System

Abstract

An object of the system according to the embodiment is to automate generation and information collection of a hearing item at the time of occurrence of an event and reduce the workload of a questioner and an answerer.SOLUTION: A system according to an embodiment includes an input unit, an analysis unit, a presentation unit, and an answer generation unit. The input unit inputs image data or a situation at the time of occurrence of an event. The analysis unit analyzes the information input by the input unit and automatically generates necessary hearing items. The presentation unit presents the hearing items generated by the analysis unit to the questioner. The answer generation unit generates an automatic answer based on the hearing items presented by the presentation 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] With conventional technology, the creation of interview items and collection of information when an event occurs was done manually, which created a problem of making the work of questioners and answerers cumbersome.

[0005] The system according to the embodiment aims to automate the generation of interview items and the collection of information when an event occurs, thereby reducing the workload of questioners and answerers. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a presentation unit, and an answer generation unit. The input unit inputs image data or a situation at the time of an event occurrence. The analysis unit analyzes the information input by the input unit and automatically generates necessary hearing items. The presentation unit presents the hearing items generated by the analysis unit to the questioner. The answer generation unit generates an automatic answer based on the hearing items presented by the presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the generation of interview items and information collection when an event occurs, thereby reducing the workload of questioners and answerers. [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 automatically generates interview questions based on image data and circumstances at the time of an event, thereby reducing the workload of questioners and answerers. The system includes an input unit for inputting image data and circumstances at the time of the event, an analysis unit for analyzing the input information and automatically generating necessary interview questions, a presentation unit for presenting the generated interview questions to the questioner, and an answer generation unit for generating an automatic answer based on the presented interview questions. For example, the questioner inputs image data and circumstances at the time of the event. The system then analyzes the input information and automatically generates necessary interview questions. For example, the system identifies the type of defect and the location of the defect from the image data and presents related questions based on that information. The system also extracts necessary information from the list of circumstances and automatically presents any missing information. The system then presents the automatically generated interview questions to the questioner, who then answers them. This allows the questioner to provide all necessary information and the answerer to collect information efficiently. Furthermore, the system generates automatic answers based on the collected information. For example, the system can present appropriate solutions based on similar past cases or a database. This significantly reduces the answerer's workload. This reduces the need for complicated interactions between questioners and answerers, improving work efficiency. For example, in the case of an inquiry about a product defect, the system can identify the type of defect from image data and automatically generate related questions, enabling a fast and accurate response. Furthermore, generating automatic answers based on collected information reduces the burden on answerers and improves work efficiency.

[0029] The system according to the embodiment includes an input unit, an analysis unit, a presentation unit, and an answer generation unit. The input unit inputs image data or a situation at the time of an event occurrence. The event occurrence includes, but is not limited to, an accident, a malfunction, an abnormal operation, etc. The image data or a situation includes, but is not limited to, a photograph, a video, a text description, etc. The input unit, for example, uploads image data captured by a user to the system. The input unit can also allow the user to describe the situation in text format. The input unit can also describe the situation using voice input. For example, a user can upload an image captured with a smartphone camera, and the system analyzes the image. Alternatively, a user can describe the situation in text format, and the system can analyze the text. The analysis unit analyzes the information input by the input unit and automatically generates the necessary interview items. The analysis is performed, for example, using an image analysis algorithm or a text analysis method, but is not limited to these examples. For example, the analysis unit identifies the type and location of the problem from the image data and generates related questions based on the identified information. The analysis unit can also extract important information from text data and generate interview items based on the extracted information. For example, an image analysis algorithm extracts features in an image and identifies the type of defect. A text analysis method extracts keywords in text and identifies important information. A presentation unit presents the hearing items generated by the analysis unit to the questioner. Presentation can be performed, for example, using a visual display or a text display, but is not limited to these examples. For example, the presentation unit displays the generated hearing items on the questioner's screen. The presentation unit can also present the hearing items by audio. For example, a visual display displays the question items in list format. A text display displays the question items in text format. An answer generation unit generates an automatic answer based on the hearing items presented by the presentation unit. The automatic answer can be generated, for example, based on similar past cases or a database, but is not limited to these examples. For example, the answer generation unit searches for similar past cases and presents an appropriate solution. The answer generation unit can also generate an automatic answer based on a database.For example, similar past cases include events of the same type or under the same conditions. The database includes a case database and an FAQ database. As a result, the system according to the embodiment can automatically generate interview questions based on image data and the situation at the time of the event occurrence, thereby reducing the workload of the questioner and the respondent. For example, the system can analyze image data and the situation and automatically generate the necessary interview questions, allowing the questioner to provide all necessary information. Furthermore, the system can reduce the workload of the respondent by generating automatic answers based on the collected information.

[0030] The answer generation unit can present a solution based on similar past cases or a database. Examples of similar past cases include, but are not limited to, events of the same type or under the same conditions. Examples of databases include, but are not limited to, a case database or an FAQ database. For example, the answer generation unit searches for similar past cases and presents an appropriate solution. The answer generation unit can also generate an automatic answer based on the database. For example, the answer generation unit presents a similar solution based on similar past cases. The database generates an appropriate answer using a case database or an FAQ database. By utilizing similar past cases and databases, the accuracy and efficiency of answers are improved. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without AI. For example, the answer generation unit can present a solution using an AI model that inputs similar past cases or a database and outputs a solution.

[0031] The input unit can automatically evaluate the resolution or quality of the image data and select an input method. Examples of the resolution or quality include, but are not limited to, the number of pixels and noise level. Examples of the input method include, but are not limited to, manual input, automatic input, and voice input. For example, in the case of low-resolution image data, the input unit requests additional images. In addition, in the case of high-resolution image data, the input unit can use the image data as is for analysis. In addition, if the image quality is insufficient, the input unit can prompt the user to retake the image. This improves the accuracy of the analysis by selecting the optimal input method depending on the quality of the image data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can select the optimal input method using an AI model that evaluates the resolution and quality of the image data.

[0032] The input unit can complete input content by referring to the user's past input history when inputting information. Examples of past input history include, but are not limited to, the most recent input history and input history under specific conditions. Examples of input content completion include, but are not limited to, predictive input and the use of templates. For example, the input unit automatically completes information previously input by the user. The input unit can also suggest related information from the user's past input history. The input unit can also predict and complete input content based on information previously input by the user. This enables completion of input content by referring to the past input history, improving input efficiency. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can complete input content using an AI model that uses the past input history as input and completes input content.

[0033] The input unit can adjust the input method based on the user's current environment during input. Examples of the current environment include, but are not limited to, network conditions and device status. Examples of input method adjustment include, but are not limited to, data compression based on network speed. For example, the input unit supports offline input when the network is unstable. The input unit can also support real-time input when the network is stable. The input unit can also adjust the input method based on the battery status of the user's device. Adjusting the input method based on the user's environment improves input efficiency and accuracy. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the input method using an AI model that adjusts the input method based on the network conditions and device status.

[0034] The input unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. Examples of highly relevant information include, but are not limited to, information about nearby geographical locations and examples from the same region. For example, when the user is in a specific region, the input unit prioritizes inputting information related to that region. Furthermore, when the user is moving, the input unit can prioritize inputting information related to the user's current location. Furthermore, when the user is in a specific location, the input unit can prioritize inputting information related to the location. This improves input efficiency by prioritized input of highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the input content using an AI model that receives geographical location information and prioritizes input of highly relevant information.

[0035] The input unit can analyze the user's social media activity and input related information at the time of input. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related information includes, for example, but is not limited to, posts on the same topic and related hashtags. The input unit can suggest related input content based on information shared by the user on social media. The input unit can also analyze the user's social media activity and input related information. The input unit can also input related information based on the activities of the user's friends on social media. This allows for efficient input of related information by analyzing social media activity. Some or all of the above-described processing in the input unit can be performed, for example, using AI, or can be performed without AI. For example, the input unit can adjust the input content using an AI model that inputs social media activity and inputs related information.

[0036] The input unit can customize the input method by reflecting the user's past feedback during input. Examples of past feedback include, but are not limited to, user evaluation comments and survey results. Examples of customizing the input method include, but are not limited to, changing settings based on the user's preferences. The input unit customizes the input method based on, for example, feedback provided by the user in the past. The input unit can also suggest an optimal input method based on the user's past feedback. The input unit can also improve the input method by reflecting the user's past feedback. By reflecting the past feedback, the input method is optimized and user convenience is improved. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the input content using an AI model that customizes the input method using the past feedback as input.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input data. The importance of the input data includes, for example, the urgency of the data and the scope of its impact, but is not limited to these examples. The level of detail of the analysis includes, for example, a simple analysis and a detailed analysis, but is not limited to these examples. For example, the analysis unit performs a detailed analysis for important data. The analysis unit can also perform a simplified analysis for less important data. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the input data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis content using an AI model that uses the importance of the input data as input and adjusts the level of detail of the analysis.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, text data and image data. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit applies an image analysis algorithm to image data. For text data, the analysis unit can also apply a natural language processing algorithm. For numerical data, the analysis unit can also apply a statistical analysis algorithm. This improves the accuracy of the analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis content using an AI model that inputs the data category and applies an appropriate analysis algorithm.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis results of the same user or analysis results under the same conditions. The analysis unit improves the accuracy of the analysis, for example, based on the user's past analysis results. The analysis unit can also propose an optimal analysis method based on the user's past analysis results. The analysis unit can also improve the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can adjust the analysis content using an AI model that uses past analysis results as input and improves the accuracy of the analysis.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The time of data submission includes, for example, but is not limited to, the submission date and time and the submission order. The analysis priority includes, for example, but is not limited to, the urgency and importance. For example, the analysis unit prioritizes analysis of data submitted earlier. The analysis unit can also postpone data submitted later. The analysis unit can also determine the analysis priority based on the time of submission. This enables efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis content using an AI model that uses the submission time as an input and determines the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, data on the same topic or related data. Examples of the order of analysis include, but are not limited to, analyzing data with high relevance first. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone data with low relevance. The analysis unit can also adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis content using an AI model that uses the relevance of the data as input and adjusts the order of analysis.

[0042] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, survey results and past usage history. Examples of the use of technical terms include, but are not limited to, providing definitions of technical terms. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid technical terms when the user's level of expertise is low. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for providing analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the analysis content using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms.

[0043] The presentation unit can adjust the level of detail of the presentation based on the importance of the hearing item during presentation. The importance of the hearing item includes, but is not limited to, the urgency of the question and the scope of impact of the question. The level of detail of the presentation includes, but is not limited to, a simplified display or a detailed display. For example, the presentation unit presents detailed information for an important hearing item. The presentation unit can also present simplified information for a hearing item with a low level of importance. The presentation unit can also adjust the level of detail of the presentation according to the importance of the hearing item. This enables efficient information presentation by adjusting the level of detail of the presentation according to the importance of the hearing item. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the presentation content using an AI model that uses the importance of the hearing item as an input and adjusts the level of detail of the presentation.

[0044] The presentation unit can apply different presentation algorithms depending on the category of the hearing item when presenting the information. Examples of categories of hearing items include, but are not limited to, technical questions and business questions. Examples of presentation algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the presentation unit applies a technical presentation algorithm to technical hearing items. For non-technical hearing items, the presentation unit can also apply a general presentation algorithm. The presentation unit can also apply different presentation algorithms depending on the category of the hearing item. This enables appropriate information presentation by applying a presentation algorithm depending on the category of the hearing item. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the presentation content using an AI model that receives the category of the hearing item as input and applies an appropriate presentation algorithm.

[0045] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results when presenting content. Past presentation results include, but are not limited to, presentation results by the same user or presentation results under the same conditions. The presentation unit improves the accuracy of presentation, for example, based on the user's past presentation results. The presentation unit can also suggest an optimal presentation method based on the user's past presentation results. The presentation unit can also improve the presentation algorithm by referring to the user's past presentation results. This improves the accuracy of presentation by referring to the past presentation results. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can adjust the presentation content using an AI model that uses the past presentation results as input and improves the accuracy of presentation.

[0046] The presentation unit can determine the presentation priority based on the submission time of the hearing items when presenting them. The submission time of the hearing items includes, for example, but is not limited to, the submission date and time and the submission order. The presentation priority includes, for example, but is not limited to, the urgency and importance. For example, the presentation unit prioritizes the presentation of hearing items submitted earlier. The presentation unit can also postpone hearing items submitted later. The presentation unit can also determine the presentation priority based on the submission time. This enables efficient information presentation by determining the presentation priority based on the submission time of the hearing items. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the presentation content using an AI model that uses the submission time as an input and determines the presentation priority.

[0047] The presentation unit can adjust the order of presentation based on the relevance of the hearing items when presenting them. Examples of the relevance of the hearing items include, but are not limited to, the same topic or related questions. Examples of the order of presentation include, but are not limited to, presenting highly relevant questions first. For example, the presentation unit prioritizes the presentation of highly relevant hearing items. The presentation unit can also postpone less relevant hearing items. The presentation unit can also adjust the order of presentation based on the relevance of the hearing items. This enables efficient information presentation by adjusting the order of presentation based on the relevance of the hearing items. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the presentation content using an AI model that uses the relevance of the hearing items as input and adjusts the order of presentation.

[0048] The presentation unit may adjust the use of technical terms in the presentation depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, survey results and past usage history. Examples of the use of technical terms include, but are not limited to, providing definitions of technical terms. For example, the presentation unit may use a lot of technical terms when the user's level of expertise is high. The presentation unit may also avoid technical terms when the user's level of expertise is low. The presentation unit may also adjust the use of technical terms in the presentation depending on the user's level of expertise. This allows the user to be provided with information that is easy to understand. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit may adjust the presentation content using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms.

[0049] The answer generation unit can adjust the level of detail of the answer based on the importance of the hearing item when generating an answer. The importance of the hearing item includes, for example, but is not limited to, the urgency of the question and the scope of impact of the question. The level of detail of the answer includes, for example, but is not limited to, a simple answer or a detailed answer. For example, the answer generation unit generates a detailed answer for an important hearing item. The answer generation unit can also generate a simplified answer for a hearing item with a low level of importance. The answer generation unit can also adjust the level of detail of the answer depending on the importance of the hearing item. This enables efficient answers to be generated by adjusting the level of detail of the answer depending on the importance of the hearing item. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can adjust the content of the answer using an AI model that inputs the importance of the hearing item and adjusts the level of detail of the answer.

[0050] When generating an answer, the answer generation unit can apply different answer algorithms depending on the category of the hearing item. The categories of hearing items include, but are not limited to, technical questions and business questions. The answer algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the answer generation unit applies a technical answer algorithm to technical hearing items. The answer generation unit can also apply a general answer algorithm to non-technical hearing items. The answer generation unit can also apply different answer algorithms depending on the category of the hearing item. This enables appropriate answers to be generated by applying an answer algorithm according to the category of the hearing item. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can adjust the content of the answer using an AI model that inputs the category of the hearing item and applies an appropriate answer algorithm.

[0051] When generating an answer, the answer generation unit can improve the accuracy of the answer by referring to the user's past answer results. Past answer results include, but are not limited to, answer results by the same user or answer results under the same conditions. The answer generation unit improves the accuracy of the answer, for example, based on the user's past answer results. The answer generation unit can also suggest an optimal answer method based on the user's past answer results. The answer generation unit can also improve the answer algorithm by referring to the user's past answer results. In this way, the accuracy of the answer is improved by referring to the past answer results. Some or all of the above-mentioned processing in the answer generation unit may be performed, for example, using AI or may be performed without using AI. For example, the answer generation unit can adjust the content of the answer using an AI model that uses past answer results as input and improves the accuracy of the answer.

[0052] When generating answers, the answer generation unit can determine the priority of answers based on the submission times of the hearing items. The submission times of the hearing items include, for example, but are not limited to, the submission date and time and the submission order. The priority of answers includes, for example, but are not limited to, the urgency and importance. For example, the answer generation unit can prioritize generating answers for hearing items submitted early. The answer generation unit can also postpone generating answers for hearing items submitted late. The answer generation unit can also determine the priority of answers based on the submission times. This enables efficient answers by determining the priority of answers based on the submission times of the hearing items. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can adjust the content of the answer using an AI model that uses the submission time as input and determines the priority of answers.

[0053] When generating answers, the answer generation unit can adjust the order of answers based on the relevance of the hearing items. Examples of the relevance of the hearing items include, but are not limited to, questions on the same topic or related questions. Examples of the order of answers include, but are not limited to, answering questions starting with the most relevant questions. For example, the answer generation unit can prioritize generating answers for highly relevant hearing items. The answer generation unit can also postpone generating answers for less relevant hearing items. The answer generation unit can also adjust the order of answers based on the relevance of the hearing items. This enables efficient answers to be generated by adjusting the order of answers based on the relevance of the hearing items. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can adjust the content of an answer using an AI model that uses the relevance of the hearing items as input and adjusts the order of answers.

[0054] When generating an answer, the answer generation unit can adjust the use of technical terms in the answer depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, survey results and past usage history. Examples of the use of technical terms include, but are not limited to, providing definitions of technical terms. For example, the answer generation unit uses a lot of technical terms when the user's level of expertise is high. The answer generation unit can also avoid using technical terms when the user's level of expertise is low. The answer generation unit can also adjust the use of technical terms in the answer depending on the user's level of expertise. This allows the user to be provided with an answer that is easy to understand. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without AI. For example, the answer generation unit can adjust the content of the answer using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The input unit can learn the user's past behavioral patterns and suggest the optimal input method. For example, it can prioritize and suggest input methods that the user has frequently used in the past. Also, if the user tends to use a specific input method during a specific time period, it can suggest an input method suitable for that time period. Also, if the user uses a specific device, it can suggest the input method optimal for that device. In this way, by learning the user's past behavioral patterns, the efficiency and accuracy of input are improved. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can adjust the input content using an AI model that inputs past behavioral patterns and suggests the optimal input method.

[0057] The input unit can detect the user's current activity status and suggest the optimal input timing. For example, if the user is in a meeting, the input can be delayed. Also, if the user is taking a break, the input can be promoted. Also, if the user is moving, the input can be paused. In this way, the input timing can be adjusted according to the user's activity status, thereby improving the efficiency and accuracy of input. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can adjust the input content using an AI model that inputs the user's activity status and suggests the optimal input timing.

[0058] The input unit can analyze the user's past input data and suggest the optimal input format. For example, it can prioritize the suggested format that the user has used in the past. Also, if the user prefers a specific data format, it can suggest that format. Also, if the user uses a specific input method, it can suggest the optimal format for that method. In this way, by analyzing the user's past input data, the efficiency and accuracy of input are improved. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can adjust the input content using an AI model that uses past input data as input and suggests the optimal input format.

[0059] The input unit can detect the user's current device status and suggest the optimal input interface. For example, if the user is using a smartphone, it can prioritize touch input. If the user is using a PC, it can suggest keyboard input. If the user is using a tablet, it can suggest stylus input. This improves input efficiency and accuracy by suggesting the optimal input interface according to the user's device status. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the input content using an AI model that inputs the device status and suggests the optimal input interface.

[0060] The input unit can analyze the user's past input errors and provide input guidance to prevent errors. For example, it can identify errors that the user frequently made in the past and display guidance to prevent those errors. Furthermore, if the user is prone to making errors in a specific input field, it can also warn the user about that field. Furthermore, if the user is prone to making errors using a specific input method, it can also suggest improvements to that method. By analyzing the user's past input errors, the accuracy of input is improved. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without AI. For example, the input unit can adjust the input content using an AI model that uses past input errors as input and provides input guidance to prevent errors.

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

[0062] Step 1: The input unit inputs image data or a situation at the time of an event occurrence. The occurrence of an event includes, but is not limited to, an accident, a malfunction, an abnormal operation, etc. The image data or a situation includes, but is not limited to, a photograph, a video, a text description, etc. For example, the input unit uploads image data taken by a user to the system. The input unit also allows the user to describe the situation in text format. Furthermore, the input unit can also describe the situation using voice input. For example, the user can upload an image taken with a smartphone camera, and the system analyzes the image. The user can also describe the situation in text format, and the system can analyze the text. Step 2: The analysis unit analyzes the information input by the input unit and automatically generates the necessary interview items. The analysis is performed using, for example, an image analysis algorithm or a text analysis method, but is not limited to these examples. For example, the analysis unit identifies the type of defect and the location of the defect from the image data, and generates related questions based on that. The analysis unit can also extract important information from text data and generate interview items based on that. For example, an image analysis algorithm extracts features from the image and identifies the type of defect. A text analysis method extracts keywords from the text and identifies important information. Step 3: The presentation unit presents the hearing items generated by the analysis unit to the questioner. The presentation may be performed using, for example, a visual display or a text display, but is not limited to these examples. For example, the presentation unit displays the generated hearing items on the questioner's screen. The presentation unit can also present the hearing items by voice. For example, the visual display displays the question items in list format. The text display displays the question items in text format. Step 4: The answer generation unit generates an automatic answer based on the hearing items presented by the presentation unit. The automatic answer is generated based on, for example, similar past cases or a database, but is not limited to these examples. For example, the answer generation unit searches for similar past cases and presents an appropriate solution. The answer generation unit can also generate an automatic answer based on a database. For example, similar past cases include events of the same type or events under the same conditions. The database includes a case database or an FAQ database.

[0063] (Example 2) A system according to an embodiment of the present invention automatically generates interview questions based on image data and circumstances at the time of an event, thereby reducing the workload of questioners and answerers. The system includes an input unit for inputting image data and circumstances at the time of the event, an analysis unit for analyzing the input information and automatically generating necessary interview questions, a presentation unit for presenting the generated interview questions to the questioner, and an answer generation unit for generating an automatic answer based on the presented interview questions. For example, the questioner inputs image data and circumstances at the time of the event. The system then analyzes the input information and automatically generates necessary interview questions. For example, the system identifies the type of defect and the location of the defect from the image data and presents related questions based on that information. The system also extracts necessary information from the list of circumstances and automatically presents any missing information. The system then presents the automatically generated interview questions to the questioner, who then answers them. This allows the questioner to provide all necessary information and the answerer to collect information efficiently. Furthermore, the system generates automatic answers based on the collected information. For example, the system can present appropriate solutions based on similar past cases or a database. This significantly reduces the answerer's workload. This reduces the need for complicated interactions between questioners and answerers, improving work efficiency. For example, in the case of an inquiry about a product defect, the system can identify the type of defect from image data and automatically generate related questions, enabling a fast and accurate response. Furthermore, generating automatic answers based on collected information reduces the burden on answerers and improves work efficiency.

[0064] The system according to the embodiment includes an input unit, an analysis unit, a presentation unit, and an answer generation unit. The input unit inputs image data or a situation at the time of an event occurrence. The event occurrence includes, but is not limited to, an accident, a malfunction, an abnormal operation, etc. The image data or a situation includes, but is not limited to, a photograph, a video, a text description, etc. The input unit, for example, uploads image data captured by a user to the system. The input unit can also allow the user to describe the situation in text format. The input unit can also describe the situation using voice input. For example, a user can upload an image captured with a smartphone camera, and the system analyzes the image. Alternatively, a user can describe the situation in text format, and the system can analyze the text. The analysis unit analyzes the information input by the input unit and automatically generates the necessary interview items. The analysis is performed, for example, using an image analysis algorithm or a text analysis method, but is not limited to these examples. For example, the analysis unit identifies the type and location of the problem from the image data and generates related questions based on the identified information. The analysis unit can also extract important information from text data and generate interview items based on the extracted information. For example, an image analysis algorithm extracts features in an image and identifies the type of defect. A text analysis method extracts keywords in text and identifies important information. A presentation unit presents the hearing items generated by the analysis unit to the questioner. Presentation can be performed, for example, using a visual display or a text display, but is not limited to these examples. For example, the presentation unit displays the generated hearing items on the questioner's screen. The presentation unit can also present the hearing items by audio. For example, a visual display displays the question items in list format. A text display displays the question items in text format. An answer generation unit generates an automatic answer based on the hearing items presented by the presentation unit. The automatic answer can be generated, for example, based on similar past cases or a database, but is not limited to these examples. For example, the answer generation unit searches for similar past cases and presents an appropriate solution. The answer generation unit can also generate an automatic answer based on a database.For example, similar past cases include events of the same type or under the same conditions. The database includes a case database and an FAQ database. As a result, the system according to the embodiment can automatically generate interview questions based on image data and the situation at the time of the event occurrence, thereby reducing the workload of the questioner and the respondent. For example, the system can analyze image data and the situation and automatically generate the necessary interview questions, allowing the questioner to provide all necessary information. Furthermore, the system can reduce the workload of the respondent by generating automatic answers based on the collected information.

[0065] The answer generation unit can present a solution based on similar past cases or a database. Examples of similar past cases include, but are not limited to, events of the same type or under the same conditions. Examples of databases include, but are not limited to, a case database or an FAQ database. For example, the answer generation unit searches for similar past cases and presents an appropriate solution. The answer generation unit can also generate an automatic answer based on the database. For example, the answer generation unit presents a similar solution based on similar past cases. The database generates an appropriate answer using a case database or an FAQ database. By utilizing similar past cases and databases, the accuracy and efficiency of answers are improved. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without AI. For example, the answer generation unit can present a solution using an AI model that inputs similar past cases or a database and outputs a solution.

[0066] The input unit can estimate the user's emotions and adjust the timing of input based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can delay the timing of input to allow the user to relax. Furthermore, if the user is relaxed, the input unit can also speed up the timing of input to allow the user to input information smoothly. Furthermore, if the user is in a hurry, the input unit can optimize the timing of input to allow the user to input information quickly. This adjusts the input timing according to the user's emotions, reducing the user's stress and realizing efficient input. 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 input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0067] The input unit can automatically evaluate the resolution or quality of the image data and select an input method. Examples of the resolution or quality include, but are not limited to, the number of pixels and noise level. Examples of the input method include, but are not limited to, manual input, automatic input, and voice input. For example, in the case of low-resolution image data, the input unit requests additional images. In addition, in the case of high-resolution image data, the input unit can use the image data as is for analysis. In addition, if the image quality is insufficient, the input unit can prompt the user to retake the image. This improves the accuracy of the analysis by selecting the optimal input method depending on the quality of the image data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can select the optimal input method using an AI model that evaluates the resolution and quality of the image data.

[0068] The input unit can complete input content by referring to the user's past input history when inputting information. Examples of past input history include, but are not limited to, the most recent input history and input history under specific conditions. Examples of input content completion include, but are not limited to, predictive input and the use of templates. For example, the input unit automatically completes information previously input by the user. The input unit can also suggest related information from the user's past input history. The input unit can also predict and complete input content based on information previously input by the user. This enables completion of input content by referring to the past input history, improving input efficiency. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can complete input content using an AI model that uses the past input history as input and completes input content.

[0069] The input unit can adjust the input method based on the user's current environment during input. Examples of the current environment include, but are not limited to, network conditions and device status. Examples of input method adjustment include, but are not limited to, data compression based on network speed. For example, the input unit supports offline input when the network is unstable. The input unit can also support real-time input when the network is stable. The input unit can also adjust the input method based on the battery status of the user's device. Adjusting the input method based on the user's environment improves input efficiency and accuracy. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the input method using an AI model that adjusts the input method based on the network conditions and device status.

[0070] The input unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. Examples of prioritization of input content include, but are not limited to, importance and urgency. For example, when the user is stressed, the input unit can prioritize presenting important input content. Furthermore, when the user is relaxed, the input unit can prioritize presenting detailed input content. Furthermore, when the user is in a hurry, the input unit can prioritize presenting the most important input content. Thus, by prioritizing input content according to the user's emotions, important information can be input preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the input unit may be performed using, for example, an AI. For example, the input unit can input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0071] The input unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. Examples of highly relevant information include, but are not limited to, information about nearby geographical locations and examples from the same region. For example, when the user is in a specific region, the input unit prioritizes inputting information related to that region. Furthermore, when the user is moving, the input unit can prioritize inputting information related to the user's current location. Furthermore, when the user is in a specific location, the input unit can prioritize inputting information related to the location. This improves input efficiency by prioritized input of highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the input content using an AI model that receives geographical location information and prioritizes input of highly relevant information.

[0072] The input unit can analyze the user's social media activity and input related information at the time of input. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. Related information includes, for example, but is not limited to, posts on the same topic and related hashtags. The input unit can suggest related input content based on information shared by the user on social media. The input unit can also analyze the user's social media activity and input related information. The input unit can also input related information based on the activities of the user's friends on social media. This allows for efficient input of related information by analyzing social media activity. Some or all of the above-described processing in the input unit can be performed, for example, using AI, or can be performed without AI. For example, the input unit can adjust the input content using an AI model that inputs social media activity and inputs related information.

[0073] The input unit can customize the input method by reflecting the user's past feedback during input. Examples of past feedback include, but are not limited to, user evaluation comments and survey results. Examples of customizing the input method include, but are not limited to, changing settings based on the user's preferences. The input unit customizes the input method based on, for example, feedback provided by the user in the past. The input unit can also suggest an optimal input method based on the user's past feedback. The input unit can also improve the input method by reflecting the user's past feedback. By reflecting the past feedback, the input method is optimized and user convenience is improved. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the input content using an AI model that customizes the input method using the past feedback as input.

[0074] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. Examples of presentation methods of the analysis include, but are not limited to, graph display and text display. For example, the analysis unit uses a simple presentation method when the user is stressed. The analysis unit can also use a detailed presentation method when the user is relaxed. The analysis unit can also use a concise presentation method when the user is in a hurry. This allows the analysis results to be easily understood by adjusting the presentation method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input data. The importance of the input data includes, for example, the urgency of the data and the scope of its impact, but is not limited to these examples. The level of detail of the analysis includes, for example, a simple analysis and a detailed analysis, but is not limited to these examples. For example, the analysis unit performs a detailed analysis for important data. The analysis unit can also perform a simplified analysis for less important data. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the input data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis content using an AI model that uses the importance of the input data as input and adjusts the level of detail of the analysis.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, text data and image data. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit applies an image analysis algorithm to image data. For text data, the analysis unit can also apply a natural language processing algorithm. For numerical data, the analysis unit can also apply a statistical analysis algorithm. This improves the accuracy of the analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis content using an AI model that inputs the data category and applies an appropriate analysis algorithm.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, analysis results of the same user or analysis results under the same conditions. The analysis unit improves the accuracy of the analysis, for example, based on the user's past analysis results. The analysis unit can also propose an optimal analysis method based on the user's past analysis results. The analysis unit can also improve the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can adjust the analysis content using an AI model that uses past analysis results as input and improves the accuracy of the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Examples of the length of the analysis include, but are not limited to, a concise analysis or a detailed analysis. For example, the analysis unit can provide a short analysis result when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise analysis result when the user is in a hurry. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an appropriate analysis result for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. The time of data submission includes, for example, but is not limited to, the submission date and time and the submission order. The analysis priority includes, for example, but is not limited to, the urgency and importance. For example, the analysis unit prioritizes analysis of data submitted earlier. The analysis unit can also postpone data submitted later. The analysis unit can also determine the analysis priority based on the time of submission. This enables efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis content using an AI model that uses the submission time as an input and determines the analysis priority.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, data on the same topic or related data. Examples of the order of analysis include, but are not limited to, analyzing data with high relevance first. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone data with low relevance. The analysis unit can also adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis content using an AI model that uses the relevance of the data as input and adjusts the order of analysis.

[0081] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, survey results and past usage history. Examples of the use of technical terms include, but are not limited to, providing definitions of technical terms. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid technical terms when the user's level of expertise is low. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for providing analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the analysis content using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms.

[0082] The presentation unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. Examples of presentation methods include, but are not limited to, visual display and text display. For example, the presentation unit can use a simple presentation method when the user is stressed. For example, the presentation unit can use a detailed presentation method when the user is relaxed. For example, the presentation unit can use a concise presentation method when the user is in a hurry. This allows the presentation method to be adjusted according to the user's emotion, providing the user with information that is easy to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0083] The presentation unit can adjust the level of detail of the presentation based on the importance of the hearing item during presentation. The importance of the hearing item includes, but is not limited to, the urgency of the question and the scope of impact of the question. The level of detail of the presentation includes, but is not limited to, a simplified display or a detailed display. For example, the presentation unit presents detailed information for an important hearing item. The presentation unit can also present simplified information for a hearing item with a low level of importance. The presentation unit can also adjust the level of detail of the presentation according to the importance of the hearing item. This enables efficient information presentation by adjusting the level of detail of the presentation according to the importance of the hearing item. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the presentation content using an AI model that uses the importance of the hearing item as an input and adjusts the level of detail of the presentation.

[0084] The presentation unit can apply different presentation algorithms depending on the category of the hearing item when presenting the information. Examples of categories of hearing items include, but are not limited to, technical questions and business questions. Examples of presentation algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the presentation unit applies a technical presentation algorithm to technical hearing items. For non-technical hearing items, the presentation unit can also apply a general presentation algorithm. The presentation unit can also apply different presentation algorithms depending on the category of the hearing item. This enables appropriate information presentation by applying a presentation algorithm depending on the category of the hearing item. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the presentation content using an AI model that receives the category of the hearing item as input and applies an appropriate presentation algorithm.

[0085] The presentation unit can improve the accuracy of presentation by referring to the user's past presentation results when presenting content. Past presentation results include, but are not limited to, presentation results by the same user or presentation results under the same conditions. The presentation unit improves the accuracy of presentation, for example, based on the user's past presentation results. The presentation unit can also suggest an optimal presentation method based on the user's past presentation results. The presentation unit can also improve the presentation algorithm by referring to the user's past presentation results. This improves the accuracy of presentation by referring to the past presentation results. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can adjust the presentation content using an AI model that uses the past presentation results as input and improves the accuracy of presentation.

[0086] The presentation unit can estimate the user's emotion and adjust the length of the presentation based on the estimated user's emotion. Examples of presentation lengths include, but are not limited to, brief presentations and detailed presentations. For example, the presentation unit can provide a short presentation when the user is stressed. The presentation unit can also provide a detailed presentation when the user is relaxed. The presentation unit can also provide a brief presentation when the user is in a hurry. By adjusting the length of the presentation according to the user's emotion, appropriate information can be provided to the user. The emotion estimation is realized 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 presentation unit can be performed using, for example, an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0087] The presentation unit can determine the presentation priority based on the submission time of the hearing items when presenting them. The submission time of the hearing items includes, for example, but is not limited to, the submission date and time and the submission order. The presentation priority includes, for example, but is not limited to, the urgency and importance. For example, the presentation unit prioritizes the presentation of hearing items submitted earlier. The presentation unit can also postpone hearing items submitted later. The presentation unit can also determine the presentation priority based on the submission time. This enables efficient information presentation by determining the presentation priority based on the submission time of the hearing items. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the presentation content using an AI model that uses the submission time as an input and determines the presentation priority.

[0088] The presentation unit can adjust the order of presentation based on the relevance of the hearing items when presenting them. Examples of the relevance of the hearing items include, but are not limited to, the same topic or related questions. Examples of the order of presentation include, but are not limited to, presenting highly relevant questions first. For example, the presentation unit prioritizes the presentation of highly relevant hearing items. The presentation unit can also postpone less relevant hearing items. The presentation unit can also adjust the order of presentation based on the relevance of the hearing items. This enables efficient information presentation by adjusting the order of presentation based on the relevance of the hearing items. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can adjust the presentation content using an AI model that uses the relevance of the hearing items as input and adjusts the order of presentation.

[0089] The presentation unit may adjust the use of technical terms in the presentation depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, survey results and past usage history. Examples of the use of technical terms include, but are not limited to, providing definitions of technical terms. For example, the presentation unit may use a lot of technical terms when the user's level of expertise is high. The presentation unit may also avoid technical terms when the user's level of expertise is low. The presentation unit may also adjust the use of technical terms in the presentation depending on the user's level of expertise. This allows the user to be provided with information that is easy to understand. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit may adjust the presentation content using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms.

[0090] The answer generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. Examples of answer expression methods include, but are not limited to, visual display and text display. For example, if the user is stressed, the answer generation unit uses a simple and easy-to-understand expression method. Furthermore, if the user is relaxed, the answer generation unit can use an expression method that includes detailed explanations. Furthermore, if the user is in a hurry, the answer generation unit can use a concise expression method that focuses on the main points. By adjusting the way the answer is expressed based on the user's emotions, an answer that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the answer generation unit may be performed using, for example, an AI. For example, the answer generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0091] The answer generation unit can adjust the level of detail of the answer based on the importance of the hearing item when generating an answer. The importance of the hearing item includes, for example, but is not limited to, the urgency of the question and the scope of impact of the question. The level of detail of the answer includes, for example, but is not limited to, a simple answer or a detailed answer. For example, the answer generation unit generates a detailed answer for an important hearing item. The answer generation unit can also generate a simplified answer for a hearing item with a low level of importance. The answer generation unit can also adjust the level of detail of the answer depending on the importance of the hearing item. This enables efficient answers to be generated by adjusting the level of detail of the answer depending on the importance of the hearing item. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can adjust the content of the answer using an AI model that inputs the importance of the hearing item and adjusts the level of detail of the answer.

[0092] When generating an answer, the answer generation unit can apply different answer algorithms depending on the category of the hearing item. The categories of hearing items include, but are not limited to, technical questions and business questions. The answer algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the answer generation unit applies a technical answer algorithm to technical hearing items. The answer generation unit can also apply a general answer algorithm to non-technical hearing items. The answer generation unit can also apply different answer algorithms depending on the category of the hearing item. This enables appropriate answers to be generated by applying an answer algorithm according to the category of the hearing item. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can adjust the content of the answer using an AI model that inputs the category of the hearing item and applies an appropriate answer algorithm.

[0093] When generating an answer, the answer generation unit can improve the accuracy of the answer by referring to the user's past answer results. Past answer results include, but are not limited to, answer results by the same user or answer results under the same conditions. The answer generation unit improves the accuracy of the answer, for example, based on the user's past answer results. The answer generation unit can also suggest an optimal answer method based on the user's past answer results. The answer generation unit can also improve the answer algorithm by referring to the user's past answer results. In this way, the accuracy of the answer is improved by referring to the past answer results. Some or all of the above-mentioned processing in the answer generation unit may be performed, for example, using AI or may be performed without using AI. For example, the answer generation unit can adjust the content of the answer using an AI model that uses past answer results as input and improves the accuracy of the answer.

[0094] The answer generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. The length of the answer can include, but is not limited to, a concise answer or a detailed answer. For example, the answer generation unit can provide a short answer when the user is stressed. The answer generation unit can also provide a detailed answer when the user is relaxed. The answer generation unit can also provide a concise answer when the user is in a hurry. By adjusting the length of the answer according to the user's emotions, an appropriate answer can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answer generation unit can be performed using, for example, an AI. For example, the answer generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0095] When generating answers, the answer generation unit can determine the priority of answers based on the submission times of the hearing items. The submission times of the hearing items include, for example, but are not limited to, the submission date and time and the submission order. The priority of answers includes, for example, but are not limited to, the urgency and importance. For example, the answer generation unit can prioritize generating answers for hearing items submitted early. The answer generation unit can also postpone generating answers for hearing items submitted late. The answer generation unit can also determine the priority of answers based on the submission times. This enables efficient answers by determining the priority of answers based on the submission times of the hearing items. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can adjust the content of the answer using an AI model that uses the submission time as input and determines the priority of answers.

[0096] When generating answers, the answer generation unit can adjust the order of answers based on the relevance of the hearing items. Examples of the relevance of the hearing items include, but are not limited to, questions on the same topic or related questions. Examples of the order of answers include, but are not limited to, answering questions starting with the most relevant questions. For example, the answer generation unit can prioritize generating answers for highly relevant hearing items. The answer generation unit can also postpone generating answers for less relevant hearing items. The answer generation unit can also adjust the order of answers based on the relevance of the hearing items. This enables efficient answers to be generated by adjusting the order of answers based on the relevance of the hearing items. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer generation unit can adjust the content of an answer using an AI model that uses the relevance of the hearing items as input and adjusts the order of answers.

[0097] When generating an answer, the answer generation unit can adjust the use of technical terms in the answer depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, survey results and past usage history. Examples of the use of technical terms include, but are not limited to, providing definitions of technical terms. For example, the answer generation unit uses a lot of technical terms when the user's level of expertise is high. The answer generation unit can also avoid using technical terms when the user's level of expertise is low. The answer generation unit can also adjust the use of technical terms in the answer depending on the user's level of expertise. This allows the user to be provided with an answer that is easy to understand. Some or all of the above-described processing in the answer generation unit may be performed using, for example, AI, or may be performed without AI. For example, the answer generation unit can adjust the content of the answer using an AI model that uses the user's level of expertise as input and adjusts the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, presentation unit, and answer generation 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 input unit can input image data and situations using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information to generate necessary hearing items. The presentation unit presents hearing items to the questioner using, for example, the display 40A or speaker 40B of the smart device 14. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an automatic answer based on the collected information. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, presentation unit, and answer generation 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 input unit can input image data and situations using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information to generate the necessary hearing items. The presentation unit presents the hearing items to the questioner, for example, using the display and speaker 240 of the smart glasses 214. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an automatic answer based on the collected information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, presentation unit, and answer generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit can input image data and situations using the camera 42 or microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information to generate necessary hearing items. The presentation unit presents hearing items to the questioner, for example, using the display 343 or speaker 240 of the headset-type terminal 314. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an automatic answer based on the collected information. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, presentation unit, and answer generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input image data and situations using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information to generate the necessary hearing items. The presentation unit presents the hearing items to the questioner, for example, using the display or speaker 240 of the robot 414. The answer generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an automatic answer based on the collected information.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is stressed, important analyses can be prioritized. Also, if the user is relaxed, detailed analyses can be prioritized. Also, if the user is in a hurry, simple analyses can be prioritized to provide results quickly. By adjusting the analysis priority according to the user's emotions, optimal analysis results can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] The input unit can learn the user's past behavioral patterns and suggest the optimal input method. For example, it can prioritize and suggest input methods that the user has frequently used in the past. Also, if the user tends to use a specific input method during a specific time period, it can suggest an input method suitable for that time period. Also, if the user uses a specific device, it can suggest the input method optimal for that device. In this way, by learning the user's past behavioral patterns, the efficiency and accuracy of input are improved. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can adjust the input content using an AI model that inputs past behavioral patterns and suggests the optimal input method.

[0101] The analysis unit can estimate the user's emotions and adjust the analysis feedback method based on the estimated user emotions. For example, if the user is feeling stressed, positive feedback can be provided preferentially. Also, if the user is relaxed, detailed feedback can be provided. Also, if the user is in a hurry, brief feedback can be provided. By adjusting the analysis feedback method according to the user's emotions, optimal feedback can be provided to the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0102] The input unit can detect the user's current activity status and suggest the optimal input timing. For example, if the user is in a meeting, the input can be delayed. Also, if the user is taking a break, the input can be promoted. Also, if the user is moving, the input can be paused. In this way, the input timing can be adjusted according to the user's activity status, thereby improving the efficiency and accuracy of input. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can adjust the input content using an AI model that inputs the user's activity status and suggests the optimal input timing.

[0103] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user emotions. For example, if the user is stressed, a simple and intuitive graph can be used. If the user is relaxed, a graph containing detailed data can be used. If the user is in a hurry, a graph emphasizing the main points can be used. By adjusting the visual presentation of the analysis according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0104] The input unit can analyze the user's past input data and suggest the optimal input format. For example, it can prioritize the suggested format that the user has used in the past. Also, if the user prefers a specific data format, it can suggest that format. Also, if the user uses a specific input method, it can suggest the optimal format for that method. In this way, by analyzing the user's past input data, the efficiency and accuracy of input are improved. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can adjust the input content using an AI model that uses past input data as input and suggests the optimal input format.

[0105] The analysis unit can estimate the user's emotions and adjust the analysis notification method based on the estimated user emotions. For example, if the user is feeling stressed, the notification can be made less. If the user is relaxed, a detailed notification can be made. If the user is in a hurry, a brief notification can be made. By adjusting the analysis notification method according to the user's emotions, the optimal notification can be provided to the user. Emotion estimation is realized 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0106] The input unit can detect the user's current device status and suggest the optimal input interface. For example, if the user is using a smartphone, it can prioritize touch input. If the user is using a PC, it can suggest keyboard input. If the user is using a tablet, it can suggest stylus input. This improves input efficiency and accuracy by suggesting the optimal input interface according to the user's device status. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the input content using an AI model that inputs the device status and suggests the optimal input interface.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, a simple and intuitive display method can be used. If the user is relaxed, a display method including detailed data can be used. If the user is in a hurry, a display method emphasizing the main points can be used. By adjusting the display method of the analysis results according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is realized 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0108] The input unit can analyze the user's past input errors and provide input guidance to prevent errors. For example, it can identify errors that the user frequently made in the past and display guidance to prevent those errors. Furthermore, if the user is prone to making errors in a specific input field, it can also warn the user about that field. Furthermore, if the user is prone to making errors using a specific input method, it can also suggest improvements to that method. By analyzing the user's past input errors, the accuracy of input is improved. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without AI. For example, the input unit can adjust the input content using an AI model that uses past input errors as input and provides input guidance to prevent errors.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The input unit inputs image data or a situation at the time of an event occurrence. The occurrence of an event includes, but is not limited to, an accident, a malfunction, an abnormal operation, etc. The image data or a situation includes, but is not limited to, a photograph, a video, a text description, etc. For example, the input unit uploads image data taken by a user to the system. The input unit also allows the user to describe the situation in text format. Furthermore, the input unit can also describe the situation using voice input. For example, the user can upload an image taken with a smartphone camera, and the system analyzes the image. The user can also describe the situation in text format, and the system can analyze the text. Step 2: The analysis unit analyzes the information input by the input unit and automatically generates the necessary interview items. The analysis is performed using, for example, an image analysis algorithm or a text analysis method, but is not limited to these examples. For example, the analysis unit identifies the type of defect and the location of the defect from the image data, and generates related questions based on that. The analysis unit can also extract important information from text data and generate interview items based on that. For example, an image analysis algorithm extracts features from the image and identifies the type of defect. A text analysis method extracts keywords from the text and identifies important information. Step 3: The presentation unit presents the hearing items generated by the analysis unit to the questioner. The presentation may be performed using, for example, a visual display or a text display, but is not limited to these examples. For example, the presentation unit displays the generated hearing items on the questioner's screen. The presentation unit can also present the hearing items by voice. For example, the visual display displays the question items in list format. The text display displays the question items in text format. Step 4: The answer generation unit generates an automatic answer based on the hearing items presented by the presentation unit. The automatic answer is generated based on, for example, similar past cases or a database, but is not limited to these examples. For example, the answer generation unit searches for similar past cases and presents an appropriate solution. The answer generation unit can also generate an automatic answer based on a database. For example, similar past cases include events of the same type or events under the same conditions. The database includes a case database or an FAQ database.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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. an input unit for inputting image data or a situation at the time of occurrence of an event; an analysis unit that analyzes the information input by the input unit and automatically generates necessary hearing items; a presentation unit that presents the hearing items generated by the analysis unit to a questioner; an answer generation unit that generates an automatic answer based on the hearing items presented by the presentation unit; A system characterized by:

2. The answer generation unit Providing solutions based on similar past cases and databases 2. The system of claim 1.

3. The input unit Estimate the user's emotions and adjust the timing of input based on the estimated user emotions.

2. The system of claim 1.

4. The input unit Automatically assess the resolution or quality of image data and select the input method 2. The system of claim 1.

5. The input unit When typing, the system will refer to the user's past input history to complete the input.

2. The system of claim 1.

6. The input unit As you type, adjust your input method based on your current environment 2. The system of claim 1.

7. The input unit Estimate the user's emotions and prioritize input content based on the estimated user emotions.

2. The system of claim 1.

8. The input unit As you type, it takes your geographic location into account to prioritize relevant information.

2. The system of claim 1.

9. The input unit Analyze your social media activity and populate relevant information as you type 2. The system of claim 1.

Images