system

Abstract

The system according to this embodiment aims to propose an optimal purchase plan and guide the timing of purchase by utilizing point reward information. [Solution] The system according to the embodiment comprises an acquisition unit, a proposal unit, an optimization unit, and a guide unit. The acquisition unit acquires point redemption information. The proposal unit proposes the optimal purchase plan based on the point redemption information acquired by the acquisition unit. The optimization unit optimizes sales and coupon information based on the purchase plan proposed by the proposal unit. The guide unit guides the timing of the purchase based on the information optimized by the optimization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that point reduction was complicated and difficult to understand, and it was difficult to find the optimal purchase timing.

[0005] The system according to the embodiment aims to propose an optimal purchase plan by utilizing point reduction information and guide the purchase timing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a proposal unit, an optimization unit, and a guide unit. The acquisition unit acquires point reward information. The proposal unit proposes an optimal purchase plan based on the point reward information acquired by the acquisition unit. The optimization unit optimizes sales and coupon information based on the purchase plan proposed by the proposal unit. The guide unit guides the timing of the purchase based on the information optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can utilize point reward information to propose an optimal purchase plan and guide the timing of the purchase. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The shopping support system according to an embodiment of the present invention is a system that utilizes AI to improve the shopping experience. This shopping support system uses AI to acquire the latest point reward information in conjunction with e-commerce sites in order to link real-time point information. Next, the AI ​​provides an optimal purchase plan simply by the user stating what they want. Furthermore, it suggests the best sales and coupon usage methods on e-book sales sites and other e-commerce sites. Finally, it guides the user through a purchase strategy that takes into account specific stores and sales events. This system simplifies complex point systems, achieves savings through optimized purchase plans, and improves the user experience. For example, the AI ​​acquires the latest point reward information in conjunction with e-commerce sites. In this process, the AI ​​collects point information in real time and provides it to the user. For example, if a user purchases a specific product, the AI ​​immediately acquires the latest point reward information for that product and notifies the user. Next, the AI ​​provides an optimal purchase plan simply by the user stating what they want. The user creates a wish list and inputs it into the AI. Based on the user's list, the AI ​​proposes an optimal purchase plan. For example, if a specific product is on sale, it suggests the best time to purchase that product. Furthermore, it suggests the best ways to use sales and coupons on e-book sales sites and other e-commerce sites. The AI ​​collects sales and coupon information from each shopping site and suggests the best way to use them to the user. For example, if a coupon is available for a particular product, it suggests the best time to use that coupon. Finally, it guides users through purchasing strategies that take into account specific stores and sales events. The AI ​​collects sales event information from each shopping site and suggests the best purchasing strategy to the user. For example, it suggests how to maximize point rewards by purchasing a specific product during a sale period at a specific store. This simplifies complex point systems, enables savings through optimized purchasing plans, and improves the user experience. By following the AI's suggestions, users can earn points more efficiently and purchase products at the optimal time.This allows shopping support systems to improve the user's shopping experience.

[0029] The shopping support system according to the embodiment comprises an acquisition unit, a proposal unit, an optimization unit, and a guide unit. The acquisition unit acquires point reward information. The acquisition unit acquires the latest point reward information in conjunction with, for example, an e-commerce site. The acquisition unit collects point information in real time and provides it to the user. For example, when a specific product is purchased, the acquisition unit immediately acquires the latest point reward information for that product and notifies the user. The proposal unit proposes the optimal purchase plan based on the point reward information acquired by the acquisition unit. The proposal unit, for example, has the user create a wish list and input it into the AI. The proposal unit proposes the optimal purchase plan based on the user's list. For example, if a specific product is on sale, it proposes the optimal timing to purchase that product. The optimization unit optimizes sale and coupon information based on the purchase plan proposed by the proposal unit. The optimization unit proposes the optimal way to use sales and coupons on, for example, e-book sales sites and other e-commerce sites. The optimization unit collects sale information and coupon information from each shopping site and proposes the optimal way to use them to the user. For example, if a coupon is available for a specific product, it proposes the optimal timing to use that coupon. The guiding unit guides the timing of purchases based on information optimized by the optimization unit. The guiding unit guides purchase strategies that take into account specific stores and sales events, for example. The guiding unit collects sales event information from each shopping site and proposes the optimal purchase strategy to the user. For example, it proposes a way to maximize point rewards by purchasing specific products during a sale period at a specific store. In this way, the shopping support system can improve the user's shopping experience. Some or all of the above processing in the acquisition unit, proposal unit, optimization unit, and guiding unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use AI to acquire the latest point reward information in conjunction with e-commerce sites. The proposal unit can use AI to propose the optimal purchase plan based on the user's wish list. The optimization unit can use AI to optimize sales and coupon information.The guidance system can use AI to guide users on the timing of their purchases. This allows the shopping assistance system to improve the user's shopping experience.

[0030] The acquisition unit acquires point reward information. For example, the acquisition unit acquires the latest point reward information in conjunction with e-commerce sites. Specifically, the acquisition unit uses APIs of multiple e-commerce sites to collect point reward information provided by each site in real time. This allows the acquisition unit to instantly acquire and notify the user of the latest point reward information for a particular product when the user purchases that product. For example, if a user searches for a product on a specific e-commerce site, the acquisition unit instantly acquires the point reward information for that product and notifies the user. Furthermore, the acquisition unit can analyze the user's purchase history and search history to prioritize acquiring point reward information for products that the user is likely to be interested in. This allows the user to efficiently obtain point reward information that matches their interests. In addition, the acquisition unit can provide information in real time by adjusting the update frequency of point reward information. For example, by increasing the update frequency of point reward information during specific times or sales periods, the acquisition unit can provide users with the latest information. This allows the acquisition unit to always provide users with the latest point reward information and improve the shopping experience.

[0031] The suggestion unit proposes the optimal purchase plan based on point reward information acquired by the acquisition unit. For example, the suggestion unit allows users to create a wish list and input it into the AI. Specifically, when a user creates a wish list, the suggestion unit inputs that list into the AI, which then analyzes the user's purchase history, search history, and point reward information. This allows the AI ​​to propose the optimal purchase plan to the user. For example, if a particular product is on sale, it can suggest the best time to purchase it. The suggestion unit can also learn the user's purchasing patterns and preferences and propose products and services that suit the user. This allows users to efficiently find products that match their interests. Furthermore, the suggestion unit integrates information from multiple e-commerce sites to propose the optimal purchase plan to the user. For example, if a particular product is sold on multiple sites, the suggestion unit can compare point reward information and sale information from each site and propose the most advantageous purchase plan to the user. This allows the suggestion unit to provide users with the optimal purchase plan and improve their shopping experience.

[0032] The optimization unit optimizes sales and coupon information based on the purchase plans proposed by the proposal unit. Specifically, the optimization unit proposes the best way to use sales and coupons on e-book sales sites and other e-commerce sites. For example, if a coupon is available for a particular product, it will suggest the best time to use that coupon. The optimization unit collects sales and coupon information from each shopping site and proposes the best way to use it to the user. This allows the user to use coupons at the most advantageous time. In addition, the optimization unit can analyze the user's purchase history and search history to propose sales and coupon information that suits the user. This allows the user to efficiently obtain sales and coupon information that matches their interests. Furthermore, the optimization unit enables real-time information provision by adjusting the update frequency of sales and coupon information. For example, by increasing the update frequency of sales and coupon information during specific times or sales periods, it can provide users with the latest information. In this way, the optimization unit can always provide users with the latest sales and coupon information and improve the shopping experience.

[0033] The Guide Department guides users on the best time to purchase based on information optimized by the Optimization Department. Specifically, the Guide Department provides purchasing strategies that take into account specific stores and sales events. For example, it might suggest how to maximize point rewards by purchasing specific products during sales periods at specific stores. The Guide Department collects sales event information from various shopping sites and proposes the optimal purchasing strategy to users. This allows users to purchase products at the most advantageous time. The Guide Department can also analyze users' purchase and search history to propose purchasing strategies tailored to them. This allows users to efficiently obtain purchasing strategies that match their interests. Furthermore, the Guide Department provides users with the latest information by offering purchase timing guidance in real time. For example, by providing purchase timing guidance during specific time periods or sales periods, it can suggest the optimal time to purchase to users. In this way, the Guide Department can always provide users with the latest purchase timing guidance, improving their shopping experience.

[0034] The acquisition unit can acquire the latest point reward information in conjunction with e-commerce sites. For example, the acquisition unit acquires the latest point reward information in conjunction with e-commerce sites. The acquisition unit collects point information in real time and provides it to the user. For example, when a specific product is purchased, the acquisition unit immediately acquires the latest point reward information for that product and notifies the user. In this way, the latest point reward information can be acquired by linking with e-commerce sites. The latest point reward information includes, but is not limited to, the point reward rate and reward conditions for specific stores or products. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to acquire the latest point reward information in conjunction with e-commerce sites.

[0035] The suggestion unit can propose the optimal purchase plan based on the user's wish list. For example, the suggestion unit can input the user's wish list into the AI. Based on the user's list, the suggestion unit proposes the optimal purchase plan. For example, if a particular product is on sale, it can suggest the best time to purchase that product. This allows for suggestions tailored to the user's needs by proposing the optimal purchase plan based on the user's wish list. The wish list may include, but is not limited to, items manually added by the user or automatically generated based on past purchase history. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can use AI to propose the optimal purchase plan based on the user's wish list.

[0036] The optimization unit can suggest the best sales and coupon usage methods on e-book sales sites and other e-commerce sites. For example, the optimization unit can suggest the best sales and coupon usage methods on e-book sales sites and other e-commerce sites. The optimization unit collects sales and coupon information from each shopping site and suggests the best usage methods to the user. For example, if a coupon is available for a particular product, it suggests the best timing to use that coupon. This increases the user's savings by suggesting the best sales and coupon usage methods on e-book sales sites and other e-commerce sites. The best sales and coupon usage methods include, but are not limited to, discount rates, usage conditions, and usage timing. Some or all of the processing described above in the optimization unit may be performed using, for example, AI, or not using AI. For example, the optimization unit can use AI to suggest the best sales and coupon usage methods on e-book sales sites and other e-commerce sites.

[0037] The guide unit can guide purchasing strategies based on specific stores and sales events. For example, the guide unit guides purchasing strategies that take specific stores and sales events into consideration. The guide unit collects sales event information from each shopping site and proposes the optimal purchasing strategy to the user. For example, it suggests how to maximize point rewards by purchasing specific products during a sale period at a specific store. This optimizes the user's purchasing timing by guiding purchasing strategies that take specific stores and sales events into consideration. Specific stores and sales events include, but are not limited to, specific brands, seasonal sales, and limited-time events. Some or all of the processing described above in the guide unit may be performed using, for example, AI, or not. For example, the guide unit can use AI to guide purchasing strategies that take specific stores and sales events into consideration.

[0038] The acquisition unit can analyze the user's past purchase history and prioritize the acquisition of optimal point reward information. For example, the acquisition unit can prioritize the acquisition of relevant point reward information based on the product categories the user has purchased in the past. The acquisition unit can also prioritize the acquisition of point reward information from stores the user frequently uses. The acquisition unit can also prioritize the acquisition of point reward information from when the user has purchased high-value items in the past. This allows the acquisition unit to prioritize the acquisition of highly relevant point reward information by analyzing the user's past purchase history. Past purchase history includes, but is not limited to, purchase date and time, purchased items, and purchase frequency. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not. For example, the acquisition unit can use AI to analyze the user's past purchase history and prioritize the acquisition of optimal point reward information.

[0039] The acquisition unit can filter point reward information based on the user's current purchasing intent and products of interest when acquiring it. For example, the acquisition unit can filter point reward information based on product categories that the user is currently interested in. The acquisition unit can also prioritize acquiring point reward information for products that the user is considering purchasing. The acquisition unit can also acquire point reward information during times when the user's purchasing intent is high. This allows the system to provide users with useful information by filtering point reward information based on their current purchasing intent and products of interest. Current purchasing intent and products of interest include, but are not limited to, browsing history, items in the shopping cart, and search keywords. Some or all of the processing described above in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to filter point reward information based on the user's current purchasing intent and products of interest when acquiring it.

[0040] The acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring point reward information. For example, the acquisition unit can prioritize acquiring point reward information from stores near the user's current location. The acquisition unit can also prioritize acquiring point reward information from areas frequently visited by the user. The acquisition unit can also prioritize acquiring point reward information from stores along the user's travel route. This allows the acquisition unit to provide highly relevant point reward information to the user by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the processing described above in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to prioritize acquiring highly relevant information by considering the user's geographical location when acquiring point reward information.

[0041] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring point reward information. For example, the acquisition unit can acquire point reward information for products and services that the user has shown interest in on social media. The acquisition unit can also prioritize acquiring point reward information for brands and stores that the user follows. The acquisition unit can also analyze the user's purchasing intent on social media and acquire relevant point reward information. In this way, by analyzing the user's social media activity, it is possible to provide point reward information that is highly relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to analyze the user's social media activity and acquire relevant information when acquiring point reward information.

[0042] The suggestion function can adjust the level of detail in its suggestions based on the importance of the products. For example, for highly important products, the suggestion function may offer a detailed description and multiple purchase options. For less important products, it may offer a concise description and basic purchase options. For moderately important products, it may offer a description with a moderate level of detail and several purchase options. This allows the suggestion function to provide users with appropriate information by adjusting the level of detail in its suggestions based on the importance of the products. Product importance includes, but is not limited to, price, demand, and user interest. Some or all of the processing described above in the suggestion function may be performed using, for example, AI, or not. For example, the suggestion function can use AI to adjust the level of detail in its suggestions based on the importance of the products when making suggestions.

[0043] The suggestion function can apply different suggestion algorithms depending on the product category when making suggestions. For example, in the case of electronic devices, the suggestion function may make suggestions that emphasize technical specifications and reviews. In the case of fashion items, the suggestion function may also make suggestions based on trends and styles. In the case of food products, the suggestion function may also make suggestions that emphasize nutritional information and recipes. By applying different suggestion algorithms depending on the product category, it becomes possible to make suggestions that are optimal for the user. Product categories include, but are not limited to, electronic devices, clothing, and food products. Some or all of the processing described above in the suggestion function may be performed using, for example, AI, or not using AI. For example, the suggestion function can use AI to apply different suggestion algorithms depending on the product category when making suggestions.

[0044] The proposal department can prioritize proposals based on the product submission timing when submitting a proposal. For example, the proposal department can immediately submit a proposal for urgent products. For less urgent products, the proposal department can submit a proposal according to the user's convenience. For seasonal or limited-edition products, the proposal department can submit a proposal at an appropriate time. This allows for proposals to be submitted at the appropriate time for the user by prioritizing proposals based on the product submission timing. Product submission timing includes, but is not limited to, the release date, sale start date, and coupon expiration date. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can use AI to prioritize proposals based on the product submission timing when submitting a proposal.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit may prioritize suggesting products that the user has shown interest in. The suggestion unit may also prioritize suggesting highly relevant products based on the user's purchase history. The suggestion unit may also adjust the order of suggestions based on the user's current purchasing intent. This allows for suggestions that are more relevant to the user by adjusting the order of suggestions based on the relevance of the products. Product relevance includes, but is not limited to, products in the same category, past purchase history, and the user's level of interest. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit may use AI to adjust the order of suggestions based on the relevance of the products when making suggestions.

[0046] The optimization unit can analyze the user's past purchasing behavior during optimization to select the most relevant sales and coupon information. For example, the optimization unit can select the most relevant information based on sales and coupon information the user has used in the past. The optimization unit can also select highly relevant sales and coupon information based on the user's past purchasing behavior. The optimization unit can analyze the user's past purchasing behavior and select the most effective sales and coupon information. This allows the system to provide highly relevant sales and coupon information by analyzing the user's past purchasing behavior. Past purchasing behavior includes, but is not limited to, purchase date and time, purchased items, and purchase frequency. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or not. For example, the optimization unit can use AI to analyze the user's past purchasing behavior and select the most relevant sales and coupon information during optimization.

[0047] The optimization unit can customize the optimization methods based on the user's current lifestyle during optimization. For example, if the user is busy, the optimization unit may suggest concise and to-the-point sales and coupon information. If the user is relaxed, the optimization unit may also suggest detailed sales and coupon information. The optimization unit can also customize the most suitable sales and coupon information according to the user's lifestyle. This allows the system to provide the user with the most relevant information by customizing the optimization methods according to the user's current lifestyle. Current lifestyle includes, but is not limited to, income, family structure, and lifestyle. Some or all of the processing described above in the optimization unit may be performed using, for example, AI, or not. For example, the optimization unit can use AI to customize the optimization methods based on the user's current lifestyle during optimization.

[0048] The optimization unit can select the most relevant sales and coupon information by considering the user's geographical location during optimization. For example, the optimization unit may prioritize optimizing sales and coupon information from stores near the user's current location. The optimization unit can also prioritize optimizing sales and coupon information from areas the user frequently visits. The optimization unit can also prioritize optimizing sales and coupon information from stores along the user's travel route. This allows the system to provide the user with sales and coupon information that is highly relevant to them by considering their geographical location. Optimal sales and coupon information may include, but is not limited to, discount rates, usage conditions, and usage timing. Some or all of the processing described above in the optimization unit may be performed using, for example, AI, or not. For example, the optimization unit may use AI to select the most relevant sales and coupon information by considering the user's geographical location during optimization.

[0049] The optimization unit can analyze the user's social media activity and propose optimization methods during the optimization process. For example, the optimization unit can optimize sales and coupon information for products and services that the user has shown interest in on social media. The optimization unit can also prioritize and optimize sales and coupon information for brands and stores that the user follows. The optimization unit can also analyze the user's purchasing intent on social media and propose the most suitable sales and coupon information. In this way, by analyzing the user's social media activity, it is possible to provide the user with sales and coupon information that is highly relevant to them. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or not using AI. For example, the optimization unit can use AI to analyze the user's social media activity and propose optimization methods during the optimization process.

[0050] The guidance unit can suggest the optimal timing for purchase by referring to the user's past purchase history during the guidance process. For example, the guidance unit can suggest relevant purchase timings based on the product categories the user has previously purchased. The guidance unit can also suggest purchase timings for stores the user frequently visits. The guidance unit can also suggest purchase timings when the user has previously purchased high-priced items. This allows the guidance unit to suggest highly relevant purchase timings by referring to the user's past purchase history. Past purchase history includes, but is not limited to, purchase date and time, purchased items, and purchase frequency. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can use AI to suggest the optimal timing for purchase by referring to the user's past purchase history during the guidance process.

[0051] The guidance unit can customize the timing of a purchase based on the user's current purchasing intent during the guidance process. For example, the guidance unit can customize the timing of a purchase based on the product categories the user is currently interested in. The guidance unit can also suggest the timing of a purchase for products the user is considering buying. The guidance unit can also suggest a purchase timing during times when the user's purchasing intent is high. This allows the system to suggest the optimal time for a purchase by customizing the timing of a purchase based on the user's current purchasing intent. Current purchasing intent includes, but is not limited to, browsing history, items in the shopping cart, and search keywords. Some or all of the processing described above in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can use AI to customize the timing of a purchase based on the user's current purchasing intent during the guidance process.

[0052] The guidance unit can suggest the optimal timing for purchases while considering the user's geographical location. For example, the guidance unit may prioritize suggesting purchase timings for stores near the user's current location. It may also prioritize suggesting purchase timings for areas the user frequently visits. It may also prioritize suggesting purchase timings for stores along the user's travel route. By considering the user's geographical location, it can suggest purchase timings that are highly relevant to the user. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the processing described above in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can use AI to suggest the optimal timing for purchases while considering the user's geographical location.

[0053] The guidance unit can analyze the user's social media activity during the guidance process and suggest methods for timing purchases. For example, the guidance unit can suggest the timing for purchasing products or services that the user has shown interest in on social media. The guidance unit can also prioritize suggesting purchase timings for brands and stores that the user follows. The guidance unit can also analyze the user's purchasing intent on social media and suggest relevant purchase timings. In this way, by analyzing the user's social media activity, it can suggest purchase timings that are highly relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can use AI to analyze the user's social media activity during the guidance process and suggest methods for timing purchases.

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

[0055] The acquisition unit can analyze the user's purchase history and acquire point reward information based on past purchase patterns. For example, the acquisition unit can prioritize acquiring relevant point reward information based on the product categories the user has purchased in the past. Furthermore, the acquisition unit can also prioritize acquiring point reward information from stores the user frequently uses. This allows the system to provide highly relevant point reward information by analyzing the user's past purchase history. Past purchase history includes, but is not limited to, the date and time of purchase, the products purchased, and the frequency of purchase. Some or all of the processing described above in the acquisition unit may be performed using AI or not.

[0056] The optimization unit can suggest optimal sales and coupon information by taking into account the user's geographical location. For example, it can prioritize suggesting sales and coupon information from stores near the user's current location. Furthermore, it can prioritize suggesting sales and coupon information from areas the user frequently visits. In this way, by considering the user's geographical location, it is possible to provide sales and coupon information that is highly relevant to the user. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the processing described above in the optimization unit may be performed using AI, or it may be performed without using AI.

[0057] The guide function can analyze a user's social media activity and suggest relevant purchase timings. For example, it can suggest the timing for purchasing products or services that the user has shown interest in on social media. Furthermore, it can prioritize suggesting purchase timings for brands and stores that the user follows. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant purchase timings for the user. Social media activity includes, but is not limited to, posts, likes, and follower counts. Some or all of the processing described above in the guide function may be performed using AI or not.

[0058] The suggestion section can adjust the level of detail in its suggestions based on the importance of the products. For example, for highly important products, it can offer detailed descriptions and multiple purchase options. For less important products, it can offer concise descriptions and basic purchase options. This allows the system to provide users with appropriate information by adjusting the level of detail in its suggestions based on the importance of the products. Product importance includes, but is not limited to, price, demand, and user interest. Some or all of the processing described above in the suggestion section may be performed using AI or not.

[0059] The guide unit can suggest the optimal timing for purchase by referring to the user's past purchase history. For example, it can suggest relevant purchase timings based on the product categories the user has purchased in the past. Furthermore, it can also suggest purchase timings for stores the user frequently visits. In this way, by referring to the user's past purchase history, it is possible to provide highly relevant purchase timings. Past purchase history includes, but is not limited to, the date and time of purchase, the products purchased, and the frequency of purchase. Some or all of the above processing in the guide unit may be performed using AI or not.

[0060] The acquisition unit can filter point reward information based on the user's current purchasing intent and products of interest. For example, it can filter point reward information based on the product categories the user is currently interested in. Furthermore, it can prioritize the acquisition of point reward information for products the user is considering purchasing. This allows the system to provide users with useful information by filtering point reward information based on their current purchasing intent and products of interest. Current purchasing intent and products of interest include, but are not limited to, browsing history, items in the shopping cart, and search keywords. Some or all of the processing described above in the acquisition unit may be performed using AI or not.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The acquisition unit acquires point reward information. The acquisition unit acquires the latest point reward information, for example, by linking with an e-commerce site. The acquisition unit collects point information in real time and provides it to the user. For example, when a specific product is purchased, the acquisition unit immediately acquires the latest point reward information for that product and notifies the user. Step 2: The suggestion unit proposes the optimal purchase plan based on the point redemption information acquired by the acquisition unit. For example, the suggestion unit has the user create a wish list and input it into the AI. Based on the user's list, the suggestion unit proposes the optimal purchase plan. For example, if a particular product is on sale, it will suggest the best time to purchase that product. Step 3: The optimization unit optimizes sales and coupon information based on the purchase plan proposed by the proposal unit. The optimization unit proposes the best way to use sales and coupons on e-book sales sites and other e-commerce sites, for example. The optimization unit collects sales and coupon information from each shopping site and proposes the best way to use it to the user. For example, if a coupon is available for a specific product, it will propose the best time to use that coupon. Step 4: The guide unit guides the user on the timing of their purchase based on the information optimized by the optimization unit. The guide unit guides the user on purchase strategies that take into account specific stores and sales events, for example. The guide unit collects sales event information from each shopping site and proposes the best purchase strategy to the user. For example, it may suggest how to maximize point rewards by purchasing specific products during a sale period at a specific store.

[0063] (Example of form 2) The shopping support system according to an embodiment of the present invention is a system that utilizes AI to improve the shopping experience. This shopping support system uses AI to acquire the latest point reward information in conjunction with e-commerce sites in order to link real-time point information. Next, the AI ​​provides an optimal purchase plan simply by the user stating what they want. Furthermore, it suggests the best sales and coupon usage methods on e-book sales sites and other e-commerce sites. Finally, it guides the user through a purchase strategy that takes into account specific stores and sales events. This system simplifies complex point systems, achieves savings through optimized purchase plans, and improves the user experience. For example, the AI ​​acquires the latest point reward information in conjunction with e-commerce sites. In this process, the AI ​​collects point information in real time and provides it to the user. For example, if a user purchases a specific product, the AI ​​immediately acquires the latest point reward information for that product and notifies the user. Next, the AI ​​provides an optimal purchase plan simply by the user stating what they want. The user creates a wish list and inputs it into the AI. Based on the user's list, the AI ​​proposes an optimal purchase plan. For example, if a specific product is on sale, it suggests the best time to purchase that product. Furthermore, it suggests the best ways to use sales and coupons on e-book sales sites and other e-commerce sites. The AI ​​collects sales and coupon information from each shopping site and suggests the best way to use them to the user. For example, if a coupon is available for a particular product, it suggests the best time to use that coupon. Finally, it guides users through purchasing strategies that take into account specific stores and sales events. The AI ​​collects sales event information from each shopping site and suggests the best purchasing strategy to the user. For example, it suggests how to maximize point rewards by purchasing a specific product during a sale period at a specific store. This simplifies complex point systems, enables savings through optimized purchasing plans, and improves the user experience. By following the AI's suggestions, users can earn points more efficiently and purchase products at the optimal time.This allows shopping support systems to improve the user's shopping experience.

[0064] The shopping support system according to the embodiment comprises an acquisition unit, a proposal unit, an optimization unit, and a guide unit. The acquisition unit acquires point reward information. The acquisition unit acquires the latest point reward information in conjunction with, for example, an e-commerce site. The acquisition unit collects point information in real time and provides it to the user. For example, when a specific product is purchased, the acquisition unit immediately acquires the latest point reward information for that product and notifies the user. The proposal unit proposes the optimal purchase plan based on the point reward information acquired by the acquisition unit. The proposal unit, for example, has the user create a wish list and input it into the AI. The proposal unit proposes the optimal purchase plan based on the user's list. For example, if a specific product is on sale, it proposes the optimal timing to purchase that product. The optimization unit optimizes sale and coupon information based on the purchase plan proposed by the proposal unit. The optimization unit proposes the optimal way to use sales and coupons on, for example, e-book sales sites and other e-commerce sites. The optimization unit collects sale information and coupon information from each shopping site and proposes the optimal way to use them to the user. For example, if a coupon is available for a specific product, it proposes the optimal timing to use that coupon. The guiding unit guides the timing of purchases based on information optimized by the optimization unit. The guiding unit guides purchase strategies that take into account specific stores and sales events, for example. The guiding unit collects sales event information from each shopping site and proposes the optimal purchase strategy to the user. For example, it proposes a way to maximize point rewards by purchasing specific products during a sale period at a specific store. In this way, the shopping support system can improve the user's shopping experience. Some or all of the above processing in the acquisition unit, proposal unit, optimization unit, and guiding unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use AI to acquire the latest point reward information in conjunction with e-commerce sites. The proposal unit can use AI to propose the optimal purchase plan based on the user's wish list. The optimization unit can use AI to optimize sales and coupon information.The guidance system can use AI to guide users on the timing of their purchases. This allows the shopping assistance system to improve the user's shopping experience.

[0065] The acquisition unit acquires point reward information. For example, the acquisition unit acquires the latest point reward information in conjunction with e-commerce sites. Specifically, the acquisition unit uses APIs of multiple e-commerce sites to collect point reward information provided by each site in real time. This allows the acquisition unit to instantly acquire and notify the user of the latest point reward information for a particular product when the user purchases that product. For example, if a user searches for a product on a specific e-commerce site, the acquisition unit instantly acquires the point reward information for that product and notifies the user. Furthermore, the acquisition unit can analyze the user's purchase history and search history to prioritize acquiring point reward information for products that the user is likely to be interested in. This allows the user to efficiently obtain point reward information that matches their interests. In addition, the acquisition unit can provide information in real time by adjusting the update frequency of point reward information. For example, by increasing the update frequency of point reward information during specific times or sales periods, the acquisition unit can provide users with the latest information. This allows the acquisition unit to always provide users with the latest point reward information and improve the shopping experience.

[0066] The suggestion unit proposes the optimal purchase plan based on point reward information acquired by the acquisition unit. For example, the suggestion unit allows users to create a wish list and input it into the AI. Specifically, when a user creates a wish list, the suggestion unit inputs that list into the AI, which then analyzes the user's purchase history, search history, and point reward information. This allows the AI ​​to propose the optimal purchase plan to the user. For example, if a particular product is on sale, it can suggest the best time to purchase it. The suggestion unit can also learn the user's purchasing patterns and preferences and propose products and services that suit the user. This allows users to efficiently find products that match their interests. Furthermore, the suggestion unit integrates information from multiple e-commerce sites to propose the optimal purchase plan to the user. For example, if a particular product is sold on multiple sites, the suggestion unit can compare point reward information and sale information from each site and propose the most advantageous purchase plan to the user. This allows the suggestion unit to provide users with the optimal purchase plan and improve their shopping experience.

[0067] The optimization unit optimizes sales and coupon information based on the purchase plans proposed by the proposal unit. Specifically, the optimization unit proposes the best way to use sales and coupons on e-book sales sites and other e-commerce sites. For example, if a coupon is available for a particular product, it will suggest the best time to use that coupon. The optimization unit collects sales and coupon information from each shopping site and proposes the best way to use it to the user. This allows the user to use coupons at the most advantageous time. In addition, the optimization unit can analyze the user's purchase history and search history to propose sales and coupon information that suits the user. This allows the user to efficiently obtain sales and coupon information that matches their interests. Furthermore, the optimization unit enables real-time information provision by adjusting the update frequency of sales and coupon information. For example, by increasing the update frequency of sales and coupon information during specific times or sales periods, it can provide users with the latest information. In this way, the optimization unit can always provide users with the latest sales and coupon information and improve the shopping experience.

[0068] The Guide Department guides users on the best time to purchase based on information optimized by the Optimization Department. Specifically, the Guide Department provides purchasing strategies that take into account specific stores and sales events. For example, it might suggest how to maximize point rewards by purchasing specific products during sales periods at specific stores. The Guide Department collects sales event information from various shopping sites and proposes the optimal purchasing strategy to users. This allows users to purchase products at the most advantageous time. The Guide Department can also analyze users' purchase and search history to propose purchasing strategies tailored to them. This allows users to efficiently obtain purchasing strategies that match their interests. Furthermore, the Guide Department provides users with the latest information by offering purchase timing guidance in real time. For example, by providing purchase timing guidance during specific time periods or sales periods, it can suggest the optimal time to purchase to users. In this way, the Guide Department can always provide users with the latest purchase timing guidance, improving their shopping experience.

[0069] The acquisition unit can acquire the latest point reward information in conjunction with e-commerce sites. For example, the acquisition unit acquires the latest point reward information in conjunction with e-commerce sites. The acquisition unit collects point information in real time and provides it to the user. For example, when a specific product is purchased, the acquisition unit immediately acquires the latest point reward information for that product and notifies the user. In this way, the latest point reward information can be acquired by linking with e-commerce sites. The latest point reward information includes, but is not limited to, the point reward rate and reward conditions for specific stores or products. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to acquire the latest point reward information in conjunction with e-commerce sites.

[0070] The suggestion unit can propose the optimal purchase plan based on the user's wish list. For example, the suggestion unit can input the user's wish list into the AI. Based on the user's list, the suggestion unit proposes the optimal purchase plan. For example, if a particular product is on sale, it can suggest the best time to purchase that product. This allows for suggestions tailored to the user's needs by proposing the optimal purchase plan based on the user's wish list. The wish list may include, but is not limited to, items manually added by the user or automatically generated based on past purchase history. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can use AI to propose the optimal purchase plan based on the user's wish list.

[0071] The optimization unit can suggest the best sales and coupon usage methods on e-book sales sites and other e-commerce sites. For example, the optimization unit can suggest the best sales and coupon usage methods on e-book sales sites and other e-commerce sites. The optimization unit collects sales and coupon information from each shopping site and suggests the best usage methods to the user. For example, if a coupon is available for a particular product, it suggests the best timing to use that coupon. This increases the user's savings by suggesting the best sales and coupon usage methods on e-book sales sites and other e-commerce sites. The best sales and coupon usage methods include, but are not limited to, discount rates, usage conditions, and usage timing. Some or all of the processing described above in the optimization unit may be performed using, for example, AI, or not using AI. For example, the optimization unit can use AI to suggest the best sales and coupon usage methods on e-book sales sites and other e-commerce sites.

[0072] The guide unit can guide purchasing strategies based on specific stores and sales events. For example, the guide unit guides purchasing strategies that take specific stores and sales events into consideration. The guide unit collects sales event information from each shopping site and proposes the optimal purchasing strategy to the user. For example, it suggests how to maximize point rewards by purchasing specific products during a sale period at a specific store. This optimizes the user's purchasing timing by guiding purchasing strategies that take specific stores and sales events into consideration. Specific stores and sales events include, but are not limited to, specific brands, seasonal sales, and limited-time events. Some or all of the processing described above in the guide unit may be performed using, for example, AI, or not. For example, the guide unit can use AI to guide purchasing strategies that take specific stores and sales events into consideration.

[0073] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring point reward information based on the estimated user emotions. For example, if the user is stressed, the acquisition unit can acquire point reward information at a time when the user can relax. If the user is excited, the acquisition unit can also acquire and notify the user of point reward information immediately. If the user is tired, the acquisition unit can also acquire point reward information during breaks or relaxation times. By adjusting the timing of acquiring point reward information according to the user's emotions, information can be provided at the optimal time for the user. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can use AI to estimate the user's emotions and adjust the timing of acquiring point reward information based on the estimated user emotions.

[0074] The acquisition unit can analyze the user's past purchase history and prioritize the acquisition of optimal point reward information. For example, the acquisition unit can prioritize the acquisition of relevant point reward information based on the product categories the user has purchased in the past. The acquisition unit can also prioritize the acquisition of point reward information from stores the user frequently uses. The acquisition unit can also prioritize the acquisition of point reward information from when the user has purchased high-value items in the past. This allows the acquisition unit to prioritize the acquisition of highly relevant point reward information by analyzing the user's past purchase history. Past purchase history includes, but is not limited to, purchase date and time, purchased items, and purchase frequency. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not. For example, the acquisition unit can use AI to analyze the user's past purchase history and prioritize the acquisition of optimal point reward information.

[0075] The acquisition unit can filter point reward information based on the user's current purchasing intent and products of interest when acquiring it. For example, the acquisition unit can filter point reward information based on product categories that the user is currently interested in. The acquisition unit can also prioritize acquiring point reward information for products that the user is considering purchasing. The acquisition unit can also acquire point reward information during times when the user's purchasing intent is high. This allows the system to provide users with useful information by filtering point reward information based on their current purchasing intent and products of interest. Current purchasing intent and products of interest include, but are not limited to, browsing history, items in the shopping cart, and search keywords. Some or all of the processing described above in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to filter point reward information based on the user's current purchasing intent and products of interest when acquiring it.

[0076] The acquisition unit can estimate the user's emotions and determine the priority of point reward information to acquire based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit will prioritize acquiring high-priority point reward information. If the user is in a hurry, the acquisition unit can also prioritize acquiring point reward information that is immediately available. If the user is excited, the acquisition unit can also prioritize acquiring point reward information with many benefits. In this way, by prioritizing point reward information according to the user's emotions, information that is important to the user can be provided preferentially. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can use AI to estimate the user's emotions and determine the priority of point reward information to acquire based on the estimated user emotions.

[0077] The acquisition unit can prioritize acquiring highly relevant information by considering the user's geographical location when acquiring point reward information. For example, the acquisition unit can prioritize acquiring point reward information from stores near the user's current location. The acquisition unit can also prioritize acquiring point reward information from areas frequently visited by the user. The acquisition unit can also prioritize acquiring point reward information from stores along the user's travel route. This allows the acquisition unit to provide highly relevant point reward information to the user by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the processing described above in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to prioritize acquiring highly relevant information by considering the user's geographical location when acquiring point reward information.

[0078] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring point reward information. For example, the acquisition unit can acquire point reward information for products and services that the user has shown interest in on social media. The acquisition unit can also prioritize acquiring point reward information for brands and stores that the user follows. The acquisition unit can also analyze the user's purchasing intent on social media and acquire relevant point reward information. In this way, by analyzing the user's social media activity, it is possible to provide point reward information that is highly relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can use AI to analyze the user's social media activity and acquire relevant information when acquiring point reward information.

[0079] The suggestion unit can estimate the user's emotions and adjust the presentation of the purchase plan based on those emotions. For example, if the user is relaxed, the suggestion unit may suggest a purchase plan with detailed explanations. If the user is in a hurry, the suggestion unit may suggest a concise and to-the-point purchase plan. If the user is excited, the suggestion unit may suggest a visually appealing purchase plan. By adjusting the presentation of the purchase plan according to the user's emotions, it becomes possible to make suggestions that are easy for the user to understand. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can use AI to estimate the user's emotions and adjust the presentation of the purchase plan based on those emotions.

[0080] The suggestion function can adjust the level of detail in its suggestions based on the importance of the products. For example, for highly important products, the suggestion function may offer a detailed description and multiple purchase options. For less important products, it may offer a concise description and basic purchase options. For moderately important products, it may offer a description with a moderate level of detail and several purchase options. This allows the suggestion function to provide users with appropriate information by adjusting the level of detail in its suggestions based on the importance of the products. Product importance includes, but is not limited to, price, demand, and user interest. Some or all of the processing described above in the suggestion function may be performed using, for example, AI, or not. For example, the suggestion function can use AI to adjust the level of detail in its suggestions based on the importance of the products when making suggestions.

[0081] The suggestion function can apply different suggestion algorithms depending on the product category when making suggestions. For example, in the case of electronic devices, the suggestion function may make suggestions that emphasize technical specifications and reviews. In the case of fashion items, the suggestion function may also make suggestions based on trends and styles. In the case of food products, the suggestion function may also make suggestions that emphasize nutritional information and recipes. By applying different suggestion algorithms depending on the product category, it becomes possible to make suggestions that are optimal for the user. Product categories include, but are not limited to, electronic devices, clothing, and food products. Some or all of the processing described above in the suggestion function may be performed using, for example, AI, or not using AI. For example, the suggestion function can use AI to apply different suggestion algorithms depending on the product category when making suggestions.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is relaxed, the suggestion unit may make a longer suggestion with detailed explanations. If the user is in a hurry, the suggestion unit may make a concise and short suggestion. If the user is excited, the suggestion unit may make a visually appealing suggestion. By adjusting the length of the suggestion according to the user's emotions, it becomes possible to make suggestions that are easy for the user to understand. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can use AI to estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions.

[0083] The proposal department can prioritize proposals based on the product submission timing when submitting a proposal. For example, the proposal department can immediately submit a proposal for urgent products. For less urgent products, the proposal department can submit a proposal according to the user's convenience. For seasonal or limited-edition products, the proposal department can submit a proposal at an appropriate time. This allows for proposals to be submitted at the appropriate time for the user by prioritizing proposals based on the product submission timing. Product submission timing includes, but is not limited to, the release date, sale start date, and coupon expiration date. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can use AI to prioritize proposals based on the product submission timing when submitting a proposal.

[0084] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. For example, the suggestion unit may prioritize suggesting products that the user has shown interest in. The suggestion unit may also prioritize suggesting highly relevant products based on the user's purchase history. The suggestion unit may also adjust the order of suggestions based on the user's current purchasing intent. This allows for suggestions that are more relevant to the user by adjusting the order of suggestions based on the relevance of the products. Product relevance includes, but is not limited to, products in the same category, past purchase history, and the user's level of interest. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit may use AI to adjust the order of suggestions based on the relevance of the products when making suggestions.

[0085] The optimization unit can estimate the user's emotions and adjust the optimization method for sales and coupon information based on the estimated emotions. For example, if the user is relaxed, the optimization unit can suggest detailed sales information and coupon usage methods. If the user is in a hurry, the optimization unit can also suggest concise and to-the-point sales information and coupon usage methods. If the user is excited, the optimization unit can also suggest visually appealing sales information and coupon usage methods. In this way, by adjusting the optimization method for sales and coupon information according to the user's emotions, information that is easy for the user to understand can be provided. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can use AI to estimate the user's emotions and adjust the optimization method for sales and coupon information based on the estimated emotions.

[0086] The optimization unit can analyze the user's past purchasing behavior during optimization to select the most relevant sales and coupon information. For example, the optimization unit can select the most relevant information based on sales and coupon information the user has used in the past. The optimization unit can also select highly relevant sales and coupon information based on the user's past purchasing behavior. The optimization unit can analyze the user's past purchasing behavior and select the most effective sales and coupon information. This allows the system to provide highly relevant sales and coupon information by analyzing the user's past purchasing behavior. Past purchasing behavior includes, but is not limited to, purchase date and time, purchased items, and purchase frequency. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or not. For example, the optimization unit can use AI to analyze the user's past purchasing behavior and select the most relevant sales and coupon information during optimization.

[0087] The optimization unit can customize the optimization methods based on the user's current lifestyle during optimization. For example, if the user is busy, the optimization unit may suggest concise and to-the-point sales and coupon information. If the user is relaxed, the optimization unit may also suggest detailed sales and coupon information. The optimization unit can also customize the most suitable sales and coupon information according to the user's lifestyle. This allows the system to provide the user with the most relevant information by customizing the optimization methods according to the user's current lifestyle. Current lifestyle includes, but is not limited to, income, family structure, and lifestyle. Some or all of the processing described above in the optimization unit may be performed using, for example, AI, or not. For example, the optimization unit can use AI to customize the optimization methods based on the user's current lifestyle during optimization.

[0088] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is relaxed, the optimization unit will prioritize optimizing high-priority sales and coupon information. If the user is in a hurry, the optimization unit can also prioritize optimizing immediately available sales and coupon information. If the user is excited, the optimization unit can also prioritize optimizing sales and coupon information with many benefits. In this way, by determining optimization priorities according to the user's emotions, information important to the user can be provided preferentially. User emotions are estimated using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can use AI to estimate the user's emotions and determine optimization priorities based on the estimated emotions.

[0089] The optimization unit can select the most relevant sales and coupon information by considering the user's geographical location during optimization. For example, the optimization unit may prioritize optimizing sales and coupon information from stores near the user's current location. The optimization unit can also prioritize optimizing sales and coupon information from areas the user frequently visits. The optimization unit can also prioritize optimizing sales and coupon information from stores along the user's travel route. This allows the system to provide the user with sales and coupon information that is highly relevant to them by considering their geographical location. Optimal sales and coupon information may include, but is not limited to, discount rates, usage conditions, and usage timing. Some or all of the processing described above in the optimization unit may be performed using, for example, AI, or not. For example, the optimization unit may use AI to select the most relevant sales and coupon information by considering the user's geographical location during optimization.

[0090] The optimization unit can analyze the user's social media activity and propose optimization methods during the optimization process. For example, the optimization unit can optimize sales and coupon information for products and services that the user has shown interest in on social media. The optimization unit can also prioritize and optimize sales and coupon information for brands and stores that the user follows. The optimization unit can also analyze the user's purchasing intent on social media and propose the most suitable sales and coupon information. In this way, by analyzing the user's social media activity, it is possible to provide the user with sales and coupon information that is highly relevant to them. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the optimization unit may be performed using, for example, AI, or not using AI. For example, the optimization unit can use AI to analyze the user's social media activity and propose optimization methods during the optimization process.

[0091] The guide unit can estimate the user's emotions and adjust the purchase timing guidance method based on the estimated user emotions. For example, if the user is relaxed, the guide unit can provide detailed purchase timing guidance. If the user is in a hurry, the guide unit can provide concise and to-the-point purchase timing guidance. If the user is excited, the guide unit can provide visually appealing purchase timing guidance. By adjusting the purchase timing guidance method according to the user's emotions, information that is easy for the user to understand can be provided. User emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI or not. For example, the guide unit can use AI to estimate the user's emotions and adjust the purchase timing guidance method based on the estimated user emotions.

[0092] The guidance unit can suggest the optimal timing for purchase by referring to the user's past purchase history during the guidance process. For example, the guidance unit can suggest relevant purchase timings based on the product categories the user has previously purchased. The guidance unit can also suggest purchase timings for stores the user frequently visits. The guidance unit can also suggest purchase timings when the user has previously purchased high-priced items. This allows the guidance unit to suggest highly relevant purchase timings by referring to the user's past purchase history. Past purchase history includes, but is not limited to, purchase date and time, purchased items, and purchase frequency. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can use AI to suggest the optimal timing for purchase by referring to the user's past purchase history during the guidance process.

[0093] The guidance unit can customize the timing of a purchase based on the user's current purchasing intent during the guidance process. For example, the guidance unit can customize the timing of a purchase based on the product categories the user is currently interested in. The guidance unit can also suggest the timing of a purchase for products the user is considering buying. The guidance unit can also suggest a purchase timing during times when the user's purchasing intent is high. This allows the system to suggest the optimal time for a purchase by customizing the timing of a purchase based on the user's current purchasing intent. Current purchasing intent includes, but is not limited to, browsing history, items in the shopping cart, and search keywords. Some or all of the processing described above in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can use AI to customize the timing of a purchase based on the user's current purchasing intent during the guidance process.

[0094] The guide unit can estimate the user's emotions and prioritize purchase timings based on those estimated emotions. For example, if the user is relaxed, the guide unit will prioritize suggesting high-priority purchase timings. If the user is in a hurry, the guide unit can also prioritize suggesting immediately available purchase timings. If the user is excited, the guide unit can also prioritize suggesting purchase timings with many benefits. By prioritizing purchase timings according to the user's emotions, the guide unit can prioritize providing information that is important to the user. User emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the guide unit may be performed using AI or not. For example, the guide unit can use AI to estimate the user's emotions and prioritize purchase timings based on those estimated emotions.

[0095] The guidance unit can suggest the optimal timing for purchases while considering the user's geographical location. For example, the guidance unit may prioritize suggesting purchase timings for stores near the user's current location. It may also prioritize suggesting purchase timings for areas the user frequently visits. It may also prioritize suggesting purchase timings for stores along the user's travel route. By considering the user's geographical location, it can suggest purchase timings that are highly relevant to the user. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the processing described above in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can use AI to suggest the optimal timing for purchases while considering the user's geographical location.

[0096] The guidance unit can analyze the user's social media activity during the guidance process and suggest methods for timing purchases. For example, the guidance unit can suggest the timing for purchasing products or services that the user has shown interest in on social media. The guidance unit can also prioritize suggesting purchase timings for brands and stores that the user follows. The guidance unit can also analyze the user's purchasing intent on social media and suggest relevant purchase timings. In this way, by analyzing the user's social media activity, it can suggest purchase timings that are highly relevant to the user. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can use AI to analyze the user's social media activity during the guidance process and suggest methods for timing purchases.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The acquisition unit can analyze the user's purchase history and acquire point reward information based on past purchase patterns. For example, the acquisition unit can prioritize acquiring relevant point reward information based on the product categories the user has purchased in the past. Furthermore, the acquisition unit can also prioritize acquiring point reward information from stores the user frequently uses. This allows the system to provide highly relevant point reward information by analyzing the user's past purchase history. Past purchase history includes, but is not limited to, the date and time of purchase, the products purchased, and the frequency of purchase. Some or all of the processing described above in the acquisition unit may be performed using AI or not.

[0099] The suggestion section can estimate the user's emotions and adjust the purchase plan based on those emotions. For example, if the user is relaxed, it can suggest a purchase plan that includes detailed explanations. If the user is in a hurry, it can suggest a concise and to-the-point purchase plan. Furthermore, if the user is excited, it can suggest a visually appealing purchase plan. By adjusting the purchase plan according to the user's emotions, it becomes possible to make suggestions that are easy for the user to understand. The estimation of the user's emotions is performed using an emotion engine or generative AI, etc. Some or all of the processing described above in the suggestion section may be performed using AI or not.

[0100] The optimization unit can suggest optimal sales and coupon information by taking into account the user's geographical location. For example, it can prioritize suggesting sales and coupon information from stores near the user's current location. Furthermore, it can prioritize suggesting sales and coupon information from areas the user frequently visits. In this way, by considering the user's geographical location, it is possible to provide sales and coupon information that is highly relevant to the user. Geographical location information includes, but is not limited to, GPS data, IP addresses, and location services. Some or all of the processing described above in the optimization unit may be performed using AI, or it may be performed without using AI.

[0101] The guide function can analyze a user's social media activity and suggest relevant purchase timings. For example, it can suggest the timing for purchasing products or services that the user has shown interest in on social media. Furthermore, it can prioritize suggesting purchase timings for brands and stores that the user follows. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant purchase timings for the user. Social media activity includes, but is not limited to, posts, likes, and follower counts. Some or all of the processing described above in the guide function may be performed using AI or not.

[0102] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring point reward information based on the estimated emotions. For example, if the user is feeling stressed, point reward information can be acquired at a time when the user can relax. If the user is excited, point reward information can be acquired and notified immediately. In this way, by adjusting the timing of acquiring point reward information according to the user's emotions, information can be provided at the optimal time for the user. The estimation of the user's emotions is performed using an emotion engine or generative AI. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI.

[0103] The suggestion section can adjust the level of detail in its suggestions based on the importance of the products. For example, for highly important products, it can offer detailed descriptions and multiple purchase options. For less important products, it can offer concise descriptions and basic purchase options. This allows the system to provide users with appropriate information by adjusting the level of detail in its suggestions based on the importance of the products. Product importance includes, but is not limited to, price, demand, and user interest. Some or all of the processing described above in the suggestion section may be performed using AI or not.

[0104] The optimization unit can estimate the user's emotions and adjust the optimization method for sales and coupon information based on the estimated emotions. For example, if the user is relaxed, it can suggest detailed sales information and coupon usage instructions. If the user is in a hurry, it can suggest concise and to-the-point sales information and coupon usage instructions. In this way, by adjusting the optimization method for sales and coupon information according to the user's emotions, information that is easy for the user to understand can be provided. The estimation of the user's emotions is performed using an emotion engine or generative AI. Some or all of the above processing in the optimization unit may be performed using AI or not.

[0105] The guide unit can suggest the optimal timing for purchase by referring to the user's past purchase history. For example, it can suggest relevant purchase timings based on the product categories the user has purchased in the past. Furthermore, it can also suggest purchase timings for stores the user frequently visits. In this way, by referring to the user's past purchase history, it is possible to provide highly relevant purchase timings. Past purchase history includes, but is not limited to, the date and time of purchase, the products purchased, and the frequency of purchase. Some or all of the above processing in the guide unit may be performed using AI or not.

[0106] The acquisition unit can filter point reward information based on the user's current purchasing intent and products of interest. For example, it can filter point reward information based on the product categories the user is currently interested in. Furthermore, it can prioritize the acquisition of point reward information for products the user is considering purchasing. This allows the system to provide users with useful information by filtering point reward information based on their current purchasing intent and products of interest. Current purchasing intent and products of interest include, but are not limited to, browsing history, items in the shopping cart, and search keywords. Some or all of the processing described above in the acquisition unit may be performed using AI or not.

[0107] The guidance unit can estimate the user's emotions and adjust the guidance method for purchase timing based on the estimated emotions. For example, if the user is relaxed, it can provide purchase timing guidance that includes detailed explanations. If the user is in a hurry, it can provide concise and to-the-point purchase timing guidance. In this way, by adjusting the purchase timing guidance method according to the user's emotions, information that is easy for the user to understand can be provided. The estimation of the user's emotions is performed using an emotion engine or generative AI, etc. Some or all of the above processing in the guidance unit may be performed using AI or not using AI.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The acquisition unit acquires point reward information. The acquisition unit acquires the latest point reward information, for example, by linking with an e-commerce site. The acquisition unit collects point information in real time and provides it to the user. For example, when a specific product is purchased, the acquisition unit immediately acquires the latest point reward information for that product and notifies the user. Step 2: The suggestion unit proposes the optimal purchase plan based on the point redemption information acquired by the acquisition unit. For example, the suggestion unit has the user create a wish list and input it into the AI. Based on the user's list, the suggestion unit proposes the optimal purchase plan. For example, if a particular product is on sale, it will suggest the best time to purchase that product. Step 3: The optimization unit optimizes sales and coupon information based on the purchase plan proposed by the proposal unit. The optimization unit proposes the best way to use sales and coupons on e-book sales sites and other e-commerce sites, for example. The optimization unit collects sales and coupon information from each shopping site and proposes the best way to use it to the user. For example, if a coupon is available for a specific product, it will propose the best time to use that coupon. Step 4: The guide unit guides the user on the timing of their purchase based on the information optimized by the optimization unit. The guide unit guides the user on purchase strategies that take into account specific stores and sales events, for example. The guide unit collects sales event information from each shopping site and proposes the best purchase strategy to the user. For example, it may suggest how to maximize point rewards by purchasing specific products during a sale period at a specific store.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the acquisition unit, proposal unit, optimization unit, and guide unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and collects point redemption information in real time and provides it to the user. The proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and proposes an optimal purchase plan based on the user's wish list. The optimization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and optimizes sales and coupon information. The guide unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and guides the timing of purchases. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the acquisition unit, proposal unit, optimization unit, and guide unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and collects point redemption information in real time and provides it to the user. The proposal unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and proposes an optimal purchase plan based on the user's wish list. The optimization unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and optimizes sales and coupon information. The guide unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and guides the timing of purchase. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the acquisition unit, proposal unit, optimization unit, and guide unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and collects point redemption information in real time and provides it to the user. The proposal unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and proposes an optimal purchase plan based on the user's wish list. The optimization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and optimizes sales and coupon information. The guide unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and guides the timing of purchase. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the acquisition unit, proposal unit, optimization unit, and guide unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and collects point redemption information in real time and provides it to the user. The proposal unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and proposes an optimal purchase plan based on the user's wish list. The optimization unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and optimizes sales and coupon information. The guide unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and guides the timing of purchases. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The acquisition unit obtains point redemption information, A proposal unit proposes the optimal purchase plan based on the point redemption information acquired by the acquisition unit, An optimization unit optimizes sales and coupon information based on the purchase plan proposed by the aforementioned proposal unit, The system includes a guide unit that guides the purchase timing based on information optimized by the optimization unit. A system characterized by the following features. (Note 2) The acquisition unit is, Get the latest point reward information by linking with e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We suggest the optimal purchase plan based on the user's wish list. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, We propose the best ways to use sales and coupons on e-book sales sites and other e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide portion is Guide your purchasing strategy based on specific stores and sales events. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of point reward information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, By analyzing the user's past purchase history, the system prioritizes obtaining the most suitable point reward information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When retrieving point reward information, filtering is performed based on the user's current purchasing intent and products of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the user's emotions and determines the priority of point reward information to retrieve based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When retrieving point reward information, the system prioritizes retrieving highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When obtaining point reward information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, The system estimates the user's emotions and adjusts how the purchase plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, We estimate user sentiment and adjust how we optimize sales and coupon information based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, During optimization, the system analyzes users' past purchasing behavior to select the most suitable sales and coupon information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, During optimization, the optimization methods are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, the system selects the most suitable sales and coupon information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, we analyze users' social media activity and propose optimization methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide portion is We estimate user emotions and adjust the purchase timing guidance method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide portion is During the guided tour, we refer to the user's past purchase history to suggest the optimal timing for purchase. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide portion is During the guided tour, the timing of the purchase is customized based on the user's current purchasing intent. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide portion is It estimates the user's emotions and prioritizes the timing of purchases based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide portion is During the guided tour, the system will suggest the optimal purchase timing based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide portion is During the guidance process, we analyze the user's social media activity and suggest ways to time their purchase. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition unit obtains point redemption information, A proposal unit proposes the optimal purchase plan based on the point redemption information acquired by the acquisition unit, An optimization unit optimizes sales and coupon information based on the purchase plan proposed by the aforementioned proposal unit, The system includes a guide unit that guides the purchase timing based on information optimized by the optimization unit. A system characterized by the following features.

2. The acquisition unit is, Get the latest point reward information by linking with e-commerce sites. The system according to feature 1.

3. The aforementioned proposal section is, We suggest the optimal purchase plan based on the user's wish list. The system according to feature 1.

4. The optimization unit, We propose the best ways to use sales and coupons on e-book sales sites and other e-commerce sites. The system according to feature 1.

5. The aforementioned guide section is Guide your purchasing strategy based on specific stores and sales events. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of point reward information acquisition based on the estimated user emotions. The system according to feature 1.

7. The acquisition unit is, By analyzing the user's past purchase history, the system prioritizes obtaining the most suitable point reward information. The system according to feature 1.

8. The acquisition unit is, When retrieving point reward information, filtering is performed based on the user's current purchasing intent and products of interest. The system according to feature 1.

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