system

Abstract

The system according to the embodiment aims to provide appropriate recommendations to users by utilizing information about purchased products and purchase locations. [Solution] A system according to an embodiment includes an acquisition unit, a generation unit, and a recommendation unit. The acquisition unit acquires information about purchased products and purchase locations. The generation unit generates a prompt based on the information acquired by the acquisition unit. The recommendation unit makes a recommendation to a user based on the prompt generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately utilize information about purchased products and purchase locations to provide appropriate recommendations to users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate recommendations to users by utilizing information about purchased products and purchase locations. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a generation unit, and a recommendation unit. The acquisition unit acquires information on purchased products and purchase locations. The generation unit generates a prompt based on the information acquired by the acquisition unit. The recommendation unit makes a recommendation to a user based on the prompt generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can make appropriate recommendations to users by utilizing information about purchased products and purchase locations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An electronic payment system according to an embodiment of the present invention acquires information about purchased products and their locations, generates prompts using a generation AI, and provides appropriate recommendations to users. When a user purchases a product, the electronic payment system generates an electronic receipt. The electronic receipt includes information about the purchased product and its location. Next, the generation AI generates a prompt based on the information in the generated electronic receipt. The prompt is generated taking into account the user's purchase history, current location information, and other factors. Based on the generated prompt, appropriate recommendations are provided to the user. For example, if a user purchases camping gear, camping spots that are easily accessible from the purchase location are suggested. Similarly, if a user purchases sports equipment, nearby sports facilities are suggested. Several specific examples are conceivable. Furthermore, technical details of the electronic receipt function are also described. Information about purchased products and their locations is automatically acquired through the electronic payment system, and an electronic receipt is generated. At this time, measures such as data encryption and anonymization are implemented to protect the user's privacy. This improves the user's purchasing experience and promotes the use of the electronic payment system. This allows the electronic payment system to improve the user's purchasing experience and promote the use of the electronic payment system. For example, users can easily obtain information and services related to the products they have purchased and can then purchase further related products and services, which will promote the use of electronic payment systems and improve user satisfaction.

[0029] An electronic payment system according to an embodiment includes an acquisition unit, a generation unit, and a recommendation unit. The acquisition unit acquires information on purchased products and purchase locations. The purchased products include, but are not limited to, for example, food, clothing, and electronic devices. The purchase locations include, but are not limited to, for example, online stores and physical stores. The acquisition unit acquires purchase information, for example, through the electronic payment system. The generation unit uses a generation AI to generate a prompt based on the information acquired by the acquisition unit. The prompt is generated in the form of, for example, a text message or a notification, but is not limited to, for example. The generation unit generates the prompt by taking into account, for example, a user's purchase history, current location information, past recommendation history, etc. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The recommendation unit makes an appropriate recommendation to the user based on the generated prompt. The recommendation is made in the form of, for example, a product suggestion or a service suggestion, but is not limited to, for example. For example, if a user purchases camping goods, the recommendation unit can suggest camping spots that are easily accessible from the place of purchase. Furthermore, if a user purchases sports equipment, the recommendation unit can also suggest nearby sports facilities. This allows the electronic payment system according to the embodiment to make appropriate recommendations based on the purchase information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that takes the generated prompt as input and outputs recommendations.

[0030] The acquisition unit can acquire information about the purchased product and the purchase location through an electronic payment system. Examples of electronic payment systems include, but are not limited to, credit cards, electronic money, and mobile payments. The acquisition unit acquires the purchase information using, for example, a credit card. The acquisition unit can also acquire the purchase information using electronic money. Furthermore, the acquisition unit can also acquire the purchase information using mobile payment. In this way, accurate information can be obtained by acquiring the purchase information through the electronic payment system. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the purchase information acquired through the electronic payment system into the generation AI and cause the generation AI to analyze the purchase information.

[0031] The generation unit can generate prompts based on the user's purchase history, current location information, and past recommendation history. The generation unit generates prompts, for example, taking into account the user's purchase history. For example, the generation unit generates prompts based on purchase history from the past year. The generation unit can also generate prompts based on purchase history from a specific category. The generation unit can also generate prompts based on current location information. For example, the generation unit generates prompts based on GPS information. The generation unit can also generate prompts based on Wi-Fi location information. The generation unit can also generate prompts based on past recommendation history. For example, the generation unit generates prompts based on recommendation history from the past six months. The generation unit can also generate prompts based on recommendation history from a specific category. This allows for more appropriate prompts to be generated by taking the user's history and location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's purchase history and location information into the generation AI and cause the generation AI to generate a prompt.

[0032] The recommendation unit can make appropriate recommendations to the user based on the generated prompt. The recommendation unit, for example, makes product suggestions based on the generated prompt. For example, if a user purchases camping goods, the recommendation unit can suggest camping spots that are easily accessible from the place of purchase. Furthermore, if a user purchases sports equipment, the recommendation unit can also suggest nearby sports facilities. Furthermore, the recommendation unit can also make service suggestions based on the generated prompt. For example, if a user purchases travel-related products, the recommendation unit can suggest nearby tourist spots. Furthermore, if a user dined at a restaurant, the recommendation unit can also suggest nearby cafes. This makes it possible to make appropriate recommendations based on the generated prompt. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that uses the generated prompt as input and outputs recommendations.

[0033] When a user purchases camping gear, the recommendation unit can suggest camping spots that are easily accessible from the location of the purchase. For example, when a user purchases camping gear, the recommendation unit can suggest camping spots within 30 minutes by car from the location of the purchase. The recommendation unit can also suggest camping spots within an hour by public transportation. Furthermore, the recommendation unit can suggest optimal camping spots taking into account the user's current location information. For example, the recommendation unit can suggest nearby camping spots based on the user's GPS information. The recommendation unit can also suggest optimal camping spots based on the user's Wi-Fi location information. This makes it possible to suggest appropriate camping spots when purchasing camping gear. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's purchase information and location information into a generation AI and cause the generation AI to suggest camping spots.

[0034] The recommendation unit can suggest nearby sports facilities when a user purchases sports equipment. For example, when a user purchases sports equipment, the recommendation unit can suggest nearby gyms. The recommendation unit can also suggest nearby swimming pools. Furthermore, the recommendation unit can suggest nearby tennis courts. For example, the recommendation unit can suggest the most suitable sports facility taking into account the user's current location information. The recommendation unit can suggest nearby sports facilities based on the user's GPS information. The recommendation unit can also suggest the most suitable sports facility based on the user's Wi-Fi location information. This makes it possible to suggest appropriate sports facilities when purchasing sports equipment. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's purchase information and location information into the generation AI and cause the generation AI to suggest sports facilities.

[0035] The acquisition unit can take measures to encrypt and anonymize data to protect the user's privacy. The acquisition unit, for example, encrypts the data. For example, the acquisition unit encrypts the data using AES encryption. The acquisition unit can also encrypt the data using RSA encryption. The acquisition unit can also anonymize the data. For example, the acquisition unit anonymizes the data by deleting personal information. The acquisition unit can also pseudonymize the data. This makes it possible to acquire information while protecting the user's privacy. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can have the generation AI perform the data encryption and anonymization processing.

[0036] The acquisition unit can analyze the user's past purchase history and select the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of product categories that the user frequently purchases. The acquisition unit can also prioritize acquisition of products that the user tends to purchase during specific time periods from the user's purchase history. Furthermore, the acquisition unit can analyze the user's purchase history and prioritize acquisition of purchase information from specific stores. In this way, by analyzing the past purchase history, information can be acquired in the optimal method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's purchase history data into the generation AI and cause the generation AI to select the optimal acquisition method.

[0037] When acquiring purchase information, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is health-conscious, the acquisition unit can prioritize acquiring health-related product information. Furthermore, if the user is traveling, the acquisition unit can also prioritize acquiring travel-related product information. Furthermore, if the user has started a new hobby, the acquisition unit can also prioritize acquiring product information related to that hobby. This makes it possible to acquire information according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0038] When acquiring purchase information, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, if the user uses voice input, the acquisition unit can acquire the purchase information using voice recognition technology. Furthermore, if the user uses text input, the acquisition unit can also acquire the purchase information using text analysis technology. Furthermore, if the user uses image input, the acquisition unit can also acquire the purchase information using image recognition technology. This makes it possible to acquire information using the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data into a generation AI and cause the generation AI to select the optimal acquisition means.

[0039] When acquiring purchase information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit can prioritize acquiring product information related to that area. Furthermore, when the user is traveling, the acquisition unit can also prioritize acquiring product information related to the area of ​​the user's travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring store information around the user's home. This makes it possible to acquire highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0040] When acquiring purchase information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit can acquire product information related to locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related product information. Furthermore, the acquisition unit can also acquire related product information by referring to the activities of the user's friends on social media. In this way, related information can be acquired by analyzing social media activity. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to acquire related information.

[0041] When acquiring purchase information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the acquisition unit prioritizes acquisition of product categories that the user has previously rated highly. The acquisition unit can also exclude acquisition of product categories that the user has previously rated poorly. Furthermore, the acquisition unit can analyze the user's past feedback and suggest an optimal acquisition method. This allows information to be acquired in an optimal manner by reflecting past feedback. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can input the user's feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0042] When generating a prompt, the generation unit can adjust the level of detail of the prompt based on the importance of the purchased item. For example, the generation unit generates a detailed prompt when an expensive item is purchased. The generation unit can also generate a concise prompt when an everyday item is purchased. Furthermore, the generation unit can generate a special prompt when an item for a special event is purchased. This makes it possible to generate a prompt with a level of detail according to the importance of the purchased item. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of the purchased item into the generation AI and cause the generation AI to adjust the level of detail of the prompt.

[0043] When generating a prompt, the generation unit can apply different generation algorithms depending on the category of the purchased item. For example, when a product in the food category is purchased, the generation unit generates a prompt that suggests a recipe. Furthermore, when a product in the fashion category is purchased, the generation unit can also generate a prompt that suggests an outfit coordination. Furthermore, when a product in the electronics category is purchased, the generation unit can also generate a prompt on how to use the product. This makes it possible to generate a prompt using an optimal algorithm depending on the category of the purchased item. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input category data of the purchased item into the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0044] When generating a prompt, the generation unit can improve the accuracy of generation by referring to the user's past prompt results. For example, the generation unit refers to patterns of prompts that the user has previously rated highly. The generation unit can also exclude patterns of prompts that the user has previously rated poorly. Furthermore, the generation unit can analyze the user's past prompt results and generate an optimal prompt. In this way, by referring to the past prompt results, the accuracy of generation can be improved. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past prompt result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] When generating prompts, the generation unit can determine the priority of the prompts based on the submission time of the purchased items. For example, when a highly urgent item is purchased, the generation unit generates prompts with priority. The generation unit can also generate prompts with normal priority when an everyday item is purchased. Furthermore, the generation unit can generate prompts with special priority when an item for a special event is purchased. This makes it possible to generate prompts with priority according to the submission time of the purchased items. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input purchase item submission time data into the generation AI and have the generation AI determine the priority of the prompts.

[0046] When generating prompts, the generation unit can adjust the order of prompts based on the relevance of the purchased items. For example, if the relevance of the purchased items is high, the generation unit may generate prompts preferentially. Also, if the relevance of the purchased items is low, the generation unit may generate prompts later. Furthermore, the generation unit can analyze the relevance of the purchased items and generate prompts in an optimal order. This allows prompts to be generated in an order according to the relevance of the purchased items. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input relevance data of the purchased items into the generation AI and cause the generation AI to adjust the order of the prompts.

[0047] When generating a prompt, the generation unit can adjust the use of technical terminology in the prompt depending on the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate a prompt that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can generate a simple prompt that avoids technical terminology. Furthermore, the generation unit can analyze the user's level of expertise and generate an optimal prompt. This allows for the generation of an appropriate prompt according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the prompt.

[0048] When making a recommendation, the recommendation unit can analyze the user's past purchasing behavior and select the optimal recommendation method. For example, the recommendation unit prioritizes recommending product categories that the user has previously rated highly. The recommendation unit can also recommend products by excluding product categories that the user has previously rated poorly. Furthermore, the recommendation unit can analyze the user's past purchasing behavior and propose the optimal recommendation method. In this way, the optimal recommendation method can be selected by analyzing past purchasing behavior. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's purchasing behavior data into the generation AI and cause the generation AI to select the optimal recommendation method.

[0049] When making a recommendation, the recommendation unit can customize the means of recommendation based on the user's current lifestyle situation. For example, if the user is health-conscious, the recommendation unit can recommend health-related products. Also, if the user is traveling, the recommendation unit can recommend travel-related products. Furthermore, if the user has started a new hobby, the recommendation unit can recommend products related to that hobby. This makes it possible to make appropriate recommendations according to the user's lifestyle situation. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's lifestyle situation data into the generation AI and cause the generation AI to customize the means of recommendation.

[0050] When making a recommendation, the recommendation unit can improve the recommendation method by reflecting user feedback. For example, the recommendation unit preferentially uses recommendation methods that users have previously rated highly. The recommendation unit can also exclude recommendation methods that users have previously rated poorly. Furthermore, the recommendation unit can analyze user feedback and propose an optimal recommendation method. This allows the recommendation method to be improved by reflecting user feedback. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input user feedback data into a generation AI and cause the generation AI to improve the recommendation method.

[0051] When making a recommendation, the recommendation unit can select the optimal recommendation method taking into account the user's geographical location information. For example, if the user is in a specific area, the recommendation unit can recommend products and services related to that area. Furthermore, if the user is traveling, the recommendation unit can also recommend products and services related to the area of ​​the user's travel destination. Furthermore, if the user is at home, the recommendation unit can also recommend stores and services near the user's home. This allows optimal recommendations to be made based on the user's geographical location information. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal recommendation method.

[0052] When making a recommendation, the recommendation unit can analyze the user's social media activity and suggest a means of recommendation. For example, the recommendation unit can recommend products and services related to places where the user has checked in on social media. The recommendation unit can also analyze the content of the user's social media posts and recommend related products and services. Furthermore, the recommendation unit can recommend related products and services by referring to the activities of the user's friends on social media. In this way, related products and services can be recommended by analyzing social media activity. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's social media data into a generation AI and have the generation AI suggest a means of recommendation.

[0053] When making a recommendation, the recommendation unit can customize the recommendation method by reflecting the user's past feedback. For example, the recommendation unit preferentially uses recommendation methods that the user has previously rated highly. The recommendation unit can also exclude recommendation methods that the user has previously rated poorly. Furthermore, the recommendation unit can analyze the user's past feedback and propose the optimal recommendation method. In this way, the optimal recommendation method can be proposed by reflecting the past feedback. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input user feedback data into the generation AI and cause the generation AI to customize the recommendation method.

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

[0055] The acquisition unit can monitor a user's purchasing behavior in real time and detect abnormal patterns. For example, the acquisition unit can issue a warning if the user suddenly purchases a high-priced item that the user does not normally purchase. The acquisition unit can also generate an alert if the user purchases multiple high-priced items in a short period of time. Furthermore, the acquisition unit can request additional authentication if the user's purchasing behavior significantly differs from the normal pattern. This allows for enhanced security by monitoring a user's purchasing behavior and detecting abnormal patterns.

[0056] The generation unit can predict future purchasing behavior based on the user's purchasing history. For example, if the user has purchased a specific product in a specific season in the past, the generation unit can suggest similar products as that season approaches. The generation unit can also predict products that the user will purchase regularly and send reminders. Furthermore, the generation unit can analyze the user's purchasing patterns and predict products that the user is likely to purchase next. This makes it possible to predict future purchasing behavior based on the user's purchasing history and make appropriate suggestions.

[0057] The recommendation unit can provide customized coupons based on the user's purchasing behavior. For example, if the user frequently purchases products of a particular brand, the recommendation unit can provide coupons for that brand. Also, if the user purchases products in a particular category, the recommendation unit can provide coupons for that category. Furthermore, the recommendation unit can provide coupons that can be used at specific stores based on the user's purchasing history. This can provide customized coupons based on the user's purchasing behavior, thereby increasing purchasing motivation.

[0058] The acquisition unit can analyze the user's purchasing behavior and promote environmentally conscious purchasing behavior. For example, if the user purchases an environmentally friendly product, the acquisition unit can highlight that information. The acquisition unit can also award points each time the user purchases an environmentally conscious product. Furthermore, the acquisition unit can analyze the user's purchasing behavior and suggest environmentally conscious products. In this way, the user's purchasing behavior can be analyzed and environmentally conscious purchasing behavior can be promoted.

[0059] The generation unit can make health-conscious suggestions based on the user's purchasing behavior. For example, if the user purchases a health food, the generation unit can suggest recipes related to that food. Also, if the user purchases a fitness-related product, the generation unit can suggest a training plan. Furthermore, the generation unit can provide health-related advice based on the user's purchasing history. This makes it possible to make health-conscious suggestions based on the user's purchasing behavior.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The acquisition unit acquires information about purchased products and purchase locations. Purchased products include food, clothing, electronic devices, etc., and purchase locations include online stores and physical stores. The acquisition unit acquires the purchase information through, for example, an electronic payment system. Step 2: The generator uses the generation AI to generate a prompt based on the information acquired by the acquirer. The prompt is generated in the form of a text message, notification, or other format. The generator generates the prompt by taking into account the user's purchase history, current location information, past recommendation history, and other factors. Step 3: The recommendation unit makes appropriate recommendations to the user based on the generated prompts. Recommendations are made in the form of product suggestions, service suggestions, etc. For example, if a user purchases camping gear, the unit can suggest camping spots that are easily accessible from the place of purchase. Similarly, if a user purchases sports equipment, the unit can suggest nearby sports facilities. The processing in the recommendation unit is sometimes performed using AI.

[0062] (Example 2) An electronic payment system according to an embodiment of the present invention acquires information about purchased products and their locations, generates prompts using a generation AI, and provides appropriate recommendations to users. When a user purchases a product, the electronic payment system generates an electronic receipt. The electronic receipt includes information about the purchased product and its location. Next, the generation AI generates a prompt based on the information in the generated electronic receipt. The prompt is generated taking into account the user's purchase history, current location information, and other factors. Based on the generated prompt, appropriate recommendations are provided to the user. For example, if a user purchases camping gear, camping spots that are easily accessible from the purchase location are suggested. Similarly, if a user purchases sports equipment, nearby sports facilities are suggested. Several specific examples are conceivable. Furthermore, technical details of the electronic receipt function are also described. Information about purchased products and their locations is automatically acquired through the electronic payment system, and an electronic receipt is generated. At this time, measures such as data encryption and anonymization are implemented to protect the user's privacy. This improves the user's purchasing experience and promotes the use of the electronic payment system. This allows the electronic payment system to improve the user's purchasing experience and promote the use of the electronic payment system. For example, users can easily obtain information and services related to the products they have purchased and can then purchase further related products and services, which will promote the use of electronic payment systems and improve user satisfaction.

[0063] An electronic payment system according to an embodiment includes an acquisition unit, a generation unit, and a recommendation unit. The acquisition unit acquires information on purchased products and purchase locations. The purchased products include, but are not limited to, for example, food, clothing, and electronic devices. The purchase locations include, but are not limited to, for example, online stores and physical stores. The acquisition unit acquires purchase information, for example, through the electronic payment system. The generation unit uses a generation AI to generate a prompt based on the information acquired by the acquisition unit. The prompt is generated in the form of, for example, a text message or a notification, but is not limited to, for example. The generation unit generates the prompt by taking into account, for example, a user's purchase history, current location information, past recommendation history, etc. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The recommendation unit makes an appropriate recommendation to the user based on the generated prompt. The recommendation is made in the form of, for example, a product suggestion or a service suggestion, but is not limited to, for example. For example, if a user purchases camping goods, the recommendation unit can suggest camping spots that are easily accessible from the place of purchase. Furthermore, if a user purchases sports equipment, the recommendation unit can also suggest nearby sports facilities. This allows the electronic payment system according to the embodiment to make appropriate recommendations based on the purchase information. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that takes the generated prompt as input and outputs recommendations.

[0064] The acquisition unit can acquire information about the purchased product and the purchase location through an electronic payment system. Examples of electronic payment systems include, but are not limited to, credit cards, electronic money, and mobile payments. The acquisition unit acquires the purchase information using, for example, a credit card. The acquisition unit can also acquire the purchase information using electronic money. Furthermore, the acquisition unit can also acquire the purchase information using mobile payment. In this way, accurate information can be obtained by acquiring the purchase information through the electronic payment system. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the purchase information acquired through the electronic payment system into the generation AI and cause the generation AI to analyze the purchase information.

[0065] The generation unit can generate prompts based on the user's purchase history, current location information, and past recommendation history. The generation unit generates prompts, for example, taking into account the user's purchase history. For example, the generation unit generates prompts based on purchase history from the past year. The generation unit can also generate prompts based on purchase history from a specific category. The generation unit can also generate prompts based on current location information. For example, the generation unit generates prompts based on GPS information. The generation unit can also generate prompts based on Wi-Fi location information. The generation unit can also generate prompts based on past recommendation history. For example, the generation unit generates prompts based on recommendation history from the past six months. The generation unit can also generate prompts based on recommendation history from a specific category. This allows for more appropriate prompts to be generated by taking the user's history and location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the user's purchase history and location information into the generation AI and cause the generation AI to generate a prompt.

[0066] The recommendation unit can make appropriate recommendations to the user based on the generated prompt. The recommendation unit, for example, makes product suggestions based on the generated prompt. For example, if a user purchases camping goods, the recommendation unit can suggest camping spots that are easily accessible from the place of purchase. Furthermore, if a user purchases sports equipment, the recommendation unit can also suggest nearby sports facilities. Furthermore, the recommendation unit can also make service suggestions based on the generated prompt. For example, if a user purchases travel-related products, the recommendation unit can suggest nearby tourist spots. Furthermore, if a user dined at a restaurant, the recommendation unit can also suggest nearby cafes. This makes it possible to make appropriate recommendations based on the generated prompt. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can make recommendations using an AI model that uses the generated prompt as input and outputs recommendations.

[0067] When a user purchases camping gear, the recommendation unit can suggest camping spots that are easily accessible from the location of the purchase. For example, when a user purchases camping gear, the recommendation unit can suggest camping spots within 30 minutes by car from the location of the purchase. The recommendation unit can also suggest camping spots within an hour by public transportation. Furthermore, the recommendation unit can suggest optimal camping spots taking into account the user's current location information. For example, the recommendation unit can suggest nearby camping spots based on the user's GPS information. The recommendation unit can also suggest optimal camping spots based on the user's Wi-Fi location information. This makes it possible to suggest appropriate camping spots when purchasing camping gear. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's purchase information and location information into a generation AI and cause the generation AI to suggest camping spots.

[0068] The recommendation unit can suggest nearby sports facilities when a user purchases sports equipment. For example, when a user purchases sports equipment, the recommendation unit can suggest nearby gyms. The recommendation unit can also suggest nearby swimming pools. Furthermore, the recommendation unit can suggest nearby tennis courts. For example, the recommendation unit can suggest the most suitable sports facility taking into account the user's current location information. The recommendation unit can suggest nearby sports facilities based on the user's GPS information. The recommendation unit can also suggest the most suitable sports facility based on the user's Wi-Fi location information. This makes it possible to suggest appropriate sports facilities when purchasing sports equipment. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's purchase information and location information into the generation AI and cause the generation AI to suggest sports facilities.

[0069] The acquisition unit can take measures to encrypt and anonymize data to protect the user's privacy. The acquisition unit, for example, encrypts the data. For example, the acquisition unit encrypts the data using AES encryption. The acquisition unit can also encrypt the data using RSA encryption. The acquisition unit can also anonymize the data. For example, the acquisition unit anonymizes the data by deleting personal information. The acquisition unit can also pseudonymize the data. This makes it possible to acquire information while protecting the user's privacy. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can have the generation AI perform the data encryption and anonymization processing.

[0070] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring purchase information based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can acquire information immediately after purchase and quickly make recommendations. Alternatively, if the user is relaxed, the acquisition unit can acquire information some time after purchase and slowly make recommendations. Furthermore, if the user is in a hurry, the acquisition unit can acquire information simultaneously with purchase and immediately make recommendations. This allows information to be acquired at an appropriate time depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI, or without an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0071] The acquisition unit can analyze the user's past purchase history and select the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of product categories that the user frequently purchases. The acquisition unit can also prioritize acquisition of products that the user tends to purchase during specific time periods from the user's purchase history. Furthermore, the acquisition unit can analyze the user's purchase history and prioritize acquisition of purchase information from specific stores. In this way, by analyzing the past purchase history, information can be acquired in the optimal method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's purchase history data into the generation AI and cause the generation AI to select the optimal acquisition method.

[0072] When acquiring purchase information, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is health-conscious, the acquisition unit can prioritize acquiring health-related product information. Furthermore, if the user is traveling, the acquisition unit can also prioritize acquiring travel-related product information. Furthermore, if the user has started a new hobby, the acquisition unit can also prioritize acquiring product information related to that hobby. This makes it possible to acquire information according to the user's living situation and areas of interest. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0073] When acquiring purchase information, the acquisition unit can select the optimal acquisition means depending on the user's input method. For example, if the user uses voice input, the acquisition unit can acquire the purchase information using voice recognition technology. Furthermore, if the user uses text input, the acquisition unit can also acquire the purchase information using text analysis technology. Furthermore, if the user uses image input, the acquisition unit can also acquire the purchase information using image recognition technology. This makes it possible to acquire information using the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data into a generation AI and cause the generation AI to select the optimal acquisition means.

[0074] The acquisition unit can estimate the user's emotions and determine the priority of the purchase information to be acquired based on the estimated user's emotions. For example, when the user is excited, the acquisition unit can prioritize acquiring highly relevant information. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring detailed information. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring information that covers the main points. This allows appropriate information to be acquired preferentially by determining the priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using an AI, for example, or without an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0075] When acquiring purchase information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit can prioritize acquiring product information related to that area. Furthermore, when the user is traveling, the acquisition unit can also prioritize acquiring product information related to the area of ​​the user's travel destination. Furthermore, when the user is at home, the acquisition unit can also prioritize acquiring store information around the user's home. This makes it possible to acquire highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0076] When acquiring purchase information, the acquisition unit can analyze the user's social media activity and acquire related information. For example, the acquisition unit can acquire product information related to locations where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related product information. Furthermore, the acquisition unit can also acquire related product information by referring to the activities of the user's friends on social media. In this way, related information can be acquired by analyzing social media activity. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to acquire related information.

[0077] When acquiring purchase information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. For example, the acquisition unit prioritizes acquisition of product categories that the user has previously rated highly. The acquisition unit can also exclude acquisition of product categories that the user has previously rated poorly. Furthermore, the acquisition unit can analyze the user's past feedback and suggest an optimal acquisition method. This allows information to be acquired in an optimal manner by reflecting past feedback. Some or all of the above-described processing in the acquisition unit may be performed, for example, using AI or without AI. For example, the acquisition unit can input the user's feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0078] The generation unit can estimate the user's emotions and adjust the prompt expression method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a prompt with a gentle expression. If the user is in a hurry, the generation unit can also generate a concise and to-the-point prompt. Furthermore, if the user is excited, the generation unit can generate a visually stimulating prompt. This allows prompts to be generated using an expression method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the prompt expression method.

[0079] When generating a prompt, the generation unit can adjust the level of detail of the prompt based on the importance of the purchased item. For example, the generation unit generates a detailed prompt when an expensive item is purchased. The generation unit can also generate a concise prompt when an everyday item is purchased. Furthermore, the generation unit can generate a special prompt when an item for a special event is purchased. This makes it possible to generate a prompt with a level of detail according to the importance of the purchased item. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of the purchased item into the generation AI and cause the generation AI to adjust the level of detail of the prompt.

[0080] When generating a prompt, the generation unit can apply different generation algorithms depending on the category of the purchased item. For example, when a product in the food category is purchased, the generation unit generates a prompt that suggests a recipe. Furthermore, when a product in the fashion category is purchased, the generation unit can also generate a prompt that suggests an outfit coordination. Furthermore, when a product in the electronics category is purchased, the generation unit can also generate a prompt on how to use the product. This makes it possible to generate a prompt using an optimal algorithm depending on the category of the purchased item. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input category data of the purchased item into the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0081] When generating a prompt, the generation unit can improve the accuracy of generation by referring to the user's past prompt results. For example, the generation unit refers to patterns of prompts that the user has previously rated highly. The generation unit can also exclude patterns of prompts that the user has previously rated poorly. Furthermore, the generation unit can analyze the user's past prompt results and generate an optimal prompt. In this way, by referring to the past prompt results, the accuracy of generation can be improved. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past prompt result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0082] The generation unit can estimate the user's emotions and adjust the length of the prompts based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point prompts. If the user is relaxed, the generation unit can also generate longer prompts with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate visually stimulating prompts. This allows prompts to be generated with lengths that correspond to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the prompts.

[0083] When generating prompts, the generation unit can determine the priority of the prompts based on the submission time of the purchased items. For example, when a highly urgent item is purchased, the generation unit generates prompts with priority. The generation unit can also generate prompts with normal priority when an everyday item is purchased. Furthermore, the generation unit can generate prompts with special priority when an item for a special event is purchased. This makes it possible to generate prompts with priority according to the submission time of the purchased items. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input purchase item submission time data into the generation AI and have the generation AI determine the priority of the prompts.

[0084] When generating prompts, the generation unit can adjust the order of prompts based on the relevance of the purchased items. For example, if the relevance of the purchased items is high, the generation unit may generate prompts preferentially. Also, if the relevance of the purchased items is low, the generation unit may generate prompts later. Furthermore, the generation unit can analyze the relevance of the purchased items and generate prompts in an optimal order. This allows prompts to be generated in an order according to the relevance of the purchased items. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit may input relevance data of the purchased items into the generation AI and cause the generation AI to adjust the order of the prompts.

[0085] When generating a prompt, the generation unit can adjust the use of technical terminology in the prompt depending on the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate a prompt that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can generate a simple prompt that avoids technical terminology. Furthermore, the generation unit can analyze the user's level of expertise and generate an optimal prompt. This allows for the generation of an appropriate prompt according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the prompt.

[0086] The recommendation unit can estimate the user's emotions and adjust the recommendation method based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is in a hurry, the recommendation unit can also provide concise recommendations. Furthermore, if the user is excited, the recommendation unit can also provide visually stimulating recommendations. This allows recommendations to be made in a manner that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the recommendation method.

[0087] When making a recommendation, the recommendation unit can analyze the user's past purchasing behavior and select the optimal recommendation method. For example, the recommendation unit prioritizes recommending product categories that the user has previously rated highly. The recommendation unit can also recommend products by excluding product categories that the user has previously rated poorly. Furthermore, the recommendation unit can analyze the user's past purchasing behavior and propose the optimal recommendation method. In this way, the optimal recommendation method can be selected by analyzing past purchasing behavior. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's purchasing behavior data into the generation AI and cause the generation AI to select the optimal recommendation method.

[0088] When making a recommendation, the recommendation unit can customize the means of recommendation based on the user's current lifestyle situation. For example, if the user is health-conscious, the recommendation unit can recommend health-related products. Also, if the user is traveling, the recommendation unit can recommend travel-related products. Furthermore, if the user has started a new hobby, the recommendation unit can recommend products related to that hobby. This makes it possible to make appropriate recommendations according to the user's lifestyle situation. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's lifestyle situation data into the generation AI and cause the generation AI to customize the means of recommendation.

[0089] When making a recommendation, the recommendation unit can improve the recommendation method by reflecting user feedback. For example, the recommendation unit preferentially uses recommendation methods that users have previously rated highly. The recommendation unit can also exclude recommendation methods that users have previously rated poorly. Furthermore, the recommendation unit can analyze user feedback and propose an optimal recommendation method. This allows the recommendation method to be improved by reflecting user feedback. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input user feedback data into a generation AI and cause the generation AI to improve the recommendation method.

[0090] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated user emotions. For example, if the user is relaxed, the recommendation unit can prioritize detailed recommendations. Furthermore, if the user is in a hurry, the recommendation unit can prioritize brief recommendations. Furthermore, if the user is excited, the recommendation unit can prioritize visually stimulating recommendations. This allows recommendations to be prioritized according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of recommendations.

[0091] When making a recommendation, the recommendation unit can select the optimal recommendation method taking into account the user's geographical location information. For example, if the user is in a specific area, the recommendation unit can recommend products and services related to that area. Furthermore, if the user is traveling, the recommendation unit can also recommend products and services related to the area of ​​the user's travel destination. Furthermore, if the user is at home, the recommendation unit can also recommend stores and services near the user's home. This allows optimal recommendations to be made based on the user's geographical location information. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal recommendation method.

[0092] When making a recommendation, the recommendation unit can analyze the user's social media activity and suggest a means of recommendation. For example, the recommendation unit can recommend products and services related to places where the user has checked in on social media. The recommendation unit can also analyze the content of the user's social media posts and recommend related products and services. Furthermore, the recommendation unit can recommend related products and services by referring to the activities of the user's friends on social media. In this way, related products and services can be recommended by analyzing social media activity. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's social media data into a generation AI and have the generation AI suggest a means of recommendation.

[0093] When making a recommendation, the recommendation unit can customize the recommendation method by reflecting the user's past feedback. For example, the recommendation unit preferentially uses recommendation methods that the user has previously rated highly. The recommendation unit can also exclude recommendation methods that the user has previously rated poorly. Furthermore, the recommendation unit can analyze the user's past feedback and propose the optimal recommendation method. In this way, the optimal recommendation method can be proposed by reflecting the past feedback. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input user feedback data into the generation AI and cause the generation AI to customize the recommendation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, generation unit, and recommendation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires information on purchased products and purchase locations via the control unit 46A of the smart device 14, and processes the information via the specific processing unit 290 of the data processing device 12. The generation unit generates prompts using a generation AI via the specific processing unit 290 of the data processing device 12. The recommendation unit makes appropriate recommendations to the user based on the prompts generated by the control unit 46A of the smart device 14. The acquisition unit can, for example, estimate the user's emotions and adjust the timing of acquiring purchase information based on the estimated emotions. The emotion estimation is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, generation unit, and recommendation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires information on the purchased product and the purchase location by the control unit 46A of the smart glasses 214, and processes the information by the specific processing unit 290 of the data processing device 12. The generation unit generates a prompt using a generation AI by the specific processing unit 290 of the data processing device 12. The recommendation unit makes an appropriate recommendation to the user based on the prompt generated by the control unit 46A of the smart glasses 214. The acquisition unit can, for example, estimate the user's emotion and adjust the timing of acquiring purchase information based on the estimated emotion. The emotion estimation is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, generation unit, and recommendation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires information about the purchased product and the purchase location by the control unit 46A of the headset-type terminal 314, and processes the information by the specific processing unit 290 of the data processing device 12. The generation unit generates a prompt using a generation AI by the specific processing unit 290 of the data processing device 12. The recommendation unit makes an appropriate recommendation to the user based on the prompt generated by the control unit 46A of the headset-type terminal 314. The acquisition unit can, for example, estimate the user's emotions and adjust the timing of acquiring purchase information based on the estimated emotions. The emotion estimation is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, generation unit, and recommendation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires information about the purchased product and the place of purchase by the control unit 46A of the robot 414, and processes the information by the specific processing unit 290 of the data processing device 12. The generation unit generates a prompt using a generation AI by the specific processing unit 290 of the data processing device 12. The recommendation unit makes an appropriate recommendation to the user based on the prompt generated by the control unit 46A of the robot 414. The acquisition unit can, for example, estimate the user's emotions and adjust the timing of acquiring purchase information based on the estimated emotions. The emotion estimation is realized by the specific processing unit 290 of the data processing device 12.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The acquisition unit can monitor a user's purchasing behavior in real time and detect abnormal patterns. For example, the acquisition unit can issue a warning if the user suddenly purchases a high-priced item that the user does not normally purchase. The acquisition unit can also generate an alert if the user purchases multiple high-priced items in a short period of time. Furthermore, the acquisition unit can request additional authentication if the user's purchasing behavior significantly differs from the normal pattern. This allows for enhanced security by monitoring a user's purchasing behavior and detecting abnormal patterns.

[0096] The generation unit can predict future purchasing behavior based on the user's purchasing history. For example, if the user has purchased a specific product in a specific season in the past, the generation unit can suggest similar products as that season approaches. The generation unit can also predict products that the user will purchase regularly and send reminders. Furthermore, the generation unit can analyze the user's purchasing patterns and predict products that the user is likely to purchase next. This makes it possible to predict future purchasing behavior based on the user's purchasing history and make appropriate suggestions.

[0097] The recommendation unit can provide customized coupons based on the user's purchasing behavior. For example, if the user frequently purchases products of a particular brand, the recommendation unit can provide coupons for that brand. Also, if the user purchases products in a particular category, the recommendation unit can provide coupons for that category. Furthermore, the recommendation unit can provide coupons that can be used at specific stores based on the user's purchasing history. This can provide customized coupons based on the user's purchasing behavior, thereby increasing purchasing motivation.

[0098] The acquisition unit can analyze the user's purchasing behavior and promote environmentally conscious purchasing behavior. For example, if the user purchases an environmentally friendly product, the acquisition unit can highlight that information. The acquisition unit can also award points each time the user purchases an environmentally conscious product. Furthermore, the acquisition unit can analyze the user's purchasing behavior and suggest environmentally conscious products. In this way, the user's purchasing behavior can be analyzed and environmentally conscious purchasing behavior can be promoted.

[0099] The generation unit can make health-conscious suggestions based on the user's purchasing behavior. For example, if the user purchases a health food, the generation unit can suggest recipes related to that food. Also, if the user purchases a fitness-related product, the generation unit can suggest a training plan. Furthermore, the generation unit can provide health-related advice based on the user's purchasing history. This makes it possible to make health-conscious suggestions based on the user's purchasing behavior.

[0100] The acquisition unit can estimate the user's emotions and adjust the method for acquiring purchase information based on the estimated emotions. For example, when the user is feeling stressed, the acquisition unit can quickly acquire purchase information and provide concise recommendations. When the user is relaxed, the acquisition unit can also acquire detailed purchase information and provide detailed recommendations. Furthermore, when the user is excited, the acquisition unit can acquire visually attractive purchase information and provide visually stimulating recommendations. This makes it possible to adjust the method for acquiring purchase information according to the user's emotions.

[0101] The generation unit can estimate the user's emotions and adjust the content of the prompt based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a prompt with a gentle expression. If the user is in a hurry, the generation unit can also generate a prompt that is concise and to the point. Furthermore, if the user is excited, the generation unit can also generate a visually stimulating prompt. In this way, it is possible to generate a prompt with content that corresponds to the user's emotions.

[0102] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated emotions. For example, if the user is feeling stressed, the recommendation unit can suggest products and services that will help the user relax. Also, if the user is relaxed, the recommendation unit can suggest new hobbies or products and services that will pique the user's interest. Furthermore, if the user is excited, the recommendation unit can suggest active products and services. This allows recommendations to be made at a timing that suits the user's emotions.

[0103] The acquisition unit can estimate the user's emotions and adjust the frequency of acquisition of purchase information based on the estimated emotions. For example, if the user is feeling stressed, the acquisition unit can reduce the frequency of acquisition of purchase information to reduce the burden on the user. Furthermore, if the user is relaxed, the acquisition unit can increase the frequency of acquisition of purchase information and provide detailed recommendations. Furthermore, if the user is excited, the acquisition unit can adjust the frequency of acquisition of purchase information and provide visually stimulating recommendations. This makes it possible to adjust the frequency of acquisition of purchase information according to the user's emotions.

[0104] The generation unit can estimate the user's emotion and adjust the format of the prompt based on the estimated emotion. For example, the generation unit can generate a text prompt when the user is relaxed. The generation unit can also generate an audio prompt when the user is in a hurry. Furthermore, the generation unit can generate a visually stimulating video prompt when the user is excited. In this way, the prompt can be generated in a format that corresponds to the user's emotion.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The acquisition unit acquires information about purchased products and purchase locations. Purchased products include food, clothing, electronic devices, etc., and purchase locations include online stores and physical stores. The acquisition unit acquires the purchase information through, for example, an electronic payment system. Step 2: The generator uses the generation AI to generate a prompt based on the information acquired by the acquirer. The prompt is generated in the form of a text message, notification, or other format. The generator generates the prompt by taking into account the user's purchase history, current location information, past recommendation history, and other factors. Step 3: The recommendation unit makes appropriate recommendations to the user based on the generated prompts. Recommendations are made in the form of product suggestions, service suggestions, etc. For example, if a user purchases camping gear, the unit can suggest camping spots that are easily accessible from the place of purchase. Similarly, if a user purchases sports equipment, the unit can suggest nearby sports facilities. The processing in the recommendation unit is sometimes performed using AI.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires information about a purchased product and a purchase location; a generating unit that generates a prompt based on the information acquired by the acquiring unit; a recommendation unit that makes a recommendation to a user based on the prompt generated by the generation unit. A system characterized by:

2. The acquisition unit Obtain information about the products and places of purchase through electronic payment systems 2. The system of claim 1.

3. The generation unit Generate prompts based on the user's purchase history, current location, and past recommendation history 2. The system of claim 1.

4. The recommendation unit Make appropriate recommendations to the user based on the generated prompts 2. The system of claim 1.

5. The recommendation unit When a user purchases camping gear, the app suggests camping spots that are easily accessible from the place of purchase.

2. The system of claim 1.

6. The recommendation unit If a user purchases sports equipment, suggest nearby sports facilities 2. The system of claim 1.

7. The acquisition unit Encrypt and anonymize data to protect user privacy 2. The system of claim 1.

8. The acquisition unit To estimate a user's emotions and adjust the timing of acquiring purchase information based on the estimated user's emotions.

2. The system of claim 1.

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