system

Abstract

The system according to this embodiment aims to allow users to search for products using natural language and to provide them with appropriate products. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a search unit, a provision unit, a storage unit, a utilization unit, and a proposal unit. The reception unit receives product searches from users using natural language. The analysis unit analyzes the information received by the reception unit. The search unit searches for relevant products based on the information analyzed by the analysis unit. The provision unit provides the products found by the search unit to the user. The storage unit stores the information provided by the provision unit. The utilization unit uses the information stored by the storage unit for the company's product development and promotion. The proposal unit provides products that match the user based on the information obtained by the utilization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 as a 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, when a user searches for a product in natural language, there is a risk that the search results may not be provided appropriately.

[0005] The system according to the embodiment aims to enable a user to search for a product in natural language and provide an appropriate product.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a provision unit, a storage unit, a utilization unit, and a proposal unit. The reception unit receives product searches from users using natural language. The analysis unit analyzes the information received by the reception unit. The search unit searches for relevant products based on the information analyzed by the analysis unit. The provision unit provides the products found by the search unit to the user. The storage unit stores the information provided by the provision unit. The utilization unit uses the information stored by the storage unit for the company's product development and promotion. The proposal unit provides products that match the user based on the information obtained by the utilization unit. [Effects of the Invention]

[0007] The system according to this embodiment can allow users to search for products using natural language and provide them with appropriate products. [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 I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The product search system according to an embodiment of the present invention is a system that enables the searching of various products using natural language. This product search system allows users to search for products using natural language through a messaging app. This system can search for and suggest relevant products simply by the user entering a vague question such as, "Do you sell something like this?" The searched information is also saved and used for the company's product development and promotion. For example, a user might enter a specific request such as, "I want some new clothes, but I'd prefer a hoodie, short-sleeved, with a tiger pattern, made of thick fabric, and not too flashy." This information is analyzed by AI, and relevant products are searched for. Next, the search results are presented to the user. The user can select and purchase from the presented products. The search results are also saved in a database and used for the company's product development and promotion. This allows companies to understand the genuine voices of users and provide products that match their needs. An excellent feature of this service is that it allows users to search for products whose names they don't know through natural language search. This allows companies to understand unmet user needs, and even if a product isn't available, users can feel satisfied by having their voice heard by the company. For example, if a user enters "I want some new clothes, but I'd prefer a hoodie, short-sleeved, with a tiger pattern, made of thick fabric, and not too flashy," the AI ​​analyzes the request and searches for suitable products. The search results are presented to the user, who can then select and purchase an item. The search data is also stored in a database and used for product development and promotion by companies. This allows companies to understand users' genuine needs and provide products that match their preferences. This system provides users with a smooth purchasing experience without information loss, and companies can hear genuine user feedback. This creates a win-win situation for both users and companies. As a result, the product search system can efficiently receive, analyze, search, provide, store, utilize, and suggest products based on users' natural language searches.

[0029] The product search system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a provision unit, a storage unit, a utilization unit, and a suggestion unit. The reception unit receives product searches from users using natural language. The reception unit can, for example, receive natural language text entered by the user through a messaging application. The reception unit can also convert voice input into text and accept it. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's intent using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can use AI to analyze the user's input and generate appropriate search queries. The search unit searches for relevant products based on the information analyzed by the analysis unit. The search unit searches for product information in the database using, for example, a search algorithm. The search unit can use AI to search for products based on the user's intent. The provision unit provides the products found by the search unit to the user. The provision unit displays the search results, for example, through a user interface. The provision unit can use AI to provide the search results to the user in the most optimal format. The storage unit stores information provided by the provision unit. The storage unit stores, for example, search history and user selection information in a database. The storage unit can efficiently store data using AI. The utilization unit uses the information stored by the storage unit for corporate product development and promotion. The utilization unit analyzes the stored information using, for example, data analysis technology and provides it to the company. The utilization unit can effectively utilize the stored information using AI. The suggestion unit provides products that match the user based on the information obtained by the utilization unit. The suggestion unit suggests related products based on, for example, the user's search history and selection information. The suggestion unit can suggest the most suitable products to the user using AI. As a result, the product search system according to this embodiment can efficiently receive, analyze, search, provide, store, utilize, and suggest products based on the user's natural language product search.

[0030] The reception desk accepts product searches from users using natural language. For example, it can receive natural language text entered by users through messaging apps. Specifically, users can use smartphones or computers to enter product questions and search keywords in a chat format. Furthermore, the reception desk can also accept voice input converted into text. In the case of voice input, when a user speaks into a microphone, the voice data is converted into text data using speech recognition technology. Speech recognition technology involves a process that analyzes the characteristics of the voice and converts it into the most appropriate text using a language model. This allows users to perform product searches by voice without using their hands. The reception desk centrally manages this input data and transmits it to the analysis department. In addition, the reception desk can monitor user input in real time and provide input assistance or correction suggestions as needed. For example, if a user enters an incorrect word, the reception desk automatically corrects the word and generates the correct search query. The reception desk can also provide a suggestion function based on the user's past search history and preferences. This allows users to perform product searches more efficiently and improves the overall user experience of the system.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis to analyze the user's intent. Morphological analysis divides the input text into words and identifies the part of speech and meaning of each word. Grammatical analysis analyzes the sentence structure and clarifies relationships such as subject, predicate, and object. Semantic analysis understands the meaning of the entire sentence and identifies what the user is seeking. The analysis unit can use AI to analyze user input and generate appropriate search queries. The AI ​​uses machine learning algorithms to learn patterns from large amounts of data and analyze user intent with high accuracy. For example, if a user inputs "I'm looking for a cheap smartphone," the analysis unit extracts the keywords "cheap" and "smartphone" and generates search queries based on price range and product category. Furthermore, the analysis unit can also generate more personalized search queries by considering the user's past search history and preferences. This allows the analysis unit to accurately understand user intent and build a foundation for providing optimal search results.

[0032] The search unit searches for relevant products based on information analyzed by the analysis unit. For example, the search unit uses search algorithms to search for product information within the database. These search algorithms include keyword matching, ranking algorithms, and filtering techniques. Keyword matching compares the search query generated by the analysis unit with product information in the database to identify matching products. Ranking algorithms rank search results based on user preferences and past behavior, displaying the most relevant products at the top. Filtering techniques narrow down search results based on criteria such as price range, brand, and rating. The search unit can also use AI to search for products based on user intent. The AI ​​uses machine learning models to learn user preferences and behavioral patterns, providing optimal search results. For example, if a user has frequently searched for products of a particular brand in the past, the search unit will prioritize displaying products of that brand. Furthermore, the search unit can provide the latest product information based on real-time updated data. This allows the search unit to quickly and accurately find and provide products that best suit the user's needs.

[0033] The service provider delivers products found by the search provider to the user. The service provider displays search results, for example, through a user interface. The user interface is provided through a web browser or mobile application and is designed to allow users to easily view search results. The service provider can use AI to deliver search results in the most optimal format for the user. The AI ​​analyzes the user's past behavior and preferences to select the optimal display format and layout. For example, if the user prefers visual information, the service provider will display product images larger and add detailed descriptions. Also, if the user prioritizes price, the service provider will highlight the price information. Furthermore, the service provider provides filtering and sorting functions for search results, making it easy for users to find products that meet their needs. For example, search results can be narrowed down by conditions such as price, rating, and popularity. This allows the service provider to provide users with the most optimal search results and improve the user experience.

[0034] The storage unit stores information provided by the provisioning unit. For example, the storage unit stores search history and user selection information in a database. The database is built using a high-speed, high-capacity storage system, enabling efficient storage of user search history and preference information. The storage unit can store data efficiently using AI. The AI ​​maximizes storage utilization efficiency by using data compression and optimization techniques. For example, it saves storage capacity by automatically detecting and deleting duplicate data. The storage unit also protects data using encryption technology to ensure data security. This allows for efficient data storage while protecting user privacy. Furthermore, the storage unit regularly backs up stored data and takes measures to prevent data loss. This enables the storage unit to achieve reliable data storage and improve the overall stability of the system.

[0035] The user unit utilizes the information stored by the storage unit for the company's product development and promotion. For example, the user unit analyzes the stored information using data analysis technologies and provides it to the company. These data analysis technologies include statistical analysis, machine learning, and data mining. Statistical analysis identifies user behavior patterns and preferences based on stored data, which is then used for product development and marketing strategies. Machine learning learns patterns from large amounts of data to predict future trends and demand. Data mining extracts useful information from data to support corporate decision-making. The user unit can effectively utilize stored information using AI. Based on the data analysis results, AI provides companies with specific suggestions and action plans. For example, if a particular product is found to be popular, AI can propose a strategy to focus its promotion on that product. It can also provide insights for developing new products or improving existing ones based on user preferences. This allows the user unit to help companies respond quickly to market needs and enhance their competitiveness.

[0036] The suggestion department provides products that match the user based on information obtained by the utilization department. For example, the suggestion department suggests relevant products based on the user's search history and selection information. The suggestion department can use AI to suggest the most suitable products to the user. The AI ​​uses collaborative filtering and content-based filtering technologies to analyze the user's preferences and behavior patterns and select the most suitable products. Collaborative filtering suggests products to users with similar preferences based on the preferences and behavior of other users. Content-based filtering suggests products that match the user's preferences based on the product's features and attributes. For example, if a user has frequently purchased products from a particular brand in the past, new products or related products from that brand will be suggested. Also, if a user prefers products in a particular category, products belonging to that category will be prioritized. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestion algorithm. This allows the suggestion department to always suggest the most suitable products to the user and improve the user experience.

[0037] The reception desk can analyze the user's past search history and select the most suitable reception method. For example, the reception desk can automatically display search keywords that the user has frequently used in the past as suggestions. For example, the reception desk can prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest search methods to be used during specific time periods based on the user's past search history. By analyzing the user's past search history, the reception desk can select the most suitable reception method and improve user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past search history data into a generating AI and have the generating AI select the most suitable reception method.

[0038] The reception unit can filter product searches based on the user's current purchasing intent and areas of interest. For example, if the user has a high purchasing intent, the reception unit can prioritize displaying highly relevant products. For example, if the user is interested in a particular area of ​​interest, the reception unit can prioritize displaying products in that area. For example, if the user has a low purchasing intent, the reception unit can suggest products that might interest them. In this way, by filtering based on the user's purchasing intent and areas of interest, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's purchasing intent data into a generating AI and have the generating AI perform the filtering.

[0039] The reception unit can prioritize requests that are highly relevant to the user's geographical location when receiving product searches. For example, if the user is in a specific region, the reception unit can prioritize displaying products related to that region. For example, if the user is traveling, the reception unit can prioritize displaying products related to the user's travel destination. For example, if the user is at home, the reception unit can prioritize displaying products that can be purchased at stores near the user's home. In this way, by considering the user's geographical location, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant requests.

[0040] The reception unit can analyze the user's social media activity and accept relevant requests when a product search is received. For example, the reception unit can prioritize displaying products mentioned by the user on social media. For example, the reception unit can prioritize displaying products from brands or stores that the user follows. For example, the reception unit can suggest relevant products based on the user's interests on social media. In this way, by analyzing the user's social media activity, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI execute the reception of relevant requests.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the search request during the analysis. For example, the analysis unit can perform a detailed analysis for high-importance search requests. For example, the analysis unit can perform a concise analysis for low-importance search requests. For example, the analysis unit can perform an analysis with an appropriate level of detail for medium-importance search requests. By adjusting the level of detail of the analysis based on the importance of the search request, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input search request importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the search request during analysis. For example, for a search request in the fashion category, the analysis unit can apply a fashion-specific analysis algorithm. For example, for a search request in the electronics category, the analysis unit can apply a electronics-specific analysis algorithm. For example, for a search request in the food category, the analysis unit can apply a food-specific analysis algorithm. By applying the appropriate analysis algorithm according to the category of the search request, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the search request into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on when the search requests were submitted. For example, the analysis unit may prioritize the analysis of the most recent search requests. For example, the analysis unit may postpone the analysis of older search requests. For example, the analysis unit may prioritize the analysis of search requests submitted within a specific time period. This enables efficient analysis by determining the priority of analysis based on when the search requests were submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input search request submission time data into a generating AI and have the generating AI determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the search requests during analysis. For example, the analysis unit may prioritize the analysis of highly relevant search requests. For example, the analysis unit may postpone the analysis of less relevant search requests. For example, the analysis unit may moderately analyze search requests with moderate relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the search requests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the search requests into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The search unit can improve search accuracy by considering the interrelationships between products during a search. For example, the search unit can group related products and display them in the search results. For example, the search unit can prioritize displaying highly relevant products by considering the interrelationships between products. For example, the search unit can analyze the interrelationships between products and suggest products that match the user's interests. This improves search accuracy by considering the interrelationships between products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input product interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0046] The search unit can perform searches while considering the attribute information of the product providers. For example, the search unit can prioritize displaying products from highly reliable providers by considering the reliability of the providers. For example, the search unit can prioritize displaying products from highly-rated providers by considering the provider's ratings. For example, the search unit can prioritize displaying products from nearby providers by considering the provider's geographical information. In this way, by considering the attribute information of the product providers, highly reliable products can be prioritized. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input provider attribute information into a generating AI and have the generating AI perform the search.

[0047] The search unit can perform searches while considering the geographical distribution of products. For example, the search unit can prioritize displaying products that are close to the user's current location. For example, the search unit can prioritize displaying products related to a specific region. For example, the search unit can display geographically dispersed products in a balanced manner. In this way, by considering the geographical distribution of products, it is possible to prioritize displaying products that are highly relevant to the user. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of products into a generating AI and have the generating AI perform the search.

[0048] The search unit can improve search accuracy by referring to related literature on products during the search process. For example, the search unit can provide detailed product information based on related literature. For example, the search unit can prioritize displaying highly reliable products by referring to related literature. For example, the search unit can analyze related literature on products and suggest products that match the user's interests. This improves search accuracy by referring to related literature on products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data on products into a generating AI and have the generating AI perform the search.

[0049] The delivery unit can select the optimal delivery method by referring to the user's past purchase history at the time of delivery. For example, the delivery unit can suggest related products based on products the user has purchased in the past. For example, the delivery unit can analyze the user's purchase history and suggest products that may be of interest. For example, the delivery unit can select the optimal delivery method by considering the user's purchase patterns. This allows the delivery unit to provide highly relevant products by referring to the user's past purchase history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal delivery method.

[0050] The delivery unit can customize the means of delivery based on the user's current purchase intent at the time of delivery. For example, if the user shows high purchase intent, the delivery unit can provide an immediate purchase link. For example, if the user shows low purchase intent, the delivery unit can provide detailed product information to pique their interest. For example, the delivery unit can provide special promotions or discount information according to the user's purchase intent. This makes it possible to deliver products more effectively by customizing the means of delivery according to the user's purchase intent. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input user purchase intent data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0051] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing products related to that region. For example, if the user is traveling, the service provider can provide products related to the travel destination. For example, if the user is at home, the service provider can provide products that can be purchased at stores near the user's home. In this way, by considering the user's geographical location information, highly relevant products can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0052] The service provider can analyze the user's social media activity and propose a means of provision at the time of provision. For example, the service provider can prioritize providing products that the user has mentioned on social media. For example, the service provider can provide products from brands or stores that the user follows. For example, the service provider can propose relevant products based on the user's interests on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant products. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute a proposal for a means of provision.

[0053] The storage unit can optimize the storage algorithm by referring to previously saved data during storage. For example, the storage unit can select the optimal storage method based on previously saved data. For example, the storage unit can analyze previously saved data and apply an efficient storage algorithm. For example, the storage unit can select a storage method that avoids duplication by referring to previously saved data. This allows for the application of an efficient storage algorithm by referring to previously saved data. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input previously saved data into a generating AI and have the generating AI perform the optimization of the storage algorithm.

[0054] The storage unit can weight the stored data based on when the search request was submitted. For example, the storage unit can prioritize storing data based on the most recent search request. For example, the storage unit can postpone storing data based on past search requests. For example, the storage unit can weight and store data based on search requests submitted within a specific time period. This enables efficient data storage by weighting the stored data based on when the search request was submitted. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input search request submission time data into a generating AI and have the generating AI perform the weighting of the stored data.

[0055] The user unit can optimize its usage algorithm by referring to past usage data at the time of use. For example, the user unit can select the optimal usage method based on data used in the past. For example, the user unit can analyze past usage data and apply an efficient usage algorithm. For example, the user unit can select a usage method that avoids duplication by referring to past usage data. In this way, an efficient usage algorithm can be applied by referring to past usage data. Some or all of the above processing in the user unit may be performed using AI, for example, or without using AI. For example, the user unit can input past usage data into a generating AI and have the generating AI perform the optimization of the usage algorithm.

[0056] The user unit can weight the data used based on the submission date of the stored data when it is used. For example, the user unit can prioritize the use of the most recent stored data. For example, the user unit can postpone the use of older stored data. For example, the user unit can weight the use of stored data submitted during a specific time period. This enables efficient data utilization by weighting the data used based on the submission date of the stored data. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can input the data on the submission date of the stored data into a generating AI and have the generating AI perform the weighting of the data used.

[0057] The suggestion unit can select the optimal suggestion method by referring to the user's past purchase history when making suggestions. For example, the suggestion unit can suggest related products based on products the user has purchased in the past. For example, the suggestion unit can analyze the user's purchase history and suggest products that might interest them. For example, the suggestion unit can select the optimal suggestion method by considering the user's purchase patterns. This allows the suggestion unit to suggest highly relevant products by referring to the user's past purchase history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal suggestion method.

[0058] The suggestion unit can customize the suggestion method based on the user's current purchase intent when making a suggestion. For example, if the user shows high purchase intent, the suggestion unit can provide an immediate purchase link. For example, if the user shows low purchase intent, the suggestion unit can provide detailed product information to pique their interest. For example, the suggestion unit can provide special promotions or discount information according to the user's purchase intent. This allows for more effective product suggestions by customizing the suggestion method according to the user's purchase intent. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user purchase intent data into a generating AI and have the generating AI perform the customization of the suggestion method.

[0059] The suggestion unit can select the optimal suggestion method when making suggestions, taking into account the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can prioritize suggesting products related to that region. For example, if the user is traveling, the suggestion unit can suggest products related to the travel destination. For example, if the user is at home, the suggestion unit can suggest products that can be purchased at stores near the user's home. In this way, by considering the user's geographical location information, it is possible to suggest highly relevant products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal suggestion method.

[0060] The suggestion unit can analyze the user's social media activity and propose methods for making suggestions. For example, the suggestion unit can prioritize suggesting products mentioned by the user on social media. For example, the suggestion unit can suggest products from brands or stores that the user follows. For example, the suggestion unit can suggest relevant products based on the user's interests on social media. In this way, by analyzing the user's social media activity, it is possible to suggest highly relevant products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI execute suggestions for methods of making suggestions.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The reception desk can analyze the user's past search history and select the most suitable reception method. For example, the reception desk can automatically display search keywords that the user has frequently used in the past as suggestions. For example, the reception desk can prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest search methods to be used during specific time periods based on the user's past search history. By analyzing the user's past search history, the reception desk can select the most suitable reception method and improve user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past search history data into a generating AI and have the generating AI select the most suitable reception method.

[0063] The reception unit can filter product searches based on the user's current purchasing intent and areas of interest. For example, if the user has a high purchasing intent, the reception unit can prioritize displaying highly relevant products. For example, if the user is interested in a particular area of ​​interest, the reception unit can prioritize displaying products in that area. For example, if the user has a low purchasing intent, the reception unit can suggest products that might interest them. In this way, by filtering based on the user's purchasing intent and areas of interest, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's purchasing intent data into a generating AI and have the generating AI perform the filtering.

[0064] The reception unit can prioritize requests that are highly relevant to the user's geographical location when receiving product searches. For example, if the user is in a specific region, the reception unit can prioritize displaying products related to that region. For example, if the user is traveling, the reception unit can prioritize displaying products related to the user's travel destination. For example, if the user is at home, the reception unit can prioritize displaying products that can be purchased at stores near the user's home. In this way, by considering the user's geographical location, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant requests.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the search request during the analysis. For example, the analysis unit can perform a detailed analysis for high-importance search requests. For example, the analysis unit can perform a concise analysis for low-importance search requests. For example, the analysis unit can perform an analysis with an appropriate level of detail for medium-importance search requests. By adjusting the level of detail of the analysis based on the importance of the search request, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input search request importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0066] The search unit can improve search accuracy by considering the interrelationships between products during a search. For example, the search unit can group related products and display them in the search results. For example, the search unit can prioritize displaying highly relevant products by considering the interrelationships between products. For example, the search unit can analyze the interrelationships between products and suggest products that match the user's interests. This improves search accuracy by considering the interrelationships between products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input product interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk accepts product searches from users using natural language. For example, it can receive natural language text entered by users through messaging apps. It can also accept voice input converted to text. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes the user's intent using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Using AI, it can analyze the user's input and generate appropriate search queries. Step 3: The search unit searches for relevant products based on the information analyzed by the analysis unit. For example, it searches for product information in the database using a search algorithm. AI can be used to search for products based on the user's intent. Step 4: The delivery unit provides the user with the products found by the search unit. For example, it displays the search results through the user interface. Using AI, the search results can be provided to the user in the most optimal format. Step 5: The storage unit stores the information provided by the provision unit. For example, it stores search history and user selection information in a database. AI can be used to store data efficiently. Step 6: The utilization unit uses the information stored by the storage unit for the company's product development and promotion. For example, it analyzes the stored information using data analysis technology and provides it to the company. AI can be used to effectively utilize the stored information. Step 7: The suggestion unit provides products that match the user based on the information obtained by the utilization unit. For example, it suggests related products based on the user's search history and selection information. AI can be used to suggest the most suitable products for the user.

[0069] (Example of form 2) The product search system according to an embodiment of the present invention is a system that enables the searching of various products using natural language. This product search system allows users to search for products using natural language through a messaging app. This system can search for and suggest relevant products simply by the user entering a vague question such as, "Do you sell something like this?" The searched information is also saved and used for the company's product development and promotion. For example, a user might enter a specific request such as, "I want some new clothes, but I'd prefer a hoodie, short-sleeved, with a tiger pattern, made of thick fabric, and not too flashy." This information is analyzed by AI, and relevant products are searched for. Next, the search results are presented to the user. The user can select and purchase from the presented products. The search results are also saved in a database and used for the company's product development and promotion. This allows companies to understand the genuine voices of users and provide products that match their needs. An excellent feature of this service is that it allows users to search for products whose names they don't know through natural language search. This allows companies to understand unmet user needs, and even if a product isn't available, users can feel satisfied by having their voice heard by the company. For example, if a user enters "I want some new clothes, but I'd prefer a hoodie, short-sleeved, with a tiger pattern, made of thick fabric, and not too flashy," the AI ​​analyzes the request and searches for suitable products. The search results are presented to the user, who can then select and purchase an item. The search data is also stored in a database and used for product development and promotion by companies. This allows companies to understand users' genuine needs and provide products that match their preferences. This system provides users with a smooth purchasing experience without information loss, and companies can hear genuine user feedback. This creates a win-win situation for both users and companies. As a result, the product search system can efficiently receive, analyze, search, provide, store, utilize, and suggest products based on users' natural language searches.

[0070] The product search system according to this embodiment comprises a reception unit, an analysis unit, a search unit, a provision unit, a storage unit, a utilization unit, and a suggestion unit. The reception unit receives product searches from users using natural language. The reception unit can, for example, receive natural language text entered by the user through a messaging application. The reception unit can also convert voice input into text and accept it. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's intent using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can use AI to analyze the user's input and generate appropriate search queries. The search unit searches for relevant products based on the information analyzed by the analysis unit. The search unit searches for product information in the database using, for example, a search algorithm. The search unit can use AI to search for products based on the user's intent. The provision unit provides the products found by the search unit to the user. The provision unit displays the search results, for example, through a user interface. The provision unit can use AI to provide the search results to the user in the most optimal format. The storage unit stores information provided by the provision unit. The storage unit stores, for example, search history and user selection information in a database. The storage unit can efficiently store data using AI. The utilization unit uses the information stored by the storage unit for corporate product development and promotion. The utilization unit analyzes the stored information using, for example, data analysis technology and provides it to the company. The utilization unit can effectively utilize the stored information using AI. The suggestion unit provides products that match the user based on the information obtained by the utilization unit. The suggestion unit suggests related products based on, for example, the user's search history and selection information. The suggestion unit can suggest the most suitable products to the user using AI. As a result, the product search system according to this embodiment can efficiently receive, analyze, search, provide, store, utilize, and suggest products based on the user's natural language product search.

[0071] The reception desk accepts product searches from users using natural language. For example, it can receive natural language text entered by users through messaging apps. Specifically, users can use smartphones or computers to enter product questions and search keywords in a chat format. Furthermore, the reception desk can also accept voice input converted into text. In the case of voice input, when a user speaks into a microphone, the voice data is converted into text data using speech recognition technology. Speech recognition technology involves a process that analyzes the characteristics of the voice and converts it into the most appropriate text using a language model. This allows users to perform product searches by voice without using their hands. The reception desk centrally manages this input data and transmits it to the analysis department. In addition, the reception desk can monitor user input in real time and provide input assistance or correction suggestions as needed. For example, if a user enters an incorrect word, the reception desk automatically corrects the word and generates the correct search query. The reception desk can also provide a suggestion function based on the user's past search history and preferences. This allows users to perform product searches more efficiently and improves the overall user experience of the system.

[0072] The analysis unit analyzes the information received by the reception unit. The analysis unit uses natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis to analyze the user's intent. Morphological analysis divides the input text into words and identifies the part of speech and meaning of each word. Grammatical analysis analyzes the sentence structure and clarifies relationships such as subject, predicate, and object. Semantic analysis understands the meaning of the entire sentence and identifies what the user is seeking. The analysis unit can use AI to analyze user input and generate appropriate search queries. The AI ​​uses machine learning algorithms to learn patterns from large amounts of data and analyze user intent with high accuracy. For example, if a user inputs "I'm looking for a cheap smartphone," the analysis unit extracts the keywords "cheap" and "smartphone" and generates search queries based on price range and product category. Furthermore, the analysis unit can also generate more personalized search queries by considering the user's past search history and preferences. This allows the analysis unit to accurately understand user intent and build a foundation for providing optimal search results.

[0073] The search unit searches for relevant products based on information analyzed by the analysis unit. For example, the search unit uses search algorithms to search for product information within the database. These search algorithms include keyword matching, ranking algorithms, and filtering techniques. Keyword matching compares the search query generated by the analysis unit with product information in the database to identify matching products. Ranking algorithms rank search results based on user preferences and past behavior, displaying the most relevant products at the top. Filtering techniques narrow down search results based on criteria such as price range, brand, and rating. The search unit can also use AI to search for products based on user intent. The AI ​​uses machine learning models to learn user preferences and behavioral patterns, providing optimal search results. For example, if a user has frequently searched for products of a particular brand in the past, the search unit will prioritize displaying products of that brand. Furthermore, the search unit can provide the latest product information based on real-time updated data. This allows the search unit to quickly and accurately find and provide products that best suit the user's needs.

[0074] The service provider delivers products found by the search provider to the user. The service provider displays search results, for example, through a user interface. The user interface is provided through a web browser or mobile application and is designed to allow users to easily view search results. The service provider can use AI to deliver search results in the most optimal format for the user. The AI ​​analyzes the user's past behavior and preferences to select the optimal display format and layout. For example, if the user prefers visual information, the service provider will display product images larger and add detailed descriptions. Also, if the user prioritizes price, the service provider will highlight the price information. Furthermore, the service provider provides filtering and sorting functions for search results, making it easy for users to find products that meet their needs. For example, search results can be narrowed down by conditions such as price, rating, and popularity. This allows the service provider to provide users with the most optimal search results and improve the user experience.

[0075] The storage unit stores information provided by the provisioning unit. For example, the storage unit stores search history and user selection information in a database. The database is built using a high-speed, high-capacity storage system, enabling efficient storage of user search history and preference information. The storage unit can store data efficiently using AI. The AI ​​maximizes storage utilization efficiency by using data compression and optimization techniques. For example, it saves storage capacity by automatically detecting and deleting duplicate data. The storage unit also protects data using encryption technology to ensure data security. This allows for efficient data storage while protecting user privacy. Furthermore, the storage unit regularly backs up stored data and takes measures to prevent data loss. This enables the storage unit to achieve reliable data storage and improve the overall stability of the system.

[0076] The user unit utilizes the information stored by the storage unit for the company's product development and promotion. For example, the user unit analyzes the stored information using data analysis technologies and provides it to the company. These data analysis technologies include statistical analysis, machine learning, and data mining. Statistical analysis identifies user behavior patterns and preferences based on stored data, which is then used for product development and marketing strategies. Machine learning learns patterns from large amounts of data to predict future trends and demand. Data mining extracts useful information from data to support corporate decision-making. The user unit can effectively utilize stored information using AI. Based on the data analysis results, AI provides companies with specific suggestions and action plans. For example, if a particular product is found to be popular, AI can propose a strategy to focus its promotion on that product. It can also provide insights for developing new products or improving existing ones based on user preferences. This allows the user unit to help companies respond quickly to market needs and enhance their competitiveness.

[0077] The suggestion department provides products that match the user based on information obtained by the utilization department. For example, the suggestion department suggests relevant products based on the user's search history and selection information. The suggestion department can use AI to suggest the most suitable products to the user. The AI ​​uses collaborative filtering and content-based filtering technologies to analyze the user's preferences and behavior patterns and select the most suitable products. Collaborative filtering suggests products to users with similar preferences based on the preferences and behavior of other users. Content-based filtering suggests products that match the user's preferences based on the product's features and attributes. For example, if a user has frequently purchased products from a particular brand in the past, new products or related products from that brand will be suggested. Also, if a user prefers products in a particular category, products belonging to that category will be prioritized. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy of its suggestion algorithm. This allows the suggestion department to always suggest the most suitable products to the user and improve the user experience.

[0078] The reception unit can estimate the user's emotions and adjust the timing of product search requests based on the estimated emotions. For example, if the user is stressed, the reception unit can request a product search during a time when the user can relax. If the user is excited, the reception unit can request a product search immediately, providing a quick response. If the user is tired, the reception unit can request a product search using a simple interface. By adjusting the timing of product search requests according to the user's emotions, product searches can be requested at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception desk can analyze the user's past search history and select the most suitable reception method. For example, the reception desk can automatically display search keywords that the user has frequently used in the past as suggestions. For example, the reception desk can prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest search methods to be used during specific time periods based on the user's past search history. By analyzing the user's past search history, the reception desk can select the most suitable reception method and improve user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past search history data into a generating AI and have the generating AI select the most suitable reception method.

[0080] The reception unit can filter product searches based on the user's current purchasing intent and areas of interest. For example, if the user has a high purchasing intent, the reception unit can prioritize displaying highly relevant products. For example, if the user is interested in a particular area of ​​interest, the reception unit can prioritize displaying products in that area. For example, if the user has a low purchasing intent, the reception unit can suggest products that might interest them. In this way, by filtering based on the user's purchasing intent and areas of interest, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's purchasing intent data into a generating AI and have the generating AI perform the filtering.

[0081] The reception unit can estimate the user's emotions and determine the priority of incoming search requests based on the estimated emotions. For example, if the user is in a hurry, the reception unit will process the search request with the highest priority. If the user is relaxed, the reception unit can process the search request with the normal priority. If the user is excited, the reception unit can process the search request quickly. This allows for a faster and more appropriate response by prioritizing search requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception unit can prioritize requests that are highly relevant to the user's geographical location when receiving product searches. For example, if the user is in a specific region, the reception unit can prioritize displaying products related to that region. For example, if the user is traveling, the reception unit can prioritize displaying products related to the user's travel destination. For example, if the user is at home, the reception unit can prioritize displaying products that can be purchased at stores near the user's home. In this way, by considering the user's geographical location, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant requests.

[0083] The reception unit can analyze the user's social media activity and accept relevant requests when a product search is received. For example, the reception unit can prioritize displaying products mentioned by the user on social media. For example, the reception unit can prioritize displaying products from brands or stores that the user follows. For example, the reception unit can suggest relevant products based on the user's interests on social media. In this way, by analyzing the user's social media activity, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI execute the reception of relevant requests.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the search request during the analysis. For example, the analysis unit can perform a detailed analysis for high-importance search requests. For example, the analysis unit can perform a concise analysis for low-importance search requests. For example, the analysis unit can perform an analysis with an appropriate level of detail for medium-importance search requests. By adjusting the level of detail of the analysis based on the importance of the search request, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input search request importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the search request during analysis. For example, for a search request in the fashion category, the analysis unit can apply a fashion-specific analysis algorithm. For example, for a search request in the electronics category, the analysis unit can apply a electronics-specific analysis algorithm. For example, for a search request in the food category, the analysis unit can apply a food-specific analysis algorithm. By applying the appropriate analysis algorithm according to the category of the search request, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the search request into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can determine the priority of analysis based on when the search requests were submitted. For example, the analysis unit may prioritize the analysis of the most recent search requests. For example, the analysis unit may postpone the analysis of older search requests. For example, the analysis unit may prioritize the analysis of search requests submitted within a specific time period. This enables efficient analysis by determining the priority of analysis based on when the search requests were submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input search request submission time data into a generating AI and have the generating AI determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the search requests during analysis. For example, the analysis unit may prioritize the analysis of highly relevant search requests. For example, the analysis unit may postpone the analysis of less relevant search requests. For example, the analysis unit may moderately analyze search requests with moderate relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the search requests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the search requests into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0090] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is relaxed, the search unit can apply broad search criteria. For example, if the user is in a hurry, the search unit can apply strict search criteria. For example, if the user is excited, the search unit can prioritize searching for visually appealing products. By adjusting the search criteria according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The search unit can improve search accuracy by considering the interrelationships between products during a search. For example, the search unit can group related products and display them in the search results. For example, the search unit can prioritize displaying highly relevant products by considering the interrelationships between products. For example, the search unit can analyze the interrelationships between products and suggest products that match the user's interests. This improves search accuracy by considering the interrelationships between products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input product interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0092] The search unit can perform searches while considering the attribute information of the product providers. For example, the search unit can prioritize displaying products from highly reliable providers by considering the reliability of the providers. For example, the search unit can prioritize displaying products from highly-rated providers by considering the provider's ratings. For example, the search unit can prioritize displaying products from nearby providers by considering the provider's geographical information. In this way, by considering the attribute information of the product providers, highly reliable products can be prioritized. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input provider attribute information into a generating AI and have the generating AI perform the search.

[0093] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated emotions. For example, if the user is relaxed, the search unit can display detailed search results. If the user is in a hurry, the search unit can display concise search results. If the user is excited, the search unit can display visually appealing search results. By adjusting the order in which search results are displayed according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The search unit can perform searches while considering the geographical distribution of products. For example, the search unit can prioritize displaying products that are close to the user's current location. For example, the search unit can prioritize displaying products related to a specific region. For example, the search unit can display geographically dispersed products in a balanced manner. In this way, by considering the geographical distribution of products, it is possible to prioritize displaying products that are highly relevant to the user. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input geographical distribution data of products into a generating AI and have the generating AI perform the search.

[0095] The search unit can improve search accuracy by referring to related literature on products during the search process. For example, the search unit can provide detailed product information based on related literature. For example, the search unit can prioritize displaying highly reliable products by referring to related literature. For example, the search unit can analyze related literature on products and suggest products that match the user's interests. This improves search accuracy by referring to related literature on products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input related literature data on products into a generating AI and have the generating AI perform the search.

[0096] The service provider can estimate the user's emotions and adjust how the offerings are displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display detailed product information. If the user is in a hurry, the service provider can display concise product information. If the user is excited, the service provider can display visually appealing product information. By adjusting how the offerings are displayed according to the user's emotions, more appropriate product information can be provided. 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0097] The delivery unit can select the optimal delivery method by referring to the user's past purchase history at the time of delivery. For example, the delivery unit can suggest related products based on products the user has purchased in the past. For example, the delivery unit can analyze the user's purchase history and suggest products that may be of interest. For example, the delivery unit can select the optimal delivery method by considering the user's purchase patterns. This allows the delivery unit to provide highly relevant products by referring to the user's past purchase history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal delivery method.

[0098] The delivery unit can customize the means of delivery based on the user's current purchase intent at the time of delivery. For example, if the user shows high purchase intent, the delivery unit can provide an immediate purchase link. For example, if the user shows low purchase intent, the delivery unit can provide detailed product information to pique their interest. For example, the delivery unit can provide special promotions or discount information according to the user's purchase intent. This makes it possible to deliver products more effectively by customizing the means of delivery according to the user's purchase intent. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input user purchase intent data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0099] The service provider can estimate the user's emotions and determine the priority of offerings based on the estimated emotions. For example, if the user is in a hurry, the service provider can prioritize offering the most relevant products. If the user is relaxed, the service provider can provide detailed product information. If the user is excited, the service provider can prioritize offering visually appealing products. This allows for the provision of more appropriate product information by prioritizing offerings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing products related to that region. For example, if the user is traveling, the service provider can provide products related to the travel destination. For example, if the user is at home, the service provider can provide products that can be purchased at stores near the user's home. In this way, by considering the user's geographical location information, highly relevant products can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0101] The service provider can analyze the user's social media activity and propose a means of provision at the time of provision. For example, the service provider can prioritize providing products that the user has mentioned on social media. For example, the service provider can provide products from brands or stores that the user follows. For example, the service provider can propose relevant products based on the user's interests on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant products. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute a proposal for a means of provision.

[0102] The storage unit can estimate the user's emotions and select data to store based on the estimated emotions. For example, the storage unit can prioritize storing product information that the user has shown high interest in. For example, the storage unit can postpone storing product information that the user has shown low interest in. For example, the storage unit can select and store highly relevant product information based on the user's emotions. This allows for the storage of more relevant data by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The storage unit can optimize the storage algorithm by referring to previously saved data during storage. For example, the storage unit can select the optimal storage method based on previously saved data. For example, the storage unit can analyze previously saved data and apply an efficient storage algorithm. For example, the storage unit can select a storage method that avoids duplication by referring to previously saved data. This allows for the application of an efficient storage algorithm by referring to previously saved data. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input previously saved data into a generating AI and have the generating AI perform the optimization of the storage algorithm.

[0104] The storage unit can estimate the user's emotions and adjust the storage frequency based on the estimated emotions. For example, if the user shows high interest, the storage unit will save data frequently. For example, if the user shows low interest, the storage unit can reduce the storage frequency. For example, the storage unit can set an appropriate storage frequency based on the user's emotions. This allows for more appropriate data storage by adjusting the storage frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0105] The storage unit can weight the stored data based on when the search request was submitted. For example, the storage unit can prioritize storing data based on the most recent search request. For example, the storage unit can postpone storing data based on past search requests. For example, the storage unit can weight and store data based on search requests submitted within a specific time period. This enables efficient data storage by weighting the stored data based on when the search request was submitted. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input search request submission time data into a generating AI and have the generating AI perform the weighting of the stored data.

[0106] The user unit can estimate the user's emotions and select data to use based on the estimated emotions. For example, the user unit can prioritize using data that the user has shown high interest in. For example, the user unit can postpone using data that the user has shown low interest in. For example, the user unit can select and use highly relevant data based on the user's emotions. This allows for the use of more relevant data by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the user unit may be performed using AI, or not using AI. For example, the user unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The user unit can optimize its usage algorithm by referring to past usage data at the time of use. For example, the user unit can select the optimal usage method based on data used in the past. For example, the user unit can analyze past usage data and apply an efficient usage algorithm. For example, the user unit can select a usage method that avoids duplication by referring to past usage data. In this way, an efficient usage algorithm can be applied by referring to past usage data. Some or all of the above processing in the user unit may be performed using AI, for example, or without using AI. For example, the user unit can input past usage data into a generating AI and have the generating AI perform the optimization of the usage algorithm.

[0108] The user unit can estimate the user's emotions and adjust the frequency of use based on the estimated emotions. For example, if the user shows high interest, the user unit will use the data frequently. For example, if the user shows low interest, the user unit can reduce the frequency of use. For example, the user unit can set an appropriate frequency of use based on the user's emotions. This allows for more appropriate data use by adjusting the frequency of use according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The user unit can weight the data used based on the submission date of the stored data when it is used. For example, the user unit can prioritize the use of the most recent stored data. For example, the user unit can postpone the use of older stored data. For example, the user unit can weight the use of stored data submitted during a specific time period. This enables efficient data utilization by weighting the data used based on the submission date of the stored data. Some or all of the above processing in the user unit may be performed using AI, for example, or without AI. For example, the user unit can input the data on the submission date of the stored data into a generating AI and have the generating AI perform the weighting of the data used.

[0110] The suggestion unit can estimate the user's emotions and adjust its suggestion method based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can make detailed suggestions. If the user is in a hurry, the suggestion unit can make concise suggestions. If the user is excited, the suggestion unit can make visually appealing suggestions. By adjusting the suggestion method according to the user's emotions, more appropriate product suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The suggestion unit can select the optimal suggestion method by referring to the user's past purchase history when making suggestions. For example, the suggestion unit can suggest related products based on products the user has purchased in the past. For example, the suggestion unit can analyze the user's purchase history and suggest products that might interest them. For example, the suggestion unit can select the optimal suggestion method by considering the user's purchase patterns. This allows the suggestion unit to suggest highly relevant products by referring to the user's past purchase history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal suggestion method.

[0112] The suggestion unit can customize the suggestion method based on the user's current purchase intent when making a suggestion. For example, if the user shows high purchase intent, the suggestion unit can provide an immediate purchase link. For example, if the user shows low purchase intent, the suggestion unit can provide detailed product information to pique their interest. For example, the suggestion unit can provide special promotions or discount information according to the user's purchase intent. This allows for more effective product suggestions by customizing the suggestion method according to the user's purchase intent. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user purchase intent data into a generating AI and have the generating AI perform the customization of the suggestion method.

[0113] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting the most relevant products. For example, if the user is relaxed, the suggestion unit can suggest detailed product information. For example, if the user is excited, the suggestion unit can prioritize suggesting visually appealing products. This allows for more appropriate product suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The suggestion unit can select the optimal suggestion method when making suggestions, taking into account the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can prioritize suggesting products related to that region. For example, if the user is traveling, the suggestion unit can suggest products related to the travel destination. For example, if the user is at home, the suggestion unit can suggest products that can be purchased at stores near the user's home. In this way, by considering the user's geographical location information, it is possible to suggest highly relevant products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal suggestion method.

[0115] The suggestion unit can analyze the user's social media activity and propose methods for making suggestions. For example, the suggestion unit can prioritize suggesting products mentioned by the user on social media. For example, the suggestion unit can suggest products from brands or stores that the user follows. For example, the suggestion unit can suggest relevant products based on the user's interests on social media. In this way, by analyzing the user's social media activity, it is possible to suggest highly relevant products. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media data into a generating AI and have the generating AI execute suggestions for methods of making suggestions.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The reception desk can analyze the user's past search history and select the most suitable reception method. For example, the reception desk can automatically display search keywords that the user has frequently used in the past as suggestions. For example, the reception desk can prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest search methods to be used during specific time periods based on the user's past search history. By analyzing the user's past search history, the reception desk can select the most suitable reception method and improve user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past search history data into a generating AI and have the generating AI select the most suitable reception method.

[0118] The reception unit can estimate the user's emotions and adjust the timing of product search requests based on the estimated emotions. For example, if the user is stressed, the reception unit can request a product search during a time when the user can relax. If the user is excited, the reception unit can request a product search immediately, providing a quick response. If the user is tired, the reception unit can request a product search using a simple interface. By adjusting the timing of product search requests according to the user's emotions, product searches can be requested at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The reception unit can filter product searches based on the user's current purchasing intent and areas of interest. For example, if the user has a high purchasing intent, the reception unit can prioritize displaying highly relevant products. For example, if the user is interested in a particular area of ​​interest, the reception unit can prioritize displaying products in that area. For example, if the user has a low purchasing intent, the reception unit can suggest products that might interest them. In this way, by filtering based on the user's purchasing intent and areas of interest, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's purchasing intent data into a generating AI and have the generating AI perform the filtering.

[0120] The reception unit can estimate the user's emotions and determine the priority of incoming search requests based on the estimated emotions. For example, if the user is in a hurry, the reception unit will process the search request with the highest priority. If the user is relaxed, the reception unit can process the search request with the normal priority. If the user is excited, the reception unit can process the search request quickly. This allows for a faster and more appropriate response by prioritizing search requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The reception unit can prioritize requests that are highly relevant to the user's geographical location when receiving product searches. For example, if the user is in a specific region, the reception unit can prioritize displaying products related to that region. For example, if the user is traveling, the reception unit can prioritize displaying products related to the user's travel destination. For example, if the user is at home, the reception unit can prioritize displaying products that can be purchased at stores near the user's home. In this way, by considering the user's geographical location, highly relevant products can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant requests.

[0122] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 emotion data into a generative AI and have the generative AI perform emotion estimation.

[0123] The analysis unit can adjust the level of detail of the analysis based on the importance of the search request during the analysis. For example, the analysis unit can perform a detailed analysis for high-importance search requests. For example, the analysis unit can perform a concise analysis for low-importance search requests. For example, the analysis unit can perform an analysis with an appropriate level of detail for medium-importance search requests. By adjusting the level of detail of the analysis based on the importance of the search request, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input search request importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0124] The search unit can estimate the user's emotions and adjust the search criteria based on the estimated emotions. For example, if the user is relaxed, the search unit can apply broad search criteria. For example, if the user is in a hurry, the search unit can apply strict search criteria. For example, if the user is excited, the search unit can prioritize searching for visually appealing products. By adjusting the search criteria according to the user's emotions, more appropriate search results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI, or not using AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0125] The search unit can improve search accuracy by considering the interrelationships between products during a search. For example, the search unit can group related products and display them in the search results. For example, the search unit can prioritize displaying highly relevant products by considering the interrelationships between products. For example, the search unit can analyze the interrelationships between products and suggest products that match the user's interests. This improves search accuracy by considering the interrelationships between products. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input product interrelationship data into a generating AI and have the generating AI perform the search accuracy improvement.

[0126] The service provider can estimate the user's emotions and adjust how the offerings are displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display detailed product information. If the user is in a hurry, the service provider can display concise product information. If the user is excited, the service provider can display visually appealing product information. By adjusting how the offerings are displayed according to the user's emotions, more appropriate product information can be provided. 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk accepts product searches from users using natural language. For example, it can receive natural language text entered by users through messaging apps. It can also accept voice input converted to text. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes the user's intent using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Using AI, it can analyze the user's input and generate appropriate search queries. Step 3: The search unit searches for relevant products based on the information analyzed by the analysis unit. For example, it searches for product information in the database using a search algorithm. AI can be used to search for products based on the user's intent. Step 4: The delivery unit provides the user with the products found by the search unit. For example, it displays the search results through the user interface. Using AI, the search results can be provided to the user in the most optimal format. Step 5: The storage unit stores the information provided by the provision unit. For example, it stores search history and user selection information in a database. AI can be used to store data efficiently. Step 6: The utilization unit uses the information stored by the storage unit for the company's product development and promotion. For example, it analyzes the stored information using data analysis technology and provides it to the company. AI can be used to effectively utilize the stored information. Step 7: The suggestion unit provides products that match the user based on the information obtained by the utilization unit. For example, it suggests related products based on the user's search history and selection information. AI can be used to suggest the most suitable products for the user.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, provision unit, storage unit, utilization unit, and proposal unit, is implemented, for example, by 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 can receive natural language text entered by the user through a messaging application. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's intent using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The search unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and searches for product information in the database 24 using a search algorithm. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and displays the search results through a user interface. The storage unit is implemented, for example, by the database 24 of the data processing unit 12 and stores search history and user selection information. The utilization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the stored information and provides it to companies. The suggestion function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests relevant products based on the user's search history and selection information. The correspondence between each function and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, provision unit, storage unit, utilization unit, and proposal unit, is implemented, for example, by 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, allowing the user to receive natural language text through voice input. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the user's intent using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The search unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and searches for product information in the database 24 using a search algorithm. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214, and provides search results by voice. The storage unit is implemented, for example, by the database 24 of the data processing unit 12, and stores search history and user selection information. The utilization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the stored information and provides it to companies. The suggestion function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests relevant products based on the user's search history and selection information. The correspondence between each function and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, provision unit, storage unit, utilization unit, and proposal unit, is implemented by, for example, 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, allowing the user to receive natural language text through voice input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the user's intent using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and searches for product information in the database 24 using a search algorithm. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, and displays the search results through the user interface. The storage unit is implemented by, for example, the database 24 of the data processing unit 12, and stores search history and user selection information. The utilization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the stored information and provides it to companies. The suggestion function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests relevant products based on the user's search history and selection information. The correspondence between each function and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the reception unit, analysis unit, search unit, provision unit, storage unit, utilization unit, and proposal 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, allowing the user to receive natural language text through voice input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the user's intent using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and searches for product information in the database 24 using a search algorithm. The provision unit is implemented by, for example, the speaker 240 of the robot 414, and provides search results by voice. The storage unit is implemented by, for example, the database 24 of the data processing unit 12, and stores search history and user selection information. The utilization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the stored information and provides it to companies. The suggestion function is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests relevant products based on the user's search history and selection information. The correspondence between each function and the device or control unit is not limited to the example described above and can be modified in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) A reception desk that accepts product searches from users using natural language, An analysis unit that analyzes the information received by the reception unit, A search unit searches for the corresponding product based on the information analyzed by the aforementioned analysis unit, A supply unit that provides the product found by the search unit to the user, A storage unit for storing the information provided by the aforementioned provisioning unit, The aforementioned storage unit has a utilization unit that uses the information stored by the storage unit for the company's product development and promotion, The system includes a suggestion unit that provides products that match the user based on the information obtained by the aforementioned utilization unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of product search requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past search history and select the most suitable method of acceptance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When a product search is submitted, filtering is performed based on the user's current purchasing intent and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of incoming search requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving product search requests, the system prioritizes 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 7) The aforementioned reception unit is When a product search is submitted, 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 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the search request. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the search request. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the search request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the search requests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, It estimates user sentiment and adjusts search criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, we improve search accuracy by considering the interrelationships between products. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the search is performed while taking into account the attribute information of the product provider. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, It estimates the user's sentiment and adjusts the order in which search results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, consider the geographical distribution of the products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned search unit, When searching, refer to related literature for products to improve search accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the product, the system will refer to the user's past purchase history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, At the time of delivery, the delivery method will be customized based on the user's current purchasing intent. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned storage unit is The system estimates the user's emotions and selects data to store based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned storage unit is When saving, the saving algorithm is optimized by referring to previously saved data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned storage unit is It estimates the user's emotions and adjusts the frequency of saving based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned storage unit is When saving, the saved data is weighted based on when the search request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned utilization unit is, The system estimates the user's emotions and selects usage data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned utilization unit is, When using the service, the usage algorithm is optimized by referring to past usage data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned utilization unit is, It estimates the user's emotions and adjusts the frequency of use based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned utilization unit is, When using the data, the data used will be weighted based on when the saved data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, the system selects the most suitable proposal method by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making a proposal, customize the proposal method based on the user's current purchasing intent. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, When making a proposal, the optimal proposal method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest methods for making the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 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 accepts product searches from users using natural language, An analysis unit that analyzes the information received by the reception unit, A search unit searches for the corresponding product based on the information analyzed by the aforementioned analysis unit, A supply unit that provides the product found by the search unit to the user, A storage unit for storing the information provided by the aforementioned provisioning unit, The aforementioned storage unit has a utilization unit that uses the information stored by the storage unit for the company's product development and promotion, The system includes a suggestion unit that provides products that match the user based on the information obtained by the aforementioned utilization unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of product search requests based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past search history and select the most suitable method of acceptance. The system according to feature 1.

4. The aforementioned reception unit is When a product search is submitted, filtering is performed based on the user's current purchasing intent and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of incoming search requests based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving product search requests, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When a product search is submitted, the system analyzes the user's social media activity and accepts relevant requests. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the search request. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the search request. The system according to feature 1.

Images