System

Abstract

An object of the system according to the embodiment is to appropriately answer a question of a user based on an instruction manual.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a reception unit, and a provision unit. The collection part collects data of the instruction manual. The analysis unit analyzes the data collected by the collection unit. The reception unit receives a question from a user. The providing unit analyzes the question received by the receiving unit and provides an appropriate answer.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] With conventional technology, many users did not understand the instruction manuals, which resulted in a heavy burden on customer support.

[0005] The system according to the embodiment aims to provide appropriate answers to user questions based on the instruction manual. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reception unit, and a provision unit. The collection unit collects data from instruction manuals. The analysis unit analyzes the data collected by the collection unit. The reception unit receives questions from users. The provision unit analyzes the questions received by the reception unit and provides appropriate answers. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately answer the user's questions based on the instruction manual. [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 chatbot system according to an embodiment of the present invention collects instruction manual data, analyzes it using a generation AI, and provides answers to user questions. The chatbot system collects instruction manual data, analyzes it using a generation AI, and provides appropriate answers to user questions, thereby improving user convenience and reducing customer support workload. For example, the chatbot system digitizes the contents of instruction manuals and inputs them into a generation AI. The generation AI then analyzes the instruction manual data and generates appropriate answers to user questions. For example, when a user asks, "How do I use this microwave?", the generation AI analyzes the microwave's instruction manual and provides information on how to use it. This allows users to quickly obtain the information they need without reading the instruction manual. This also reduces customer support workload. For example, by using a chatbot, users no longer need to contact customer support, thereby reducing the burden on support staff. This allows the chatbot system to improve user convenience and reduce customer support workload. For example, by using a chatbot, users no longer need to contact customer support, thereby reducing the burden on support staff.

[0029] A chatbot system according to an embodiment includes a collection unit, an analysis unit, a reception unit, and a provision unit. The collection unit collects data from instruction manuals. For example, the collection unit digitizes the contents of the instruction manuals and inputs them into the generation AI. For example, the collection unit scans instruction manuals for home appliances and electronic devices and inputs them as text data to the generation AI. The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, the analysis unit analyzes each section and item of the instruction manual and registers the contents of each in a database. The analysis unit uses the generation AI to understand the contents of the instruction manual and build a database for generating appropriate answers to questions from users. The reception unit accepts questions from users. For example, when a user inputs a question to the chatbot, the reception unit causes the generation AI to analyze the question and search the database for an appropriate answer to provide it. The provision unit analyzes the question accepted by the reception unit and provides an appropriate answer. For example, when a user asks, "Please tell me how to use this microwave," the provision unit causes the generation AI to analyze the microwave's instruction manual and provide information on how to use it. This enables the chatbot system according to the embodiment to efficiently collect and analyze instruction manual data, accept questions, and provide answers.

[0030] The collection unit can digitize the contents of the instruction manual and input it into the generation AI. For example, the collection unit scans the contents of the instruction manual and converts it into digital data, which is then input into the generation AI. For example, the collection unit scans the instruction manual for a home appliance or electronic device and inputs it as text data into the generation AI. The collection unit can also digitize the contents of the instruction manual using OCR technology and input it into the generation AI. For example, the collection unit scans the contents of the instruction manual with a high-resolution scanner and converts it into text information using OCR technology. In this way, the digitization of the contents of the instruction manual makes it possible for the generation AI to analyze it.

[0031] The analysis unit can analyze each section and item of the instruction manual and register the contents of each in a database. The analysis unit can, for example, use a generation AI to analyze each section and item of the instruction manual and register the contents of each in a database. The analysis unit can, for example, use a generation AI to analyze each section and item of the instruction manual and register the contents of each in a database. The analysis unit can, for example, use a generation AI to understand the contents of the instruction manual and build a database for generating appropriate answers to questions from users. For example, the analysis unit can analyze each section and item of the instruction manual and register the contents of each in a database. In this way, by registering the contents of the instruction manual in a database, appropriate answers can be provided to questions from users.

[0032] The providing unit can generate appropriate answers to questions from users. For example, when a user inputs a question to a chatbot, the providing unit causes the generation AI to analyze the question and search a database for an appropriate answer to provide it. For example, when a user asks, "Please tell me how to use this microwave," the providing unit causes the generation AI to analyze the microwave's instruction manual and provide information on how to use it. This provides appropriate answers to questions from users, improving user convenience.

[0033] When a user inputs a question to the chatbot, the providing unit allows the generation AI to analyze the question and search for and provide an appropriate answer from a database. For example, when a user inputs a question to the chatbot, the providing unit allows the generation AI to analyze the question and search for and provide an appropriate answer from a database. For example, when a user asks, "Please tell me how to use this microwave," the providing unit allows the generation AI to analyze the microwave's instruction manual and provide information on how to use it. The providing unit uses the generation AI to generate an appropriate answer to a question from the user. For example, the providing unit allows the generation AI to analyze the user's question and search for and provide an appropriate answer from a database. This makes it possible to quickly answer the user's question through the chatbot.

[0034] The providing unit can provide information described in the instruction manual, such as how to use the home appliance and how to troubleshoot it. For example, when a user asks, "How do I use this microwave?", the providing unit causes the generation AI to analyze the microwave's instruction manual and provide information on how to use it. The providing unit uses the generation AI to provide the information described in the instruction manual. For example, the providing unit causes the generation AI to analyze the user's question, search a database for an appropriate answer, and provide it. This improves user convenience by providing the information described in the instruction manual.

[0035] The collection unit can optimize the collection method based on the type and model of the product when collecting instruction manuals. For example, when collecting instruction manuals, the collection unit optimizes the collection method based on the type and model of the product. For example, in the case of a home appliance, the collection unit selects the optimal collection method based on the product model number. For example, in the case of an electronic device, the collection unit uses different collection means depending on the type of product. For example, in the case of an automobile-related instruction manual, the collection unit customizes the collection method based on the model and year of the product. This allows instruction manuals to be collected efficiently by optimizing the collection method according to the type and model of the product.

[0036] When collecting instruction manuals, the collection unit can select the items to be collected based on the frequency of use of the product and the user's usage history. For example, when collecting instruction manuals, the collection unit selects the items to be collected based on the frequency of use of the product and the user's usage history. For example, the collection unit prioritizes collecting instruction manuals for products that are frequently used by the user. For example, the collection unit analyzes the user's usage history and collects instruction manuals for frequently used products. For example, the collection unit prioritizes collecting instruction manuals for products for which the user has reported problems in the past. In this way, by selecting the items to be collected based on the frequency of use and the usage history, necessary instruction manuals can be collected preferentially.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting instruction manuals. For example, when collecting instruction manuals, the collection unit selects the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects instruction manuals using voice recognition technology. For example, when the user uses text input, the collection unit collects instruction manuals using text analysis technology. For example, when the user uses image input, the collection unit collects instruction manuals using image recognition technology. This allows instruction manuals to be collected efficiently by selecting the collection means depending on the user's input method.

[0038] When collecting instruction manuals, the collection unit can prioritize collecting highly relevant instruction manuals by taking into account the user's geographical location information. For example, when collecting instruction manuals, the collection unit prioritizes collecting highly relevant instruction manuals by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting instruction manuals for products sold in that area. For example, when the user is traveling, the collection unit collects instruction manuals for products that the user may use at the travel destination. For example, when the user moves, the collection unit collects instruction manuals for products that the user will use in the new area. In this way, by taking into account the geographical location information, highly relevant instruction manuals can be collected preferentially.

[0039] The collection unit can analyze the user's social media activity when collecting instruction manuals and collect related instruction manuals. For example, when collecting instruction manuals, the collection unit analyzes the user's social media activity and collects related instruction manuals. For example, if the user posts a product they purchased on social media, the collection unit collects instruction manuals for that product. For example, the collection unit collects instruction manuals for products in which the user is interested from the user's social media activity. For example, the collection unit collects instruction manuals for products shared by the user's friends on social media. In this way, related instruction manuals can be collected efficiently by analyzing social media activity.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting instruction manuals. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting instruction manuals. For example, the collection unit improves the collection method based on feedback provided by the user in the past. For example, the collection unit preferentially uses a specific collection means based on the user's feedback. For example, the collection unit analyzes the user's feedback and optimizes the collection method. In this way, the collection method can be optimized by reflecting the past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the instruction manual during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the instruction manual during analysis. For example, the analysis unit performs a detailed analysis for an instruction manual with high importance. For example, the analysis unit performs a simplified analysis for an instruction manual with low importance. For example, the analysis unit adjusts the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the instruction manual during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the instruction manual during analysis. For example, the analysis unit applies an analysis algorithm dedicated to home appliances to an instruction manual for a home appliance. For example, the analysis unit applies an analysis algorithm dedicated to electronic devices to an instruction manual for an electronic device. For example, the analysis unit applies an analysis algorithm dedicated to automobiles to an instruction manual related to automobiles. In this way, by applying an analysis algorithm depending on the category, the accuracy of the analysis is improved.

[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 improves the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis based on the analysis results performed by the user in the past. For example, the analysis unit analyzes the user's past analysis history and optimizes the analysis algorithm. For example, the analysis unit improves the accuracy of the analysis by referring to the user's feedback. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0044] The analysis unit can determine the analysis priority based on the publication date of the instruction manual during analysis. The analysis unit, for example, determines the analysis priority based on the publication date of the instruction manual during analysis. The analysis unit, for example, prioritizes analysis of the latest instruction manual. The analysis unit, for example, analyzes older instruction manuals later. The analysis unit, for example, adjusts the analysis priority based on the publication date. In this way, by determining the analysis priority based on the publication date, analysis can be performed efficiently.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the instruction manuals during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the instruction manuals during analysis. The analysis unit, for example, prioritizes analyzing instruction manuals for products that the user uses frequently. The analysis unit, for example, prioritizes analyzing instruction manuals for products that the user is most interested in. The analysis unit, for example, adjusts the order of analysis based on the relevance of the instruction manuals. In this way, by adjusting the order of analysis based on the relevance, analysis can be performed efficiently.

[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 adjusts 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 performs an analysis that makes heavy use of technical terms. For example, if the user is a beginner, the analysis unit performs an analysis that avoids technical terms. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0047] 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. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests question contents that will be used in a specific time period from the user's past question history. In this way, by referring to the past question history, questions can be received in the optimal method for the user.

[0048] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving a question. For example, the reception unit performs filtering based on the user's current situation and areas of interest when receiving a question. For example, the reception unit preferentially receives questions about products the user is currently using. For example, the reception unit filters related questions based on the user's areas of interest. For example, the reception unit suggests optimal questions depending on the user's current situation (time of day, location, etc.). In this way, by filtering based on the current situation and areas of interest, highly relevant questions can be preferentially received.

[0049] The reception unit can select the optimal reception means depending on the user's input method when receiving a question. For example, the reception unit selects the optimal reception means depending on the user's input method when receiving a question. For example, if the user uses voice input, the reception unit receives the question using voice recognition technology. For example, if the user uses text input, the reception unit receives the question using text analysis technology. For example, if the user uses image input, the reception unit receives the question using image recognition technology. This allows questions to be received efficiently by selecting the optimal reception means depending on the input method.

[0050] When accepting a question, the acceptance unit can prioritize accepting highly relevant questions by taking into consideration the user's geographical location information. For example, when accepting a question, the acceptance unit prioritizes accepting highly relevant questions by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the acceptance unit prioritizes accepting questions about problems that are likely to occur in that area. For example, when the user is traveling, the acceptance unit accepts questions about problems that may occur at the user's travel destination. For example, when the user has moved, the acceptance unit accepts questions about problems that may occur in the new area. In this way, by taking into consideration the geographical location information, highly relevant questions can be prioritized.

[0051] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit analyzes the user's social media activity when receiving a question and receives related questions. For example, the reception unit preferentially receives questions about products the user purchased on social media. For example, the reception unit receives questions about products the user is interested in based on the user's social media activity. For example, the reception unit receives questions about products shared by the user's friends on social media. In this way, related questions can be efficiently received by analyzing social media activity.

[0052] 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. For example, the reception unit improves the reception method based on feedback provided by the user in the past. For example, the reception unit preferentially uses a specific reception means based on the user's feedback. For example, the reception unit analyzes the user's feedback and optimizes the reception method. In this way, the reception method can be optimized by reflecting the past feedback.

[0053] The providing unit can adjust the level of detail of the answer based on the importance of the question when providing an answer. For example, the providing unit adjusts the level of detail of the answer based on the importance of the question when providing an answer. For example, the providing unit provides a detailed answer to a question of high importance. For example, the providing unit provides a simplified answer to a question of low importance. For example, the providing unit adjusts the level of detail of the answer according to the importance. In this way, by adjusting the level of detail of the answer according to the importance, answers can be provided efficiently.

[0054] The providing unit can apply different answer algorithms depending on the category of the question when providing an answer. For example, the providing unit applies different answer algorithms depending on the category of the question when providing an answer. For example, the providing unit applies an answer algorithm dedicated to home appliances to a question about home appliances. For example, the providing unit applies an answer algorithm dedicated to electronic devices to a question about electronic devices. For example, the providing unit applies an answer algorithm dedicated to automobiles to a question related to automobiles. In this way, the accuracy of the answer is improved by applying an answer algorithm depending on the category.

[0055] The providing unit can improve the accuracy of the answer by referring to the user's past answer results when providing an answer. For example, the providing unit improves the accuracy of the answer by referring to the user's past answer results when providing an answer. For example, the providing unit improves the accuracy of the answer based on answer results received by the user in the past. For example, the providing unit analyzes the user's past question history and optimizes the answer algorithm. For example, the providing unit improves the accuracy of the answer by referring to the user's feedback. In this way, the accuracy of the answer is improved by referring to the past answer results.

[0056] The providing unit can determine the priority of answers based on the time of submission of the questions when providing answers. For example, the providing unit determines the priority of answers based on the time of submission of the questions when providing answers. For example, the providing unit provides answers preferentially to the most recent questions. For example, the providing unit provides answers to older questions later. For example, the providing unit adjusts the priority of answers based on the time of submission. In this way, answers can be provided efficiently by determining the priority of answers based on the time of submission.

[0057] The providing unit can adjust the order of answers based on the relevance of the question when providing answers. The providing unit, for example, adjusts the order of answers based on the relevance of the question when providing answers. The providing unit, for example, provides answers preferentially to questions that users frequently ask. The providing unit, for example, provides answers preferentially to questions that users are most interested in. The providing unit, for example, adjusts the order of answers based on the relevance of the question. In this way, answers can be provided efficiently by adjusting the order of answers based on the relevance.

[0058] The providing unit can adjust the use of technical terms in the answer according to the user's level of expertise when providing an answer. For example, the providing unit adjusts the use of technical terms in the answer according to the user's level of expertise when providing an answer. For example, if the user has technical knowledge, the providing unit provides an answer that uses a lot of technical terms. For example, if the user is a beginner, the providing unit provides an answer that avoids technical terms. For example, the providing unit adjusts the use of technical terms in the answer according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, an answer that is easy for the user to understand can be provided.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The chatbot system can also analyze the user's past question history and automatically suggest answers to similar questions. For example, if a user has previously asked about how to use a microwave, the system can determine that the user is likely to ask a similar question next time and prepare a relevant answer in advance. Also, if a user frequently asks questions about a specific product, the system can prioritize providing detailed information about that product. This allows the system to respond to user questions quickly and accurately.

[0061] The chatbot system can also provide region-specific information by taking into account the user's geographic location. For example, if the user is in a specific region, it can provide information about services and products available in that region. If the user is traveling, it can provide information about how to use the service and things to be aware of at the travel destination. Furthermore, if the user has moved, it can provide information that will be useful for living in the new region. This allows it to provide appropriate information according to the user's geographic location.

[0062] The chatbot system can also analyze a user's social media activity and provide relevant information. For example, if a user mentions a particular product on social media, it can provide detailed information about that product. It can also suggest related products and services based on information shared by the user's friends on social media. Furthermore, it can provide customized relevant information based on the user's interests. This allows it to provide appropriate information based on the user's social media activity.

[0063] The chatbot system can also improve the quality of answers by reflecting the user's past feedback. For example, the content and format of answers can be improved based on the user's past feedback. Also, specific answering methods can be prioritized based on the user's feedback. Furthermore, the chatbot system can analyze the user's feedback and optimize the answering algorithm. This allows the quality of answers to be improved by reflecting the past feedback.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects instruction manual data. For example, the contents of the instruction manual are digitized and input into the generation AI. The collection unit scans instruction manuals for home appliances and electronic devices and inputs them into the generation AI as text data. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis unit analyzes each section and item in the instruction manual and registers the contents of each in a database. The analysis unit understands the contents of the instruction manual and builds a database to generate appropriate answers to questions from users. Step 3: The reception unit accepts questions from users. For example, when a user inputs a question into the chatbot, the generation AI analyzes the question and searches the database for an appropriate answer to provide. Step 4: The provider analyzes the question received by the receiver and provides an appropriate answer. For example, if a user asks, "How do I use this microwave?", the generator AI analyzes the microwave's instruction manual and provides information on how to use it.

[0066] (Example 2) A chatbot system according to an embodiment of the present invention collects instruction manual data, analyzes it using a generation AI, and provides answers to user questions. The chatbot system collects instruction manual data, analyzes it using a generation AI, and provides appropriate answers to user questions, thereby improving user convenience and reducing customer support workload. For example, the chatbot system digitizes the contents of instruction manuals and inputs them into a generation AI. The generation AI then analyzes the instruction manual data and generates appropriate answers to user questions. For example, when a user asks, "How do I use this microwave?", the generation AI analyzes the microwave's instruction manual and provides information on how to use it. This allows users to quickly obtain the information they need without reading the instruction manual. This also reduces customer support workload. For example, by using a chatbot, users no longer need to contact customer support, thereby reducing the burden on support staff. This allows the chatbot system to improve user convenience and reduce customer support workload. For example, by using a chatbot, users no longer need to contact customer support, thereby reducing the burden on support staff.

[0067] A chatbot system according to an embodiment includes a collection unit, an analysis unit, a reception unit, and a provision unit. The collection unit collects data from instruction manuals. For example, the collection unit digitizes the contents of the instruction manuals and inputs them into the generation AI. For example, the collection unit scans instruction manuals for home appliances and electronic devices and inputs them as text data to the generation AI. The analysis unit uses the generation AI to analyze the data collected by the collection unit. For example, the analysis unit analyzes each section and item of the instruction manual and registers the contents of each in a database. The analysis unit uses the generation AI to understand the contents of the instruction manual and build a database for generating appropriate answers to questions from users. The reception unit accepts questions from users. For example, when a user inputs a question to the chatbot, the reception unit causes the generation AI to analyze the question and search the database for an appropriate answer to provide it. The provision unit analyzes the question accepted by the reception unit and provides an appropriate answer. For example, when a user asks, "Please tell me how to use this microwave," the provision unit causes the generation AI to analyze the microwave's instruction manual and provide information on how to use it. This enables the chatbot system according to the embodiment to efficiently collect and analyze instruction manual data, accept questions, and provide answers.

[0068] The collection unit can digitize the contents of the instruction manual and input it into the generation AI. For example, the collection unit scans the contents of the instruction manual and converts it into digital data, which is then input into the generation AI. For example, the collection unit scans the instruction manual for a home appliance or electronic device and inputs it as text data into the generation AI. The collection unit can also digitize the contents of the instruction manual using OCR technology and input it into the generation AI. For example, the collection unit scans the contents of the instruction manual with a high-resolution scanner and converts it into text information using OCR technology. In this way, the digitization of the contents of the instruction manual makes it possible for the generation AI to analyze it.

[0069] The analysis unit can analyze each section and item of the instruction manual and register the contents of each in a database. The analysis unit can, for example, use a generation AI to analyze each section and item of the instruction manual and register the contents of each in a database. The analysis unit can, for example, use a generation AI to analyze each section and item of the instruction manual and register the contents of each in a database. The analysis unit can, for example, use a generation AI to understand the contents of the instruction manual and build a database for generating appropriate answers to questions from users. For example, the analysis unit can analyze each section and item of the instruction manual and register the contents of each in a database. In this way, by registering the contents of the instruction manual in a database, appropriate answers can be provided to questions from users.

[0070] The providing unit can generate appropriate answers to questions from users. For example, when a user inputs a question to a chatbot, the providing unit causes the generation AI to analyze the question and search a database for an appropriate answer to provide it. For example, when a user asks, "Please tell me how to use this microwave," the providing unit causes the generation AI to analyze the microwave's instruction manual and provide information on how to use it. This provides appropriate answers to questions from users, improving user convenience.

[0071] When a user inputs a question to the chatbot, the providing unit allows the generation AI to analyze the question and search for and provide an appropriate answer from a database. For example, when a user inputs a question to the chatbot, the providing unit allows the generation AI to analyze the question and search for and provide an appropriate answer from a database. For example, when a user asks, "Please tell me how to use this microwave," the providing unit allows the generation AI to analyze the microwave's instruction manual and provide information on how to use it. The providing unit uses the generation AI to generate an appropriate answer to a question from the user. For example, the providing unit allows the generation AI to analyze the user's question and search for and provide an appropriate answer from a database. This makes it possible to quickly answer the user's question through the chatbot.

[0072] The providing unit can provide information described in the instruction manual, such as how to use the home appliance and how to troubleshoot it. For example, when a user asks, "How do I use this microwave?", the providing unit causes the generation AI to analyze the microwave's instruction manual and provide information on how to use it. The providing unit uses the generation AI to provide the information described in the instruction manual. For example, the providing unit causes the generation AI to analyze the user's question, search a database for an appropriate answer, and provide it. This improves user convenience by providing the information described in the instruction manual.

[0073] The collection unit can estimate the user's emotions and adjust the timing of collecting the instruction manual based on the estimated user's emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of collecting the instruction manual based on the estimated user's emotions. For example, if the user is stressed, the collection unit delays the collection timing to collect when the user is relaxed. For example, if the user is in a hurry, the collection unit advances the collection timing to collect the instruction manual quickly. For example, if the user is excited, the collection unit adjusts the collection timing to collect when the user is calm. In this way, by adjusting the collection timing according to the user's emotions, the instruction manual can be collected at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The collection unit can optimize the collection method based on the type and model of the product when collecting instruction manuals. For example, when collecting instruction manuals, the collection unit optimizes the collection method based on the type and model of the product. For example, in the case of a home appliance, the collection unit selects the optimal collection method based on the product model number. For example, in the case of an electronic device, the collection unit uses different collection means depending on the type of product. For example, in the case of an automobile-related instruction manual, the collection unit customizes the collection method based on the model and year of the product. This allows instruction manuals to be collected efficiently by optimizing the collection method according to the type and model of the product.

[0075] When collecting instruction manuals, the collection unit can select the items to be collected based on the frequency of use of the product and the user's usage history. For example, when collecting instruction manuals, the collection unit selects the items to be collected based on the frequency of use of the product and the user's usage history. For example, the collection unit prioritizes collecting instruction manuals for products that are frequently used by the user. For example, the collection unit analyzes the user's usage history and collects instruction manuals for frequently used products. For example, the collection unit prioritizes collecting instruction manuals for products for which the user has reported problems in the past. In this way, by selecting the items to be collected based on the frequency of use and the usage history, necessary instruction manuals can be collected preferentially.

[0076] The collection unit can select the optimal collection means depending on the user's input method when collecting instruction manuals. For example, when collecting instruction manuals, the collection unit selects the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects instruction manuals using voice recognition technology. For example, when the user uses text input, the collection unit collects instruction manuals using text analysis technology. For example, when the user uses image input, the collection unit collects instruction manuals using image recognition technology. This allows instruction manuals to be collected efficiently by selecting the collection means depending on the user's input method.

[0077] The collection unit can estimate the user's emotions and determine the priority of the instruction manuals to be collected based on the estimated user's emotions. For example, the collection unit estimates the user's emotions and determines the priority of the instruction manuals to be collected based on the estimated user's emotions. For example, when the user is stressed, the collection unit prioritizes collecting instruction manuals of high importance. For example, when the user is relaxed, the collection unit collects instruction manuals of low importance as well. For example, when the user is in a hurry, the collection unit prioritizes collecting instruction manuals that are needed quickly. In this way, by determining the priority of the instruction manuals to be collected according to the user's emotions, important instruction manuals can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] When collecting instruction manuals, the collection unit can prioritize collecting highly relevant instruction manuals by taking into account the user's geographical location information. For example, when collecting instruction manuals, the collection unit prioritizes collecting highly relevant instruction manuals by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting instruction manuals for products sold in that area. For example, when the user is traveling, the collection unit collects instruction manuals for products that the user may use at the travel destination. For example, when the user moves, the collection unit collects instruction manuals for products that the user will use in the new area. In this way, by taking into account the geographical location information, highly relevant instruction manuals can be collected preferentially.

[0079] The collection unit can analyze the user's social media activity when collecting instruction manuals and collect related instruction manuals. For example, when collecting instruction manuals, the collection unit analyzes the user's social media activity and collects related instruction manuals. For example, if the user posts a product they purchased on social media, the collection unit collects instruction manuals for that product. For example, the collection unit collects instruction manuals for products in which the user is interested from the user's social media activity. For example, the collection unit collects instruction manuals for products shared by the user's friends on social media. In this way, related instruction manuals can be collected efficiently by analyzing social media activity.

[0080] The collection unit can customize the collection method by reflecting the user's past feedback when collecting instruction manuals. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting instruction manuals. For example, the collection unit improves the collection method based on feedback provided by the user in the past. For example, the collection unit preferentially uses a specific collection means based on the user's feedback. For example, the collection unit analyzes the user's feedback and optimizes the collection method. In this way, the collection method can be optimized by reflecting the past feedback.

[0081] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit estimates the user's emotion and adjusts the way the analysis is presented based on the estimated user's emotion. For example, if the user is feeling stressed, the analysis unit uses a simple and easy-to-understand presentation. For example, if the user is relaxed, the analysis unit uses a presentation that includes detailed information. For example, if the user is in a hurry, the analysis unit uses a presentation that focuses on the main points. This allows the analysis presentation to be adjusted according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the instruction manual during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the instruction manual during analysis. For example, the analysis unit performs a detailed analysis for an instruction manual with high importance. For example, the analysis unit performs a simplified analysis for an instruction manual with low importance. For example, the analysis unit adjusts the depth of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the instruction manual during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the instruction manual during analysis. For example, the analysis unit applies an analysis algorithm dedicated to home appliances to an instruction manual for a home appliance. For example, the analysis unit applies an analysis algorithm dedicated to electronic devices to an instruction manual for an electronic device. For example, the analysis unit applies an analysis algorithm dedicated to automobiles to an instruction manual related to automobiles. In this way, by applying an analysis algorithm depending on the category, the accuracy of the analysis is improved.

[0084] 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 improves the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis based on the analysis results performed by the user in the past. For example, the analysis unit analyzes the user's past analysis history and optimizes the analysis algorithm. For example, the analysis unit improves the accuracy of the analysis by referring to the user's feedback. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, if the user is excited, the analysis unit performs a visually stimulating analysis. This allows for adjusting the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The analysis unit can determine the analysis priority based on the publication date of the instruction manual during analysis. The analysis unit, for example, determines the analysis priority based on the publication date of the instruction manual during analysis. The analysis unit, for example, prioritizes analysis of the latest instruction manual. The analysis unit, for example, analyzes older instruction manuals later. The analysis unit, for example, adjusts the analysis priority based on the publication date. In this way, by determining the analysis priority based on the publication date, analysis can be performed efficiently.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the instruction manuals during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the instruction manuals during analysis. The analysis unit, for example, prioritizes analyzing instruction manuals for products that the user uses frequently. The analysis unit, for example, prioritizes analyzing instruction manuals for products that the user is most interested in. The analysis unit, for example, adjusts the order of analysis based on the relevance of the instruction manuals. In this way, by adjusting the order of analysis based on the relevance, analysis can be performed efficiently.

[0088] 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 adjusts 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 performs an analysis that makes heavy use of technical terms. For example, if the user is a beginner, the analysis unit performs an analysis that avoids technical terms. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0089] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated user emotions. For example, the reception unit estimates the user's emotions and adjusts the method of receiving questions based on the estimated user emotions. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input steps. For example, when the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. For example, when the user is in a hurry, the reception unit prioritizes voice input and allows questions to be received quickly. This allows questions to be received in a more appropriate manner by adjusting the method of receiving questions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] 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. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests question contents that will be used in a specific time period from the user's past question history. In this way, by referring to the past question history, questions can be received in the optimal method for the user.

[0091] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving a question. For example, the reception unit performs filtering based on the user's current situation and areas of interest when receiving a question. For example, the reception unit preferentially receives questions about products the user is currently using. For example, the reception unit filters related questions based on the user's areas of interest. For example, the reception unit suggests optimal questions depending on the user's current situation (time of day, location, etc.). In this way, by filtering based on the current situation and areas of interest, highly relevant questions can be preferentially received.

[0092] The reception unit can select the optimal reception means depending on the user's input method when receiving a question. For example, the reception unit selects the optimal reception means depending on the user's input method when receiving a question. For example, if the user uses voice input, the reception unit receives the question using voice recognition technology. For example, if the user uses text input, the reception unit receives the question using text analysis technology. For example, if the user uses image input, the reception unit receives the question using image recognition technology. This allows questions to be received efficiently by selecting the optimal reception means depending on the input method.

[0093] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, the reception unit 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 stressed, the reception unit prioritizes receiving questions of high importance. For example, when the user is relaxed, the reception unit prioritizes receiving questions including questions of low importance. For example, when the user is in a hurry, the reception unit prioritizes receiving questions that require a quick response. In this way, by determining the priority of questions according to the user's emotions, important questions can be received preferentially. Emotion estimation is realized using an emotion estimation function using, 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.

[0094] When accepting a question, the acceptance unit can prioritize accepting highly relevant questions by taking into consideration the user's geographical location information. For example, when accepting a question, the acceptance unit prioritizes accepting highly relevant questions by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the acceptance unit prioritizes accepting questions about problems that are likely to occur in that area. For example, when the user is traveling, the acceptance unit accepts questions about problems that may occur at the user's travel destination. For example, when the user has moved, the acceptance unit accepts questions about problems that may occur in the new area. In this way, by taking into consideration the geographical location information, highly relevant questions can be prioritized.

[0095] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit analyzes the user's social media activity when receiving a question and receives related questions. For example, the reception unit preferentially receives questions about products the user purchased on social media. For example, the reception unit receives questions about products the user is interested in based on the user's social media activity. For example, the reception unit receives questions about products shared by the user's friends on social media. In this way, related questions can be efficiently received by analyzing social media activity.

[0096] 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. For example, the reception unit improves the reception method based on feedback provided by the user in the past. For example, the reception unit preferentially uses a specific reception means based on the user's feedback. For example, the reception unit analyzes the user's feedback and optimizes the reception method. In this way, the reception method can be optimized by reflecting the past feedback.

[0097] The providing 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 providing unit estimates the user's emotions and adjusts the way the answer is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit uses a simple and easy-to-understand expression. For example, if the user is relaxed, the providing unit uses an expression that includes detailed information. For example, if the user is in a hurry, the providing unit uses an expression that focuses on the main points. This allows the way the answer is expressed to be adjusted according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] The providing unit can adjust the level of detail of the answer based on the importance of the question when providing an answer. For example, the providing unit adjusts the level of detail of the answer based on the importance of the question when providing an answer. For example, the providing unit provides a detailed answer to a question of high importance. For example, the providing unit provides a simplified answer to a question of low importance. For example, the providing unit adjusts the level of detail of the answer according to the importance. In this way, by adjusting the level of detail of the answer according to the importance, answers can be provided efficiently.

[0099] The providing unit can apply different answer algorithms depending on the category of the question when providing an answer. For example, the providing unit applies different answer algorithms depending on the category of the question when providing an answer. For example, the providing unit applies an answer algorithm dedicated to home appliances to a question about home appliances. For example, the providing unit applies an answer algorithm dedicated to electronic devices to a question about electronic devices. For example, the providing unit applies an answer algorithm dedicated to automobiles to a question related to automobiles. In this way, the accuracy of the answer is improved by applying an answer algorithm depending on the category.

[0100] The providing unit can improve the accuracy of the answer by referring to the user's past answer results when providing an answer. For example, the providing unit improves the accuracy of the answer by referring to the user's past answer results when providing an answer. For example, the providing unit improves the accuracy of the answer based on answer results received by the user in the past. For example, the providing unit analyzes the user's past question history and optimizes the answer algorithm. For example, the providing unit improves the accuracy of the answer by referring to the user's feedback. In this way, the accuracy of the answer is improved by referring to the past answer results.

[0101] The providing unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, the providing unit estimates the user's emotions and adjusts the length of the answer based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit provides a short, to-the-point answer. For example, if the user is relaxed, the providing unit provides a longer answer with detailed explanations. For example, if the user is excited, the providing unit provides an answer with a visually stimulating effect. This allows the length of the answer to be adjusted according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The providing unit can determine the priority of answers based on the time of submission of the questions when providing answers. For example, the providing unit determines the priority of answers based on the time of submission of the questions when providing answers. For example, the providing unit provides answers preferentially to the most recent questions. For example, the providing unit provides answers to older questions later. For example, the providing unit adjusts the priority of answers based on the time of submission. In this way, answers can be provided efficiently by determining the priority of answers based on the time of submission.

[0103] The providing unit can adjust the order of answers based on the relevance of the question when providing answers. The providing unit, for example, adjusts the order of answers based on the relevance of the question when providing answers. The providing unit, for example, provides answers preferentially to questions that users frequently ask. The providing unit, for example, provides answers preferentially to questions that users are most interested in. The providing unit, for example, adjusts the order of answers based on the relevance of the question. In this way, answers can be provided efficiently by adjusting the order of answers based on the relevance.

[0104] The providing unit can adjust the use of technical terms in the answer according to the user's level of expertise when providing an answer. For example, the providing unit adjusts the use of technical terms in the answer according to the user's level of expertise when providing an answer. For example, if the user has technical knowledge, the providing unit provides an answer that uses a lot of technical terms. For example, if the user is a beginner, the providing unit provides an answer that avoids technical terms. For example, the providing unit adjusts the use of technical terms in the answer according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the level of expertise, an answer that is easy for the user to understand can be provided. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, reception unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit scans an instruction manual using the camera 42 of the smart device 14 and inputs the scanned data into the generation AI as digital data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the data in the instruction manual using the generation AI. The reception unit is realized, for example, by the reception device 38 of the smart device 14 and receives questions from the user. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer based on the analyzed data and provides the answer to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, reception unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit scans an instruction manual using the camera 42 of the smart glasses 214 and inputs the scanned data into the generation AI as digital data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the data in the instruction manual using the generation AI. The reception unit is realized, for example, by the microphone 238 of the smart glasses 214 and receives questions from the user. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer based on the analyzed data and provides the answer to the user via the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, reception unit, and provision unit described above 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 scans an instruction manual using the camera 42 of the headset-type terminal 314 and inputs the scanned data into the generation AI as digital data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the data in the instruction manual using the generation AI. The reception unit is realized, for example, by the microphone 238 of the headset-type terminal 314 and receives questions from the user. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer based on the analyzed data and provides the answer to the user through the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, reception unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit scans an instruction manual using the camera 42 of the robot 414 and inputs the scanned data into the generation AI as digital data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the data in the instruction manual using the generation AI. The reception unit is realized, for example, by the microphone 238 of the robot 414 and receives questions from the user. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer based on the analyzed data and provides the answer to the user via the speaker 240 of the robot 414.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The chatbot system can further estimate the user's emotions and adjust the tone of the response based on the estimated emotions. For example, if the user is feeling stressed, the response providing unit can provide an answer in a gentle tone. If the user is relaxed, the response providing unit can provide an answer in a friendly tone. If the user is in a hurry, the response providing unit can provide an answer in a concise and quick tone. This makes it possible to improve user satisfaction by providing an answer in an appropriate tone according to the user's emotions.

[0107] The chatbot system can also analyze the user's past question history and automatically suggest answers to similar questions. For example, if a user has previously asked about how to use a microwave, the system can determine that the user is likely to ask a similar question next time and prepare a relevant answer in advance. Also, if a user frequently asks questions about a specific product, the system can prioritize providing detailed information about that product. This allows the system to respond to user questions quickly and accurately.

[0108] The chatbot system can also provide region-specific information by taking into account the user's geographic location. For example, if the user is in a specific region, it can provide information about services and products available in that region. If the user is traveling, it can provide information about how to use the service and things to be aware of at the travel destination. Furthermore, if the user has moved, it can provide information that will be useful for living in the new region. This allows it to provide appropriate information according to the user's geographic location.

[0109] The chatbot system can also analyze a user's social media activity and provide relevant information. For example, if a user mentions a particular product on social media, it can provide detailed information about that product. It can also suggest related products and services based on information shared by the user's friends on social media. Furthermore, it can provide customized relevant information based on the user's interests. This allows it to provide appropriate information based on the user's social media activity.

[0110] The chatbot system can also improve the quality of answers by reflecting the user's past feedback. For example, the content and format of answers can be improved based on the user's past feedback. Also, specific answering methods can be prioritized based on the user's feedback. Furthermore, the chatbot system can analyze the user's feedback and optimize the answering algorithm. This allows the quality of answers to be improved by reflecting the past feedback.

[0111] The chatbot system can further estimate the user's emotions and adjust the level of detail of the answer based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide a concise and to-the-point answer. If the user is relaxed, the providing unit can provide an answer with detailed explanations. If the user is in a hurry, the providing unit can provide a quick answer. This allows the system to provide an answer with an appropriate level of detail according to the user's emotions.

[0112] The chatbot system can further estimate the user's emotions and adjust the visual presentation of the answer based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide an answer with a simple and visually calming design. If the user is relaxed, the providing unit can provide an answer with a visually rich design. If the user is excited, the providing unit can provide an answer with a visually stimulating effect. This makes it possible to provide an answer with an appropriate visual presentation according to the user's emotions.

[0113] The chatbot system can further estimate the user's emotions and adjust the order of answers based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide important information with priority. If the user is relaxed, the providing unit can provide detailed information in an orderly manner. If the user is in a hurry, the providing unit can quickly provide information that covers the main points. This makes it possible to provide answers in an appropriate order according to the user's emotions.

[0114] The chatbot system can further estimate the user's emotions and adjust the use of technical terms in the answer based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can provide a simple answer that avoids technical terms. If the user is relaxed, the providing unit can provide a detailed answer that includes technical terms. If the user is in a hurry, the providing unit can provide a concise answer that gets to the point. This makes it possible to provide answers using appropriate technical terms according to the user's emotions.

[0115] The chatbot system can further estimate the user's emotions and adjust the format of the answer based on the estimated emotions. For example, if the user is stressed, the providing unit can provide an answer in bullet points or short paragraphs. If the user is relaxed, the providing unit can provide a longer answer with detailed explanations. If the user is in a hurry, the providing unit can provide an answer in a concise format that focuses on the main points. This allows the system to provide an answer in an appropriate format depending on the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects instruction manual data. For example, the contents of the instruction manual are digitized and input into the generation AI. The collection unit scans instruction manuals for home appliances and electronic devices and inputs them into the generation AI as text data. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit. The analysis unit analyzes each section and item in the instruction manual and registers the contents of each in a database. The analysis unit understands the contents of the instruction manual and builds a database to generate appropriate answers to questions from users. Step 3: The reception unit accepts questions from users. For example, when a user inputs a question into the chatbot, the generation AI analyzes the question and searches the database for an appropriate answer to provide. Step 4: The provider analyzes the question received by the receiver and provides an appropriate answer. For example, if a user asks, "How do I use this microwave?", the generator AI analyzes the microwave's instruction manual and provides information on how to use it.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

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

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. 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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

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

[0155] 7, a 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.

[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 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.

[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 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).

[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] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 data on instruction manuals; an analysis unit that analyzes the data collected by the collection unit; a reception unit that receives questions from users; a providing unit that analyzes the question received by the receiving unit and provides an appropriate answer. A system characterized by:

2. The collecting unit The contents of the instruction manual are digitized and input into the generation AI.

2. The system of claim 1.

3. The analysis unit Analyze each section and item in the instruction manual and register the contents of each in a database 2. The system of claim 1.

4. The providing unit Generate appropriate answers to user questions 2. The system of claim 1.

5. The providing unit When a user enters a question into the chatbot, the generative AI analyzes the question and searches the database for an appropriate answer to provide.

2. The system of claim 1.

6. The providing unit Providing information found in the instruction manual, such as how to use and troubleshoot your appliances 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of collecting instruction manuals based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting instruction manuals, optimize collection methods based on product type and model 2. The system of claim 1.

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