system

Abstract

The system according to this embodiment aims to provide explanations and usage recommendations tailored to the user's needs and circumstances. [Solution] The system according to the embodiment comprises an acquisition unit, a collection unit, an analysis unit, a recommendation unit, and a troubleshooting unit. The acquisition unit acquires the user's payment information. The collection unit collects information on home appliances and equipment purchased based on the information acquired by the acquisition unit. The analysis unit analyzes the information collected by the acquisition unit and understands the contents of the instruction manual. The recommendation unit provides recommendations for explanations and usage methods tailored to the user's needs and situation based on the analysis results obtained by the analysis unit. The troubleshooting unit provides troubleshooting information based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to understand the instruction manuals of purchased household appliances and equipment and obtain information for proper use.

[0005] The system according to the embodiment aims to provide explanations and usage recommendations tailored to the needs and situations of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a collection unit, an analysis unit, a recommendation unit, and a troubleshooting unit. The acquisition unit acquires the user's payment information. The collection unit collects information on purchased home appliances and equipment based on the information acquired by the acquisition unit. The analysis unit analyzes the information collected by the acquisition unit and understands the contents of the instruction manual. The recommendation unit provides recommendations for explanations and usage methods tailored to the user's needs and circumstances based on the analysis results obtained by the analysis unit. The troubleshooting unit provides troubleshooting information based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide explanations and usage recommendations tailored to the user's needs and circumstances. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 2, 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 system according to an embodiment of the present invention is a system that links the user's payment information and uses AI to analyze the instruction manuals of purchased home appliances and devices. This system acquires the user's payment information and collects information about the purchased home appliances and devices. Next, the AI ​​analyzes this information and understands the contents of the instruction manual. Based on the analysis results, it provides explanations and usage recommendations tailored to the user's needs and circumstances. It also provides information to help with troubleshooting. As a result, users can effectively use home appliances and devices without hassle. It makes technology-driven living simpler and easier to use. In this way, the system analyzes information about home appliances and devices based on the user's payment information and provides explanations and usage recommendations tailored to individual needs, allowing users to effectively use the products.

[0029] The system according to this embodiment comprises an acquisition unit, a data collection unit, an analysis unit, a recommendation unit, and a troubleshooting unit. The acquisition unit acquires the user's payment information. The acquisition unit can acquire payment information such as credit card information, purchase history, and payment method. The acquisition unit can also acquire the user's payment information in real time. Furthermore, the acquisition unit can also acquire payment information using API integration. For example, the acquisition unit acquires credit card information and collects information on home appliances and equipment based on the purchase history. The data collection unit collects information on home appliances and equipment purchased based on the information acquired by the acquisition unit. The data collection unit can collect information such as product name, model number, purchase date, and usage instructions. Furthermore, the data collection unit can also collect information on related home appliances and equipment based on the user's purchase history. Furthermore, the data collection unit can also collect information on home appliances and equipment by referring to databases on the internet. For example, the data collection unit searches databases on the internet based on the product name and model number and collects relevant information. The analysis unit analyzes the information collected by the acquisition unit and understands the contents of the instruction manual. The analysis unit can analyze the contents of the instruction manual using technologies such as text analysis, image analysis, and data mining. The analysis unit can also understand the contents of the instruction manual using AI. Furthermore, the analysis unit can provide more accurate information by comprehensively analyzing the text and images of the instruction manual. For example, the analysis unit can extract important information from the instruction manual using text analysis and extract visual information using image analysis. The recommendation unit provides recommendations for explanations and usage tailored to the user's needs and circumstances based on the analysis results obtained by the analysis unit. The recommendation unit can provide recommendations such as product usage, related product suggestions, and maintenance methods. It can also provide customized advice based on the user's usage and environment. Furthermore, the recommendation unit can provide optimal recommendations by referring to the user's past usage history. For example, the recommendation unit provides relevant recommendations based on the user's past usage history. The troubleshooting unit provides troubleshooting information based on the analysis results obtained by the analysis unit.The troubleshooting unit can provide information such as interpretation of error messages, repair methods, and support contact information. It can also refer to past troubleshooting data to provide the optimal solution. Furthermore, the troubleshooting unit can provide customized solutions based on the user's current usage and environment. For example, the troubleshooting unit provides the optimal solution based on the user's current usage. As a result, the system according to this embodiment analyzes information about home appliances and devices based on the user's payment information and provides explanations and usage recommendations tailored to individual needs, enabling the user to effectively utilize the product.

[0030] The acquisition unit retrieves user payment information. For example, it can retrieve payment information such as credit card information, purchase history, and payment method. Specifically, the acquisition unit securely retrieves credit card information used by users when shopping online or at physical stores and stores it in encrypted format. Regarding purchase history, it retrieves detailed information about products and services previously purchased by the user and stores this information in a database. Regarding payment methods, it records the payment method selected by the user, such as credit card, debit card, electronic money, and bank transfer. Furthermore, the acquisition unit can retrieve user payment information in real time. For example, when a user makes a new purchase, that information is immediately reflected in the system. This allows the system to always keep track of the user's latest purchase history and payment status. The acquisition unit can also retrieve payment information using API integration. For example, it can use APIs from credit card companies and payment service providers to automatically retrieve user payment information and import it into the system. This eliminates the need for manual data entry and allows for efficient information collection. The acquisition unit retrieves credit card information and collects information on home appliances and equipment based on purchase history. For example, when a user purchases a specific home appliance, the system retrieves detailed information about that product (product name, model number, purchase date, etc.) and registers it in the system. This allows the retrieval unit to efficiently collect information on related home appliances and equipment based on the user's payment information, enriching the system's overall database.

[0031] The data collection unit collects information on home appliances and equipment purchased based on the information acquired by the data acquisition unit. For example, the data collection unit can collect information such as product name, model number, purchase date, and usage instructions. Specifically, the data collection unit searches the database for the product name and model number of home appliances purchased by the user and retrieves detailed information. It also identifies and records the exact purchase date from the user's purchase history. Regarding usage instructions, it collects information from product manuals and the manufacturer's official website and provides it to the user. Furthermore, the data collection unit can also collect information on related home appliances and equipment based on the user's purchase history. For example, if a user has purchased multiple home appliances of a particular brand, the unit can collect information on other products of that brand and suggest them to the user. This improves compatibility and usability by allowing the user to use products from a consistent brand. The data collection unit can also collect information on home appliances and equipment by referring to databases on the internet. For example, the data collection unit searches internet databases based on the product name and model number and collects relevant information. This includes product specifications, user reviews, and maintenance information. This allows the data collection unit to provide comprehensive information about the home appliances and equipment purchased by the user, helping them to effectively utilize the products.

[0032] The analysis unit analyzes the information collected by the data collection unit to understand the contents of the instruction manual. The analysis unit can analyze the contents of the instruction manual using technologies such as text analysis, image analysis, and data mining. Specifically, it uses text analysis to extract important information from the instruction manual and organize the information necessary for the user. For example, it extracts product usage instructions, precautions, and maintenance procedures and presents them to the user in an easy-to-understand manner. It also uses image analysis to extract visual information from diagrams and photographs included in the instruction manual and provides it to the user. This makes it easier for the user to understand how to use the product based on visual information. Furthermore, the analysis unit can also understand the contents of the instruction manual using AI. For example, it uses natural language processing technology to analyze the text of the instruction manual and automatically understand product usage and troubleshooting procedures. This allows the analysis unit to provide users with more accurate information. Moreover, the analysis unit can provide even more accurate information by analyzing the text and images of the instruction manual from multiple angles. For example, it uses text analysis to extract important information from the instruction manual and image analysis to extract visual information. This allows the analysis unit to provide users with comprehensive information and effectively communicate product usage and maintenance methods.

[0033] The recommendation unit provides explanations and usage recommendations tailored to the user's needs and circumstances, based on the analysis results obtained by the analysis unit. For example, the recommendation unit can provide recommendations on product usage, related product suggestions, and maintenance methods. Specifically, it provides detailed procedures and precautions for using the product the user has purchased, supporting the user in effectively utilizing the product. Regarding related product suggestions, it proposes accessories and products with additional functions that are compatible with the product the user has purchased, allowing the user to maximize the product's functionality. Furthermore, it provides regular maintenance procedures and precautions, offering advice to extend the product's lifespan. The recommendation unit can also provide customized advice based on the user's usage and environment. For example, if a user is using the product in a specific environment, it suggests usage and maintenance methods suitable for that environment, enabling the user to use the product in optimal condition. Additionally, the recommendation unit can provide optimal recommendations by referring to the user's past usage history. For example, it provides relevant recommendations based on products the user has previously purchased and functions they have used, allowing the user to receive optimal advice based on their usage history.

[0034] The troubleshooting unit provides troubleshooting information based on the analysis results obtained by the analysis unit. For example, the troubleshooting unit can provide information such as the interpretation of error messages, repair methods, and support contact information. Specifically, it explains the meaning and cause of error messages encountered by the user while using the product and proposes appropriate solutions. It also provides detailed explanations of repair methods, including specific procedures, necessary tools, and parts, to support users in performing repairs themselves. Furthermore, it provides contact information for the product manufacturer and support center to ensure users receive prompt support. The troubleshooting unit can also provide optimal solutions by referring to past trouble data. For example, it proposes the most effective solution based on data from past troubles with the same product. This allows users to resolve problems quickly and reliably. Additionally, the troubleshooting unit can provide customized solutions based on the user's current usage and environment. For example, if a user is using the product in a specific environment, it proposes a troubleshooting method suitable for that environment. This allows users to obtain the best solution for their situation. Thus, the system according to this embodiment analyzes information about home appliances and devices based on the user's payment information and provides explanations and usage recommendations tailored to individual needs, enabling users to effectively utilize the product.

[0035] The data acquisition unit can analyze the user's past payment history and select the optimal acquisition method. For example, the acquisition unit may prioritize acquiring payment methods that the user frequently uses. The acquisition unit can also select an acquisition method for a specific time period based on the user's past payment history. Furthermore, the acquisition unit can analyze the user's past payment history and select the most efficient acquisition method. This allows for efficient information acquisition by selecting the optimal acquisition method through analysis of the user's past payment history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past payment history data into a generating AI and have the generating AI select the optimal acquisition method.

[0036] The acquisition unit can filter payment information based on the user's current purchasing trends and areas of interest when acquiring it. For example, the acquisition unit can prioritize acquiring information related to products recently purchased by the user. The acquisition unit can also analyze the user's current purchasing trends and filter relevant payment information. Furthermore, the acquisition unit can acquire highly relevant payment information based on the user's areas of interest. This allows for the acquisition of highly relevant information by filtering information based on the user's purchasing trends and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user purchasing trend data into a generating AI and have the generating AI perform the filtering.

[0037] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring payment information. For example, the acquisition unit can prioritize the acquisition of information about products purchased by the user in a specific region. The acquisition unit can also acquire highly relevant payment information based on the user's current location. Furthermore, the acquisition unit can acquire optimal payment information by considering the user's geographical location. This allows for the priority acquisition of highly relevant information by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant information.

[0038] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring payment information. For example, the acquisition unit prioritizes acquiring payment information for products mentioned by the user on social media. The acquisition unit can also analyze the user's social media activity and acquire relevant payment information. Furthermore, the acquisition unit can acquire highly relevant payment information based on the user's areas of interest on social media. This allows for the acquisition of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI acquire relevant information.

[0039] The data collection unit can select the optimal information collection method by referring to the user's past purchase history during collection. For example, the data collection unit may prioritize collecting information on products the user has previously purchased. The data collection unit can also refer to the user's past purchase history and collect relevant information. Furthermore, the data collection unit can analyze the user's past purchase history and select the optimal information collection method. This allows for efficient information collection by selecting the optimal method based on the user's past purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal information collection method.

[0040] The data collection unit can customize information based on the user's current living situation and usage environment during collection. For example, the data collection unit collects relevant information based on the user's current living situation. The data collection unit can also collect optimal information based on the user's usage environment. Furthermore, the data collection unit can customize information considering the user's living situation and usage environment. This allows for the collection of highly relevant information by customizing information based on the user's living situation and usage environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user living situation data into a generating AI and have the generating AI perform the information customization.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, the data collection unit can prioritize the collection of information about products purchased by the user in a specific region. The data collection unit can also collect highly relevant information based on the user's current location. Furthermore, the data collection unit can collect the most relevant information by considering the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0042] The analysis unit can analyze the contents of the instruction manual in detail during the analysis process and extract information tailored to specific usage scenarios. For example, the analysis unit can analyze the contents of the instruction manual in detail and extract basic setup methods. It can also analyze the contents of the instruction manual and extract optimal usage methods for specific situations. Furthermore, the analysis unit can analyze the contents of the instruction manual in detail and extract information tailored to user needs. This allows for the extraction of information tailored to specific usage scenarios by analyzing the contents of the instruction manual in detail. 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 the text data of the instruction manual into a generating AI and have the generating AI perform the extraction of information tailored to specific usage scenarios.

[0043] The analysis unit can provide more accurate information by comprehensively analyzing the text and images of the instruction manual during the analysis process. For example, the analysis unit can analyze the text of the instruction manual and extract important information. The analysis unit can also analyze the images of the instruction manual and extract visual information. Furthermore, the analysis unit can provide more accurate information by comprehensively analyzing the text and images of the instruction manual. In this way, more accurate information can be provided by comprehensively analyzing the text and images of the instruction manual. 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 text data and image data of the instruction manual into a generating AI and have the generating AI perform a comprehensive analysis.

[0044] The analysis unit can improve the accuracy of its analysis by referring to related literature and supplementary materials in the instruction manual during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to related literature in the instruction manual. The analysis unit can also improve the accuracy of its analysis by referring to supplementary materials in the instruction manual. Furthermore, the analysis unit can perform a more accurate analysis by referring to related literature and supplementary materials in the instruction manual. As a result, the accuracy of the analysis is improved by referring to related literature and supplementary materials in the instruction manual. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input related literature data from the instruction manual into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0045] The recommendation unit can provide optimal recommendations by referring to the user's past usage history when making recommendations. For example, the recommendation unit can refer to the user's past usage history and provide relevant recommendations. The recommendation unit can also analyze the user's past usage history and provide optimal recommendations. Furthermore, the recommendation unit can provide customized recommendations based on the user's past usage history. This allows for the provision of highly relevant recommendations by referring to the user's past usage history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past usage history data into a generating AI and have the generating AI perform the task of providing optimal recommendations.

[0046] The recommendation unit can provide customized advice based on the user's current usage and environment when making recommendations. For example, the recommendation unit can provide optimal advice based on the user's current usage. It can also provide customized advice based on the user's usage environment. Furthermore, the recommendation unit can provide optimal advice considering the user's current usage and environment. This allows for the provision of more appropriate advice by providing customized advice based on the user's usage and environment. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input user usage data into a generating AI and have the generating AI perform the task of providing customized advice.

[0047] The recommendation unit can provide optimal recommendations by considering the user's geographical location information. For example, the recommendation unit can provide recommendations related to products used by the user in a specific region. Furthermore, the recommendation unit can provide highly relevant recommendations based on the user's current location. In addition, the recommendation unit can provide optimal recommendations by considering the user's geographical location information. This allows for the provision of highly relevant recommendations by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal recommendations.

[0048] The troubleshooting unit can provide the optimal solution by referring to past trouble data during troubleshooting. For example, the troubleshooting unit can refer to past trouble data and provide solutions for similar problems. The troubleshooting unit can also analyze past trouble data and provide the optimal solution. Furthermore, the troubleshooting unit can provide customized solutions based on past trouble data. In this way, the optimal solution can be provided by referring to past trouble data. Some or all of the above processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input past trouble data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0049] The troubleshooting unit can provide customized solutions based on the user's current usage and environment during troubleshooting. For example, the troubleshooting unit can provide the optimal solution based on the user's current usage. It can also provide customized solutions based on the user's usage environment. Furthermore, the troubleshooting unit can provide the optimal solution considering the user's current usage and environment. This allows for the provision of more appropriate solutions by providing customized solutions based on the user's usage and environment. Some or all of the above-described processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input user usage data into a generating AI and have the generating AI perform the task of providing customized solutions.

[0050] The troubleshooting unit can provide the optimal solution during troubleshooting by taking into account the user's geographical location information. For example, the troubleshooting unit can provide troubleshooting methods related to products used by the user in a specific region. Furthermore, the troubleshooting unit can also provide highly relevant troubleshooting methods based on the user's current location. In addition, the troubleshooting unit can provide the optimal troubleshooting method by taking into account the user's geographical location information. This allows for the provision of highly relevant solutions by considering the user's geographical location information. Some or all of the above processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The acquisition unit can predict future purchases based on the user's purchase history and collect information related to those predicted purchases in advance. For example, the acquisition unit can analyze the usage period of products the user has purchased in the past and the replacement timing of consumables to predict the next products or parts that will be needed. The acquisition unit can also analyze the user's purchasing patterns and predict seasonal purchasing trends. Furthermore, the acquisition unit can predict the purchase of related products based on the user's life events (e.g., moving or marriage). This improves user convenience by collecting and providing the information the user needs in advance.

[0053] The data collection unit can collect reviews and ratings of relevant products based on the user's purchase history. For example, it can collect reviews from other users of products purchased by the user and prioritize displaying products with high ratings. It can also collect ratings for similar products from the user's purchase history. Furthermore, it can filter reviews of relevant products based on the user's purchase history to provide more reliable information. This allows users to make better purchasing decisions by referring to ratings from other users of products they have purchased.

[0054] The analysis unit can analyze product usage patterns based on the user's purchase history and propose an optimal maintenance schedule. For example, the analysis unit can analyze the frequency and environment of use of the product purchased by the user and propose appropriate maintenance timing. The analysis unit can also predict the product's lifespan and notify the user when replacement is needed. Furthermore, the analysis unit can customize maintenance methods based on the user's usage patterns. This allows users to effectively use the product over a long period of time.

[0055] The recommendation system can provide promotional information on relevant products based on the user's purchase history. For example, it can provide discount information related to products the user has previously purchased. It can also provide campaign information on relevant products based on the user's purchase history. Furthermore, the recommendation system can customize promotional information for specific products based on the user's purchase history. This allows users to make more advantageous purchases by receiving relevant product promotional information.

[0056] The troubleshooting department can analyze past trouble trends based on the user's purchase history and propose preventative measures. For example, the troubleshooting department can analyze data on troubles the user has experienced in the past and propose preventative measures to avoid similar problems occurring. Furthermore, the troubleshooting department can analyze trouble trends for specific products based on the user's purchase history and notify users of points to be aware of. In addition, the troubleshooting department can provide advice to prevent problems from occurring in the first place, based on the user's purchase history. This allows users to prevent problems before they occur.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The acquisition unit retrieves the user's payment information. The acquisition unit can retrieve payment information such as credit card information, purchase history, and payment method. The acquisition unit can also retrieve the user's payment information in real time. Furthermore, the acquisition unit can also retrieve payment information using API integration. Step 2: The collection unit collects information about purchased home appliances and equipment based on the information acquired by the acquisition unit. The collection unit can collect information such as product name, model number, purchase date, and usage instructions. The collection unit can also collect information about related home appliances and equipment based on the user's purchase history. Furthermore, the collection unit can also collect information about home appliances and equipment by referring to databases on the internet. Step 3: The analysis unit analyzes the information collected by the collection unit to understand the content of the instruction manual. The analysis unit can analyze the content of the instruction manual using techniques such as text analysis, image analysis, and data mining. The analysis unit can also understand the content of the instruction manual using AI. Furthermore, the analysis unit can provide more accurate information by analyzing the text and images of the instruction manual from multiple angles. Step 4: The recommendation unit provides explanations and usage recommendations tailored to the user's needs and circumstances based on the analysis results obtained by the analysis unit. The recommendation unit can provide recommendations such as how to use the product, suggestions for related products, and maintenance methods. It can also provide customized advice based on the user's usage situation and environment. Furthermore, the recommendation unit can provide optimal recommendations by referring to the user's past usage history. Step 5: The troubleshooting unit provides troubleshooting information based on the analysis results obtained by the analysis unit. The troubleshooting unit can provide information such as interpretation of error messages, repair methods, and support contact information. The troubleshooting unit can also provide the best solution by referring to past trouble data. Furthermore, the troubleshooting unit can provide a customized solution based on the user's current usage and environment.

[0059] (Example of form 2) The system according to an embodiment of the present invention is a system that links the user's payment information and uses AI to analyze the instruction manuals of purchased home appliances and devices. This system acquires the user's payment information and collects information about the purchased home appliances and devices. Next, the AI ​​analyzes this information and understands the contents of the instruction manual. Based on the analysis results, it provides explanations and usage recommendations tailored to the user's needs and circumstances. It also provides information to help with troubleshooting. As a result, users can effectively use home appliances and devices without hassle. It makes technology-driven living simpler and easier to use. In this way, the system analyzes information about home appliances and devices based on the user's payment information and provides explanations and usage recommendations tailored to individual needs, allowing users to effectively use the products.

[0060] The system according to this embodiment comprises an acquisition unit, a data collection unit, an analysis unit, a recommendation unit, and a troubleshooting unit. The acquisition unit acquires the user's payment information. The acquisition unit can acquire payment information such as credit card information, purchase history, and payment method. The acquisition unit can also acquire the user's payment information in real time. Furthermore, the acquisition unit can also acquire payment information using API integration. For example, the acquisition unit acquires credit card information and collects information on home appliances and equipment based on the purchase history. The data collection unit collects information on home appliances and equipment purchased based on the information acquired by the acquisition unit. The data collection unit can collect information such as product name, model number, purchase date, and usage instructions. Furthermore, the data collection unit can also collect information on related home appliances and equipment based on the user's purchase history. Furthermore, the data collection unit can also collect information on home appliances and equipment by referring to databases on the internet. For example, the data collection unit searches databases on the internet based on the product name and model number and collects relevant information. The analysis unit analyzes the information collected by the acquisition unit and understands the contents of the instruction manual. The analysis unit can analyze the contents of the instruction manual using technologies such as text analysis, image analysis, and data mining. The analysis unit can also understand the contents of the instruction manual using AI. Furthermore, the analysis unit can provide more accurate information by comprehensively analyzing the text and images of the instruction manual. For example, the analysis unit can extract important information from the instruction manual using text analysis and extract visual information using image analysis. The recommendation unit provides recommendations for explanations and usage tailored to the user's needs and circumstances based on the analysis results obtained by the analysis unit. The recommendation unit can provide recommendations such as product usage, related product suggestions, and maintenance methods. It can also provide customized advice based on the user's usage and environment. Furthermore, the recommendation unit can provide optimal recommendations by referring to the user's past usage history. For example, the recommendation unit provides relevant recommendations based on the user's past usage history. The troubleshooting unit provides troubleshooting information based on the analysis results obtained by the analysis unit.The troubleshooting unit can provide information such as interpretation of error messages, repair methods, and support contact information. It can also refer to past troubleshooting data to provide the optimal solution. Furthermore, the troubleshooting unit can provide customized solutions based on the user's current usage and environment. For example, the troubleshooting unit provides the optimal solution based on the user's current usage. As a result, the system according to this embodiment analyzes information about home appliances and devices based on the user's payment information and provides explanations and usage recommendations tailored to individual needs, enabling the user to effectively utilize the product.

[0061] The acquisition unit retrieves user payment information. For example, it can retrieve payment information such as credit card information, purchase history, and payment method. Specifically, the acquisition unit securely retrieves credit card information used by users when shopping online or at physical stores and stores it in encrypted format. Regarding purchase history, it retrieves detailed information about products and services previously purchased by the user and stores this information in a database. Regarding payment methods, it records the payment method selected by the user, such as credit card, debit card, electronic money, and bank transfer. Furthermore, the acquisition unit can retrieve user payment information in real time. For example, when a user makes a new purchase, that information is immediately reflected in the system. This allows the system to always keep track of the user's latest purchase history and payment status. The acquisition unit can also retrieve payment information using API integration. For example, it can use APIs from credit card companies and payment service providers to automatically retrieve user payment information and import it into the system. This eliminates the need for manual data entry and allows for efficient information collection. The acquisition unit retrieves credit card information and collects information on home appliances and equipment based on purchase history. For example, when a user purchases a specific home appliance, the system retrieves detailed information about that product (product name, model number, purchase date, etc.) and registers it in the system. This allows the retrieval unit to efficiently collect information on related home appliances and equipment based on the user's payment information, enriching the system's overall database.

[0062] The data collection unit collects information on home appliances and equipment purchased based on the information acquired by the data acquisition unit. For example, the data collection unit can collect information such as product name, model number, purchase date, and usage instructions. Specifically, the data collection unit searches the database for the product name and model number of home appliances purchased by the user and retrieves detailed information. It also identifies and records the exact purchase date from the user's purchase history. Regarding usage instructions, it collects information from product manuals and the manufacturer's official website and provides it to the user. Furthermore, the data collection unit can also collect information on related home appliances and equipment based on the user's purchase history. For example, if a user has purchased multiple home appliances of a particular brand, the unit can collect information on other products of that brand and suggest them to the user. This improves compatibility and usability by allowing the user to use products from a consistent brand. The data collection unit can also collect information on home appliances and equipment by referring to databases on the internet. For example, the data collection unit searches internet databases based on the product name and model number and collects relevant information. This includes product specifications, user reviews, and maintenance information. This allows the data collection unit to provide comprehensive information about the home appliances and equipment purchased by the user, helping them to effectively utilize the products.

[0063] The analysis unit analyzes the information collected by the data collection unit to understand the contents of the instruction manual. The analysis unit can analyze the contents of the instruction manual using technologies such as text analysis, image analysis, and data mining. Specifically, it uses text analysis to extract important information from the instruction manual and organize the information necessary for the user. For example, it extracts product usage instructions, precautions, and maintenance procedures and presents them to the user in an easy-to-understand manner. It also uses image analysis to extract visual information from diagrams and photographs included in the instruction manual and provides it to the user. This makes it easier for the user to understand how to use the product based on visual information. Furthermore, the analysis unit can also understand the contents of the instruction manual using AI. For example, it uses natural language processing technology to analyze the text of the instruction manual and automatically understand product usage and troubleshooting procedures. This allows the analysis unit to provide users with more accurate information. Moreover, the analysis unit can provide even more accurate information by analyzing the text and images of the instruction manual from multiple angles. For example, it uses text analysis to extract important information from the instruction manual and image analysis to extract visual information. This allows the analysis unit to provide users with comprehensive information and effectively communicate product usage and maintenance methods.

[0064] The recommendation unit provides explanations and usage recommendations tailored to the user's needs and circumstances, based on the analysis results obtained by the analysis unit. For example, the recommendation unit can provide recommendations on product usage, related product suggestions, and maintenance methods. Specifically, it provides detailed procedures and precautions for using the product the user has purchased, supporting the user in effectively utilizing the product. Regarding related product suggestions, it proposes accessories and products with additional functions that are compatible with the product the user has purchased, allowing the user to maximize the product's functionality. Furthermore, it provides regular maintenance procedures and precautions, offering advice to extend the product's lifespan. The recommendation unit can also provide customized advice based on the user's usage and environment. For example, if a user is using the product in a specific environment, it suggests usage and maintenance methods suitable for that environment, enabling the user to use the product in optimal condition. Additionally, the recommendation unit can provide optimal recommendations by referring to the user's past usage history. For example, it provides relevant recommendations based on products the user has previously purchased and functions they have used, allowing the user to receive optimal advice based on their usage history.

[0065] The troubleshooting unit provides troubleshooting information based on the analysis results obtained by the analysis unit. For example, the troubleshooting unit can provide information such as the interpretation of error messages, repair methods, and support contact information. Specifically, it explains the meaning and cause of error messages encountered by the user while using the product and proposes appropriate solutions. It also provides detailed explanations of repair methods, including specific procedures, necessary tools, and parts, to support users in performing repairs themselves. Furthermore, it provides contact information for the product manufacturer and support center to ensure users receive prompt support. The troubleshooting unit can also provide optimal solutions by referring to past trouble data. For example, it proposes the most effective solution based on data from past troubles with the same product. This allows users to resolve problems quickly and reliably. Additionally, the troubleshooting unit can provide customized solutions based on the user's current usage and environment. For example, if a user is using the product in a specific environment, it proposes a troubleshooting method suitable for that environment. This allows users to obtain the best solution for their situation. Thus, the system according to this embodiment analyzes information about home appliances and devices based on the user's payment information and provides explanations and usage recommendations tailored to individual needs, enabling users to effectively utilize the product.

[0066] The data acquisition unit can estimate the user's emotions and adjust the timing of acquiring payment information based on the estimated emotions. For example, if the user is stressed, the data acquisition unit can delay acquiring payment information and acquire it when the user is relaxed. Furthermore, if the user is in a hurry, the data acquisition unit can quickly acquire payment information and begin analysis immediately. Additionally, if the user is relaxed, the data acquisition unit can acquire payment information at the normal time and proceed with processing smoothly. By adjusting the timing of payment information acquisition according to the user's emotions, user stress can be reduced and information can be acquired at the appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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-described processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0067] The data acquisition unit can analyze the user's past payment history and select the optimal acquisition method. For example, the acquisition unit may prioritize acquiring payment methods that the user frequently uses. The acquisition unit can also select an acquisition method for a specific time period based on the user's past payment history. Furthermore, the acquisition unit can analyze the user's past payment history and select the most efficient acquisition method. This allows for efficient information acquisition by selecting the optimal acquisition method through analysis of the user's past payment history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past payment history data into a generating AI and have the generating AI select the optimal acquisition method.

[0068] The acquisition unit can filter payment information based on the user's current purchasing trends and areas of interest when acquiring it. For example, the acquisition unit can prioritize acquiring information related to products recently purchased by the user. The acquisition unit can also analyze the user's current purchasing trends and filter relevant payment information. Furthermore, the acquisition unit can acquire highly relevant payment information based on the user's areas of interest. This allows for the acquisition of highly relevant information by filtering information based on the user's purchasing trends and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user purchasing trend data into a generating AI and have the generating AI perform the filtering.

[0069] The data acquisition unit can estimate the user's emotions and determine the priority of payment information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will postpone acquiring less important payment information. If the user is relaxed, the data acquisition unit can acquire all payment information equally. Furthermore, if the user is in a hurry, the data acquisition unit can prioritize acquiring more important payment information. In this way, by prioritizing payment information according to the user's emotions, important information can be acquired preferentially. 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 data acquisition unit may be performed using AI, or not using AI. For example, the data acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0070] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring payment information. For example, the acquisition unit can prioritize the acquisition of information about products purchased by the user in a specific region. The acquisition unit can also acquire highly relevant payment information based on the user's current location. Furthermore, the acquisition unit can acquire optimal payment information by considering the user's geographical location. This allows for the priority acquisition of highly relevant information by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant information.

[0071] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring payment information. For example, the acquisition unit prioritizes acquiring payment information for products mentioned by the user on social media. The acquisition unit can also analyze the user's social media activity and acquire relevant payment information. Furthermore, the acquisition unit can acquire highly relevant payment information based on the user's areas of interest on social media. This allows for the acquisition of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media data into a generating AI and have the generating AI acquire relevant information.

[0072] The data collection unit can estimate the user's emotions and adjust the method of collecting information about home appliances and devices based on the estimated emotions. For example, if the user is stressed, the data collection unit can select a simpler method of information collection. If the user is relaxed, the data collection unit can also select a more detailed method of information collection. Furthermore, if the user is in a hurry, the data collection unit can select a method to quickly collect information. By adjusting the information collection method according to the user's emotions, appropriate information collection becomes possible. 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The data collection unit can select the optimal information collection method by referring to the user's past purchase history during collection. For example, the data collection unit may prioritize collecting information on products the user has previously purchased. The data collection unit can also refer to the user's past purchase history and collect relevant information. Furthermore, the data collection unit can analyze the user's past purchase history and select the optimal information collection method. This allows for efficient information collection by selecting the optimal method based on the user's past purchase history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past purchase history data into a generating AI and have the generating AI select the optimal information collection method.

[0074] The data collection unit can customize information based on the user's current living situation and usage environment during collection. For example, the data collection unit collects relevant information based on the user's current living situation. The data collection unit can also collect optimal information based on the user's usage environment. Furthermore, the data collection unit can customize information considering the user's living situation and usage environment. This allows for the collection of highly relevant information by customizing information based on the user's living situation and usage environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user living situation data into a generating AI and have the generating AI perform the information customization.

[0075] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important information. If the user is relaxed, the data collection unit can collect all information equally. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting more important information. In this way, by prioritizing information according to the user's emotions, important information can be collected preferentially. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, the data collection unit can prioritize the collection of information about products purchased by the user in a specific region. The data collection unit can also collect highly relevant information based on the user's current location. Furthermore, the data collection unit can collect the most relevant information by considering the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can select a simple analysis method. If the user is relaxed, the analysis unit can also select a more detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can select a method for rapid analysis. By adjusting the analysis method according to the user's emotions, appropriate analysis becomes possible. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The analysis unit can analyze the contents of the instruction manual in detail during the analysis process and extract information tailored to specific usage scenarios. For example, the analysis unit can analyze the contents of the instruction manual in detail and extract basic setup methods. It can also analyze the contents of the instruction manual and extract optimal usage methods for specific situations. Furthermore, the analysis unit can analyze the contents of the instruction manual in detail and extract information tailored to user needs. This allows for the extraction of information tailored to specific usage scenarios by analyzing the contents of the instruction manual in detail. 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 the text data of the instruction manual into a generating AI and have the generating AI perform the extraction of information tailored to specific usage scenarios.

[0079] The analysis unit can provide more accurate information by comprehensively analyzing the text and images of the instruction manual during the analysis process. For example, the analysis unit can analyze the text of the instruction manual and extract important information. The analysis unit can also analyze the images of the instruction manual and extract visual information. Furthermore, the analysis unit can provide more accurate information by comprehensively analyzing the text and images of the instruction manual. In this way, more accurate information can be provided by comprehensively analyzing the text and images of the instruction manual. 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 text data and image data of the instruction manual into a generating AI and have the generating AI perform a comprehensive analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, a highly visible display is possible. 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] The analysis unit can improve the accuracy of its analysis by referring to related literature and supplementary materials in the instruction manual during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to related literature in the instruction manual. The analysis unit can also improve the accuracy of its analysis by referring to supplementary materials in the instruction manual. Furthermore, the analysis unit can perform a more accurate analysis by referring to related literature and supplementary materials in the instruction manual. As a result, the accuracy of the analysis is improved by referring to related literature and supplementary materials in the instruction manual. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input related literature data from the instruction manual into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0082] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated emotions. For example, if the user is stressed, the recommendation unit can provide a simple and easy-to-understand presentation. If the user is relaxed, the recommendation unit can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the recommendation unit can provide a concise presentation. In this way, by adjusting the presentation of recommendations according to the user's emotions, easy-to-understand recommendations 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 recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0083] The recommendation unit can provide optimal recommendations by referring to the user's past usage history when making recommendations. For example, the recommendation unit can refer to the user's past usage history and provide relevant recommendations. The recommendation unit can also analyze the user's past usage history and provide optimal recommendations. Furthermore, the recommendation unit can provide customized recommendations based on the user's past usage history. This allows for the provision of highly relevant recommendations by referring to the user's past usage history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past usage history data into a generating AI and have the generating AI perform the task of providing optimal recommendations.

[0084] The recommendation unit can provide customized advice based on the user's current usage and environment when making recommendations. For example, the recommendation unit can provide optimal advice based on the user's current usage. It can also provide customized advice based on the user's usage environment. Furthermore, the recommendation unit can provide optimal advice considering the user's current usage and environment. This allows for the provision of more appropriate advice by providing customized advice based on the user's usage and environment. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input user usage data into a generating AI and have the generating AI perform the task of providing customized advice.

[0085] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated emotions. For example, if the user is stressed, the recommendation unit will postpone less important recommendations. Conversely, if the user is relaxed, the recommendation unit can provide all recommendations equally. Furthermore, if the user is in a hurry, the recommendation unit can prioritize providing high-priority recommendations. In this way, by determining the priority of recommendations according to the user's emotions, important recommendations can be provided preferentially. 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 recommendation unit may be performed using AI or not using AI. For example, the recommendation unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0086] The recommendation unit can provide optimal recommendations by considering the user's geographical location information. For example, the recommendation unit can provide recommendations related to products used by the user in a specific region. Furthermore, the recommendation unit can provide highly relevant recommendations based on the user's current location. In addition, the recommendation unit can provide optimal recommendations by considering the user's geographical location information. This allows for the provision of highly relevant recommendations by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal recommendations.

[0087] The troubleshooting unit can estimate the user's emotions and adjust the troubleshooting method based on the estimated emotions. For example, if the user is stressed, the troubleshooting unit can provide a simple and quick troubleshooting method. It can also provide a more detailed troubleshooting method if the user is relaxed. Furthermore, if the user is in a hurry, the troubleshooting unit can provide a quick and effective troubleshooting method. This allows for quick and appropriate troubleshooting by adjusting the troubleshooting method 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-described processes in the troubleshooting unit may be performed using AI or not. For example, the troubleshooting unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0088] The troubleshooting unit can provide the optimal solution by referring to past trouble data during troubleshooting. For example, the troubleshooting unit can refer to past trouble data and provide solutions for similar problems. The troubleshooting unit can also analyze past trouble data and provide the optimal solution. Furthermore, the troubleshooting unit can provide customized solutions based on past trouble data. In this way, the optimal solution can be provided by referring to past trouble data. Some or all of the above processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input past trouble data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0089] The troubleshooting unit can provide customized solutions based on the user's current usage and environment during troubleshooting. For example, the troubleshooting unit can provide the optimal solution based on the user's current usage. It can also provide customized solutions based on the user's usage environment. Furthermore, the troubleshooting unit can provide the optimal solution considering the user's current usage and environment. This allows for the provision of more appropriate solutions by providing customized solutions based on the user's usage and environment. Some or all of the above-described processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input user usage data into a generating AI and have the generating AI perform the task of providing customized solutions.

[0090] The troubleshooting unit can estimate the user's emotions and determine troubleshooting priorities based on those estimated emotions. For example, if the user is stressed, the troubleshooting unit may postpone less important troubleshooting tasks. Conversely, if the user is relaxed, the troubleshooting unit may provide all troubleshooting tasks equally. Furthermore, if the user is in a hurry, the troubleshooting unit may prioritize providing more important troubleshooting tasks. This ensures that important troubleshooting tasks are prioritized by determining troubleshooting priorities 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-described processes in the troubleshooting unit may be performed using AI or not. For example, the troubleshooting unit can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0091] The troubleshooting unit can provide the optimal solution during troubleshooting by taking into account the user's geographical location information. For example, the troubleshooting unit can provide troubleshooting methods related to products used by the user in a specific region. Furthermore, the troubleshooting unit can also provide highly relevant troubleshooting methods based on the user's current location. In addition, the troubleshooting unit can provide the optimal troubleshooting method by taking into account the user's geographical location information. This allows for the provision of highly relevant solutions by considering the user's geographical location information. Some or all of the above processes in the troubleshooting unit may be performed using AI, for example, or without AI. For example, the troubleshooting unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The acquisition unit can predict future purchases based on the user's purchase history and collect information related to those predicted purchases in advance. For example, the acquisition unit can analyze the usage period of products the user has purchased in the past and the replacement timing of consumables to predict the next products or parts that will be needed. The acquisition unit can also analyze the user's purchasing patterns and predict seasonal purchasing trends. Furthermore, the acquisition unit can predict the purchase of related products based on the user's life events (e.g., moving or marriage). This improves user convenience by collecting and providing the information the user needs in advance.

[0094] The acquisition unit can estimate the user's emotions and, based on those estimates, provide follow-up services after purchase. For example, if a user is dissatisfied after a purchase, the acquisition unit can quickly direct them to customer support. If the user is satisfied, the acquisition unit can also suggest additional products or services. Furthermore, if the user is confused, the acquisition unit can provide a detailed guide on how to use the product. By providing follow-up services tailored to the user's emotions, user satisfaction can be improved.

[0095] The data collection unit can collect reviews and ratings of relevant products based on the user's purchase history. For example, it can collect reviews from other users of products purchased by the user and prioritize displaying products with high ratings. It can also collect ratings for similar products from the user's purchase history. Furthermore, it can filter reviews of relevant products based on the user's purchase history to provide more reliable information. This allows users to make better purchasing decisions by referring to ratings from other users of products they have purchased.

[0096] The data collection unit can estimate the user's emotions and adjust how information is displayed based on those estimates. For example, if the user is stressed, the unit provides simple, easy-to-read information. If the user is relaxed, it can provide more detailed information. Furthermore, if the user is in a hurry, it can provide concise information. By adjusting how information is displayed according to the user's emotions, the system enables users to quickly and appropriately obtain the information they need.

[0097] The analysis unit can analyze product usage patterns based on the user's purchase history and propose an optimal maintenance schedule. For example, the analysis unit can analyze the frequency and environment of use of the product purchased by the user and propose appropriate maintenance timing. The analysis unit can also predict the product's lifespan and notify the user when replacement is needed. Furthermore, the analysis unit can customize maintenance methods based on the user's usage patterns. This allows users to effectively use the product over a long period of time.

[0098] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will provide a simple and easy-to-understand notification. If the user is relaxed, it can also provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise notification. In this way, by adjusting the notification method according to the user's emotions, the user can receive the necessary information quickly and appropriately.

[0099] The recommendation system can provide promotional information on relevant products based on the user's purchase history. For example, it can provide discount information related to products the user has previously purchased. It can also provide campaign information on relevant products based on the user's purchase history. Furthermore, the recommendation system can customize promotional information for specific products based on the user's purchase history. This allows users to make more advantageous purchases by receiving relevant product promotional information.

[0100] The recommendation system can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if a user is stressed, the recommendation system will refrain from making recommendations. Conversely, if a user is relaxed, it can actively make recommendations. Furthermore, if a user is in a hurry, it can only make important recommendations. By adjusting the timing of recommendations according to the user's emotions, the system can reduce the user's burden and provide information at the appropriate time.

[0101] The troubleshooting department can analyze past trouble trends based on the user's purchase history and propose preventative measures. For example, the troubleshooting department can analyze data on troubles the user has experienced in the past and propose preventative measures to avoid similar problems occurring. Furthermore, the troubleshooting department can analyze trouble trends for specific products based on the user's purchase history and notify users of points to be aware of. In addition, the troubleshooting department can provide advice to prevent problems from occurring in the first place, based on the user's purchase history. This allows users to prevent problems before they occur.

[0102] The troubleshooting department can estimate the user's emotions and adjust the troubleshooting support method based on those estimates. For example, if the user is stressed, the troubleshooting department can provide quick and simple support. If the user is relaxed, it can provide more detailed support. Furthermore, if the user is in a hurry, it can provide concise support. By adjusting the support method according to the user's emotions, the department can enable users to resolve problems quickly and appropriately.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The acquisition unit retrieves the user's payment information. The acquisition unit can retrieve payment information such as credit card information, purchase history, and payment method. The acquisition unit can also retrieve the user's payment information in real time. Furthermore, the acquisition unit can also retrieve payment information using API integration. Step 2: The collection unit collects information about purchased home appliances and equipment based on the information acquired by the acquisition unit. The collection unit can collect information such as product name, model number, purchase date, and usage instructions. The collection unit can also collect information about related home appliances and equipment based on the user's purchase history. Furthermore, the collection unit can also collect information about home appliances and equipment by referring to databases on the internet. Step 3: The analysis unit analyzes the information collected by the collection unit to understand the content of the instruction manual. The analysis unit can analyze the content of the instruction manual using techniques such as text analysis, image analysis, and data mining. The analysis unit can also understand the content of the instruction manual using AI. Furthermore, the analysis unit can provide more accurate information by analyzing the text and images of the instruction manual from multiple angles. Step 4: The recommendation unit provides explanations and usage recommendations tailored to the user's needs and circumstances based on the analysis results obtained by the analysis unit. The recommendation unit can provide recommendations such as how to use the product, suggestions for related products, and maintenance methods. It can also provide customized advice based on the user's usage situation and environment. Furthermore, the recommendation unit can provide optimal recommendations by referring to the user's past usage history. Step 5: The troubleshooting unit provides troubleshooting information based on the analysis results obtained by the analysis unit. The troubleshooting unit can provide information such as interpretation of error messages, repair methods, and support contact information. The troubleshooting unit can also provide the best solution by referring to past trouble data. Furthermore, the troubleshooting unit can provide a customized solution based on the user's current usage and environment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, recommendation unit, and troubleshooting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires the user's payment information. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information on purchased home appliances and equipment based on the information acquired by the acquisition unit. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information collected by the acquisition unit to understand the contents of the instruction manual. The recommendation unit is implemented by the control unit 46A of the smart device 14 and provides recommendations for explanations and usage tailored to the user's needs and situation based on the analysis results obtained by the analysis unit. The troubleshooting unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides troubleshooting information based on the analysis results obtained by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, recommendation unit, and troubleshooting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires the user's payment information. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information on purchased home appliances and equipment based on the information acquired by the acquisition unit. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information collected by the acquisition unit to understand the contents of the instruction manual. The recommendation unit is implemented by the control unit 46A of the smart glasses 214 and provides recommendations for explanations and usage tailored to the user's needs and situation based on the analysis results obtained by the analysis unit. The troubleshooting unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides troubleshooting information based on the analysis results obtained by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, recommendation unit, and troubleshooting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires the user's payment information. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information on purchased home appliances and equipment based on the information acquired by the acquisition unit. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information collected by the acquisition unit to understand the contents of the instruction manual. The recommendation unit is implemented by the control unit 46A of the headset terminal 314 and provides recommendations for explanations and usage tailored to the user's needs and situation based on the analysis results obtained by the analysis unit. The troubleshooting unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides troubleshooting information based on the analysis results obtained by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, recommendation unit, and troubleshooting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and acquires the user's payment information. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information on purchased home appliances and equipment based on the information acquired by the acquisition unit. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information collected by the acquisition unit and understands the contents of the instruction manual. The recommendation unit is implemented by the control unit 46A of the robot 414 and provides recommendations for explanations and usage tailored to the user's needs and situation based on the analysis results obtained by the analysis unit. The troubleshooting unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides troubleshooting information based on the analysis results obtained by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) An acquisition unit that acquires user payment information, A collection unit collects information on home appliances and equipment purchased based on the information acquired by the aforementioned acquisition unit, An analysis unit analyzes the information collected by the aforementioned collection unit and understands the contents of the instruction manual, A recommendation unit provides explanations and usage recommendations tailored to the user's needs and circumstances based on the analysis results obtained by the aforementioned analysis unit, The system includes a troubleshooting unit that provides troubleshooting information based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring payment information based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Analyze the user's past payment history and select the optimal method for obtaining it. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, When acquiring payment information, filtering is performed based on the user's current purchasing trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, The system estimates the user's emotions and determines the priority of payment information to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, When acquiring payment information, the system prioritizes acquiring highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When acquiring payment information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the method of collecting information about home appliances and devices based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system selects the most suitable information collection method by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the information is customized based on the user's current lifestyle and usage environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the contents of the instruction manual are analyzed in detail, and information relevant to specific usage scenarios is extracted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the text and images in the instruction manual are analyzed from multiple angles to provide more accurate information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, refer to the relevant literature and additional materials in the instruction manual to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recommendation unit is, When making recommendations, the system refers to the user's past usage history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recommendation unit is, When making recommendations, it provides customized advice based on the user's current usage and environment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, When providing recommendations, the system takes the user's geographical location into consideration to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 23) The troubleshooting unit described above, It estimates the user's emotions and adjusts troubleshooting methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The troubleshooting unit described above, During troubleshooting, we refer to past troubleshooting data to provide the best possible solution. The system described in Appendix 1, characterized by the features described herein. (Note 25) The troubleshooting unit described above, During troubleshooting, provide customized solutions based on the user's current usage and environment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The troubleshooting unit described above, It estimates the user's emotions and prioritizes troubleshooting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The troubleshooting unit described above, During troubleshooting, we consider the user's geographical location to provide the best possible solution. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 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. An acquisition unit that acquires user payment information, A collection unit collects information on home appliances and equipment purchased based on the information acquired by the aforementioned acquisition unit, An analysis unit analyzes the information collected by the aforementioned collection unit and understands the contents of the instruction manual, A recommendation unit provides explanations and usage recommendations tailored to the user's needs and circumstances based on the analysis results obtained by the aforementioned analysis unit, The system includes a troubleshooting unit that provides troubleshooting information based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring payment information based on those emotions. The system according to feature 1.

3. The acquisition unit is, Analyze the user's past payment history and select the optimal method for obtaining it. The system according to feature 1.

4. The acquisition unit is, When acquiring payment information, filtering is performed based on the user's current purchasing trends and areas of interest. The system according to feature 1.

5. The acquisition unit is, The system estimates the user's emotions and determines the priority of payment information to acquire based on those estimated emotions. The system according to feature 1.

6. The acquisition unit is, When acquiring payment information, the system prioritizes acquiring highly relevant information by considering the user's geographical location. The system according to feature 1.

7. The acquisition unit is, When acquiring payment information, the system analyzes the user's social media activity and retrieves relevant information. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the method of collecting information about home appliances and devices based on those estimated emotions. The system according to feature 1.

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