system

Abstract

The system according to this embodiment aims to recommend the optimal shopping method based on the user's requests. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a recommendation unit. The reception unit receives requests from users. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends the optimal shopping method based on the information analyzed by the analysis unit.

Description

Technical Field

[0006] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been sufficiently done to propose an optimal shopping method based on the user's desires, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend an optimal shopping method based on the user's desires.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a recommendation unit. The reception unit receives the desires from the user. The analysis unit analyzes the information received by the reception unit. The recommendation unit recommends an optimal shopping method based on the information analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can recommend the optimal shopping method based on the user's requests. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that, upon input of desired products, location information, and shopping requests, recommends the optimal way to purchase those products. When a user inputs desired products, location information, and shopping requests, the AI ​​agent analyzes the input information and recommends the optimal shopping method. This recommendation includes price comparisons of online and offline stores, utilization of coupons and points, and suggestions that take into account the user's preferences and schedule. This allows the user to shop in the cheapest and most efficient way. For example, a user inputs requests such as "I want the shampoo Merit" or "Please tell me the cheapest store within 200m of my current location." This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information. The AI ​​agent recommends the optimal shopping method, taking into account price data from online and offline stores, coupon and point information, and the user's preferences and schedule. For example, it might recommend, "The cheapest price is 528 yen at Sugi Pharmacy within 200m of your current location." Furthermore, the AI ​​agent can also handle requests for bulk purchases. For example, in response to a request like, "I want to buy one box of curry roux, one bag of carrots, one bag of potatoes, and one bag of onions all together," it will recommend the store where you can buy them at the lowest price. For example, it might recommend, "If you're buying everything at one store, Life is the cheapest. Also, if you only look at vegetables, Yoshizuya is cheap, but they don't sell curry roux, so how about buying vegetables at Yoshizuya and curry roux at a nearby Life?" This system allows users to shop in the cheapest and most efficient way. In addition, it displays prices that take into account coupon discounts and point rewards, allowing users to shop even more economically. For example, it might recommend, "If you use 7 yen worth of Ponta points, 14 yen worth of Tomod's points, and the Wednesday 15% off coupon, you can purchase it for 1,285 yen." Furthermore, the AI ​​agent also makes recommendations that take into account the user's preferences and schedule. For example, it might make recommendations like, "The store near your office is cheaper, so it's a good idea to buy it on your way home from work."Furthermore, it can also provide recommendations based on vague instructions. For example, in response to a request like, "I want a sweet and delicious coffee-flavored protein powder," it might recommend, "How about RAYS's café au lait flavored protein powder? It has good reviews regarding its taste and is the cheapest option. It's currently only available online, and the cheapest price is 3,306 yen." In this way, the AI ​​agent takes the user's shopping requests as input, autonomously collects the necessary information, and recommends the optimal shopping method, making the user's shopping smarter. As a result, the AI ​​agent system can efficiently receive and analyze user requests and recommend the most suitable shopping method.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a recommendation unit. The reception unit receives requests from users. User requests include, but are not limited to, requests to purchase products or use services. The reception unit can, for example, receive requests entered by the user using a smartphone or personal computer. The reception unit can also receive user requests using voice input. For example, if a user enters "I want Merit shampoo" by voice, the reception unit will receive the request. Furthermore, the reception unit can estimate the user's emotions and adjust the method of receiving requests based on the estimated emotions of the user. For example, if the user is stressed, it can provide a simple interface and minimize the input procedure. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using methods such as data mining and statistical analysis. For example, the analysis unit collects price data of products entered by the user and analyzes the optimal shopping method. The analysis unit can also perform analysis considering the user's past purchase history and preferences. For example, it can estimate the user's preferences based on data of products the user has purchased in the past and analyze the optimal shopping method. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide multiple options. If the user is in a hurry, it can perform a quick analysis and provide the most efficient options. The recommendation unit recommends the optimal shopping method based on the information analyzed by the analysis unit. For example, the recommendation unit can compare prices at online and offline stores and recommend the cheapest store. For example, the recommendation unit can collect price data from online stores and recommend the lowest price. The recommendation unit can also display prices considering the use of coupons and points. For example, the recommendation unit can automatically apply any coupons the user has and display the final price.Furthermore, the recommendation unit can also make recommendations that take into account the user's preferences and schedule. For example, the recommendation unit can recommend products that match the user's preferences based on their past purchase history. The recommendation unit can also make recommendations based on ambiguous instructions. For example, if the recommendation unit receives an ambiguous request, it can provide a list of related products. As a result, the AI ​​agent system according to this embodiment can efficiently receive and analyze the user's requests and recommend the optimal shopping method.

[0030] The reception desk receives requests from users. These requests may include, but are not limited to, requests to purchase products or use services. The reception desk can receive requests entered by users using smartphones or personal computers. It can also receive requests using voice input. For example, if a user voice-inputs "I want Merit shampoo," the reception desk will accept the request. Furthermore, the reception desk can estimate the user's emotions and adjust the request processing method based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. When receiving user requests, the reception desk uses natural language processing technology to accurately understand the user's intent. For example, if a user inputs "I'm looking for a cheap smartphone," the reception desk analyzes the keyword "cheap" and extracts information about price ranges. In the case of voice input, speech recognition technology is used to convert the user's utterance into text, accurately understanding the request. Furthermore, the reception desk can refer to the user's past request history and accept requests while considering the user's tendencies and preferences. For example, it can prioritize suggesting similar luxury brand products to users who have frequently purchased luxury brand items in the past. The reception desk can also provide real-time feedback when accepting user requests. For example, it can immediately display a list of related products and services in response to the user's request, helping the user narrow down their options. This allows the reception desk to receive user requests quickly and accurately, improving user satisfaction.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using methods such as data mining and statistical analysis. For example, the analysis unit collects price data of products entered by the user and analyzes the optimal shopping method. The analysis unit can also perform analysis considering the user's past purchase history and preferences. For example, the analysis unit estimates the user's preferences based on data of products the user has purchased in the past and analyzes the optimal shopping method. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide multiple options. If the user is in a hurry, it can perform a rapid analysis and provide the most efficient options. The analysis unit utilizes AI technology to find the optimal solution to the user's needs. For example, it uses natural language processing technology to analyze the user's needs in detail and extract relevant information. It also uses machine learning algorithms to learn from the user's past behavior data and predict user preferences and trends. Furthermore, the analysis unit can use clustering technology to group users with similar needs and provide common recommendations. For example, it can suggest new products related to a user group that has purchased the same product multiple times. Furthermore, the analytics unit can analyze data in real time and respond quickly to user requests. For instance, if a user is in a hurry, it can perform a rapid analysis and provide the most efficient option. The analytics unit can also collect user feedback and continuously improve the accuracy of its analysis algorithms. This allows the analytics unit to provide optimal solutions to user requests and increase user satisfaction.

[0032] The recommendation department recommends the optimal shopping method based on information analyzed by the analysis department. For example, the recommendation department compares prices at online and offline stores and recommends the cheapest store. For example, the recommendation department collects price data from online stores and recommends the lowest price. The recommendation department can also display prices considering the use of coupons and points. For example, the recommendation department automatically applies any coupons the user has and displays the final price. Furthermore, the recommendation department can make recommendations that take into account the user's preferences and plans. For example, the recommendation department recommends products that match the user's preferences based on their past purchase history. The recommendation department can also make recommendations from ambiguous instructions. For example, if the recommendation department enters a vague request, it will provide a list of related products. The recommendation department utilizes AI technology to suggest the most suitable products and services for the user's needs. For example, collaborative filtering technology is used to recommend products suitable for the user based on the purchase history and rating data of other users. Content-based filtering technology is also used to suggest similar products based on the user's past purchase history and preferences. Furthermore, the recommendation system can make recommendations considering the user's real-time situation. For instance, if the user is in a hurry, it prioritizes suggesting products that can be purchased quickly. Conversely, if the user is relaxed, it provides detailed product information and reviews to allow for careful selection. The recommendation system can collect user feedback and continuously improve the accuracy of its recommendations. For example, user ratings of recommendations are used to learn from that data and incorporate it into future recommendations. The recommendation system can also analyze user purchasing behavior and evaluate the effectiveness of its recommendations. This allows the recommendation system to suggest the most suitable products and services to users, thereby improving user satisfaction.

[0033] The recommendation unit can perform price comparisons between online and offline stores. For example, the recommendation unit can collect price data from online stores and recommend the lowest price. The recommendation unit can also collect price data from offline stores and recommend the lowest price. Furthermore, the recommendation unit can compare prices between online and offline stores and recommend the cheapest store. This allows the recommendation unit to recommend the cheapest store by comparing prices between online and offline stores. Price comparisons include, but are not limited to, comparing prices for the same product, and considering shipping costs and taxes. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recommendation unit can input online and offline store price data into a generating AI and have the generating AI recommend the store with the lowest price.

[0034] The recommendation section can display prices that take into account the use of coupons and points. For example, the recommendation section can automatically apply any coupons the user has and display the final price. The recommendation section can also display the final price considering the points the user has available. For example, the recommendation section can display the final price considering the points the user has available. The recommendation section can also display the optimal price considering the expiration dates of coupons and points. For example, the recommendation section can display the optimal price considering the expiration dates of coupons and points. By displaying prices that take into account the use of coupons and points, users can shop even more economically. The use of coupons and points includes, but is not limited to, discount rates and point expiration dates. Some or all of the above processing in the recommendation section may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data on coupons and points that the user possesses into a generating AI, and then have the generating AI display the final price.

[0035] The recommendation system can make recommendations that take into account the user's preferences and schedule. For example, the recommendation system can recommend products that match the user's preferences based on their past purchase history. The recommendation system can also refer to the user's calendar information and make recommendations that match their schedule. For example, the recommendation system can refer to the user's calendar information and make recommendations that match their schedule. The recommendation system can also recommend the optimal timing for purchase based on the user's preferences and schedule. For example, the recommendation system can recommend the optimal timing for purchase based on the user's preferences and schedule. In this way, by making recommendations that take into account the user's preferences and schedule, it is possible to propose the most suitable shopping method for the user. User preferences and schedules include, but are not limited to, past purchase history and calendar information. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recommendation unit can input the user's past purchase history and calendar information into the generative AI and have the generative AI perform the task of recommending the optimal shopping method.

[0036] The recommendation unit can make recommendations based on vague instructions. For example, if a user enters a vague request, the recommendation unit will provide a list of relevant products. The recommendation unit can also recommend the most suitable products based on the user's past purchase history and preferences when the user enters a vague request. For example, if a user enters a vague request, the recommendation unit will recommend the most suitable products based on the user's past purchase history and preferences. Furthermore, if a user enters a vague request, the recommendation unit can have its generating AI automatically analyze the request and recommend the most suitable products. For example, if a user enters a vague request, the generating AI will automatically analyze the request and recommend the most suitable products. In this way, by making recommendations based on vague instructions, the system can address the user's vague requests. Vague instructions include, for example, interpretation using natural language processing techniques, but are not limited to such examples. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recommendation unit can input the user's vague requests into a generative AI and have the generative AI recommend the most suitable products.

[0037] The recommendation system can recommend the cheapest store for bulk purchase requests. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest store. The recommendation system can also recommend the cheapest way to purchase items by combining multiple stores. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest way to purchase items by combining multiple stores. The recommendation system can also recommend the cheapest way to purchase items by utilizing coupons and points. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest way to purchase items by utilizing coupons and points. In this way, by recommending the cheapest store for bulk purchase requests, users can shop more efficiently. Bulk purchases include, but are not limited to, purchasing a certain quantity or more, or specific product combinations. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the user's bulk purchase request into a generative AI and have the generative AI recommend the store where the items can be purchased at the lowest price.

[0038] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest requests to be used during specific time periods based on the user's past request history. This allows the reception desk to select the optimal reception method by analyzing the user's past request history. Past request history includes, but is not limited to, database usage and log analysis. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not. For example, the reception desk can input the user's past request history into a generative AI and have the generative AI select the optimal reception method.

[0039] The reception desk can filter requests based on the user's current situation and areas of interest. For example, the reception desk can prioritize requests for nearby stores based on the user's current location. The reception desk can also filter requests based on the user's areas of interest (e.g., health foods or eco-friendly products). The reception desk can also filter requests based on the user's current situation (e.g., whether they are in a hurry). This allows for more appropriate requests to be received by filtering requests based on the user's current situation and areas of interest. Current situation and areas of interest include, but are not limited to, the use of real-time data and survey results. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input data on the user's current situation and areas of interest into a generating AI, and have the AI ​​perform filtering of requests.

[0040] The reception desk can prioritize requests that are highly relevant to the user, taking into account the user's geographical location information. For example, the reception desk can prioritize requests for stores near the user's current location. The reception desk can also prioritize requests for specific regions. For example, the reception desk can prioritize requests for specific regions if the user is traveling. For example, the reception desk can prioritize requests for travel destinations if the user is traveling. By considering the user's geographical location information when receiving requests, it becomes possible to receive more appropriate requests. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI prioritize receiving highly relevant requests.

[0041] The reception desk can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can prioritize requests related to products mentioned by the user on social media. The reception desk can also prioritize requests related to brands followed by the user on social media. The reception desk can also prioritize requests related to events the user is participating in on social media. This allows the reception desk to accept relevant requests by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of posts and follower analysis. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not. For example, the reception desk can input data on the user's social media activity into a generative AI and have the generative AI process the acceptance of relevant requests.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, for high-priority requests, the analysis unit performs a detailed analysis to provide the optimal option. The analysis unit can also perform a simplified analysis for low-priority requests to quickly provide options. For example, for low-priority requests, the analysis unit performs a simplified analysis to quickly provide options. The analysis unit can also perform a balanced analysis for medium-priority requests to provide appropriate options. By adjusting the level of detail of the analysis based on the importance of the request, more appropriate analysis becomes possible. The importance of a request includes, but is not limited to, the urgency for the user and the scope of impact. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the importance of requests into the generating AI and have the generating AI adjust the level of detail in the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the request during analysis. For example, the analysis unit can apply a price comparison algorithm to requests related to products. The analysis unit can also apply a geographic analysis algorithm to requests related to location information. For example, the analysis unit can apply a coupon or points utilization algorithm to requests related to shopping. By applying different analysis algorithms depending on the category of the request, more appropriate analysis becomes possible. The categories of requests include, for example, types of products and types of services, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the category of requests into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0044] The analysis unit can adjust the order of analysis based on when the requests were submitted. For example, the analysis unit may prioritize the analysis of recently submitted requests. The analysis unit may also postpone the analysis of older requests. The analysis unit can also dynamically adjust the order of analysis based on the submission date. This allows for more appropriate analysis by adjusting the order of analysis based on the submission date of the requests. The submission date of a request includes, but is not limited to, timestamps and submission history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit can input data on the submission date of requests into a generating AI and have the generating AI adjust the order of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the requests during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant requests. The analysis unit can also postpone the analysis of less relevant requests. For example, the analysis unit may postpone the analysis of less relevant requests. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the requests. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the requests. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the requests. The relevance of requests includes, but is not limited to, similarity of content and related topics. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input data on the relevance of requests into a generative AI and have the generative AI adjust the order of analysis.

[0046] The recommendation system can adjust the level of detail in its recommendations based on the importance of the products. For example, it can provide detailed recommendations for highly important products. It can also provide simplified recommendations for less important products. Furthermore, it can provide balanced recommendations for products of moderate importance. By adjusting the level of detail based on product importance, more appropriate recommendations become possible. Product importance includes, but is not limited to, user needs and product rarity. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recommendation unit can input product importance data into the generating AI and have the generating AI adjust the level of detail of the recommendations.

[0047] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, for food recommendations, the recommendation unit can apply an algorithm that takes into account nutritional value and expiration date. For electronic device recommendations, the recommendation unit can also apply an algorithm that takes into account performance and price. For example, for clothing recommendations, the recommendation unit can apply an algorithm that takes into account size and design. By applying different recommendation algorithms depending on the product category, more appropriate recommendations become possible. Recommendation algorithms include, but are not limited to, collaborative filtering and content-based filtering. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input product category data into a generative AI and have the generative AI apply different recommendation algorithms.

[0048] The recommendation unit can determine the priority of recommendations based on when the products were submitted. For example, the recommendation unit may prioritize recommendations for recently submitted products. The recommendation unit may also postpone recommendations for older products. For example, the recommendation unit may postpone recommendations for older products. The recommendation unit may also dynamically adjust the priority of recommendations based on the submission date. For example, the recommendation unit may dynamically adjust the priority of recommendations based on the submission date. This allows for more appropriate recommendations by determining the priority of recommendations based on when the products were submitted. The submission date of a product includes, but is not limited to, timestamps and submission history. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input data on the timing of product submissions into the generating AI, and have the generating AI determine the priority of recommendations.

[0049] The recommendation unit can adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit may prioritize recommendations for highly relevant products. The recommendation unit may also postpone recommendations for less relevant products. For example, the recommendation unit may postpone recommendations for less relevant products. The recommendation unit can also dynamically adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit may dynamically adjust the order of recommendations based on the relevance of the products. This allows for more appropriate recommendations by adjusting the order of recommendations based on the relevance of the products. Product relevance includes, but is not limited to, similarity of content and related topics. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input data on the relevance of products into the generating AI, and have the generating AI adjust the order of recommendations.

[0050] The recommendation unit can perform price comparisons between online and offline stores. For example, the recommendation unit can collect price data from online stores and recommend the lowest price. The recommendation unit can also collect price data from offline stores and recommend the lowest price. Furthermore, the recommendation unit can compare prices between online and offline stores and recommend the cheapest store. This allows the recommendation unit to recommend the cheapest store by comparing prices between online and offline stores. Price comparisons include, but are not limited to, comparing prices for the same product, and considering shipping costs and taxes. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recommendation unit can input online and offline store price data into a generating AI and have the generating AI recommend the store with the lowest price.

[0051] The recommendation section can display prices that take into account the use of coupons and points. For example, the recommendation section can automatically apply any coupons the user has and display the final price. The recommendation section can also display the final price considering the points the user has available. For example, the recommendation section can display the final price considering the points the user has available. The recommendation section can also display the optimal price considering the expiration dates of coupons and points. For example, the recommendation section can display the optimal price considering the expiration dates of coupons and points. By displaying prices that take into account the use of coupons and points, users can shop even more economically. The use of coupons and points includes, but is not limited to, discount rates and point expiration dates. Some or all of the above processing in the recommendation section may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data on coupons and points that the user possesses into a generating AI, and then have the generating AI display the final price.

[0052] The recommendation system can make recommendations that take into account the user's preferences and schedule. For example, the recommendation system can recommend products that match the user's preferences based on their past purchase history. The recommendation system can also refer to the user's calendar information and make recommendations that match their schedule. For example, the recommendation system can refer to the user's calendar information and make recommendations that match their schedule. The recommendation system can also recommend the optimal timing for purchase based on the user's preferences and schedule. For example, the recommendation system can recommend the optimal timing for purchase based on the user's preferences and schedule. In this way, by making recommendations that take into account the user's preferences and schedule, it is possible to propose the most suitable shopping method for the user. User preferences and schedules include, but are not limited to, past purchase history and calendar information. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recommendation unit can input the user's past purchase history and calendar information into the generative AI and have the generative AI perform the task of recommending the optimal shopping method.

[0053] The recommendation unit can make recommendations based on vague instructions. For example, if a user enters a vague request, the recommendation unit will provide a list of relevant products. The recommendation unit can also recommend the most suitable products based on the user's past purchase history and preferences when the user enters a vague request. For example, if a user enters a vague request, the recommendation unit will recommend the most suitable products based on the user's past purchase history and preferences. Furthermore, if a user enters a vague request, the recommendation unit can have its generating AI automatically analyze the request and recommend the most suitable products. For example, if a user enters a vague request, the generating AI will automatically analyze the request and recommend the most suitable products. In this way, by making recommendations based on vague instructions, the system can address the user's vague requests. Vague instructions include, for example, interpretation using natural language processing techniques, but are not limited to such examples. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recommendation unit can input the user's vague requests into a generative AI and have the generative AI recommend the most suitable products.

[0054] The recommendation system can recommend the cheapest store for bulk purchase requests. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest store. The recommendation system can also recommend the cheapest way to purchase items by combining multiple stores. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest way to purchase items by combining multiple stores. The recommendation system can also recommend the cheapest way to purchase items by utilizing coupons and points. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest way to purchase items by utilizing coupons and points. In this way, by recommending the cheapest store for bulk purchase requests, users can shop more efficiently. Bulk purchases include, but are not limited to, purchasing a certain quantity or more, or specific product combinations. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the user's bulk purchase request into a generative AI and have the generative AI recommend the store where the items can be purchased at the lowest price.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analytics unit can analyze a user's purchase history and estimate the frequency of use and consumption rate of products the user has purchased in the past. For example, if a user purchases shampoo every month, the analytics unit can predict when the next purchase will be and provide a reminder. The analytics unit can also notify users when certain products are on sale if they are frequently purchased. Furthermore, the analytics unit can collect reviews and ratings of products purchased by users to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0057] The recommendation system can provide recommendations that take into account the user's health condition. For example, if a user is on a diet, it can recommend low-calorie products. If a user has allergies, it can recommend products that do not contain those allergens. Furthermore, if a user needs to consume a specific nutrient, it can recommend products that are rich in that nutrient. This allows the system to provide the most suitable products for each user's health needs.

[0058] The reception system can analyze the user's voice input to more accurately understand their intent. For example, if a user says, "I'm looking for a cheap shampoo," the reception system analyzes the keyword "cheap" and recommends low-priced shampoos. Similarly, if a user says, "I want to buy shampoo at a nearby pharmacy," the reception system analyzes the keyword "nearby pharmacy" and recommends pharmacies close to the user's current location. Furthermore, if a user says, "I want Merit shampoo," the reception system analyzes the brand name "Merit" and recommends shampoos from that brand. This allows for a more intuitive interface using voice input.

[0059] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0060] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0061] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0062] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0063] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0064] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0065] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives requests from users. These requests include requests to purchase products or use services. The reception desk accepts requests entered by users using smartphones or personal computers. It can also accept requests using voice input. Furthermore, the reception desk can estimate the user's emotions and adjust the method of receiving requests based on those estimated emotions. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using methods such as data mining and statistical analysis. For example, it collects price data of products entered by the user and analyzes the optimal shopping method. It can also perform analysis considering the user's past purchase history and preferences. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. Step 3: The recommendation unit recommends the optimal shopping method based on the information analyzed by the analysis unit. The recommendation unit compares prices at online and offline stores and recommends the cheapest store. It can also display prices considering the use of coupons and points. Furthermore, it can provide recommendations that take into account the user's preferences and schedule.

[0068] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that, upon input of desired products, location information, and shopping requests, recommends the optimal way to purchase those products. When a user inputs desired products, location information, and shopping requests, the AI ​​agent analyzes the input information and recommends the optimal shopping method. This recommendation includes price comparisons of online and offline stores, utilization of coupons and points, and suggestions that take into account the user's preferences and schedule. This allows the user to shop in the cheapest and most efficient way. For example, a user inputs requests such as "I want the shampoo Merit" or "Please tell me the cheapest store within 200m of my current location." This information is input to the AI ​​agent. Next, the AI ​​agent analyzes the input information. The AI ​​agent recommends the optimal shopping method, taking into account price data from online and offline stores, coupon and point information, and the user's preferences and schedule. For example, it might recommend, "The cheapest price is 528 yen at Sugi Pharmacy within 200m of your current location." Furthermore, the AI ​​agent can also handle requests for bulk purchases. For example, in response to a request like, "I want to buy one box of curry roux, one bag of carrots, one bag of potatoes, and one bag of onions all together," it will recommend the store where you can buy them at the lowest price. For example, it might recommend, "If you're buying everything at one store, Life is the cheapest. Also, if you only look at vegetables, Yoshizuya is cheap, but they don't sell curry roux, so how about buying vegetables at Yoshizuya and curry roux at a nearby Life?" This system allows users to shop in the cheapest and most efficient way. In addition, it displays prices that take into account coupon discounts and point rewards, allowing users to shop even more economically. For example, it might recommend, "If you use 7 yen worth of Ponta points, 14 yen worth of Tomod's points, and the Wednesday 15% off coupon, you can purchase it for 1,285 yen." Furthermore, the AI ​​agent also makes recommendations that take into account the user's preferences and schedule. For example, it might make recommendations like, "The store near your office is cheaper, so it's a good idea to buy it on your way home from work."Furthermore, it can also provide recommendations based on vague instructions. For example, in response to a request like, "I want a sweet and delicious coffee-flavored protein powder," it might recommend, "How about RAYS's café au lait flavored protein powder? It has good reviews regarding its taste and is the cheapest option. It's currently only available online, and the cheapest price is 3,306 yen." In this way, the AI ​​agent takes the user's shopping requests as input, autonomously collects the necessary information, and recommends the optimal shopping method, making the user's shopping smarter. As a result, the AI ​​agent system can efficiently receive and analyze user requests and recommend the most suitable shopping method.

[0069] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a recommendation unit. The reception unit receives requests from users. User requests include, but are not limited to, requests to purchase products or use services. The reception unit can, for example, receive requests entered by the user using a smartphone or personal computer. The reception unit can also receive user requests using voice input. For example, if a user enters "I want Merit shampoo" by voice, the reception unit will receive the request. Furthermore, the reception unit can estimate the user's emotions and adjust the method of receiving requests based on the estimated emotions of the user. For example, if the user is stressed, it can provide a simple interface and minimize the input procedure. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using methods such as data mining and statistical analysis. For example, the analysis unit collects price data of products entered by the user and analyzes the optimal shopping method. The analysis unit can also perform analysis considering the user's past purchase history and preferences. For example, it can estimate the user's preferences based on data of products the user has purchased in the past and analyze the optimal shopping method. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide multiple options. If the user is in a hurry, it can perform a quick analysis and provide the most efficient options. The recommendation unit recommends the optimal shopping method based on the information analyzed by the analysis unit. For example, the recommendation unit can compare prices at online and offline stores and recommend the cheapest store. For example, the recommendation unit can collect price data from online stores and recommend the lowest price. The recommendation unit can also display prices considering the use of coupons and points. For example, the recommendation unit can automatically apply any coupons the user has and display the final price.Furthermore, the recommendation unit can also make recommendations that take into account the user's preferences and schedule. For example, the recommendation unit can recommend products that match the user's preferences based on their past purchase history. The recommendation unit can also make recommendations based on ambiguous instructions. For example, if the recommendation unit receives an ambiguous request, it can provide a list of related products. As a result, the AI ​​agent system according to this embodiment can efficiently receive and analyze the user's requests and recommend the optimal shopping method.

[0070] The reception desk receives requests from users. These requests may include, but are not limited to, requests to purchase products or use services. The reception desk can receive requests entered by users using smartphones or personal computers. It can also receive requests using voice input. For example, if a user voice-inputs "I want Merit shampoo," the reception desk will accept the request. Furthermore, the reception desk can estimate the user's emotions and adjust the request processing method based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. When receiving user requests, the reception desk uses natural language processing technology to accurately understand the user's intent. For example, if a user inputs "I'm looking for a cheap smartphone," the reception desk analyzes the keyword "cheap" and extracts information about price ranges. In the case of voice input, speech recognition technology is used to convert the user's utterance into text, accurately understanding the request. Furthermore, the reception desk can refer to the user's past request history and accept requests while considering the user's tendencies and preferences. For example, it can prioritize suggesting similar luxury brand products to users who have frequently purchased luxury brand items in the past. The reception desk can also provide real-time feedback when accepting user requests. For example, it can immediately display a list of related products and services in response to the user's request, helping the user narrow down their options. This allows the reception desk to receive user requests quickly and accurately, improving user satisfaction.

[0071] The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using methods such as data mining and statistical analysis. For example, the analysis unit collects price data of products entered by the user and analyzes the optimal shopping method. The analysis unit can also perform analysis considering the user's past purchase history and preferences. For example, the analysis unit estimates the user's preferences based on data of products the user has purchased in the past and analyzes the optimal shopping method. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide multiple options. If the user is in a hurry, it can perform a rapid analysis and provide the most efficient options. The analysis unit utilizes AI technology to find the optimal solution to the user's needs. For example, it uses natural language processing technology to analyze the user's needs in detail and extract relevant information. It also uses machine learning algorithms to learn from the user's past behavior data and predict user preferences and trends. Furthermore, the analysis unit can use clustering technology to group users with similar needs and provide common recommendations. For example, it can suggest new products related to a user group that has purchased the same product multiple times. Furthermore, the analytics unit can analyze data in real time and respond quickly to user requests. For instance, if a user is in a hurry, it can perform a rapid analysis and provide the most efficient option. The analytics unit can also collect user feedback and continuously improve the accuracy of its analysis algorithms. This allows the analytics unit to provide optimal solutions to user requests and increase user satisfaction.

[0072] The recommendation department recommends the optimal shopping method based on information analyzed by the analysis department. For example, the recommendation department compares prices at online and offline stores and recommends the cheapest store. For example, the recommendation department collects price data from online stores and recommends the lowest price. The recommendation department can also display prices considering the use of coupons and points. For example, the recommendation department automatically applies any coupons the user has and displays the final price. Furthermore, the recommendation department can make recommendations that take into account the user's preferences and plans. For example, the recommendation department recommends products that match the user's preferences based on their past purchase history. The recommendation department can also make recommendations from ambiguous instructions. For example, if the recommendation department enters a vague request, it will provide a list of related products. The recommendation department utilizes AI technology to suggest the most suitable products and services for the user's needs. For example, collaborative filtering technology is used to recommend products suitable for the user based on the purchase history and rating data of other users. Content-based filtering technology is also used to suggest similar products based on the user's past purchase history and preferences. Furthermore, the recommendation system can make recommendations considering the user's real-time situation. For instance, if the user is in a hurry, it prioritizes suggesting products that can be purchased quickly. Conversely, if the user is relaxed, it provides detailed product information and reviews to allow for careful selection. The recommendation system can collect user feedback and continuously improve the accuracy of its recommendations. For example, user ratings of recommendations are used to learn from that data and incorporate it into future recommendations. The recommendation system can also analyze user purchasing behavior and evaluate the effectiveness of its recommendations. This allows the recommendation system to suggest the most suitable products and services to users, thereby improving user satisfaction.

[0073] The recommendation unit can perform price comparisons between online and offline stores. For example, the recommendation unit can collect price data from online stores and recommend the lowest price. The recommendation unit can also collect price data from offline stores and recommend the lowest price. Furthermore, the recommendation unit can compare prices between online and offline stores and recommend the cheapest store. This allows the recommendation unit to recommend the cheapest store by comparing prices between online and offline stores. Price comparisons include, but are not limited to, comparing prices for the same product, and considering shipping costs and taxes. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recommendation unit can input online and offline store price data into a generating AI and have the generating AI recommend the store with the lowest price.

[0074] The recommendation section can display prices that take into account the use of coupons and points. For example, the recommendation section can automatically apply any coupons the user has and display the final price. The recommendation section can also display the final price considering the points the user has available. For example, the recommendation section can display the final price considering the points the user has available. The recommendation section can also display the optimal price considering the expiration dates of coupons and points. For example, the recommendation section can display the optimal price considering the expiration dates of coupons and points. By displaying prices that take into account the use of coupons and points, users can shop even more economically. The use of coupons and points includes, but is not limited to, discount rates and point expiration dates. Some or all of the above processing in the recommendation section may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data on coupons and points that the user possesses into a generating AI, and then have the generating AI display the final price.

[0075] The recommendation system can make recommendations that take into account the user's preferences and schedule. For example, the recommendation system can recommend products that match the user's preferences based on their past purchase history. The recommendation system can also refer to the user's calendar information and make recommendations that match their schedule. For example, the recommendation system can refer to the user's calendar information and make recommendations that match their schedule. The recommendation system can also recommend the optimal timing for purchase based on the user's preferences and schedule. For example, the recommendation system can recommend the optimal timing for purchase based on the user's preferences and schedule. In this way, by making recommendations that take into account the user's preferences and schedule, it is possible to propose the most suitable shopping method for the user. User preferences and schedules include, but are not limited to, past purchase history and calendar information. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recommendation unit can input the user's past purchase history and calendar information into the generative AI and have the generative AI perform the task of recommending the optimal shopping method.

[0076] The recommendation unit can make recommendations based on vague instructions. For example, if a user enters a vague request, the recommendation unit will provide a list of relevant products. The recommendation unit can also recommend the most suitable products based on the user's past purchase history and preferences when the user enters a vague request. For example, if a user enters a vague request, the recommendation unit will recommend the most suitable products based on the user's past purchase history and preferences. Furthermore, if a user enters a vague request, the recommendation unit can have its generating AI automatically analyze the request and recommend the most suitable products. For example, if a user enters a vague request, the generating AI will automatically analyze the request and recommend the most suitable products. In this way, by making recommendations based on vague instructions, the system can address the user's vague requests. Vague instructions include, for example, interpretation using natural language processing techniques, but are not limited to such examples. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recommendation unit can input the user's vague requests into a generative AI and have the generative AI recommend the most suitable products.

[0077] The recommendation system can recommend the cheapest store for bulk purchase requests. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest store. The recommendation system can also recommend the cheapest way to purchase items by combining multiple stores. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest way to purchase items by combining multiple stores. The recommendation system can also recommend the cheapest way to purchase items by utilizing coupons and points. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest way to purchase items by utilizing coupons and points. In this way, by recommending the cheapest store for bulk purchase requests, users can shop more efficiently. Bulk purchases include, but are not limited to, purchasing a certain quantity or more, or specific product combinations. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the user's bulk purchase request into a generative AI and have the generative AI recommend the store where the items can be purchased at the lowest price.

[0078] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. The reception desk can also provide detailed input options and suggest customizable input methods if the user is relaxed. The reception desk can also prioritize voice input if the user is in a hurry, allowing for quick request input. This allows for more appropriate request processing by adjusting the request processing method according to the user's emotions. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the reception area may be performed using AI, or not using AI. For example, the reception area may input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest requests to be used during specific time periods based on the user's past request history. This allows the reception desk to select the optimal reception method by analyzing the user's past request history. Past request history includes, but is not limited to, database usage and log analysis. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not. For example, the reception desk can input the user's past request history into a generative AI and have the generative AI select the optimal reception method.

[0080] The reception desk can filter requests based on the user's current situation and areas of interest. For example, the reception desk can prioritize requests for nearby stores based on the user's current location. The reception desk can also filter requests based on the user's areas of interest (e.g., health foods or eco-friendly products). The reception desk can also filter requests based on the user's current situation (e.g., whether they are in a hurry). This allows for more appropriate requests to be received by filtering requests based on the user's current situation and areas of interest. Current situation and areas of interest include, but are not limited to, the use of real-time data and survey results. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input data on the user's current situation and areas of interest into a generating AI, and have the AI ​​perform filtering of requests.

[0081] The reception desk can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize urgent requests. The reception desk can also prioritize detailed requests if the user is relaxed. The reception desk can also prioritize requests that can be handled quickly if the user is in a hurry. This allows for more appropriate requests to be received by determining the priority of requests according to the user's emotions. Request prioritization includes, but is not limited to, urgency and importance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0082] The reception desk can prioritize requests that are highly relevant to the user, taking into account the user's geographical location information. For example, the reception desk can prioritize requests for stores near the user's current location. The reception desk can also prioritize requests for specific regions. For example, the reception desk can prioritize requests for specific regions if the user is traveling. For example, the reception desk can prioritize requests for travel destinations if the user is traveling. By considering the user's geographical location information when receiving requests, it becomes possible to receive more appropriate requests. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI prioritize receiving highly relevant requests.

[0083] The reception desk can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can prioritize requests related to products mentioned by the user on social media. The reception desk can also prioritize requests related to brands followed by the user on social media. The reception desk can also prioritize requests related to events the user is participating in on social media. This allows the reception desk to accept relevant requests by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of posts and follower analysis. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not. For example, the reception desk can input data on the user's social media activity into a generative AI and have the generative AI process the acceptance of relevant requests.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide multiple options. The analysis unit can also perform a rapid analysis and provide the most efficient option if the user is in a hurry. The analysis unit can also perform a simple analysis and provide intuitive options if the user is stressed. By adjusting the analysis method according to the user's emotions, more appropriate analysis becomes possible. Analysis methods include, but are not limited to, data mining and statistical analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, for high-priority requests, the analysis unit performs a detailed analysis to provide the optimal option. The analysis unit can also perform a simplified analysis for low-priority requests to quickly provide options. For example, for low-priority requests, the analysis unit performs a simplified analysis to quickly provide options. The analysis unit can also perform a balanced analysis for medium-priority requests to provide appropriate options. By adjusting the level of detail of the analysis based on the importance of the request, more appropriate analysis becomes possible. The importance of a request includes, but is not limited to, the urgency for the user and the scope of impact. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the importance of requests into the generating AI and have the generating AI adjust the level of detail in the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the request during analysis. For example, the analysis unit can apply a price comparison algorithm to requests related to products. The analysis unit can also apply a geographic analysis algorithm to requests related to location information. For example, the analysis unit can apply a coupon or points utilization algorithm to requests related to shopping. By applying different analysis algorithms depending on the category of the request, more appropriate analysis becomes possible. The categories of requests include, for example, types of products and types of services, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the category of requests into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing urgent requests. The analysis unit can also prioritize analyzing detailed requests if the user is relaxed. The analysis unit can also prioritize analyzing requests that can be addressed quickly if the user is in a hurry. This allows for more appropriate analysis by determining the priority of analysis according to the user's emotions. The priority of analysis includes, but is not limited to, urgency and importance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0088] The analysis unit can adjust the order of analysis based on when the requests were submitted. For example, the analysis unit may prioritize the analysis of recently submitted requests. The analysis unit may also postpone the analysis of older requests. The analysis unit can also dynamically adjust the order of analysis based on the submission date. This allows for more appropriate analysis by adjusting the order of analysis based on the submission date of the requests. The submission date of a request includes, but is not limited to, timestamps and submission history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the analysis unit can input data on the submission date of requests into a generating AI and have the generating AI adjust the order of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the requests during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant requests. The analysis unit can also postpone the analysis of less relevant requests. For example, the analysis unit may postpone the analysis of less relevant requests. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the requests. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the requests. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the requests. The relevance of requests includes, but is not limited to, similarity of content and related topics. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input data on the relevance of requests into a generative AI and have the generative AI adjust the order of analysis.

[0090] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is feeling stressed, the recommendation system can provide simple and easy-to-understand recommendations. It can also provide recommendations with more detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the recommendation system can provide concise recommendations. By adjusting the way recommendations are presented according to the user's emotions, more appropriate recommendations become possible. Examples of recommendation presentation include, but are not limited to, text length and tone of expression. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to these examples. Some or all of the processing described above in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit may input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0091] The recommendation system can adjust the level of detail in its recommendations based on the importance of the products. For example, it can provide detailed recommendations for highly important products. It can also provide simplified recommendations for less important products. Furthermore, it can provide balanced recommendations for products of moderate importance. By adjusting the level of detail based on product importance, more appropriate recommendations become possible. Product importance includes, but is not limited to, user needs and product rarity. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recommendation unit can input product importance data into the generating AI and have the generating AI adjust the level of detail of the recommendations.

[0092] The recommendation unit can apply different recommendation algorithms depending on the product category when making recommendations. For example, for food recommendations, the recommendation unit can apply an algorithm that takes into account nutritional value and expiration date. For electronic device recommendations, the recommendation unit can also apply an algorithm that takes into account performance and price. For example, for clothing recommendations, the recommendation unit can apply an algorithm that takes into account size and design. By applying different recommendation algorithms depending on the product category, more appropriate recommendations become possible. Recommendation algorithms include, but are not limited to, collaborative filtering and content-based filtering. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input product category data into a generative AI and have the generative AI apply different recommendation algorithms.

[0093] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is in a hurry, the recommendation system will provide short, concise recommendations. It can also provide longer recommendations with more detailed explanations if the user is relaxed. Furthermore, if the user is excited, the recommendation system can provide recommendations with visually stimulating effects. This allows for more appropriate recommendations by adjusting the length of recommendations according to the user's emotions. The length of recommendations may include, but is not limited to, the user's level of interest and the importance of the information. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The recommendation unit can determine the priority of recommendations based on when the products were submitted. For example, the recommendation unit may prioritize recommendations for recently submitted products. The recommendation unit may also postpone recommendations for older products. For example, the recommendation unit may postpone recommendations for older products. The recommendation unit may also dynamically adjust the priority of recommendations based on the submission date. For example, the recommendation unit may dynamically adjust the priority of recommendations based on the submission date. This allows for more appropriate recommendations by determining the priority of recommendations based on when the products were submitted. The submission date of a product includes, but is not limited to, timestamps and submission history. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input data on the timing of product submissions into the generating AI, and have the generating AI determine the priority of recommendations.

[0095] The recommendation unit can adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit may prioritize recommendations for highly relevant products. The recommendation unit may also postpone recommendations for less relevant products. For example, the recommendation unit may postpone recommendations for less relevant products. The recommendation unit can also dynamically adjust the order of recommendations based on the relevance of the products. For example, the recommendation unit may dynamically adjust the order of recommendations based on the relevance of the products. This allows for more appropriate recommendations by adjusting the order of recommendations based on the relevance of the products. Product relevance includes, but is not limited to, similarity of content and related topics. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input data on the relevance of products into the generating AI, and have the generating AI adjust the order of recommendations.

[0096] The recommendation unit can perform price comparisons between online and offline stores. For example, the recommendation unit can collect price data from online stores and recommend the lowest price. The recommendation unit can also collect price data from offline stores and recommend the lowest price. Furthermore, the recommendation unit can compare prices between online and offline stores and recommend the cheapest store. This allows the recommendation unit to recommend the cheapest store by comparing prices between online and offline stores. Price comparisons include, but are not limited to, comparing prices for the same product, and considering shipping costs and taxes. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recommendation unit can input online and offline store price data into a generating AI and have the generating AI recommend the store with the lowest price.

[0097] The recommendation section can display prices that take into account the use of coupons and points. For example, the recommendation section can automatically apply any coupons the user has and display the final price. The recommendation section can also display the final price considering the points the user has available. For example, the recommendation section can display the final price considering the points the user has available. The recommendation section can also display the optimal price considering the expiration dates of coupons and points. For example, the recommendation section can display the optimal price considering the expiration dates of coupons and points. By displaying prices that take into account the use of coupons and points, users can shop even more economically. The use of coupons and points includes, but is not limited to, discount rates and point expiration dates. Some or all of the above processing in the recommendation section may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input data on coupons and points that the user possesses into a generating AI, and then have the generating AI display the final price.

[0098] The recommendation system can make recommendations that take into account the user's preferences and schedule. For example, the recommendation system can recommend products that match the user's preferences based on their past purchase history. The recommendation system can also refer to the user's calendar information and make recommendations that match their schedule. For example, the recommendation system can refer to the user's calendar information and make recommendations that match their schedule. The recommendation system can also recommend the optimal timing for purchase based on the user's preferences and schedule. For example, the recommendation system can recommend the optimal timing for purchase based on the user's preferences and schedule. In this way, by making recommendations that take into account the user's preferences and schedule, it is possible to propose the most suitable shopping method for the user. User preferences and schedules include, but are not limited to, past purchase history and calendar information. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recommendation unit can input the user's past purchase history and calendar information into the generative AI and have the generative AI perform the task of recommending the optimal shopping method.

[0099] The recommendation unit can make recommendations based on vague instructions. For example, if a user enters a vague request, the recommendation unit will provide a list of relevant products. The recommendation unit can also recommend the most suitable products based on the user's past purchase history and preferences when the user enters a vague request. For example, if a user enters a vague request, the recommendation unit will recommend the most suitable products based on the user's past purchase history and preferences. Furthermore, if a user enters a vague request, the recommendation unit can have its generating AI automatically analyze the request and recommend the most suitable products. For example, if a user enters a vague request, the generating AI will automatically analyze the request and recommend the most suitable products. In this way, by making recommendations based on vague instructions, the system can address the user's vague requests. Vague instructions include, for example, interpretation using natural language processing techniques, but are not limited to such examples. Some or all of the above-described processes in the recommendation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the recommendation unit can input the user's vague requests into a generative AI and have the generative AI recommend the most suitable products.

[0100] The recommendation system can recommend the cheapest store for bulk purchase requests. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest store. The recommendation system can also recommend the cheapest way to purchase items by combining multiple stores. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest way to purchase items by combining multiple stores. The recommendation system can also recommend the cheapest way to purchase items by utilizing coupons and points. For example, if a user enters a request for bulk purchases, the recommendation system will recommend the cheapest way to purchase items by utilizing coupons and points. In this way, by recommending the cheapest store for bulk purchase requests, users can shop more efficiently. Bulk purchases include, but are not limited to, purchasing a certain quantity or more, or specific product combinations. Some or all of the above-described processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the user's bulk purchase request into a generative AI and have the generative AI recommend the store where the items can be purchased at the lowest price.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The analytics unit can analyze a user's purchase history and estimate the frequency of use and consumption rate of products the user has purchased in the past. For example, if a user purchases shampoo every month, the analytics unit can predict when the next purchase will be and provide a reminder. The analytics unit can also notify users when certain products are on sale if they are frequently purchased. Furthermore, the analytics unit can collect reviews and ratings of products purchased by users to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0103] The recommendation system can provide recommendations that take into account the user's health condition. For example, if a user is on a diet, it can recommend low-calorie products. If a user has allergies, it can recommend products that do not contain those allergens. Furthermore, if a user needs to consume a specific nutrient, it can recommend products that are rich in that nutrient. This allows the system to provide the most suitable products for each user's health needs.

[0104] The reception system can analyze the user's voice input to more accurately understand their intent. For example, if a user says, "I'm looking for a cheap shampoo," the reception system analyzes the keyword "cheap" and recommends low-priced shampoos. Similarly, if a user says, "I want to buy shampoo at a nearby pharmacy," the reception system analyzes the keyword "nearby pharmacy" and recommends pharmacies close to the user's current location. Furthermore, if a user says, "I want Merit shampoo," the reception system analyzes the brand name "Merit" and recommends shampoos from that brand. This allows for a more intuitive interface using voice input.

[0105] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0106] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0107] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0108] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0109] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0110] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0111] The recommendation system can predict when users need to restock products they've previously purchased, based on their purchase history, and provide reminders. For example, if a user buys shampoo every month, the system can predict when their next purchase will be and provide a reminder. It can also notify users when certain products are on sale if they frequently purchase them. Furthermore, it can collect reviews and ratings of purchased products to estimate user satisfaction. This allows for a deeper understanding of user purchasing behavior and the provision of more personalized recommendations.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk receives requests from users. These requests include requests to purchase products or use services. The reception desk accepts requests entered by users using smartphones or personal computers. It can also accept requests using voice input. Furthermore, the reception desk can estimate the user's emotions and adjust the method of receiving requests based on those estimated emotions. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using methods such as data mining and statistical analysis. For example, it collects price data of products entered by the user and analyzes the optimal shopping method. It can also perform analysis considering the user's past purchase history and preferences. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. Step 3: The recommendation unit recommends the optimal shopping method based on the information analyzed by the analysis unit. The recommendation unit compares prices at online and offline stores and recommends the cheapest store. It can also display prices considering the use of coupons and points. Furthermore, it can provide recommendations that take into account the user's preferences and schedule.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, analysis unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user requests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal shopping method based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, and recommendation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's request. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The recommendation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and recommends the optimal shopping method based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user requests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the optimal shopping method based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, and recommendation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user requests. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends the optimal shopping method based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A reception desk that receives requests from users, An analysis unit that analyzes the information received by the reception unit, The system includes a recommendation unit that recommends the optimal shopping method based on the information analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The recommendation unit is, Compare prices between online and offline stores. The system described in Appendix 1, characterized by the features described herein. (Note 3) The recommendation unit is, Display prices that take into account the use of coupons and points. The system described in Appendix 1, characterized by the features described herein. (Note 4) The recommendation unit is, Provide recommendations that take into account the user's preferences and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recommendation unit is, Making recommendations based on vague instructions The system described in Appendix 1, characterized by the features described herein. (Note 6) The recommendation unit is, For customers requesting bulk purchases, we recommend the store where you can buy them at the lowest price. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving requests, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the level of detail of the analysis is adjusted based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the request. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the order of analysis will be adjusted based on when the requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the requests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on when the product was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recommendation unit is, When making recommendations, the order of recommendations is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recommendation unit is, Compare prices between online and offline stores. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recommendation unit is, Display prices that take into account the use of coupons and points. The system described in Appendix 1, characterized by the features described herein. (Note 27) The recommendation unit is, Provide recommendations that take into account the user's preferences and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 28) The recommendation unit is, Making recommendations based on vague instructions The system described in Appendix 1, characterized by the features described herein. (Note 29) The recommendation unit is, For customers requesting bulk purchases, we recommend the store where you can buy them at the lowest price. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives requests from users, An analysis unit that analyzes the information received by the reception unit, The system includes a recommendation unit that recommends the optimal shopping method based on the information analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The recommendation unit is, Compare prices between online and offline stores. The system according to feature 1.

3. The recommendation unit is, Display prices that take into account the use of coupons and points. The system according to feature 1.

4. The recommendation unit is, Provide recommendations that take into account the user's preferences and schedule. The system according to feature 1.

5. The recommendation unit is, Making recommendations based on vague instructions The system according to feature 1.

6. The recommendation unit is, For customers requesting bulk purchases, we recommend the store where you can buy them at the lowest price. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the way we receive requests based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system according to feature 1.

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