system

Abstract

An object of a system according to an embodiment is to analyze a question and an answer from a customer and provide an appropriate recommended action.SOLUTION: A system includes a collection unit, a generation unit, and an update unit. The collection unit collects questions and answers from customers. The generation unit analyzes the question and the answer collected by the collection unit and generates a recommended action. The updating unit updates the FAQ based on the recommended action generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are not sufficient in efficiently managing customer questions and answers and providing appropriate recommended actions, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze questions and answers from customers and provide appropriate recommended actions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and an update unit. The collection unit collects questions and answers from customers. The generation unit analyzes the questions and answers collected by the collection unit and generates recommended actions. The update unit updates the FAQs based on the recommended actions generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze questions and answers from customers and provide appropriate recommended actions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention collects customer questions and answers, analyzes them using a generation AI to generate recommended actions, and updates FAQs. This system collects customer questions and answers, analyzes them using a generation AI to generate recommended actions, and updates the FAQs. For example, customer questions and their answers are collected. These include questions about product usage and troubleshooting. This information is input into the generation AI and used as training data. The generation AI then analyzes the collected questions and answers and automatically generates recommended actions for the user's problem. For example, if a user asks, "My product isn't working," the generation AI uses past data to provide recommended actions such as "Check the power" or "Try restarting." Furthermore, as new questions or issues arise, the generation AI learns in real time and updates the FAQs. For example, when a new product is released, an increase in questions about that product is expected. The generation AI learns from these new questions and generates appropriate answers. This allows the system to provide users with prompt and appropriate support and improve customer satisfaction. Furthermore, automated FAQ updates reduce the burden on support staff.

[0029] An information processing system according to an embodiment includes a collection unit, a generation unit, and an update unit. The collection unit collects questions and answers from customers. The questions and answers from customers may be in text format, audio format, or technical questions, but are not limited to these examples. The collection unit, for example, stores the questions and answers from customers in a database. The collection unit can also collect questions and answers from customers in real time. For example, the collection unit collects questions through a web form or a chatbot and stores the collected questions in a database. The generation unit uses a generation AI to analyze the questions and answers collected by the collection unit and generate recommended actions. Recommended actions include, but are not limited to, updating FAQs and notifying users. For example, the generation unit uses natural language processing technology to analyze the questions and answers and generate appropriate recommended actions. The generation unit can also generate recommended actions based on past data using a machine learning algorithm. For example, the generation unit generates recommended actions taking into account the user's past behavior history and current situation. The update unit updates the FAQs based on the recommended actions generated by the generation unit. The updating includes, for example, which parts of the FAQ to update and how to update them, but is not limited to such examples. For example, the update unit updates the FAQ based on new information from the generation unit. The update unit can also update the FAQ in real time when new questions or phenomena arise. For example, when the number of questions about a new product increases, the update unit quickly updates the FAQ about that product. This enables the information processing system according to the embodiment to efficiently collect and analyze questions and answers from customers, generate recommended actions, and update the FAQ.

[0030] The collection unit can store the questions and answers from the customer in a database. Examples of databases include, but are not limited to, relational databases and NoSQL databases. For example, the collection unit stores the questions and answers from the customer in a relational database. The collection unit can also store the questions and answers from the customer in a NoSQL database. For example, the collection unit stores the questions and answers as key-value pairs. The collection unit can also store the questions and answers in a document format. For example, the collection unit stores the questions and answers in JSON format. By storing the questions and answers in a database, they can be used for later analysis and generation of recommended actions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to classify and tag the data when storing the questions and answers in a database.

[0031] The generation unit can analyze the data from the collection unit and generate appropriate recommended actions. The generation unit can analyze questions and answers using, for example, natural language processing technology. For example, the generation unit can analyze the content of the questions and answers using a text analysis algorithm. The generation unit can also generate recommended actions based on past data using a machine learning algorithm. For example, the generation unit can generate recommended actions taking into account the user's past behavioral history and current situation. The generation unit can also generate recommended actions using a rule-based algorithm. For example, the generation unit generates recommended actions based on predefined rules. In this way, appropriate recommended actions can be generated by analyzing the data from the collection unit. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data from the collection unit to a generation AI and cause the generation AI to generate recommended actions.

[0032] The update unit can update the FAQ based on new information from the generation unit. The update unit updates the content of the FAQ based on, for example, new information from the generation unit. For example, the update unit adds, modifies, or deletes specific items from the FAQ. The update unit can also change the structure of the FAQ. For example, the update unit reorganizes FAQ categories to make it easier for users to find information. Furthermore, the update unit can update the FAQ in real time when new questions or phenomena arise. For example, when the number of questions about a new product increases, the update unit quickly updates the FAQ about that product. This makes it possible to provide the latest information by updating the FAQ based on new information from the generation unit. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input new information from the generation unit into AI and have the AI ​​update the FAQ.

[0033] The generation unit can automatically generate recommended actions for a user's problem. The generation unit, for example, uses natural language processing technology to analyze the user's problem and automatically generate recommended actions. For example, the generation unit analyzes the user's question and generates an appropriate recommended action. The generation unit can also automatically generate recommended actions based on past data using a machine learning algorithm. For example, the generation unit generates recommended actions taking into account the user's past behavioral history and current situation. The generation unit can also automatically generate recommended actions using a rule-based algorithm. For example, the generation unit generates recommended actions based on predefined rules. This enables rapid response by automatically generating recommended actions for the user's problem. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's question to the generation AI and cause the generation AI to generate recommended actions.

[0034] The update unit can update the FAQ in real time when a new question or phenomenon arises. The update unit updates the FAQ in real time when a new question or phenomenon arises, for example. For example, when the number of questions about a new product increases, the update unit quickly updates the FAQ about that product. Furthermore, when a new troubleshooting question arises, the update unit can add an answer to that question to the FAQ. Furthermore, the update unit can build a system for updating the FAQ in real time. For example, the update unit receives new information from the generation unit in real time and automatically updates the FAQ. This allows the FAQ to be updated in real time when a new question or phenomenon arises, thereby always providing the latest information. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can input information about new questions or phenomena into AI and have the AI ​​update the FAQ.

[0035] The collection unit can analyze the user's past question history and select the optimal collection method when collecting questions. For example, the collection unit analyzes the user's past question history and selects the optimal collection method when collecting questions. For example, the collection unit prioritizes collecting related questions based on the content of questions the user has frequently asked in the past. The collection unit can also select the most effective collection method (audio, text, etc.) from the user's past question history. The collection unit can also analyze the user's past question history and collect questions at the optimal timing. In this way, the optimal collection method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past question history to a generation AI and cause the generation AI to select the optimal collection method.

[0036] The collection unit can filter questions based on the user's current situation and areas of interest when collecting them. For example, the collection unit can filter questions based on the user's current situation and areas of interest when collecting questions. For example, the collection unit can prioritize collecting questions related to products currently used by the user. The collection unit can also filter and collect related questions based on the user's areas of interest. The collection unit can also collect appropriate questions depending on the user's current situation (e.g., during troubleshooting). This makes it possible to collect highly relevant questions by filtering based on the user's current situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data related to the user's current situation and areas of interest to the generation AI and have the generation AI perform the filtering.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting questions. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting questions. For example, if the user uses voice input, the collection unit can collect questions using voice recognition technology. Also, if the user uses text input, the collection unit can collect questions using text analysis technology. Also, if the user asks questions using images, the collection unit can collect questions using image recognition technology. In this way, questions can be efficiently collected by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting questions, the collection unit can prioritize collecting highly relevant questions by taking into account the user's geographical location information. For example, when collecting questions, the collection unit prioritizes collecting highly relevant questions 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 questions related to that area. The collection unit can also collect questions about issues specific to the area based on the user's geographical location information. Furthermore, when the user is traveling, the collection unit can prioritize collecting questions related to the travel destination. In this way, by taking the user's geographical location information into account, highly relevant questions can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant questions.

[0039] The collection unit can analyze the user's social media activity when collecting questions and collect related questions. For example, the collection unit analyzes the user's social media activity when collecting questions and collects related questions. For example, the collection unit collects questions related to issues the user mentioned on social media. The collection unit can also analyze the content of the user's social media posts and collect related questions. The collection unit can also collect related questions by referring to the activities of the user's friends on social media. In this way, related questions can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related questions.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting questions. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting questions. For example, the collection unit selects an optimal collection method based on feedback provided by the user in the past. The collection unit can also improve the collection method by reflecting the user's past feedback. The collection unit can also adjust the collection timing and means based on the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The generation unit can adjust the level of detail of the recommended action based on the importance of the question when generating the recommended action. For example, the generation unit adjusts the level of detail of the recommended action based on the importance of the question when generating the recommended action. For example, the generation unit provides a detailed recommended action for a question with a high importance. The generation unit can also provide a concise recommended action for a question with a low importance. The generation unit can also gradually adjust the level of detail of the recommended action according to the importance of the question. In this way, by adjusting the level of detail of the recommended action based on the importance of the question, it is possible to provide a recommended action with an appropriate level of detail. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the recommended action.

[0042] The generation unit can apply different generation algorithms depending on the question category when generating a recommended action. For example, the generation unit applies different generation algorithms depending on the question category when generating a recommended action. For example, the generation unit applies a specialized technical generation algorithm to a technical question. The generation unit can also apply a user-friendly generation algorithm to a question about how to use a product. The generation unit can also apply a problem-solving specialized generation algorithm to a question about troubleshooting. In this way, by applying different generation algorithms depending on the question category, more appropriate recommended actions can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question category data to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0043] The generation unit can improve the accuracy of generation by referring to the user's past results of recommended actions when generating recommended actions. For example, the generation unit can improve the accuracy of generation by referring to the user's past results of recommended actions when generating recommended actions. For example, the generation unit generates an optimal recommended action based on the results of recommended actions previously performed by the user. The generation unit can also analyze the user's past results of recommended actions and improve the generation algorithm. The generation unit can also improve the accuracy of recommended actions based on user feedback. In this way, the accuracy of generation can be improved by referring to the user's past results of recommended actions. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past result data of recommended actions into the generation AI and cause the generation AI to improve the accuracy of generation.

[0044] The generation unit can determine the priority of recommended actions based on the time when the question was submitted when generating recommended actions. For example, the generation unit can determine the priority of recommended actions based on the time when the question was submitted when generating recommended actions. For example, the generation unit can quickly provide recommended actions immediately after the question is submitted. The generation unit can also determine the priority based on the time when the question was submitted and provide recommended actions at an appropriate time. The generation unit can also adjust the priority of recommended actions according to the time of day when the question was submitted. In this way, by determining the priority of recommended actions based on the time when the question was submitted, recommended actions can be provided at an appropriate time. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the time when the question was submitted to the generation AI and cause the generation AI to determine the priority of recommended actions.

[0045] The generation unit can adjust the order of recommended actions based on the relevance of the question when generating recommended actions. The generation unit, for example, adjusts the order of recommended actions based on the relevance of the question when generating recommended actions. For example, the generation unit prioritizes providing recommended actions when the relevance of the question is high. The generation unit can also adjust the order of recommended actions based on the relevance of the question. The generation unit can also postpone providing recommended actions when the relevance of the question is low. In this way, by adjusting the order of recommended actions based on the relevance of the question, more relevant recommended actions can be prioritized. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of recommended actions.

[0046] The generation unit can adjust the use of technical terminology in the recommended action according to the user's level of expertise when generating the recommended action. For example, the generation unit can adjust the use of technical terminology in the recommended action according to the user's level of expertise when generating the recommended action. For example, if the user has technical expertise, the generation unit can provide a recommended action that uses technical terminology. Furthermore, if the user is a beginner, the generation unit can provide a recommended action that is easy to understand and avoids technical terminology. The generation unit can also adjust the use of technical terminology in the recommended action according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the recommended action according to the user's level of expertise, it is possible to provide a recommended action that is easier to understand. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology in the recommended action.

[0047] When updating an FAQ, the update unit can optimize the current update content by referring to past update data. When updating an FAQ, the update unit, for example, optimizes the current update content by referring to past update data. For example, the update unit determines the optimal update content based on past FAQ update data. The update unit can also analyze past update data and improve the current update content. The update unit can also optimize the FAQ update method by referring to past update data. In this way, the current update content can be optimized by referring to the past update data. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update data to a generation AI and cause the generation AI to optimize the current update content.

[0048] The update unit can apply different update methods to different question categories when updating the FAQ. For example, the update unit applies different update methods to different question categories when updating the FAQ. For example, the update unit applies a specialized technical update method to technical questions. The update unit can also apply a user-friendly update method to questions about how to use a product. The update unit can also apply an update method specialized for problem solving to questions about troubleshooting. In this way, by applying different update methods to different question categories, more appropriate FAQs can be provided. Some or all of the above-described processing in the update unit may be performed using, or without, AI, for example. For example, the update unit can input question category data to a generation AI and cause the generation AI to apply an appropriate update method.

[0049] The update unit can perform the update when updating an FAQ, taking into account the attribute information of the question submitter. For example, when updating an FAQ, the update unit performs the update while taking into account the attribute information of the question submitter. For example, if the question submitter is a beginner, the update unit provides an FAQ containing an easy-to-understand explanation. Furthermore, if the question submitter has specialized knowledge, the update unit can also provide an FAQ containing detailed technical information. Furthermore, the update unit can provide an optimal FAQ based on the attribute information (age, occupation, etc.) of the question submitter. In this way, by taking into account the attribute information of the question submitter, a more appropriate FAQ can be provided. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the attribute information of the question submitter to a generation AI and cause the generation AI to update the FAQ.

[0050] The update unit can determine the update priority based on the time when the question was submitted when updating the FAQ. The update unit, for example, determines the update priority based on the time when the question was submitted when updating the FAQ. For example, the update unit quickly updates the FAQ immediately after the question is submitted. The update unit can also determine the priority based on the time when the question was submitted and update the FAQ at an appropriate time. The update unit can also adjust the FAQ update priority according to the time of day when the question was submitted. In this way, by determining the update priority based on the time when the question was submitted, the FAQ can be updated at an appropriate time. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input question submission time data to the generation AI and cause the generation AI to determine the update priority.

[0051] The update unit can optimize the update content by referring to market data related to the question when updating the FAQ. The update unit can optimize the update content by referring to market data related to the question when updating the FAQ, for example. For example, the update unit determines optimal FAQ update content based on the related market data. The update unit can also analyze market data and improve the current FAQ update content. The update unit can also optimize the FAQ update method by referring to the market data. In this way, the update content can be optimized by referring to market data related to the question. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input related market data to a generation AI and cause the generation AI to optimize the update content.

[0052] The update unit can take into consideration the technical maturity of the question when updating the FAQ. For example, the update unit can take into consideration the technical maturity of the question when updating the FAQ. For example, the update unit can provide an FAQ containing detailed technical information for technically mature questions. The update unit can also provide an FAQ containing easy-to-understand explanations for technically immature questions. The update unit can also adjust the update content of the FAQ according to the technical maturity of the question. In this way, more appropriate FAQs can be provided by taking into consideration the technical maturity of the question. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input technical maturity data of the question to a generation AI and cause the generation AI to update the FAQ.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When collecting questions, the collection unit can analyze the user's past question history and select the optimal collection method. For example, the collection unit prioritizes collecting related questions based on the content of questions the user has frequently asked in the past. The collection unit can also select the most effective collection method (audio, text, etc.) from the user's past question history. The collection unit can also analyze the user's past question history and collect questions at the optimal timing. In this way, the optimal collection method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past question history into a generation AI and cause the generation AI to select the optimal collection method.

[0055] When collecting questions, the collection unit can filter them based on the user's current situation and areas of interest. For example, the collection unit prioritizes collecting questions related to products currently being used by the user. The collection unit can also filter and collect related questions based on the user's areas of interest. The collection unit can also collect appropriate questions depending on the user's current situation (e.g., during troubleshooting). This makes it possible to collect highly relevant questions by filtering based on the user's current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data related to the user's current situation and areas of interest to the generation AI and have the generation AI perform the filtering.

[0056] When collecting questions, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect questions using voice recognition technology. Also, if the user uses text input, the collection unit can collect questions using text analysis technology. Also, if the user uses an image to ask a question, the collection unit can collect questions using image recognition technology. This allows questions to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0057] When collecting questions, the collection unit can prioritize collecting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting questions related to that area. The collection unit can also collect questions about issues specific to the area based on the user's geographical location information. If the user is traveling, the collection unit can also prioritize collecting questions related to the travel destination. In this way, highly relevant questions can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant questions.

[0058] When collecting questions, the collection unit can analyze the user's social media activity and collect related questions. For example, the collection unit collects questions related to issues the user mentioned on social media. The collection unit can also analyze the content of the user's social media posts and collect related questions. The collection unit can also collect related questions by referring to the activities of the user's friends on social media. In this way, related questions can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related questions.

[0059] When collecting questions, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit selects the optimal collection method based on feedback provided by the user in the past. The collection unit can also improve the collection method by reflecting the user's past feedback. The collection unit can also adjust the collection timing and means based on the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0060] When generating a recommended action, the generation unit can adjust the level of detail of the recommended action based on the importance of the question. For example, the generation unit provides a detailed recommended action for a question with a high importance. The generation unit can also provide a concise recommended action for a question with a low importance. The generation unit can also gradually adjust the level of detail of the recommended action according to the importance of the question. In this way, by adjusting the level of detail of the recommended action based on the importance of the question, it is possible to provide a recommended action with an appropriate level of detail. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the recommended action.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection department collects customer questions and answers. These questions and answers can be in text, voice, or technical format. The collection department stores these questions and answers in a database. The collection department can also collect questions in real time through web forms or chatbots. Step 2: The generation unit uses generation AI to analyze the questions and answers collected by the collection unit and generate recommended actions. Recommended actions include updating FAQs and notifying users. The generation unit uses natural language processing technology and machine learning algorithms to generate appropriate recommended actions based on past data. Step 3: The updater updates the FAQ based on the recommended actions generated by the generator. The updater determines which parts of the FAQ to update and how, and updates the FAQ based on new information. It can also update the FAQ in real time when new questions or issues arise.

[0063] (Example 2) A system according to an embodiment of the present invention collects customer questions and answers, analyzes them using a generation AI to generate recommended actions, and updates FAQs. This system collects customer questions and answers, analyzes them using a generation AI to generate recommended actions, and updates the FAQs. For example, customer questions and their answers are collected. These include questions about product usage and troubleshooting. This information is input into the generation AI and used as training data. The generation AI then analyzes the collected questions and answers and automatically generates recommended actions for the user's problem. For example, if a user asks, "My product isn't working," the generation AI uses past data to provide recommended actions such as "Check the power" or "Try restarting." Furthermore, as new questions or issues arise, the generation AI learns in real time and updates the FAQs. For example, when a new product is released, an increase in questions about that product is expected. The generation AI learns from these new questions and generates appropriate answers. This allows the system to provide users with prompt and appropriate support and improve customer satisfaction. Furthermore, automated FAQ updates reduce the burden on support staff.

[0064] An information processing system according to an embodiment includes a collection unit, a generation unit, and an update unit. The collection unit collects questions and answers from customers. The questions and answers from customers may be in text format, audio format, or technical questions, but are not limited to these examples. The collection unit, for example, stores the questions and answers from customers in a database. The collection unit can also collect questions and answers from customers in real time. For example, the collection unit collects questions through a web form or a chatbot and stores the collected questions in a database. The generation unit uses a generation AI to analyze the questions and answers collected by the collection unit and generate recommended actions. Recommended actions include, but are not limited to, updating FAQs and notifying users. For example, the generation unit uses natural language processing technology to analyze the questions and answers and generate appropriate recommended actions. The generation unit can also generate recommended actions based on past data using a machine learning algorithm. For example, the generation unit generates recommended actions taking into account the user's past behavior history and current situation. The update unit updates the FAQs based on the recommended actions generated by the generation unit. The updating includes, for example, which parts of the FAQ to update and how to update them, but is not limited to such examples. For example, the update unit updates the FAQ based on new information from the generation unit. The update unit can also update the FAQ in real time when new questions or phenomena arise. For example, when the number of questions about a new product increases, the update unit quickly updates the FAQ about that product. This enables the information processing system according to the embodiment to efficiently collect and analyze questions and answers from customers, generate recommended actions, and update the FAQ.

[0065] The collection unit can store the questions and answers from the customer in a database. Examples of databases include, but are not limited to, relational databases and NoSQL databases. For example, the collection unit stores the questions and answers from the customer in a relational database. The collection unit can also store the questions and answers from the customer in a NoSQL database. For example, the collection unit stores the questions and answers as key-value pairs. The collection unit can also store the questions and answers in a document format. For example, the collection unit stores the questions and answers in JSON format. By storing the questions and answers in a database, they can be used for later analysis and generation of recommended actions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can use AI to classify and tag the data when storing the questions and answers in a database.

[0066] The generation unit can analyze the data from the collection unit and generate appropriate recommended actions. The generation unit can analyze questions and answers using, for example, natural language processing technology. For example, the generation unit can analyze the content of the questions and answers using a text analysis algorithm. The generation unit can also generate recommended actions based on past data using a machine learning algorithm. For example, the generation unit can generate recommended actions taking into account the user's past behavioral history and current situation. The generation unit can also generate recommended actions using a rule-based algorithm. For example, the generation unit generates recommended actions based on predefined rules. In this way, appropriate recommended actions can be generated by analyzing the data from the collection unit. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data from the collection unit to a generation AI and cause the generation AI to generate recommended actions.

[0067] The update unit can update the FAQ based on new information from the generation unit. The update unit updates the content of the FAQ based on, for example, new information from the generation unit. For example, the update unit adds, modifies, or deletes specific items from the FAQ. The update unit can also change the structure of the FAQ. For example, the update unit reorganizes FAQ categories to make it easier for users to find information. Furthermore, the update unit can update the FAQ in real time when new questions or phenomena arise. For example, when the number of questions about a new product increases, the update unit quickly updates the FAQ about that product. This makes it possible to provide the latest information by updating the FAQ based on new information from the generation unit. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input new information from the generation unit into AI and have the AI ​​update the FAQ.

[0068] The generation unit can automatically generate recommended actions for a user's problem. The generation unit, for example, uses natural language processing technology to analyze the user's problem and automatically generate recommended actions. For example, the generation unit analyzes the user's question and generates an appropriate recommended action. The generation unit can also automatically generate recommended actions based on past data using a machine learning algorithm. For example, the generation unit generates recommended actions taking into account the user's past behavioral history and current situation. The generation unit can also automatically generate recommended actions using a rule-based algorithm. For example, the generation unit generates recommended actions based on predefined rules. This enables rapid response by automatically generating recommended actions for the user's problem. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's question to the generation AI and cause the generation AI to generate recommended actions.

[0069] The update unit can update the FAQ in real time when a new question or phenomenon arises. The update unit updates the FAQ in real time when a new question or phenomenon arises, for example. For example, when the number of questions about a new product increases, the update unit quickly updates the FAQ about that product. Furthermore, when a new troubleshooting question arises, the update unit can add an answer to that question to the FAQ. Furthermore, the update unit can build a system for updating the FAQ in real time. For example, the update unit receives new information from the generation unit in real time and automatically updates the FAQ. This allows the FAQ to be updated in real time when a new question or phenomenon arises, thereby always providing the latest information. Some or all of the above-mentioned processing in the update unit may be performed, for example, using AI, or may be performed without using AI. For example, the update unit can input information about new questions or phenomena into AI and have the AI ​​update the FAQ.

[0070] The collection unit can estimate the user's emotions and adjust the timing of question collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of question collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit refrains from collecting questions and collects them when the user is relaxed. Furthermore, if the user is excited, the collection unit can immediately collect questions and provide a prompt response. Furthermore, if the user is calm, the collection unit can collect questions at a normal timing. Thus, by adjusting the timing of question collection according to the user's emotions, questions can be collected at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of question collection.

[0071] The collection unit can analyze the user's past question history and select the optimal collection method when collecting questions. For example, the collection unit analyzes the user's past question history and selects the optimal collection method when collecting questions. For example, the collection unit prioritizes collecting related questions based on the content of questions the user has frequently asked in the past. The collection unit can also select the most effective collection method (audio, text, etc.) from the user's past question history. The collection unit can also analyze the user's past question history and collect questions at the optimal timing. In this way, the optimal collection method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past question history to a generation AI and cause the generation AI to select the optimal collection method.

[0072] The collection unit can filter questions based on the user's current situation and areas of interest when collecting them. For example, the collection unit can filter questions based on the user's current situation and areas of interest when collecting questions. For example, the collection unit can prioritize collecting questions related to products currently used by the user. The collection unit can also filter and collect related questions based on the user's areas of interest. The collection unit can also collect appropriate questions depending on the user's current situation (e.g., during troubleshooting). This makes it possible to collect highly relevant questions by filtering based on the user's current situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data related to the user's current situation and areas of interest to the generation AI and have the generation AI perform the filtering.

[0073] The collection unit can select the optimal collection means depending on the user's input method when collecting questions. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting questions. For example, if the user uses voice input, the collection unit can collect questions using voice recognition technology. Also, if the user uses text input, the collection unit can collect questions using text analysis technology. Also, if the user asks questions using images, the collection unit can collect questions using image recognition technology. In this way, questions can be efficiently collected by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0074] The collection unit can estimate the user's emotions and determine the priority of questions to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of questions to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting urgent questions. When the user is relaxed, the collection unit can also collect questions with a normal priority. When the user is excited, the collection unit can also prioritize collecting relevant questions. This allows more appropriate questions to be collected preferentially by determining the priority of questions to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of questions.

[0075] When collecting questions, the collection unit can prioritize collecting highly relevant questions by taking into account the user's geographical location information. For example, when collecting questions, the collection unit prioritizes collecting highly relevant questions 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 questions related to that area. The collection unit can also collect questions about issues specific to the area based on the user's geographical location information. Furthermore, when the user is traveling, the collection unit can prioritize collecting questions related to the travel destination. In this way, by taking the user's geographical location information into account, highly relevant questions can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant questions.

[0076] The collection unit can analyze the user's social media activity when collecting questions and collect related questions. For example, the collection unit analyzes the user's social media activity when collecting questions and collects related questions. For example, the collection unit collects questions related to issues the user mentioned on social media. The collection unit can also analyze the content of the user's social media posts and collect related questions. The collection unit can also collect related questions by referring to the activities of the user's friends on social media. In this way, related questions can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related questions.

[0077] The collection unit can customize the collection method by reflecting the user's past feedback when collecting questions. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting questions. For example, the collection unit selects an optimal collection method based on feedback provided by the user in the past. The collection unit can also improve the collection method by reflecting the user's past feedback. The collection unit can also adjust the collection timing and means based on the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0078] The generation unit can estimate the user's emotion and adjust the presentation of the recommended action based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the presentation of the recommended action based on the estimated user emotion. For example, if the user is feeling stressed, the generation unit can provide a concise and easy-to-understand recommended action. Furthermore, if the user is relaxed, the generation unit can provide a recommended action with detailed explanations. Furthermore, if the user is excited, the generation unit can provide a visually appealing recommended action. This allows for adjusting the presentation of the recommended action according to the user's emotion, thereby providing a more appropriate recommended action. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation of the recommended action.

[0079] The generation unit can adjust the level of detail of the recommended action based on the importance of the question when generating the recommended action. For example, the generation unit adjusts the level of detail of the recommended action based on the importance of the question when generating the recommended action. For example, the generation unit provides a detailed recommended action for a question with a high importance. The generation unit can also provide a concise recommended action for a question with a low importance. The generation unit can also gradually adjust the level of detail of the recommended action according to the importance of the question. In this way, by adjusting the level of detail of the recommended action based on the importance of the question, it is possible to provide a recommended action with an appropriate level of detail. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the recommended action.

[0080] The generation unit can apply different generation algorithms depending on the question category when generating a recommended action. For example, the generation unit applies different generation algorithms depending on the question category when generating a recommended action. For example, the generation unit applies a specialized technical generation algorithm to a technical question. The generation unit can also apply a user-friendly generation algorithm to a question about how to use a product. The generation unit can also apply a problem-solving specialized generation algorithm to a question about troubleshooting. In this way, by applying different generation algorithms depending on the question category, more appropriate recommended actions can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question category data to the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0081] The generation unit can improve the accuracy of generation by referring to the user's past results of recommended actions when generating recommended actions. For example, the generation unit can improve the accuracy of generation by referring to the user's past results of recommended actions when generating recommended actions. For example, the generation unit generates an optimal recommended action based on the results of recommended actions previously performed by the user. The generation unit can also analyze the user's past results of recommended actions and improve the generation algorithm. The generation unit can also improve the accuracy of recommended actions based on user feedback. In this way, the accuracy of generation can be improved by referring to the user's past results of recommended actions. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past result data of recommended actions into the generation AI and cause the generation AI to improve the accuracy of generation.

[0082] The generation unit can estimate the user's emotion and adjust the length of the recommended action based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the recommended action based on the estimated user emotion. For example, if the user is in a hurry, the generation unit can provide a short and to-the-point recommended action. Furthermore, if the user is relaxed, the generation unit can provide a longer recommended action with detailed explanations. Furthermore, if the user is excited, the generation unit can provide a visually stimulating recommended action. This allows the length of the recommended action to be adjusted according to the user's emotion, thereby providing a more appropriate recommended action. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the recommended action.

[0083] The generation unit can determine the priority of recommended actions based on the time when the question was submitted when generating recommended actions. For example, the generation unit can determine the priority of recommended actions based on the time when the question was submitted when generating recommended actions. For example, the generation unit can quickly provide recommended actions immediately after the question is submitted. The generation unit can also determine the priority based on the time when the question was submitted and provide recommended actions at an appropriate time. The generation unit can also adjust the priority of recommended actions according to the time of day when the question was submitted. In this way, by determining the priority of recommended actions based on the time when the question was submitted, recommended actions can be provided at an appropriate time. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the time when the question was submitted to the generation AI and cause the generation AI to determine the priority of recommended actions.

[0084] The generation unit can adjust the order of recommended actions based on the relevance of the question when generating recommended actions. The generation unit, for example, adjusts the order of recommended actions based on the relevance of the question when generating recommended actions. For example, the generation unit prioritizes providing recommended actions when the relevance of the question is high. The generation unit can also adjust the order of recommended actions based on the relevance of the question. The generation unit can also postpone providing recommended actions when the relevance of the question is low. In this way, by adjusting the order of recommended actions based on the relevance of the question, more relevant recommended actions can be prioritized. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of recommended actions.

[0085] The generation unit can adjust the use of technical terminology in the recommended action according to the user's level of expertise when generating the recommended action. For example, the generation unit can adjust the use of technical terminology in the recommended action according to the user's level of expertise when generating the recommended action. For example, if the user has technical expertise, the generation unit can provide a recommended action that uses technical terminology. Furthermore, if the user is a beginner, the generation unit can provide a recommended action that is easy to understand and avoids technical terminology. The generation unit can also adjust the use of technical terminology in the recommended action according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the recommended action according to the user's level of expertise, it is possible to provide a recommended action that is easier to understand. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology in the recommended action.

[0086] The update unit can estimate the user's emotions and adjust the FAQ update method based on the estimated user emotions. The update unit, for example, estimates the user's emotions and adjusts the FAQ update method based on the estimated user emotions. For example, if the user is feeling stressed, the update unit provides a concise and easy-to-understand FAQ. Furthermore, if the user is relaxed, the update unit can provide a FAQ with detailed explanations. Furthermore, if the user is excited, the update unit can provide a visually appealing FAQ. This allows for adjusting the FAQ update method according to the user's emotions, thereby providing a more appropriate FAQ. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the update unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the FAQ update method.

[0087] When updating an FAQ, the update unit can optimize the current update content by referring to past update data. When updating an FAQ, the update unit, for example, optimizes the current update content by referring to past update data. For example, the update unit determines the optimal update content based on past FAQ update data. The update unit can also analyze past update data and improve the current update content. The update unit can also optimize the FAQ update method by referring to past update data. In this way, the current update content can be optimized by referring to the past update data. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input past update data to a generation AI and cause the generation AI to optimize the current update content.

[0088] The update unit can apply different update methods to different question categories when updating the FAQ. For example, the update unit applies different update methods to different question categories when updating the FAQ. For example, the update unit applies a specialized technical update method to technical questions. The update unit can also apply a user-friendly update method to questions about how to use a product. The update unit can also apply an update method specialized for problem solving to questions about troubleshooting. In this way, by applying different update methods to different question categories, more appropriate FAQs can be provided. Some or all of the above-described processing in the update unit may be performed using, or without, AI, for example. For example, the update unit can input question category data to a generation AI and cause the generation AI to apply an appropriate update method.

[0089] The update unit can perform the update when updating an FAQ, taking into account the attribute information of the question submitter. For example, when updating an FAQ, the update unit performs the update while taking into account the attribute information of the question submitter. For example, if the question submitter is a beginner, the update unit provides an FAQ containing an easy-to-understand explanation. Furthermore, if the question submitter has specialized knowledge, the update unit can also provide an FAQ containing detailed technical information. Furthermore, the update unit can provide an optimal FAQ based on the attribute information (age, occupation, etc.) of the question submitter. In this way, by taking into account the attribute information of the question submitter, a more appropriate FAQ can be provided. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the attribute information of the question submitter to a generation AI and cause the generation AI to update the FAQ.

[0090] The update unit can estimate the user's emotions and adjust the FAQ update frequency based on the estimated user emotions. The update unit, for example, estimates the user's emotions and adjusts the FAQ update frequency based on the estimated user emotions. For example, if the user is feeling stressed, the update unit can frequently update the FAQ to provide the latest information. Also, if the user is relaxed, the update unit can update the FAQ at a normal update frequency. Also, if the user is excited, the update unit can quickly update the FAQ to provide new information. This allows the FAQ update frequency to be adjusted according to the user's emotions, thereby updating the FAQ at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the update unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the update unit can input user emotion data into the generation AI and cause the generation AI to adjust the FAQ update frequency.

[0091] The update unit can determine the update priority based on the time when the question was submitted when updating the FAQ. The update unit, for example, determines the update priority based on the time when the question was submitted when updating the FAQ. For example, the update unit quickly updates the FAQ immediately after the question is submitted. The update unit can also determine the priority based on the time when the question was submitted and update the FAQ at an appropriate time. The update unit can also adjust the FAQ update priority according to the time of day when the question was submitted. In this way, by determining the update priority based on the time when the question was submitted, the FAQ can be updated at an appropriate time. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input question submission time data to the generation AI and cause the generation AI to determine the update priority.

[0092] The update unit can optimize the update content by referring to market data related to the question when updating the FAQ. The update unit can optimize the update content by referring to market data related to the question when updating the FAQ, for example. For example, the update unit determines optimal FAQ update content based on the related market data. The update unit can also analyze market data and improve the current FAQ update content. The update unit can also optimize the FAQ update method by referring to the market data. In this way, the update content can be optimized by referring to market data related to the question. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input related market data to a generation AI and cause the generation AI to optimize the update content.

[0093] The update unit can take into consideration the technical maturity of the question when updating the FAQ. For example, the update unit can take into consideration the technical maturity of the question when updating the FAQ. For example, the update unit can provide an FAQ containing detailed technical information for technically mature questions. The update unit can also provide an FAQ containing easy-to-understand explanations for technically immature questions. The update unit can also adjust the update content of the FAQ according to the technical maturity of the question. In this way, more appropriate FAQs can be provided by taking into consideration the technical maturity of the question. Some or all of the above-mentioned processing in the update unit can be performed using, for example, AI, or can be performed without using AI. For example, the update unit can input technical maturity data of the question to a generation AI and cause the generation AI to update the FAQ. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, and update 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 can collect questions and answers from customers using the camera 42 and microphone 38B of the smart device 14 and store them in the database 24 by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected questions and answers to generate recommended actions. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and updates the FAQ based on the generated recommended actions. Each of the collection unit, generation unit, and update unit is also realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, generation unit, and update 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 can collect questions and answers from customers using the camera 42 and microphone 238 of the smart glasses 214 and store them in the database 24 by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected questions and answers to generate recommended actions. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and updates the FAQs based on the generated recommended actions. Each of the collection unit, generation unit, and update unit is also realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and update unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect questions and answers from customers using the camera 42 and microphone 238 of the headset type terminal 314 and store them in the database 24 by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected questions and answers to generate recommended actions. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and updates the FAQ based on the generated recommended actions. Each of the collection unit, generation unit, and update unit is also realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, and update 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 can collect questions and answers from customers using the camera 42 and microphone 238 of the robot 414 and store them in the database 24 by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected questions and answers to generate recommended actions. The update unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and updates the FAQ based on the generated recommended actions. Each of the collection unit, generation unit, and update unit is also realized, for example, by the control unit 46A of the robot 414.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The collection unit can estimate the user's emotions and adjust the timing of question collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit refrains from collecting questions and collects them when the user is relaxed. Furthermore, if the user is excited, the collection unit can immediately collect questions and provide a prompt response. Furthermore, if the user is calm, the collection unit can collect questions at a normal timing. By adjusting the timing of question collection according to the user's emotions, questions can be collected at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of question collection.

[0096] When collecting questions, the collection unit can analyze the user's past question history and select the optimal collection method. For example, the collection unit prioritizes collecting related questions based on the content of questions the user has frequently asked in the past. The collection unit can also select the most effective collection method (audio, text, etc.) from the user's past question history. The collection unit can also analyze the user's past question history and collect questions at the optimal timing. In this way, the optimal collection method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past question history into a generation AI and cause the generation AI to select the optimal collection method.

[0097] When collecting questions, the collection unit can filter them based on the user's current situation and areas of interest. For example, the collection unit prioritizes collecting questions related to products currently being used by the user. The collection unit can also filter and collect related questions based on the user's areas of interest. The collection unit can also collect appropriate questions depending on the user's current situation (e.g., during troubleshooting). This makes it possible to collect highly relevant questions by filtering based on the user's current situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data related to the user's current situation and areas of interest to the generation AI and have the generation AI perform the filtering.

[0098] When collecting questions, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect questions using voice recognition technology. Also, if the user uses text input, the collection unit can collect questions using text analysis technology. Also, if the user uses an image to ask a question, the collection unit can collect questions using image recognition technology. This allows questions to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0099] The collection unit can estimate the user's emotions and determine the priority of questions to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting questions with high urgency. Furthermore, when the user is relaxed, the collection unit can also collect questions with normal priority. Furthermore, when the user is excited, the collection unit can prioritize collecting questions with high relevance. Thus, by determining the priority of questions to be collected according to the user's emotions, more appropriate questions can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of questions.

[0100] When collecting questions, the collection unit can prioritize collecting highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting questions related to that area. The collection unit can also collect questions about issues specific to the area based on the user's geographical location information. If the user is traveling, the collection unit can also prioritize collecting questions related to the travel destination. In this way, highly relevant questions can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant questions.

[0101] When collecting questions, the collection unit can analyze the user's social media activity and collect related questions. For example, the collection unit collects questions related to issues the user mentioned on social media. The collection unit can also analyze the content of the user's social media posts and collect related questions. The collection unit can also collect related questions by referring to the activities of the user's friends on social media. In this way, related questions can be collected by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related questions.

[0102] When collecting questions, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit selects the optimal collection method based on feedback provided by the user in the past. The collection unit can also improve the collection method by reflecting the user's past feedback. The collection unit can also adjust the collection timing and means based on the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0103] The generation unit can estimate the user's emotions and adjust the presentation style of the recommended action based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can provide a concise and easy-to-understand recommended action. Furthermore, if the user is relaxed, the generation unit can provide a recommended action with detailed explanations. Furthermore, if the user is excited, the generation unit can provide a visually appealing recommended action. This allows for more appropriate recommended actions to be provided by adjusting the presentation style of the recommended action according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation style of the recommended action.

[0104] When generating a recommended action, the generation unit can adjust the level of detail of the recommended action based on the importance of the question. For example, the generation unit provides a detailed recommended action for a question with a high importance. The generation unit can also provide a concise recommended action for a question with a low importance. The generation unit can also gradually adjust the level of detail of the recommended action according to the importance of the question. In this way, by adjusting the level of detail of the recommended action based on the importance of the question, it is possible to provide a recommended action with an appropriate level of detail. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the recommended action.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The collection department collects customer questions and answers. These questions and answers can be in text, voice, or technical format. The collection department stores these questions and answers in a database. The collection department can also collect questions in real time through web forms or chatbots. Step 2: The generation unit uses generation AI to analyze the questions and answers collected by the collection unit and generate recommended actions. Recommended actions include updating FAQs and notifying users. The generation unit uses natural language processing technology and machine learning algorithms to generate appropriate recommended actions based on past data. Step 3: The updater updates the FAQ based on the recommended actions generated by the generator. The updater determines which parts of the FAQ to update and how, and updates the FAQ based on new information. It can also update the FAQ in real time when new questions or issues arise.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 AI 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 questions and answers from customers; a generation unit that analyzes the questions and answers collected by the collection unit and generates recommended actions; an update unit that updates the FAQ based on the recommended action generated by the generation unit; Equipped with A system characterized by:

2. The collecting unit Store customer questions and answers in a database The system of claim 1 .

3. The generation unit Analyzes the data from the collection unit and generates appropriate recommended actions The system of claim 1 .

4. The update unit Update the FAQ based on new information from the generator The system of claim 1 .

5. The generation unit Auto-generate recommended actions for user issues The system of claim 1 .

6. The update unit Update the FAQ in real time as new questions or issues arise The system of claim 1 .

7. The collecting unit Estimate user emotions and adjust the timing of question collection based on the estimated user emotions The system of claim 1 .

8. The collecting unit When collecting questions, analyze the user's past question history and select the optimal collection method. The system of claim 1 .

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