periodic product delivery. For example, customers can select products that suit their preferences and needs and set up periodic delivery. Furthermore, the subscription sales platform automatically calculates and presents the sales commissions and profit margins to customers. This allows customers to purchase products at transparent prices. Furthermore, the subscription sales platform utilizes human augmentation and generative AI to recommend optimal products based on customer preferences and purchase history. For example, generative AI analyzes a customer's past purchase history and preferences and suggests new products based on the results. This allows retailers and manufacturers to regularly provide their products to customers, allowing customers to regularly receive products that suit their preferences and needs. Customer satisfaction is also improved as products can be purchased at transparent prices that take into account sales commissions and profit margins. Furthermore, by utilizing human augmentation and generative AI, the most suitable products can be suggested to customers, improving the purchasing experience. This allows subscription sales platforms to enable retailers and manufacturers to regularly provide their products to customers, and customers can regularly receive products that suit their tastes and needs. Customer satisfaction is also improved as products can be purchased at transparent prices that take into account sales commissions and profit margins. Furthermore, by utilizing human augmentation and generative AI, the most suitable products can be suggested to customers, improving the purchasing experience.

[0074] A subscription sales platform according to an embodiment includes a registration unit, an analysis unit, a proposal unit, a calculation unit, and a purchase unit. The registration unit registers product information. The product information includes, for example, a product name, description, price, category, sales commission, and profit margin, but is not limited to these examples. For example, when a cosmetics manufacturer registers a new skin care product, the registration unit inputs information such as the product's ingredients, usage instructions, and price. The analysis unit analyzes customer preferences and purchase history based on the product information registered by the registration unit. The analysis is performed using, for example, data mining or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the customer's past purchase history and preferences to identify products that the customer prefers. The proposal unit proposes products based on the results of the analysis by the analysis unit. The proposal is performed using, for example, a recommendation engine or a filtering algorithm, but is not limited to these examples. For example, the proposal unit proposes new skin care products based on the analysis results. The calculation unit calculates the sales commission and profit margin. The calculation is performed using, for example, a fixed commission or a percentage commission, but is not limited to these examples. For example, the calculation unit automatically calculates sales commissions and profit margins and presents them to customers. The purchase unit offers products at the prices calculated by the calculation unit. The offering may be, for example, through online sales or in-store sales, but is not limited to these examples. For example, the purchase unit offers products at the calculated prices, allowing customers to purchase products at transparent prices. As a result, the subscription sales platform according to the embodiment can efficiently perform a series of processes from registering product information to analysis, proposals, calculations, and purchases.

[0075] The registration unit can input detailed information about the product, such as the price, sales commission, and profit margin. Detailed information includes, but is not limited to, product specifications, manufacturer, and warranty information, for example. The registration unit can input detailed product specifications, for example, to provide customers with transparent information. The registration unit can also input product manufacturer information to provide customers with reliable information. Furthermore, the registration unit can input product warranty information to provide customers with information that allows them to purchase with confidence. Thus, by inputting detailed product information, transparent information can be provided to customers.

[0076] The analysis unit can analyze the customer's past purchase history and preferences. The past purchase history includes, for example, purchase date and time, purchased items, and purchase amount, but is not limited to these examples. The analysis unit, for example, analyzes the customer's purchase date and time to identify purchasing patterns. The analysis unit can also analyze purchased items to identify the customer's preferences. The analysis unit can also analyze purchase amounts to identify the customer's purchasing power. Preferences include, for example, past purchase history, browsing history, survey results, etc., but are not limited to these examples. The analysis unit, for example, analyzes the customer's browsing history to identify items in which the customer is interested. The analysis unit can also analyze survey results to identify the customer's preferences. By analyzing the customer's past purchase history and preferences, more appropriate products can be suggested.

[0077] The suggestion unit can suggest new products based on the analyzed results. New products include, but are not limited to, recently released products and products that the customer has not yet purchased. The suggestion unit can suggest, for example, recently released products to provide the customer with new options. The suggestion unit can also suggest products that the customer has not yet purchased to provide a new purchasing experience. Furthermore, the suggestion unit can suggest new products based on the customer's preferences. In this way, customer satisfaction can be improved by suggesting new products based on the analyzed results.

[0078] The calculation unit can automatically calculate the sales commission and profit margin. Examples of automatic calculation include, but are not limited to, the algorithm to be used and the timing of calculation. For example, the calculation unit can calculate the sales commission using a fixed commission. The calculation unit can also calculate the sales commission using a percentage commission. Furthermore, the calculation unit can automatically calculate the profit margin and present it to the customer. This ensures transparency in pricing by automatically calculating the sales commission and profit margin.

[0079] The purchasing department can provide products at a calculated price. Examples of calculated prices include, but are not limited to, cost, sales commission, and profit margin. For example, the purchasing department sets a price based on cost. The purchasing department can also set a price based on sales commission. Furthermore, the purchasing department can set a price based on a profit margin and present it to the customer. By providing products at calculated prices, it is possible to provide products at transparent prices to customers.

[0080] The registration unit can estimate the user's emotions and adjust the product information input method based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression recognition, voice analysis, and survey results. For example, if the user is feeling stressed, the registration unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the registration unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input to enable quick product information input. This allows the user's stress to be reduced by adjusting the product information input method according to the user's emotions. 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.

[0081] When registering product information, the registration unit can adjust the registered content taking into account the seasonality or trends of the product. Seasonality includes, but is not limited to, seasonal demand fluctuations and seasonal limited-edition products, for example. The registration unit, for example, automatically suggests seasonal trend products and optimizes the registered content. The registration unit can also perform seasonal demand forecasts based on past sales data and adjust the registered content. Furthermore, the registration unit can perform trend analysis and prioritize the registration of popular product categories. This allows the provision of optimal product information to customers by taking into account the seasonality and trends of the product.

[0082] The registration unit can include product manufacturer and supply chain information when registering product information. Examples of manufacturer include, but are not limited to, the name, address, and manufacturing process of the manufacturer. For example, the registration unit inputs detailed product manufacturer information to provide transparency to customers. The registration unit can also track each stage of the supply chain and reflect this in the registration content. Furthermore, the registration unit can include manufacturer certification information and quality assurance information in the registration content. In this way, by including product manufacturer and supply chain information, transparent information can be provided to customers.

[0083] When registering product information, the registration unit can describe in detail how to use the product and the recommended frequency of use. Examples of usage methods include, but are not limited to, usage procedures, precautions, and recommended usage environments. For example, the registration unit can describe how to use the product step by step to provide an easy-to-understand explanation to the customer. The registration unit can also specifically describe the recommended frequency of use to suggest an appropriate usage method to the customer. Furthermore, the registration unit can include videos and images about the usage method in the registered content to provide a visual explanation. By providing detailed information about how to use the product and the recommended frequency of use, the registration unit can provide an appropriate usage method to the customer.

[0084] The registration unit can estimate the user's emotions and prioritize product information based on the estimated user emotions. Priorities include, but are not limited to, importance, urgency, and user interest. For example, when the user is excited, the registration unit can prioritize popular or new products. When the user is relaxed, the registration unit can provide detailed product information to expand the user's options. When the user is in a hurry, the registration unit can provide concise information to enable the user to quickly select a product. This allows the product information to be prioritized according to the user's emotions, thereby providing product information that meets the user's needs. The emotion estimation is achieved using an emotion estimation function, such as 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.

[0085] The registration unit can emphasize the eco-friendly characteristics of the product when registering the product information. Eco-friendly characteristics include, but are not limited to, the use of renewable energy and the use of recyclable materials. For example, the registration unit can describe the eco-friendly characteristics of the product in detail to appeal to customers. The registration unit can also emphasize the use of environmentally friendly manufacturing processes and materials. Furthermore, the registration unit can include eco-friendly certifications and labels in the registration content. This allows the eco-friendly characteristics of the product to be emphasized, making it possible to provide environmentally conscious products to customers.

[0086] The registration unit can include product reviews and ratings when registering product information. Reviews include, for example, customer ratings, comments, and star ratings, but are not limited to these examples. The registration unit, for example, automatically collects customer reviews and ratings and reflects them in the registered content. The registration unit can also display product ratings in the form of star ratings or comments to provide reference information to customers. Furthermore, the registration unit can analyze past reviews and ratings and reflect improvements to the product in the registered content. In this way, by including product reviews and ratings, reference information can be provided to customers.

[0087] The registration unit can suggest related accessories and complementary products for a product when registering product information. Related accessories include, but are not limited to, attachments and optional parts for the product. For example, the registration unit can automatically suggest related accessories for the product to encourage the customer to purchase additional items. The registration unit can also suggest complementary products to improve the customer's purchasing experience. Furthermore, the registration unit can suggest related products based on the customer's past purchase history. This improves the customer's purchasing experience by suggesting related accessories and complementary products for the product.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. Examples of the analysis algorithm include, but are not limited to, machine learning algorithms and data mining techniques. For example, when the user is relaxed, the analysis unit performs a detailed analysis to provide highly accurate results. Furthermore, when the user is in a hurry, the analysis unit can perform a quick analysis to provide concise results. Furthermore, when the user is excited, the analysis unit can provide visually appealing analysis results. Thus, by adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] During analysis, the analysis unit can take into account not only the customer's purchase history but also their browsing history and products added to their cart. The browsing history includes, for example, a list of viewed products, the date and time of viewing, and the number of views, but is not limited to these examples. For example, the analysis unit analyzes the customer's browsing history to identify products in which the customer is interested. The analysis unit can also analyze products added to the cart to identify products that the customer is highly interested in purchasing. Furthermore, the analysis unit can combine the purchase history and browsing history to more accurately grasp the customer's preferences. This allows for more accurate analysis results to be provided by taking into account not only the customer's purchase history but also their browsing history and products added to their cart.

[0090] The analysis unit can reflect the customer's lifestyle and hobbies and preferences during analysis. Lifestyle includes, but is not limited to, daily behavior patterns, hobbies, and living habits, for example. The analysis unit can, for example, suggest appropriate products based on the customer's lifestyle. The analysis unit can also analyze the customer's hobbies and preferences and suggest related products. Furthermore, the analysis unit can analyze the customer's past behavior patterns and suggest products that suit the customer's lifestyle. In this way, more appropriate products can be suggested by reflecting the customer's lifestyle and hobbies and preferences.

[0091] During the analysis, the analysis unit can analyze the customer's social media activity to identify preferences. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the analysis unit can analyze the content of the customer's social media posts to identify products in which the customer is interested. The analysis unit can also suggest related products based on the activities of the customer's friends on social media. Furthermore, the analysis unit can analyze social media trends to suggest products that suit the customer. In this way, by analyzing the customer's social media activity, it is possible to identify more accurate preferences.

[0092] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. Display methods include, but are not limited to, graph display, list display, and text display. 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 display method of the analysis results to be adjusted according to the user's emotion, enabling a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0093] The analysis unit can take into account the customer's geographical location information during analysis. Geographical location information includes, but is not limited to, GPS data, address information, and local characteristics. For example, the analysis unit can suggest nearby stores and services based on the customer's current location. The analysis unit can also analyze the customer's past location information and suggest products related to frequently visited places. Furthermore, the analysis unit can suggest optimal delivery options by taking the customer's geographical location information into account. This allows for more appropriate product suggestions by taking the customer's geographical location information into account.

[0094] The analysis unit can take into account demographic information such as the customer's age and gender during analysis. Demographic information includes, but is not limited to, age, gender, occupation, and income. For example, the analysis unit can suggest appropriate products based on the customer's age. The analysis unit can also suggest related products based on the customer's gender. Furthermore, the analysis unit can combine customer demographic information to perform more accurate analysis. This allows for more appropriate product suggestions by taking into account demographic information such as the customer's age and gender.

[0095] During analysis, the analysis unit can improve the analysis algorithm by reflecting past customer feedback. Examples of feedback include, but are not limited to, customer comments, ratings, and survey results. For example, the analysis unit collects past customer feedback and reflects it in the analysis algorithm. The analysis unit can also improve the accuracy of the analysis results based on the feedback. Furthermore, the analysis unit can analyze customer feedback and identify areas for improvement in the algorithm. Thus, by reflecting past customer feedback, the accuracy of the analysis algorithm is improved.

[0096] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. Examples of presentation methods include, but are not limited to, text, images, and videos. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion method. Furthermore, if the user is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a suggestion method that focuses on the main points. This allows suggestions that are easy for the user to read to be presented by adjusting the way suggestions are presented according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0097] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the customer's past purchase history. The level of detail includes, but is not limited to, for example, the depth of information and the length of explanation. For example, the suggestion unit suggests related products in detail based on the customer's past purchase history. Furthermore, the suggestion unit can make simple suggestions when the customer has a small purchase history. Furthermore, the suggestion unit can make detailed suggestions to attract the customer's interest when the customer has a large purchase history. This allows for more appropriate suggestions to be made by adjusting the level of detail of the proposal based on the customer's past purchase history.

[0098] When making a proposal, the proposal unit can customize the proposal content based on the customer's current needs and life events. Current needs include, but are not limited to, products and services that the customer currently needs. For example, the proposal unit analyzes the customer's current needs and proposes appropriate products. The proposal unit can also propose related products based on the customer's life events (marriage, childbirth, etc.). Furthermore, the proposal unit can propose products that match the customer's current needs based on the customer's past behavioral patterns. This allows for more appropriate proposals by customizing the proposal content based on the customer's current needs and life events.

[0099] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the customer's past proposal results. Examples of the accuracy of the proposal include, but are not limited to, the success rate of the proposal and the customer's satisfaction. For example, the suggestion unit analyzes the customer's past proposal results to improve the accuracy of the proposal. The suggestion unit can also grasp the customer's preferences more accurately based on the past proposal results. Furthermore, the suggestion unit can collect feedback on the proposal results and improve the proposal algorithm. In this way, the accuracy of the proposal is improved by referring to the customer's past proposal results.

[0100] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. Examples of the length of the suggestion include, but are not limited to, the length of the sentences in the suggestion and the amount of information. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a visually stimulating suggestion. This allows the suggestion to be easily viewed by adjusting the length of the suggestion according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0101] When making a proposal, the proposal unit can adjust the proposal content taking into account the seasonality and trends of the product. Seasonality includes, but is not limited to, seasonal demand fluctuations and seasonal limited products, for example. The proposal unit, for example, proposes seasonal trend products to attract customer interest. The proposal unit can also predict seasonal demand based on past sales data and adjust the proposal content. Furthermore, the proposal unit can perform trend analysis and prioritize the proposal of popular product categories. In this way, the proposal unit can propose the most suitable product to the customer by taking into account the seasonality and trends of the product.

[0102] When making a suggestion, the suggestion unit can analyze the customer's social media activity to suggest related products. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the suggestion unit can analyze the content of the customer's posts on social media to identify products in which the customer is interested. The suggestion unit can also suggest related products by taking into account the activities of the customer's friends on social media. Furthermore, the suggestion unit can analyze social media trends to suggest products that suit the customer. In this way, more appropriate products can be suggested by analyzing the customer's social media activity.

[0103] When making a proposal, the suggestion unit can customize the proposal content by reflecting past customer feedback. Feedback includes, but is not limited to, customer comments, ratings, survey results, and the like. For example, the suggestion unit collects past customer feedback and reflects it in the proposal content. The suggestion unit can also improve the accuracy of the proposal content based on the feedback. Furthermore, the suggestion unit can analyze customer feedback and identify areas for improvement in the proposal content. In this way, the accuracy of the proposal content is improved by reflecting past customer feedback.

[0104] The calculation unit can estimate the user's emotions and adjust the price calculation method based on the estimated user emotions. Examples of the price calculation method include, but are not limited to, the algorithm used and the timing of calculation. For example, the calculation unit can provide a detailed price breakdown when the user is relaxed. Furthermore, the calculation unit can display a concise price when the user is in a hurry. Furthermore, the calculation unit can display a visually appealing price when the user is excited. This allows the price display to be easy for the user to view by adjusting the price calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The calculation unit can take into account the product supply chain costs and logistics costs when making calculations. Supply chain costs include, but are not limited to, raw material costs, manufacturing costs, and logistics costs. For example, the calculation unit calculates the costs of each stage of the supply chain in detail and reflects them in the price. The calculation unit can also set an optimal price by taking logistics costs into account. Furthermore, the calculation unit can improve the efficiency of the supply chain and reflect cost reductions in the price. This allows for more accurate pricing by taking into account the product supply chain costs and logistics costs.

[0106] The calculation unit can reflect the seasonality and trends of the product when calculating the price. Seasonality includes, but is not limited to, seasonal demand fluctuations and seasonal limited-edition products. The calculation unit adjusts the price based on, for example, seasonal demand forecasts. The calculation unit can also perform trend analysis to optimize the prices of popular products. Furthermore, the calculation unit can fluctuate the prices of seasonal products according to demand. This allows for more appropriate pricing by reflecting the seasonality and trends of the product.

[0107] When calculating, the calculation unit can apply discounts and benefits based on the customer's past purchase history. Discounts include, but are not limited to, discount rates and application conditions. For example, the calculation unit can apply a repeat customer discount based on the customer's past purchase history. The calculation unit can also provide benefits to customers with a high purchase history. Furthermore, the calculation unit can analyze past purchase history and suggest optimal discounts. In this way, customer satisfaction can be improved by applying discounts and benefits based on the customer's past purchase history.

[0108] The calculation unit can estimate the user's emotions and adjust the price display method based on the estimated user emotions. Price display methods include, but are not limited to, text display, graph display, and color coding. For example, if the user is nervous, the calculation unit can display a simple, highly visible price. Furthermore, if the user is relaxed, the calculation unit can display a detailed price breakdown. Furthermore, if the user is in a hurry, the calculation unit can display a concise price. This allows the price display method to be adjusted according to the user's emotions, making it easy for the user to read. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0109] The calculation unit can adjust the price by taking into account the eco-friendly characteristics of the product during calculation. Eco-friendly characteristics include, but are not limited to, the use of renewable energy and the use of recyclable materials. For example, the calculation unit can give preferential treatment to the price of eco-friendly products. The calculation unit can also adjust the price of products with eco-friendly manufacturing processes. Furthermore, the calculation unit can optimize the price of products with eco-friendly certifications. This enables environmentally friendly pricing by taking into account the eco-friendly characteristics of the product.

[0110] The calculation unit can adjust the price by taking into account the customer's geographical location information during calculation. Geographical location information includes, but is not limited to, GPS data, address information, and regional characteristics. For example, the calculation unit calculates the optimal delivery cost based on the customer's current location. The calculation unit can also set prices for each region by taking into account the geographical location information. Furthermore, the calculation unit can propose the optimal price based on the customer's location information. This allows for more appropriate pricing by taking into account the customer's geographical location information.

[0111] The calculation unit can improve the price calculation algorithm by reflecting past customer feedback during calculation. Examples of feedback include, but are not limited to, customer comments, ratings, and survey results. For example, the calculation unit collects past customer feedback and reflects it in the price calculation algorithm. The calculation unit can also improve the accuracy of price calculation based on the feedback. Furthermore, the calculation unit can analyze customer feedback and identify areas for improvement in the algorithm. In this way, the accuracy of price calculation is improved by reflecting past customer feedback.

[0112] The purchasing unit can estimate the user's emotions and adjust the purchasing process based on the estimated user emotions. Examples of the purchasing process include, but are not limited to, adding items to a cart, selecting a payment method, and entering a shipping address. For example, if the user is nervous, the purchasing unit can provide a simple and easy-to-understand purchasing process. If the user is relaxed, the purchasing unit can also provide a purchasing process with detailed information. Furthermore, if the user is in a hurry, the purchasing unit can also provide a quick purchasing process. This allows the purchasing process to be adjusted according to the user's emotions, making the purchasing process easy for the user to use. 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.

[0113] The purchasing unit can simplify the purchasing procedure based on the customer's past purchase history at the time of purchase. The purchasing procedure includes, but is not limited to, adding items to a cart, selecting a payment method, and inputting a shipping address. For example, the purchasing unit automatically simplifies the purchasing procedure based on the customer's past purchase history. The purchasing unit can also provide a one-click purchasing option to customers with a large purchase history. Furthermore, the purchasing unit can analyze the customer's past purchase history and suggest the optimal purchasing procedure. This simplifies the purchasing procedure based on the customer's past purchase history, thereby streamlining the purchasing process.

[0114] The purchasing department can provide purchasing options based on the customer's current needs and life events at the time of purchase. Current needs include, but are not limited to, products and services that the customer currently needs. For example, the purchasing department analyzes the customer's current needs and provides appropriate purchasing options. The purchasing department can also provide related purchasing options based on the customer's life events (marriage, childbirth, etc.). Furthermore, the purchasing department can provide purchasing options that match the customer's current needs based on the customer's past behavioral patterns. This allows for more appropriate purchases by providing purchasing options based on the customer's current needs and life events.

[0115] The purchasing department can improve the purchasing process by reflecting customer feedback at the time of purchase. Feedback includes, but is not limited to, customer comments, ratings, survey results, and the like. For example, the purchasing department collects past customer feedback and reflects it in the purchasing process. The purchasing department can also improve the accuracy of the purchasing process based on the feedback. Furthermore, the purchasing department can analyze customer feedback and identify areas for improvement in the purchasing process. In this way, the accuracy of the purchasing process can be improved by reflecting customer feedback.

[0116] The purchasing unit can estimate the user's emotions and determine purchasing priorities based on the estimated user emotions. Priorities include, but are not limited to, importance, urgency, and user interest. For example, if the user is excited, the purchasing unit can prioritize popular or new products. If the user is relaxed, the purchasing unit can provide detailed product information to expand the user's options. If the user is in a hurry, the purchasing unit can provide concise information to enable the user to quickly select a product. This allows the system to determine purchasing priorities based on the user's emotions and provide products that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0117] The purchasing department can provide optimal delivery options by taking into account the customer's geographic location information at the time of purchase. Geographic location information includes, but is not limited to, GPS data, address information, and local characteristics. The purchasing department can, for example, propose optimal delivery options based on the customer's current location. The purchasing department can also propose optimal delivery times by taking into account the geographic location information. Furthermore, the purchasing department can propose optimal delivery methods based on the customer's location information. This allows for more appropriate delivery options to be provided by taking into account the customer's geographic location information.

[0118] The purchasing department can analyze a customer's social media activity at the time of purchase to provide relevant purchasing options. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the purchasing department can analyze the content of a customer's posts on social media to provide relevant purchasing options. The purchasing department can also provide relevant purchasing options based on the activities of the customer's friends on social media. Furthermore, the purchasing department can analyze social media trends to provide purchasing options that suit the customer. In this way, by analyzing a customer's social media activity, more appropriate purchasing options can be provided.

[0119] The purchasing department can customize the purchasing process by reflecting past customer feedback at the time of purchase. Feedback includes, but is not limited to, customer comments, ratings, survey results, and the like. For example, the purchasing department collects past customer feedback and reflects it in the purchasing process. The purchasing department can also improve the accuracy of the purchasing process based on the feedback. Furthermore, the purchasing department can analyze customer feedback and identify areas for improvement in the purchasing process. In this way, the accuracy of the purchasing process can be improved by reflecting past customer feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the registration unit, analysis unit, proposal unit, calculation unit, and purchase unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 and inputs product information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes customer preferences and purchase history. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes products based on the analysis results. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates sales commissions and profit margins. The purchase unit is realized by the control unit 46A of the smart device 14 and offers products at the calculated prices. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned registration unit, analysis unit, suggestion unit, calculation unit, and purchase unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and inputs product information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes customer preferences and purchase history. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests products based on the analysis results. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates sales commissions and profit margins. The purchase unit is realized, for example, by the control unit 46A of the smart glasses 214 and offers products at the calculated prices. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, analysis unit, proposal unit, calculation unit, and purchase unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314 and inputs product information. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes customer preferences and purchase history. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes products based on the analysis results. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates sales commissions and profit margins. The purchase unit is realized by the control unit 46A of the headset type terminal 314 and offers products at the calculated prices. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, analysis unit, proposal unit, calculation unit, and purchase unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and inputs product information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes customer preferences and purchase history. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes products based on the analysis results. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates sales commissions and profit margins. The purchase unit is realized, for example, by the control unit 46A of the robot 414 and offers products at the calculated prices.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can analyze not only a customer's purchase history but also their social media activity. For example, it can analyze the products that the customer has "liked" on social media and the posts that the customer has shared to identify the customer's interests. It can also analyze the number of followers and the content of comments that the customer has made to evaluate the customer's influence. It can also analyze social media trends and suggest products that suit the customer. This allows the analysis results to be more accurate by taking into account the customer's social media activity.

[0122] The suggestion unit can estimate the customer's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the customer is feeling stressed, a simple, highly visible suggestion can be made. If the customer is relaxed, a suggestion including detailed information can be made. Furthermore, if the customer is excited, a visually appealing suggestion can be made. In this way, by adjusting the way suggestions are presented according to the customer's emotions, it is possible to make suggestions that are easy for the customer to see.

[0123] The calculation unit can adjust prices by taking into account the eco-friendly characteristics of the product. For example, it can give preferential treatment to the prices of products manufactured using renewable energy or products made from recyclable materials. It can also adjust prices of products with eco-friendly manufacturing processes. Furthermore, it can optimize prices of products with eco-friendly certifications. This enables environmentally friendly pricing by taking into account the eco-friendly characteristics of the product.

[0124] The purchasing department can estimate the customer's emotions and adjust the purchasing process based on the estimated emotions. For example, if the customer is nervous, a simple and highly visible purchasing process can be provided. If the customer is relaxed, a purchasing process with detailed information can be provided. Furthermore, if the customer is in a hurry, a quick purchasing process can be provided. This allows the purchasing process to be adjusted according to the customer's emotions, making it easy for customers to use.

[0125] The analysis unit can perform analysis taking into account the customer's geographical location information. For example, it can suggest nearby stores and services based on the customer's current location. It can also analyze the customer's past location information and suggest products related to frequently visited places. It can also suggest optimal delivery options taking into account the customer's geographical location information. This allows it to suggest more appropriate products by taking into account the customer's geographical location information.

[0126] The suggestion unit can estimate the customer's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the customer is in a hurry, a short, to-the-point suggestion can be made. If the customer is relaxed, a longer suggestion with detailed explanations can be made. Furthermore, if the customer is excited, a visually stimulating suggestion can be made. In this way, by adjusting the length of the suggestion according to the customer's emotions, it is possible to make a suggestion that is easy for the customer to view.

[0127] The calculation unit can apply discounts and special offers based on the customer's past purchase history. For example, a repeat customer discount can be applied based on the customer's past purchase history. It can also provide special offers to customers with a high purchase history. It can also analyze past purchase history and propose optimal discounts. This improves customer satisfaction by applying discounts and special offers based on the customer's past purchase history.

[0128] The purchasing department can estimate the customer's emotions and determine purchasing priorities based on the estimated emotions. For example, if the customer is excited, popular or new products can be displayed preferentially. If the customer is relaxed, detailed product information can be provided to expand the customer's choices. Furthermore, if the customer is in a hurry, concise information can be provided to enable the customer to select a product quickly. In this way, by determining purchasing priorities according to the customer's emotions, it is possible to provide products that meet the customer's needs.

[0129] The proposal unit can customize the proposal content based on the customer's current needs and life events. For example, it can analyze the customer's current needs and propose appropriate products. It can also propose related products based on the customer's life events (marriage, childbirth, etc.). It can also propose products that match the customer's current needs based on the customer's past behavioral patterns. This allows for more appropriate proposals by customizing the proposal content based on the customer's current needs and life events.

[0130] The calculation unit can estimate the customer's emotions and adjust the way prices are displayed based on the estimated emotions. For example, if the customer is nervous, a simple, highly visible price display can be displayed. If the customer is relaxed, a detailed price breakdown can be displayed. Furthermore, if the customer is in a hurry, a concise price display can be displayed. In this way, by adjusting the way prices are displayed according to the customer's emotions, it is possible to display prices that are easy for customers to see.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The registration unit registers product information. Product information includes, for example, the product name, description, price, category, sales commission, profit margin, etc. For example, when a cosmetics manufacturer registers a new skin care product, they enter information such as the product's ingredients, usage instructions, and price. Step 2: The analysis unit analyzes the customer's preferences and purchase history based on the product information registered by the registration unit. The analysis is performed using, for example, data mining or machine learning algorithms. For example, the analysis analyzes the customer's past purchase history and preferences to identify the products that the customer prefers. Step 3: The suggestion unit suggests products based on the results of the analysis by the analysis unit. The suggestion is performed, for example, using a recommendation engine or a filtering algorithm. For example, a new skin care product is suggested based on the results of the analysis. Step 4: The calculation unit calculates the sales commission and profit margin. The calculation is performed using, for example, a fixed commission or a percentage commission. For example, the sales commission and profit margin are automatically calculated and presented to the client. Step 5: The purchasing unit offers the product at the price calculated by the calculation unit. The offer may be made, for example, through online sales or in-store sales. For example, the product may be offered at the calculated price, allowing customers to purchase the product at a transparent price.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

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

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 registration unit for registering product information; an analysis unit that analyzes customer preferences and purchase history based on the product information registered by the registration unit; a suggestion unit that suggests products based on the results of the analysis by the analysis unit; a calculation unit for calculating sales commissions and profit margins; a purchasing unit that provides the product at the price calculated by the calculation unit. A system characterized by:

2. The registration unit Enter detailed product information or price, sales commission, and profit margin information 2. The system of claim 1.

3. The analysis unit Analyze customers' past purchase history and preferences The system of claim 1 .

4. The proposal unit Propose new products based on the analyzed results 2. The system of claim 1.

5. The calculation unit Automatically calculate sales commissions and profit margins 2. The system of claim 1.

6. The purchasing department Offering products at calculated prices 2. The system of claim 1.

7. The registration unit Estimate the user's emotions and adjust the product information input method based on the estimated user emotions 2. The system of claim 1.

8. The registration unit When submitting product information, adjust your submission to take into account seasonality or trends in your products.

2. The system of claim 1.

9. The registration unit Include product manufacturer and supply chain information when submitting product information 2. The system of claim 1.

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