System

Abstract

An object of a system according to an embodiment is to enable a user to simply and quickly understand how to use home appliances and devices.SOLUTION: A system includes a collection part, an analysis part, a reconfiguration part, a reception part, and an answer part. The collection unit collects a purchase history of a user. The analysis unit analyzes the data collected by the collection unit. The reconfiguration unit reconfigures the documentation based on the data analyzed by the analysis unit. The reception unit receives a question from a user. The answering unit quickly answers the question received by the receiving unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it takes time for users to understand how to use home appliances and devices, which can be stressful for users.

[0005] The system according to the embodiment aims to enable a user to easily and quickly understand how to use a home appliance or device. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reconfiguration unit, a reception unit, and an answering unit. The collection unit collects a user's purchase history. The analysis unit analyzes the data collected by the collection unit. The reconfiguration unit reconfigures the instructions based on the data analyzed by the analysis unit. The reception unit receives questions from the user. The answering unit promptly answers the questions received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily and quickly understand how to use a home appliance or device. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention analyzes a user's purchase history and provides instructions for use in a concise and easy-to-understand format. This system collects user purchase history data and uses AI to analyze it. Next, based on the analyzed data, it reconstructs the user's manual for a home appliance or device in a concise and easy-to-understand format. When a user asks a question about how to use the device, the AI ​​immediately answers and provides specific instructions. For example, the system collects the user's purchase history. For example, it collects information about the home appliances and devices purchased on an e-commerce site. This information includes the type, model, and purchase date of the purchased product. Next, the AI ​​analyzes the collected data. Based on the purchase history, the AI ​​identifies the type of home appliance or device the user is using. Next, the AI ​​analyzes the user's manual and reconstructs it in a concise and easy-to-understand format. For example, it condenses long manuals and emphasizes important points. It also uses diagrams and illustrations to make the manual easier to understand visually. This reduces the time users spend reading the manual and allows them to quickly understand how to use the device. Furthermore, when a user asks a question about how to use the device, the AI ​​immediately answers. For example, in response to a question like, "Please tell me how to use this home appliance," the AI ​​provides specific operating instructions. This allows users to quickly resolve their questions and use their home appliances and devices correctly. This allows the system to support users' lives, reducing stress associated with using home appliances and devices and making life more comfortable and efficient. This allows the system to analyze users' purchase history and provide instructions on how to use them in a concise and easy-to-understand manner, supporting users' lives. For example, when users purchase a new home appliance, they can start using it right away without having to go through the trouble of reading the manual. Also, if a question arises about how to use it, the AI ​​will provide an immediate answer, allowing the problem to be resolved without wasting time.

[0029] An information provision system according to an embodiment includes a collection unit, an analysis unit, a reconfiguration unit, a reception unit, and a response unit. The collection unit collects a user's purchase history. For example, the collection unit collects information about home appliances and devices purchased on an e-commerce site. The collected information includes the type, model, and purchase date of the purchased product. The collection unit can acquire data from the e-commerce site using, for example, an API. The collection unit can also collect purchase information manually entered by the user. With the user's permission, the collection unit can also extract purchase information from emails or receipts. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies the home appliances and devices the user uses based on the purchase history. The analysis unit analyzes the purchase history data using, for example, a machine learning algorithm. The analysis unit can also identify the home appliances and devices currently in use based on information such as the user's frequency of use and purchase date. The analysis unit can also predict future purchasing trends based on the user's past purchase history. The reconfiguration unit reconfigures the instruction manual based on the data analyzed by the analysis unit. For example, the reconstruction unit may shorten a long manual and emphasize important points. The reconstruction unit may generate a summary of the manual, for example, using natural language processing technology. The reconstruction unit may also use diagrams and illustrations to make the manual easier to understand visually. The reconstruction unit may also adjust the content of the manual according to the user's level of expertise. The reception unit receives user questions. For example, the reception unit may receive questions entered by the user in text format. The reception unit may also receive user questions using voice input. The reception unit may also receive questions based on photos of home appliances or devices taken by the user using image input. The answering unit quickly responds to the questions received by the reception unit. For example, the answering unit may provide specific operating procedures in response to the user's questions. The answering unit may generate answers to the user's questions using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes the user's questions as input and outputs specific operating procedures.For example, the generation AI provides specific operating procedures in response to a question such as, "Please tell me how to use this home appliance." This allows the information provision system according to the embodiment to analyze a user's purchase history and provide instructions on how to use the appliance in a concise and easy-to-understand format. This supports the user's daily life, reduces stress associated with using home appliances and devices, and enables a more comfortable and efficient lifestyle.

[0030] The collection unit can collect information about home appliances and devices purchased on an e-commerce site. The collection unit, for example, collects information about home appliances and devices purchased on an e-commerce site. The collected information includes the type, model, and purchase date of the purchased product. The collection unit can obtain data from the e-commerce site using, for example, an API. The collection unit can also collect purchase information manually entered by the user. Furthermore, with the user's permission, the collection unit can extract purchase information from emails or receipts. In this way, by collecting information about home appliances and devices purchased on an e-commerce site, the user's purchase history can be accurately understood. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from the e-commerce site into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can identify what home appliances and devices the user is using based on the purchase history. The analysis unit can identify what home appliances and devices the user is using based on, for example, the purchase history. The analysis unit can analyze the purchase history data using, for example, a machine learning algorithm. The analysis unit can also identify the home appliances and devices currently in use based on information such as the user's frequency of use and purchase date. Furthermore, the analysis unit can predict future purchasing trends by referring to the user's past purchase history. This can identify what home appliances and devices the user is using, thereby making it possible to provide appropriate instructions. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input purchase history data into a generation AI and cause the generation AI to identify the home appliances and devices the user is using.

[0032] The reconstruction unit can concisely summarize the instructions and emphasize important points. For example, the reconstruction unit can concisely summarize the instructions and emphasize important points. The reconstruction unit can generate a summary of the instructions using natural language processing technology, for example. The reconstruction unit can also make the instructions easier to understand visually by using diagrams and illustrations. Furthermore, the reconstruction unit can adjust the content of the instructions according to the user's level of expertise. This makes it easier for the user to understand the instructions by shortening long instructions and emphasizing important points. Some or all of the above-mentioned processing in the reconstruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconstruction unit can input text data of the instructions into a generation AI and cause the generation AI to generate a summary.

[0033] The reconstruction unit can use diagrams and illustrations to make the instructions easier to understand visually. The reconstruction unit, for example, uses diagrams and illustrations to make the instructions easier to understand visually. The reconstruction unit, for example, expresses the contents of the instructions using diagrams and illustrations to make the instructions easier to understand visually. The reconstruction unit can also adjust the contents of the diagrams and illustrations according to the user's level of expertise. Furthermore, the reconstruction unit can also emphasize important points of the instructions using diagrams and illustrations. As a result, the use of diagrams and illustrations makes the instructions easier for the user to understand visually. Some or all of the above-mentioned processing in the reconstruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconstruction unit can input text data of the instructions into a generation AI and cause the generation AI to generate diagrams and illustrations.

[0034] The answering unit can provide detailed operating procedures in response to a question such as, "Please tell me how to use this home appliance." The answering unit provides detailed operating procedures in response to a question such as, "Please tell me how to use this home appliance." The answering unit generates an answer to the user's question using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes a user's question as input and outputs specific operating procedures. For example, the generation AI provides specific operating procedures in response to a question such as, "Please tell me how to use this home appliance." By providing specific operating procedures, the user can use the home appliance or device correctly. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input a user's question to the generation AI and have the generation AI generate specific operating procedures.

[0035] The collection unit can analyze the user's past purchase history and select an appropriate collection method. The collection unit, for example, analyzes the user's past purchase history and selects the optimal collection method. The collection unit, for example, prioritizes collecting products that the user has frequently purchased in the past. The collection unit can also focus on collecting products of a specific category from the user's purchase history. Furthermore, the collection unit can analyze the user's purchase history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past purchase history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past purchase history data into a generation AI and have the generation AI select the optimal collection method.

[0036] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting the purchase history. For example, the collection unit performs filtering based on the user's current living situation and areas of interest when collecting the purchase history. For example, the collection unit prioritizes collecting products in categories in which the user is currently interested. The collection unit can also filter and collect highly relevant products according to the user's living situation. Furthermore, the collection unit can narrow the range of products to be collected based on the user's areas of interest. This makes it possible to collect highly relevant information by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to perform filtering.

[0037] The collection unit can select an appropriate collection means depending on the user's input method when collecting the purchase history. For example, when collecting the purchase history, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, when the user uses voice input, the collection unit collects the purchase history using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect the purchase history using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect the purchase history using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting a purchase history, the collection unit can prioritize collecting highly relevant histories by taking into account the user's geographical location information. For example, when collecting a purchase history, the collection unit prioritizes collecting highly relevant histories by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting histories of products purchased in the user's current area. The collection unit can also collect histories of highly relevant products based on the user's geographical location information. Furthermore, the collection unit can also prioritize collecting histories of products purchased in places the user frequently visits. In this way, highly relevant information can be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant histories.

[0039] The collection unit can analyze the user's social media activities and collect related histories when collecting the purchase history. For example, the collection unit can analyze the user's social media activities and collect related histories when collecting the purchase history. For example, the collection unit can collect a history of products purchased at places where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect a history of related products. Furthermore, the collection unit can collect a history of related products by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related histories.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting the purchase history. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting the purchase history. The collection unit optimizes the collection method, for example, based on feedback provided by the user in the past. The collection unit can also adjust the range of products to be collected based on the user's past feedback. Furthermore, the collection unit can customize the collection timing and means by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the purchase history during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the purchase history during analysis. For example, the analysis unit performs a detailed analysis on purchase history with high importance. The analysis unit can also perform a concise analysis on purchase history with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the purchase history. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input purchase history data to a generation AI and have the generation AI adjust the level of detail.

[0042] The analysis unit can apply an appropriate analysis algorithm depending on the category of the home appliance or device during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the home appliance or device during analysis. For example, the analysis unit applies a dedicated analysis algorithm to the home appliance category. The analysis unit can also apply a dedicated analysis algorithm to the device category. Furthermore, the analysis unit can select and apply an optimal analysis algorithm for each category. In this way, applying the optimal analysis algorithm for each category improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the home appliance or device to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI perform analysis to improve accuracy.

[0044] The analysis unit can determine the analysis priority based on the submission time of the purchase history during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the purchase history during analysis. For example, the analysis unit prioritizes analysis of the most recent purchase history. The analysis unit can also postpone analysis of purchase history that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input purchase history submission time data to the generation AI and have the generation AI determine the priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the purchase history during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the purchase history during analysis. For example, the analysis unit prioritizes analysis of highly relevant purchase history. The analysis unit can also postpone analysis of less relevant purchase history. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the purchase history. In this way, by adjusting the order of analysis based on the relevance of the purchase history, highly relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the purchase history to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0047] The reconstruction unit can adjust the level of detail of the reconstruction based on the importance of the instructions during reconstruction. For example, the reconstruction unit adjusts the level of detail of the reconstruction based on the importance of the instructions during reconstruction. For example, the reconstruction unit performs detailed reconstruction for instructions with high importance. The reconstruction unit can also perform concise reconstruction for instructions with low importance. Furthermore, the reconstruction unit can dynamically adjust the level of detail of the reconstruction according to the importance of the instructions. This enables efficient reconstruction by adjusting the level of detail of the reconstruction based on the importance of the instructions. Some or all of the above-mentioned processing in the reconstruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconstruction unit can input importance data of the instructions to the generation AI and cause the generation AI to adjust the level of detail.

[0048] The reconfiguration unit can apply an appropriate reconfiguration algorithm depending on the category of the home appliance or device during reconfiguration. For example, the reconfiguration unit applies different reconfiguration algorithms depending on the category of the home appliance or device during reconfiguration. For example, the reconfiguration unit applies a dedicated reconfiguration algorithm to the home appliance category. The reconfiguration unit can also apply a dedicated reconfiguration algorithm to the device category. Furthermore, the reconfiguration unit can select and apply an optimal reconfiguration algorithm for each category. In this way, applying the optimal reconfiguration algorithm for each category improves the accuracy of the reconstruction. Some or all of the above-mentioned processing in the reconfiguration unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconfiguration unit can input category data of the home appliance or device to the generation AI and cause the generation AI to apply an appropriate reconfiguration algorithm.

[0049] The reconstruction unit can improve the accuracy of reconstruction by referring to the user's past reconstruction results during reconstruction. For example, the reconstruction unit improves the accuracy of reconstruction by referring to the user's past reconstruction results during reconstruction. For example, the reconstruction unit optimizes the reconstruction algorithm based on the user's past reconstruction results. The reconstruction unit can also improve the accuracy of reconstruction from the user's past reconstruction results. Furthermore, the reconstruction unit can adjust the level of detail of reconstruction by referring to the user's past reconstruction results. This improves the accuracy of reconstruction by referring to the past reconstruction results. Some or all of the above-mentioned processing in the reconstruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconstruction unit can input past reconstruction result data into the generation AI and cause the generation AI to perform reconstruction to improve accuracy.

[0050] The reconstruction unit can determine the priority of reconstruction based on the submission date of the instruction manual during reconstruction. The reconstruction unit, for example, determines the priority of reconstruction based on the submission date of the instruction manual during reconstruction. The reconstruction unit, for example, prioritizes reconstructing the most recent instruction manual. The reconstruction unit can also postpone the oldest submitted instruction manual. Furthermore, the reconstruction unit can dynamically adjust the priority of reconstruction based on the submission date. This enables efficient reconstruction by determining the priority of reconstruction based on the submission date of the instruction manual. Some or all of the above-mentioned processing in the reconstruction unit may be performed using AI, for example, or may be performed without using AI. For example, the reconstruction unit can input the submission date data of the instruction manual to the generation AI and have the generation AI determine the priority.

[0051] The reconstructing unit can adjust the order of reconstruction based on the relevance of the instructions during reconstruction. The reconstructing unit, for example, adjusts the order of reconstruction based on the relevance of the instructions during reconstruction. For example, the reconstructing unit prioritizes reconstructing highly relevant instructions. The reconstructing unit can also postpone reconstructing less relevant instructions. Furthermore, the reconstructing unit can dynamically adjust the order of reconstruction based on the relevance of the instructions. In this way, by adjusting the order of reconstruction based on the relevance of the instructions, highly relevant information can be prioritized for reconstruction. Some or all of the above-described processing in the reconstructing unit may be performed using AI, for example, or may be performed without using AI. For example, the reconstructing unit can input relevance data of the instructions to the generating AI and cause the generating AI to adjust the order of reconstruction.

[0052] The reconstruction unit can adjust the use of technical terms in the instructions according to the user's level of expertise during reconstruction. For example, the reconstruction unit can adjust the use of technical terms in the instructions according to the user's level of expertise during reconstruction. For example, if the user has technical expertise, the reconstruction unit can provide instructions that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the reconstruction unit can also provide concise and easy-to-understand instructions. Furthermore, the reconstruction unit can dynamically adjust the use of technical terms in the instructions according to the user's level of expertise. This allows for the provision of instructions that are easy for the user to understand by adjusting the use of technical terms in the instructions according to the user's level of expertise. Some or all of the above-described processing in the reconstruction unit can be performed using, for example, AI, or can be performed without using AI. For example, the reconstruction unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] The reception unit can select the optimal reception method by referring to the user's past question history when receiving a question. For example, the reception unit selects the optimal reception method by referring to the user's past question history when receiving a question. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be used in a specific time period based on the user's past question history. In this way, the optimal question reception method can be selected by referring to the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past question history data into a generation AI and have the generation AI select the optimal reception method.

[0054] The reception unit can perform filtering based on the user's current living situation and areas of interest when receiving a question. For example, the reception unit performs filtering based on the user's current living situation and areas of interest when receiving a question. For example, the reception unit preferentially receives questions in categories in which the user is currently interested. The reception unit can also filter and receive highly relevant questions based on the user's living situation. Furthermore, the reception unit can narrow the range of questions to be received based on the user's areas of interest. In this way, highly relevant questions can be received by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to perform filtering.

[0055] The reception unit can select an appropriate reception means according to the user's input method when receiving a question. For example, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving a question. For example, when the user uses voice input, the reception unit receives the question using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also receive the question using text analysis technology. Furthermore, when the user uses image input, the reception unit can also receive the question using image recognition technology. This allows questions to be received efficiently by selecting the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0056] The reception unit can prioritize receiving highly relevant questions in consideration of the user's geographical location information when receiving a question. For example, the reception unit prioritizes receiving highly relevant questions in consideration of the user's geographical location information when receiving a question. For example, the reception unit prioritizes receiving questions related to the area where the user is currently located. The reception unit can also receive highly relevant questions based on the user's geographical location information. Furthermore, the reception unit can also prioritize receiving questions related to places the user frequently visits. In this way, highly relevant questions can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant questions.

[0057] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can receive questions about places the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. Furthermore, the reception unit can also receive related questions by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant questions can be received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related questions.

[0058] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a question. The reception unit optimizes the reception method, for example, based on feedback provided by the user in the past. The reception unit can also adjust the range of questions to be received based on the user's past feedback. Furthermore, the reception unit can customize the reception timing and method by reflecting the user's feedback. In this way, the reception method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the reception method.

[0059] The answering unit can adjust the level of detail of the answer based on the importance of the question when answering. For example, the answering unit adjusts the level of detail of the answer based on the importance of the question when answering. For example, the answering unit provides a detailed answer to a question of high importance. The answering unit can also provide a concise answer to a question of low importance. Furthermore, the answering unit can dynamically adjust the level of detail of the answer according to the importance of the question. This enables efficient answering by adjusting the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input question importance data to a generating AI and have the generating AI adjust the level of detail.

[0060] The answering unit can apply different answering algorithms depending on the question category when answering. For example, the answering unit applies different answering algorithms depending on the question category when answering. For example, the answering unit applies a dedicated answering algorithm to questions in the home appliance category. The answering unit can also apply a dedicated answering algorithm to questions in the equipment category. Furthermore, the answering unit can select and apply the optimal answering algorithm for each category. In this way, applying the optimal answering algorithm for each category improves the accuracy of the answer. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input question category data to the generation AI and cause the generation AI to apply an appropriate answering algorithm.

[0061] The answering unit can improve the accuracy of the answer by referring to the user's past answer results when answering. For example, the answering unit can improve the accuracy of the answer by referring to the user's past answer results when answering. For example, the answering unit optimizes the answer algorithm based on the user's past answer results. The answering unit can also improve the accuracy of the answer from the user's past answer results. Furthermore, the answering unit can adjust the level of detail of the answer by referring to the user's past answer results. In this way, the accuracy of the answer is improved by referring to the past answer results. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input past answer result data into the generation AI and have the generation AI execute answers to improve accuracy.

[0062] The answering unit can determine the priority of answers based on the time of submission of the question when answering. The answering unit, for example, determines the priority of answers based on the time of submission of the question when answering. For example, the answering unit prioritizes answers to the most recent questions. The answering unit can also postpone questions that were submitted earlier. Furthermore, the answering unit can dynamically adjust the priority of answers based on the time of submission. This enables efficient answers by determining the priority of answers based on the time of submission of the question. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input question submission time data into the generating AI and have the generating AI determine the priority.

[0063] The answering unit can adjust the order of answers based on the relevance of the questions when answering. For example, the answering unit adjusts the order of answers based on the relevance of the questions when answering. For example, the answering unit prioritizes answers to highly relevant questions. The answering unit can also postpone answers to less relevant questions. Furthermore, the answering unit can dynamically adjust the order of answers based on the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, highly relevant questions can be prioritized for answering. Some or all of the above-described processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input question relevance data to a generating AI and cause the generating AI to adjust the order of answers.

[0064] The answering unit can adjust the use of technical terminology in the answer depending on the user's level of expertise when answering. For example, the answering unit can adjust the use of technical terminology in the answer depending on the user's level of expertise when answering. For example, if the user has technical expertise, the answering unit can provide an answer that uses a lot of technical terminology. Also, if the user does not have technical expertise, the answering unit can provide a concise and easy-to-understand answer. Furthermore, the answering unit can dynamically adjust the use of technical terminology in the answer depending on the user's level of expertise. This allows the user to provide an answer that is easy to understand. Some or all of the above-mentioned processing in the answering unit can be performed using AI, for example, or without AI. For example, the answering unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The reconstructing unit can also analyze the user's past behavioral patterns and optimize the method for reconstructing the instructions. For example, if the user has preferred a particular format of instructions in the past, that format can be preferentially reconstructed. Also, if the user has frequently referred to specific information in the past, that information can be emphasized. Furthermore, if the user has used a particular device in the past, information related to that device can be preferentially provided. In this way, more personalized instructions can be provided by taking the user's past behavioral patterns into consideration.

[0067] The analysis unit can also analyze the user's past purchase history and predict future purchasing trends. For example, it can predict the next product the user is likely to purchase based on products that the user has frequently purchased in the past. It can also predict the purchasing trends of products related to specific seasons or events from the user's purchase history. It can also suggest highly related products based on the user's past purchase history. In this way, by analyzing the user's past purchase history, it is possible to predict future purchasing trends and make more personalized suggestions.

[0068] The reconstruction unit can also adjust the content of the instruction manual according to the user's level of expertise. For example, if the user has specialized knowledge, it can provide instructions containing detailed technical information. On the other hand, if the user does not have specialized knowledge, it can provide concise and easy-to-understand instructions. Furthermore, it can dynamically adjust the content of the instruction manual according to the user's level of expertise. In this way, by adjusting the content of the instruction manual according to the user's level of expertise, it is possible to provide instructions that are easy for the user to understand.

[0069] The reception unit can also select the optimal reception method by referring to the user's past question history. For example, questions that the user has frequently asked in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions that will be used in a specific time period based on the user's past question history. In this way, the optimal question reception method can be selected by referring to the user's past question history.

[0070] The answering unit can also apply different answering algorithms depending on the question category. For example, a dedicated answering algorithm can be applied to questions in the home appliance category. Also, a dedicated answering algorithm can be applied to questions in the equipment category. Furthermore, the answering unit can select and apply the optimal answering algorithm for each category. This improves the accuracy of answers by applying the optimal answering algorithm for each category.

[0071] The collection unit can also prioritize collection of highly relevant histories by taking into account the user's geographical location information. For example, the collection unit can prioritize collection of histories of products purchased in the area where the user is currently located. The collection unit can also collect histories of highly relevant products based on the user's geographical location information. Furthermore, the collection unit can prioritize collection of histories of products purchased in places the user frequently visits. In this way, highly relevant information can be collected preferentially by taking into account the user's geographical location information.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects the user's purchase history. For example, the collection unit collects information about home appliances and devices purchased on an e-commerce site. The collected information includes the type, model, and purchase date of the purchased product. The collection unit can obtain data from the e-commerce site using, for example, an API. The collection unit can also collect purchase information manually entered by the user. Furthermore, with the user's permission, the collection unit can extract purchase information from emails and receipts. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies what home appliances and devices the user is using based on the purchase history. The analysis unit analyzes the purchase history data using, for example, a machine learning algorithm. The analysis unit can also identify the home appliances and devices currently in use based on information such as the user's frequency of use and purchase date. Furthermore, the analysis unit can also predict future purchasing trends by referring to the user's past purchase history. Step 3: The reconstructor reconstructs the instructions based on the data analyzed by the analyzer. For example, the reconstructor may shorten long instructions and emphasize important points. The reconstructor may generate a summary of the instructions using natural language processing technology. The reconstructor may also use diagrams and illustrations to make the instructions easier to understand visually. Furthermore, the reconstructor may adjust the content of the instructions according to the user's level of expertise. Step 4: The reception unit receives a question from the user. For example, the reception unit can receive a question input by the user in text format. The reception unit can also receive a question from the user using voice input. Furthermore, the reception unit can also receive a question based on a photo of a home appliance or device taken by the user using image input. Step 5: The answering unit quickly answers the question received by the receiving unit. For example, the answering unit provides specific operating procedures in response to the user's question. The answering unit generates an answer to the user's question using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes the user's question as input and outputs specific operating procedures. For example, the generation AI provides specific operating procedures in response to the question, "Please tell me how to use this home appliance."

[0074] (Example 2) A system according to an embodiment of the present invention analyzes a user's purchase history and provides instructions for use in a concise and easy-to-understand format. This system collects user purchase history data and uses AI to analyze it. Next, based on the analyzed data, it reconstructs the user's manual for a home appliance or device in a concise and easy-to-understand format. When a user asks a question about how to use the device, the AI ​​immediately answers and provides specific instructions. For example, the system collects the user's purchase history. For example, it collects information about the home appliances and devices purchased on an e-commerce site. This information includes the type, model, and purchase date of the purchased product. Next, the AI ​​analyzes the collected data. Based on the purchase history, the AI ​​identifies the type of home appliance or device the user is using. Next, the AI ​​analyzes the user's manual and reconstructs it in a concise and easy-to-understand format. For example, it condenses long manuals and emphasizes important points. It also uses diagrams and illustrations to make the manual easier to understand visually. This reduces the time users spend reading the manual and allows them to quickly understand how to use the device. Furthermore, when a user asks a question about how to use the device, the AI ​​immediately answers. For example, in response to a question like, "Please tell me how to use this home appliance," the AI ​​provides specific operating instructions. This allows users to quickly resolve their questions and use their home appliances and devices correctly. This allows the system to support users' lives, reducing stress associated with using home appliances and devices and making life more comfortable and efficient. This allows the system to analyze users' purchase history and provide instructions on how to use them in a concise and easy-to-understand manner, supporting users' lives. For example, when users purchase a new home appliance, they can start using it right away without having to go through the trouble of reading the manual. Also, if a question arises about how to use it, the AI ​​will provide an immediate answer, allowing the problem to be resolved without wasting time.

[0075] An information provision system according to an embodiment includes a collection unit, an analysis unit, a reconfiguration unit, a reception unit, and a response unit. The collection unit collects a user's purchase history. For example, the collection unit collects information about home appliances and devices purchased on an e-commerce site. The collected information includes the type, model, and purchase date of the purchased product. The collection unit can acquire data from the e-commerce site using, for example, an API. The collection unit can also collect purchase information manually entered by the user. With the user's permission, the collection unit can also extract purchase information from emails or receipts. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies the home appliances and devices the user uses based on the purchase history. The analysis unit analyzes the purchase history data using, for example, a machine learning algorithm. The analysis unit can also identify the home appliances and devices currently in use based on information such as the user's frequency of use and purchase date. The analysis unit can also predict future purchasing trends based on the user's past purchase history. The reconfiguration unit reconfigures the instruction manual based on the data analyzed by the analysis unit. For example, the reconstruction unit may shorten a long manual and emphasize important points. The reconstruction unit may generate a summary of the manual, for example, using natural language processing technology. The reconstruction unit may also use diagrams and illustrations to make the manual easier to understand visually. The reconstruction unit may also adjust the content of the manual according to the user's level of expertise. The reception unit receives user questions. For example, the reception unit may receive questions entered by the user in text format. The reception unit may also receive user questions using voice input. The reception unit may also receive questions based on photos of home appliances or devices taken by the user using image input. The answering unit quickly responds to the questions received by the reception unit. For example, the answering unit may provide specific operating procedures in response to the user's questions. The answering unit may generate answers to the user's questions using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes the user's questions as input and outputs specific operating procedures.For example, the generation AI provides specific operating procedures in response to a question such as, "Please tell me how to use this home appliance." This allows the information provision system according to the embodiment to analyze a user's purchase history and provide instructions on how to use the appliance in a concise and easy-to-understand format. This supports the user's daily life, reduces stress associated with using home appliances and devices, and enables a more comfortable and efficient lifestyle.

[0076] The collection unit can collect information about home appliances and devices purchased on an e-commerce site. The collection unit, for example, collects information about home appliances and devices purchased on an e-commerce site. The collected information includes the type, model, and purchase date of the purchased product. The collection unit can obtain data from the e-commerce site using, for example, an API. The collection unit can also collect purchase information manually entered by the user. Furthermore, with the user's permission, the collection unit can extract purchase information from emails or receipts. In this way, by collecting information about home appliances and devices purchased on an e-commerce site, the user's purchase history can be accurately understood. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data obtained from the e-commerce site into a generation AI and have the generation AI analyze the data.

[0077] The analysis unit can identify what home appliances and devices the user is using based on the purchase history. The analysis unit can identify what home appliances and devices the user is using based on, for example, the purchase history. The analysis unit can analyze the purchase history data using, for example, a machine learning algorithm. The analysis unit can also identify the home appliances and devices currently in use based on information such as the user's frequency of use and purchase date. Furthermore, the analysis unit can predict future purchasing trends by referring to the user's past purchase history. This can identify what home appliances and devices the user is using, thereby making it possible to provide appropriate instructions. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input purchase history data into a generation AI and cause the generation AI to identify the home appliances and devices the user is using.

[0078] The reconstruction unit can concisely summarize the instructions and emphasize important points. For example, the reconstruction unit can concisely summarize the instructions and emphasize important points. The reconstruction unit can generate a summary of the instructions using natural language processing technology, for example. The reconstruction unit can also make the instructions easier to understand visually by using diagrams and illustrations. Furthermore, the reconstruction unit can adjust the content of the instructions according to the user's level of expertise. This makes it easier for the user to understand the instructions by shortening long instructions and emphasizing important points. Some or all of the above-mentioned processing in the reconstruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconstruction unit can input text data of the instructions into a generation AI and cause the generation AI to generate a summary.

[0079] The reconstruction unit can use diagrams and illustrations to make the instructions easier to understand visually. The reconstruction unit, for example, uses diagrams and illustrations to make the instructions easier to understand visually. The reconstruction unit, for example, expresses the contents of the instructions using diagrams and illustrations to make the instructions easier to understand visually. The reconstruction unit can also adjust the contents of the diagrams and illustrations according to the user's level of expertise. Furthermore, the reconstruction unit can also emphasize important points of the instructions using diagrams and illustrations. As a result, the use of diagrams and illustrations makes the instructions easier for the user to understand visually. Some or all of the above-mentioned processing in the reconstruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconstruction unit can input text data of the instructions into a generation AI and cause the generation AI to generate diagrams and illustrations.

[0080] The answering unit can provide detailed operating procedures in response to a question such as, "Please tell me how to use this home appliance." The answering unit provides detailed operating procedures in response to a question such as, "Please tell me how to use this home appliance." The answering unit generates an answer to the user's question using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes a user's question as input and outputs specific operating procedures. For example, the generation AI provides specific operating procedures in response to a question such as, "Please tell me how to use this home appliance." By providing specific operating procedures, the user can use the home appliance or device correctly. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the answering unit can input a user's question to the generation AI and have the generation AI generate specific operating procedures.

[0081] The collection unit can estimate the user's emotions and optimize the timing of collecting the purchase history based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of collecting the purchase history based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects the purchase history when the user is relaxed. Furthermore, if the user is relaxed, the collection unit can immediately collect the purchase history and quickly start analysis. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing and collect necessary information in a short time. This adjusts the collection timing according to the user's emotions, thereby reducing the user's stress. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0082] The collection unit can analyze the user's past purchase history and select an appropriate collection method. The collection unit, for example, analyzes the user's past purchase history and selects the optimal collection method. The collection unit, for example, prioritizes collecting products that the user has frequently purchased in the past. The collection unit can also focus on collecting products of a specific category from the user's purchase history. Furthermore, the collection unit can analyze the user's purchase history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the past purchase history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past purchase history data into a generation AI and have the generation AI select the optimal collection method.

[0083] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting the purchase history. For example, the collection unit performs filtering based on the user's current living situation and areas of interest when collecting the purchase history. For example, the collection unit prioritizes collecting products in categories in which the user is currently interested. The collection unit can also filter and collect highly relevant products according to the user's living situation. Furthermore, the collection unit can narrow the range of products to be collected based on the user's areas of interest. This makes it possible to collect highly relevant information by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to perform filtering.

[0084] The collection unit can select an appropriate collection means depending on the user's input method when collecting the purchase history. For example, when collecting the purchase history, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, when the user uses voice input, the collection unit collects the purchase history using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect the purchase history using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect the purchase history using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0085] The collection unit can estimate the user's emotions and prioritize the purchase history to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and determines the priority of the purchase history to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit prioritizes collecting the most recent purchase history. Furthermore, when the user is relaxed, the collection unit can also collect past purchase history. Furthermore, when the user is stressed, the collection unit can prioritize collecting purchase history of high importance. Thus, by determining the priority of the purchase history to be collected according to the user's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI set the priority.

[0086] When collecting a purchase history, the collection unit can prioritize collecting highly relevant histories by taking into account the user's geographical location information. For example, when collecting a purchase history, the collection unit prioritizes collecting highly relevant histories by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting histories of products purchased in the user's current area. The collection unit can also collect histories of highly relevant products based on the user's geographical location information. Furthermore, the collection unit can also prioritize collecting histories of products purchased in places the user frequently visits. In this way, highly relevant information can be collected preferentially by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant histories.

[0087] The collection unit can analyze the user's social media activities and collect related histories when collecting the purchase history. For example, the collection unit can analyze the user's social media activities and collect related histories when collecting the purchase history. For example, the collection unit can collect a history of products purchased at places where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect a history of related products. Furthermore, the collection unit can collect a history of related products by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related histories.

[0088] The collection unit can customize the collection method by reflecting the user's past feedback when collecting the purchase history. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting the purchase history. The collection unit optimizes the collection method, for example, based on feedback provided by the user in the past. The collection unit can also adjust the range of products to be collected based on the user's past feedback. Furthermore, the collection unit can customize the collection timing and means by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0089] The analysis unit can estimate the user's emotions and optimize the analysis presentation method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. For example, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results with visually stimulating effects when the user is excited. By adjusting the analysis presentation method according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the purchase history during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the purchase history during analysis. For example, the analysis unit performs a detailed analysis on purchase history with high importance. The analysis unit can also perform a concise analysis on purchase history with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the purchase history. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input purchase history data to a generation AI and have the generation AI adjust the level of detail.

[0091] The analysis unit can apply an appropriate analysis algorithm depending on the category of the home appliance or device during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the home appliance or device during analysis. For example, the analysis unit applies a dedicated analysis algorithm to the home appliance category. The analysis unit can also apply a dedicated analysis algorithm to the device category. Furthermore, the analysis unit can select and apply an optimal analysis algorithm for each category. In this way, applying the optimal analysis algorithm for each category improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the home appliance or device to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0092] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis from the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI perform analysis to improve accuracy.

[0093] The analysis unit can estimate the user's emotions and optimize the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0094] The analysis unit can determine the analysis priority based on the submission time of the purchase history during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the purchase history during analysis. For example, the analysis unit prioritizes analysis of the most recent purchase history. The analysis unit can also postpone analysis of purchase history that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the purchase history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input purchase history submission time data to the generation AI and have the generation AI determine the priority.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the purchase history during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the purchase history during analysis. For example, the analysis unit prioritizes analysis of highly relevant purchase history. The analysis unit can also postpone analysis of less relevant purchase history. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the purchase history. In this way, by adjusting the order of analysis based on the relevance of the purchase history, highly relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the purchase history to the generation AI and cause the generation AI to adjust the order of analysis.

[0096] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terms.

[0097] The reconstruction unit can estimate the user's emotions and optimize the reconstruction method of the instructions based on the estimated user emotions. For example, the reconstruction unit can estimate the user's emotions and adjust the reconstruction method of the instructions based on the estimated user emotions. For example, the reconstruction unit can provide detailed instructions when the user is relaxed. Furthermore, the reconstruction unit can provide concise instructions that focus on the main points when the user is in a hurry. Furthermore, the reconstruction unit can provide instructions with visually stimulating effects when the user is excited. This allows the user to easily understand instructions by adjusting the reconstruction method of the instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reconstruction unit can be performed using, for example, an AI, or without an AI. For example, the reconstruction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the reconstruction method.

[0098] The reconstruction unit can adjust the level of detail of the reconstruction based on the importance of the instructions during reconstruction. For example, the reconstruction unit adjusts the level of detail of the reconstruction based on the importance of the instructions during reconstruction. For example, the reconstruction unit performs detailed reconstruction for instructions with high importance. The reconstruction unit can also perform concise reconstruction for instructions with low importance. Furthermore, the reconstruction unit can dynamically adjust the level of detail of the reconstruction according to the importance of the instructions. This enables efficient reconstruction by adjusting the level of detail of the reconstruction based on the importance of the instructions. Some or all of the above-mentioned processing in the reconstruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconstruction unit can input importance data of the instructions to the generation AI and cause the generation AI to adjust the level of detail.

[0099] The reconfiguration unit can apply an appropriate reconfiguration algorithm depending on the category of the home appliance or device during reconfiguration. For example, the reconfiguration unit applies different reconfiguration algorithms depending on the category of the home appliance or device during reconfiguration. For example, the reconfiguration unit applies a dedicated reconfiguration algorithm to the home appliance category. The reconfiguration unit can also apply a dedicated reconfiguration algorithm to the device category. Furthermore, the reconfiguration unit can select and apply an optimal reconfiguration algorithm for each category. In this way, applying the optimal reconfiguration algorithm for each category improves the accuracy of the reconstruction. Some or all of the above-mentioned processing in the reconfiguration unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconfiguration unit can input category data of the home appliance or device to the generation AI and cause the generation AI to apply an appropriate reconfiguration algorithm.

[0100] The reconstruction unit can improve the accuracy of reconstruction by referring to the user's past reconstruction results during reconstruction. For example, the reconstruction unit improves the accuracy of reconstruction by referring to the user's past reconstruction results during reconstruction. For example, the reconstruction unit optimizes the reconstruction algorithm based on the user's past reconstruction results. The reconstruction unit can also improve the accuracy of reconstruction from the user's past reconstruction results. Furthermore, the reconstruction unit can adjust the level of detail of reconstruction by referring to the user's past reconstruction results. This improves the accuracy of reconstruction by referring to the past reconstruction results. Some or all of the above-mentioned processing in the reconstruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the reconstruction unit can input past reconstruction result data into the generation AI and cause the generation AI to perform reconstruction to improve accuracy.

[0101] The reconstruction unit can estimate the user's emotions and optimize the length of the instructions based on the estimated user emotions. The reconstruction unit, for example, estimates the user's emotions and adjusts the length of the instructions based on the estimated user emotions. For example, if the user is in a hurry, the reconstruction unit can provide short, concise instructions. For example, if the user is relaxed, the reconstruction unit can provide detailed instructions. Furthermore, if the user is excited, the reconstruction unit can provide instructions with visually stimulating effects. By adjusting the length of the instructions according to the user's emotions, the optimal instructions can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reconstruction unit can be performed using, for example, an AI. For example, the reconstruction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the instructions.

[0102] The reconstruction unit can determine the priority of reconstruction based on the submission date of the instruction manual during reconstruction. The reconstruction unit, for example, determines the priority of reconstruction based on the submission date of the instruction manual during reconstruction. The reconstruction unit, for example, prioritizes reconstructing the most recent instruction manual. The reconstruction unit can also postpone the oldest submitted instruction manual. Furthermore, the reconstruction unit can dynamically adjust the priority of reconstruction based on the submission date. This enables efficient reconstruction by determining the priority of reconstruction based on the submission date of the instruction manual. Some or all of the above-mentioned processing in the reconstruction unit may be performed using AI, for example, or may be performed without using AI. For example, the reconstruction unit can input the submission date data of the instruction manual to the generation AI and have the generation AI determine the priority.

[0103] The reconstructing unit can adjust the order of reconstruction based on the relevance of the instructions during reconstruction. The reconstructing unit, for example, adjusts the order of reconstruction based on the relevance of the instructions during reconstruction. For example, the reconstructing unit prioritizes reconstructing highly relevant instructions. The reconstructing unit can also postpone reconstructing less relevant instructions. Furthermore, the reconstructing unit can dynamically adjust the order of reconstruction based on the relevance of the instructions. In this way, by adjusting the order of reconstruction based on the relevance of the instructions, highly relevant information can be prioritized for reconstruction. Some or all of the above-described processing in the reconstructing unit may be performed using AI, for example, or may be performed without using AI. For example, the reconstructing unit can input relevance data of the instructions to the generating AI and cause the generating AI to adjust the order of reconstruction.

[0104] The reconstruction unit can adjust the use of technical terms in the instructions according to the user's level of expertise during reconstruction. For example, the reconstruction unit can adjust the use of technical terms in the instructions according to the user's level of expertise during reconstruction. For example, if the user has technical expertise, the reconstruction unit can provide instructions that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the reconstruction unit can also provide concise and easy-to-understand instructions. Furthermore, the reconstruction unit can dynamically adjust the use of technical terms in the instructions according to the user's level of expertise. This allows for the provision of instructions that are easy for the user to understand by adjusting the use of technical terms in the instructions according to the user's level of expertise. Some or all of the above-described processing in the reconstruction unit can be performed using, for example, AI, or can be performed without using AI. For example, the reconstruction unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0105] The reception unit can estimate the user's emotions and optimize the question reception method based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the question reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly receive questions. This allows optimal question reception for the user by adjusting the question reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the reception method.

[0106] The reception unit can select the optimal reception method by referring to the user's past question history when receiving a question. For example, the reception unit selects the optimal reception method by referring to the user's past question history when receiving a question. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions that will be used in a specific time period based on the user's past question history. In this way, the optimal question reception method can be selected by referring to the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past question history data into a generation AI and have the generation AI select the optimal reception method.

[0107] The reception unit can perform filtering based on the user's current living situation and areas of interest when receiving a question. For example, the reception unit performs filtering based on the user's current living situation and areas of interest when receiving a question. For example, the reception unit preferentially receives questions in categories in which the user is currently interested. The reception unit can also filter and receive highly relevant questions based on the user's living situation. Furthermore, the reception unit can narrow the range of questions to be received based on the user's areas of interest. In this way, highly relevant questions can be received by filtering based on the user's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and cause the generation AI to perform filtering.

[0108] The reception unit can select an appropriate reception means according to the user's input method when receiving a question. For example, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving a question. For example, when the user uses voice input, the reception unit receives the question using voice recognition technology. Furthermore, when the user uses text input, the reception unit can also receive the question using text analysis technology. Furthermore, when the user uses image input, the reception unit can also receive the question using image recognition technology. This allows questions to be received efficiently by selecting the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0109] The reception unit can estimate the user's emotions and set the priority of questions to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of questions to be received based on the estimated user emotions. For example, when the user is excited, the reception unit prioritizes receiving the most recent question. Furthermore, when the user is relaxed, the reception unit can also receive past questions. Furthermore, when the user is stressed, the reception unit can prioritize receiving questions of high importance. In this way, by determining the priority of questions to be received according to the user's emotions, important questions can be received preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI set the priority.

[0110] The reception unit can prioritize receiving highly relevant questions in consideration of the user's geographical location information when receiving a question. For example, the reception unit prioritizes receiving highly relevant questions in consideration of the user's geographical location information when receiving a question. For example, the reception unit prioritizes receiving questions related to the area where the user is currently located. The reception unit can also receive highly relevant questions based on the user's geographical location information. Furthermore, the reception unit can also prioritize receiving questions related to places the user frequently visits. In this way, highly relevant questions can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to receive highly relevant questions.

[0111] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can receive questions about places the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. Furthermore, the reception unit can also receive related questions by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, highly relevant questions can be received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related questions.

[0112] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a question. The reception unit optimizes the reception method, for example, based on feedback provided by the user in the past. The reception unit can also adjust the range of questions to be received based on the user's past feedback. Furthermore, the reception unit can customize the reception timing and method by reflecting the user's feedback. In this way, the reception method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the reception method.

[0113] The answering unit can estimate the user's emotions and optimize the way the answer is expressed based on the estimated user's emotions. For example, the answering unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, the answering unit can provide a detailed answer when the user is relaxed. Furthermore, the answering unit can provide a concise answer that focuses on the main points when the user is in a hurry. Furthermore, the answering unit can provide an answer with a visually stimulating effect when the user is excited. This allows the answer to be easily understood by adjusting the way the answer is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the answering unit can be performed using, for example, an AI, or without an AI. For example, the answering unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the answer is expressed.

[0114] The answering unit can adjust the level of detail of the answer based on the importance of the question when answering. For example, the answering unit adjusts the level of detail of the answer based on the importance of the question when answering. For example, the answering unit provides a detailed answer to a question of high importance. The answering unit can also provide a concise answer to a question of low importance. Furthermore, the answering unit can dynamically adjust the level of detail of the answer according to the importance of the question. This enables efficient answering by adjusting the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, AI. For example, the answering unit can input question importance data to a generating AI and have the generating AI adjust the level of detail.

[0115] The answering unit can apply different answering algorithms depending on the question category when answering. For example, the answering unit applies different answering algorithms depending on the question category when answering. For example, the answering unit applies a dedicated answering algorithm to questions in the home appliance category. The answering unit can also apply a dedicated answering algorithm to questions in the equipment category. Furthermore, the answering unit can select and apply the optimal answering algorithm for each category. In this way, applying the optimal answering algorithm for each category improves the accuracy of the answer. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input question category data to the generation AI and cause the generation AI to apply an appropriate answering algorithm.

[0116] The answering unit can improve the accuracy of the answer by referring to the user's past answer results when answering. For example, the answering unit can improve the accuracy of the answer by referring to the user's past answer results when answering. For example, the answering unit optimizes the answer algorithm based on the user's past answer results. The answering unit can also improve the accuracy of the answer from the user's past answer results. Furthermore, the answering unit can adjust the level of detail of the answer by referring to the user's past answer results. In this way, the accuracy of the answer is improved by referring to the past answer results. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input past answer result data into the generation AI and have the generation AI execute answers to improve accuracy.

[0117] The answering unit can estimate the user's emotions and optimize the length of the answer based on the estimated user emotions. For example, the answering unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. For example, if the user is in a hurry, the answering unit can provide a short and to-the-point answer. For example, if the user is relaxed, the answering unit can provide a detailed answer. Furthermore, if the user is excited, the answering unit can provide an answer with a visually stimulating effect. By adjusting the length of the answer according to the user's emotions, the answer can be optimally provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answering unit can be performed using, for example, an AI, or without an AI. For example, the answering unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the answer.

[0118] The answering unit can determine the priority of answers based on the time of submission of the question when answering. The answering unit, for example, determines the priority of answers based on the time of submission of the question when answering. For example, the answering unit prioritizes answers to the most recent questions. The answering unit can also postpone questions that were submitted earlier. Furthermore, the answering unit can dynamically adjust the priority of answers based on the time of submission. This enables efficient answers by determining the priority of answers based on the time of submission of the question. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input question submission time data into the generating AI and have the generating AI determine the priority.

[0119] The answering unit can adjust the order of answers based on the relevance of the questions when answering. For example, the answering unit adjusts the order of answers based on the relevance of the questions when answering. For example, the answering unit prioritizes answers to highly relevant questions. The answering unit can also postpone answers to less relevant questions. Furthermore, the answering unit can dynamically adjust the order of answers based on the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, highly relevant questions can be prioritized for answering. Some or all of the above-described processing in the answering unit may be performed using, or without, AI, for example. For example, the answering unit can input question relevance data to a generating AI and cause the generating AI to adjust the order of answers.

[0120] The answering unit can adjust the use of technical terminology in the answer depending on the user's level of expertise when answering. For example, the answering unit can adjust the use of technical terminology in the answer depending on the user's level of expertise when answering. For example, if the user has technical expertise, the answering unit can provide an answer that uses a lot of technical terminology. Also, if the user does not have technical expertise, the answering unit can provide a concise and easy-to-understand answer. Furthermore, the answering unit can dynamically adjust the use of technical terminology in the answer depending on the user's level of expertise. This allows the user to provide an answer that is easy to understand. Some or all of the above-mentioned processing in the answering unit can be performed using AI, for example, or without AI. For example, the answering unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reconstruction unit, reception unit, and response unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the reconstruction unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the response unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reconstruction unit, reception unit, and response unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the reconstruction unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the response unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reconstruction unit, acceptance unit, and response unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the reconstruction unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acceptance unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the response unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reconstruction unit, reception unit, and response unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the reconstruction unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the response unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis of more important data and provide results quickly. If the user is relaxed, the analysis unit can perform detailed analysis and provide comprehensive results. Furthermore, if the user is excited, the analysis unit can present the results in a visually appealing format. This allows the analysis priority to be adjusted according to the user's emotions, thereby providing the user with optimal analysis results.

[0123] The reconstructing unit can also analyze the user's past behavioral patterns and optimize the method for reconstructing the instructions. For example, if the user has preferred a particular format of instructions in the past, that format can be preferentially reconstructed. Also, if the user has frequently referred to specific information in the past, that information can be emphasized. Furthermore, if the user has used a particular device in the past, information related to that device can be preferentially provided. In this way, more personalized instructions can be provided by taking the user's past behavioral patterns into consideration.

[0124] The reception unit can also estimate the user's emotions and optimize the method of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow questions to be received quickly. In this way, by adjusting the method of receiving questions according to the user's emotions, it is possible to receive questions optimally for the user.

[0125] The answering unit can also estimate the user's emotions and optimize the way the answer is expressed based on the estimated user's emotions. For example, if the user is relaxed, a detailed answer can be provided. If the user is in a hurry, a concise answer that focuses on the main points can be provided. Furthermore, if the user is excited, an answer with a visually stimulating effect can be provided. In this way, by adjusting the way the answer is expressed according to the user's emotions, it is possible to provide answers that are easy for the user to understand.

[0126] The collection unit can also estimate the user's emotions and optimize the timing of collecting the purchase history based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect the purchase history when the user is relaxed. Also, if the user is relaxed, the purchase history can be collected immediately and analysis can be started quickly. Furthermore, if the user is in a hurry, the collection timing can be optimized to collect the necessary information in a short time. In this way, adjusting the collection timing according to the user's emotions reduces the user's stress.

[0127] The analysis unit can also analyze the user's past purchase history and predict future purchasing trends. For example, it can predict the next product the user is likely to purchase based on products that the user has frequently purchased in the past. It can also predict the purchasing trends of products related to specific seasons or events from the user's purchase history. It can also suggest highly related products based on the user's past purchase history. In this way, by analyzing the user's past purchase history, it is possible to predict future purchasing trends and make more personalized suggestions.

[0128] The reconstruction unit can also adjust the content of the instruction manual according to the user's level of expertise. For example, if the user has specialized knowledge, it can provide instructions containing detailed technical information. On the other hand, if the user does not have specialized knowledge, it can provide concise and easy-to-understand instructions. Furthermore, it can dynamically adjust the content of the instruction manual according to the user's level of expertise. In this way, by adjusting the content of the instruction manual according to the user's level of expertise, it is possible to provide instructions that are easy for the user to understand.

[0129] The reception unit can also select the optimal reception method by referring to the user's past question history. For example, questions that the user has frequently asked in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions that will be used in a specific time period based on the user's past question history. In this way, the optimal question reception method can be selected by referring to the user's past question history.

[0130] The answering unit can also apply different answering algorithms depending on the question category. For example, a dedicated answering algorithm can be applied to questions in the home appliance category. Also, a dedicated answering algorithm can be applied to questions in the equipment category. Furthermore, the answering unit can select and apply the optimal answering algorithm for each category. This improves the accuracy of answers by applying the optimal answering algorithm for each category.

[0131] The collection unit can also prioritize collection of highly relevant histories by taking into account the user's geographical location information. For example, the collection unit can prioritize collection of histories of products purchased in the area where the user is currently located. The collection unit can also collect histories of highly relevant products based on the user's geographical location information. Furthermore, the collection unit can prioritize collection of histories of products purchased in places the user frequently visits. In this way, highly relevant information can be collected preferentially by taking into account the user's geographical location information.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection unit collects the user's purchase history. For example, the collection unit collects information about home appliances and devices purchased on an e-commerce site. The collected information includes the type, model, and purchase date of the purchased product. The collection unit can obtain data from the e-commerce site using, for example, an API. The collection unit can also collect purchase information manually entered by the user. Furthermore, with the user's permission, the collection unit can extract purchase information from emails and receipts. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies what home appliances and devices the user is using based on the purchase history. The analysis unit analyzes the purchase history data using, for example, a machine learning algorithm. The analysis unit can also identify the home appliances and devices currently in use based on information such as the user's frequency of use and purchase date. Furthermore, the analysis unit can also predict future purchasing trends by referring to the user's past purchase history. Step 3: The reconstructor reconstructs the instructions based on the data analyzed by the analyzer. For example, the reconstructor may shorten long instructions and emphasize important points. The reconstructor may generate a summary of the instructions using natural language processing technology. The reconstructor may also use diagrams and illustrations to make the instructions easier to understand visually. Furthermore, the reconstructor may adjust the content of the instructions according to the user's level of expertise. Step 4: The reception unit receives a question from the user. For example, the reception unit can receive a question input by the user in text format. The reception unit can also receive a question from the user using voice input. Furthermore, the reception unit can also receive a question based on a photo of a home appliance or device taken by the user using image input. Step 5: The answering unit quickly answers the question received by the receiving unit. For example, the answering unit provides specific operating procedures in response to the user's question. The answering unit generates an answer to the user's question using, for example, a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes the user's question as input and outputs specific operating procedures. For example, the generation AI provides specific operating procedures in response to the question, "Please tell me how to use this home appliance."

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user purchase history; an analysis unit that analyzes the data collected by the collection unit; a reconstruction unit that reconstructs the instruction manual based on the data analyzed by the analysis unit; a reception unit that receives questions from users; an answering unit that quickly answers the questions received by the receiving unit; A system characterized by:

2. The collecting unit Collect information about appliances and devices purchased on e-commerce sites 2. The system of claim 1.

3. The analysis unit Identify what appliances and devices a user uses based on their purchase history 2. The system of claim 1.

4. The reconstruction unit Keep instructions concise and highlight key points 2. The system of claim 1.

5. The reconstruction unit Use diagrams and illustrations to make it easier to understand visually 2. The system of claim 1.

6. The collecting unit Estimate user emotions and optimize the timing of collecting purchase history based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze the user's past purchase history and select the appropriate collection method 2. The system of claim 1.

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