System

Abstract

An object of a system according to an embodiment is to post a question to a Q & A site outside a company while protecting confidential information.SOLUTION: A system according to an embodiment includes a question posting unit, a mask processing unit, and a posting destination selection unit. The question posting unit posts a question to an external Q & A site. The mask processing unit masks confidential information included in the question content. The posting destination selection unit selects an appropriate Q & A site based on the question content.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, there was a risk of confidential information being leaked when posting a question to an external Q&A site, which required appropriate information management.

[0005] The system according to the embodiment aims to post questions to an external Q&A site while protecting confidential information. [Means for solving the problem]

[0006] The system according to the embodiment includes a question posting unit, a mask processing unit, and a posting destination selection unit. The question posting unit posts a question to an external Q&A site. The mask processing unit masks confidential information included in the question content. The posting destination selection unit selects an appropriate Q&A site based on the question content. [Effects of the Invention]

[0007] The system according to the embodiment allows users to post questions to external Q&A sites while protecting confidential information. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The Q&A site utilization system according to an embodiment of the present invention utilizes external Q&A sites to post questions about general issues unrelated to internal technology, with the generation AI masking confidential information. This allows the Q&A site utilization system to protect internal confidential information while utilizing external knowledge.

[0029] A Q&A site utilization system according to an embodiment includes a question posting unit, a mask processing unit, and a posting destination selection unit. The question posting unit posts a question to an external Q&A site. For example, the question posting unit posts a question to a technical Q&A site such as StackOverFlow or Qiita. The question posting unit can also post a question to a business Q&A site. The mask processing unit masks confidential information included in the question. For example, the mask processing unit replaces database table names and column names with random character strings. The mask processing unit can also replace information related to specific business logic with general expressions. The mask processing unit identifies confidential information and performs masking using a generation AI (e.g., a text generation AI or a multimodal generation AI). The posting destination selection unit selects an appropriate Q&A site based on the question. For example, the posting destination selection unit evaluates the technical complexity and scope of impact of the question to select the optimal Q&A site. In addition, the posting destination selection unit can select specialized Q&A sites or general Q&A sites depending on the importance of the question content. This allows the Q&A site utilization system to protect internal confidential information while utilizing external knowledge. For example, the question posting unit posts the question content masked by the masking processing unit to an external Q&A site, and the obtained answer is used internally. The masking processing unit uses a generation AI to mask confidential information contained in the question content. The posting destination selection unit evaluates the importance of the question content and selects the optimal Q&A site.

[0030] The posting destination selection unit can evaluate the importance of the question content and automatically select a Q&A site according to the importance. For example, the posting destination selection unit uses a generation AI to analyze the question content and score the importance. For example, the importance is evaluated based on the technical complexity and scope of impact, and questions with high importance are posted to specialized Q&A sites. The posting destination selection unit can also evaluate the urgency and scope of impact of the question content and select the most appropriate Q&A site. This makes it possible to select the most appropriate Q&A site according to the importance of the question content.

[0031] The question posting unit can automatically search for past questions and answers related to the question content and post the question by adding related information to the question content. For example, the question posting unit uses a generation AI to analyze the question content and automatically search for related past questions and answers. For example, it can quote past answers to a similar problem and post the question by adding it to the question content. The question posting unit can also perform keyword searches and similarity searches of past questions and answers and add related information to the question content. This allows users to post more specific questions by adding related information.

[0032] The question posting unit can simultaneously post a question to multiple Q&A sites and integrate and analyze the answers from each site. For example, the question posting unit can simultaneously post a question to multiple Q&A sites and automatically collect answers from each site. For example, it can simultaneously post a question to StackOverFlow and Qiita, and integrate and analyze the answers obtained. The question posting unit can also integrate answers from multiple Q&A sites and select the most appropriate answer. This allows answers from multiple Q&A sites to be integrated and analyzed.

[0033] The question posting unit converts the question content into a video format and posts it on a video sharing site to obtain a visual answer. The question posting unit, for example, converts the question content into a video format and posts it on a video sharing site. For example, an SQL query problem can be explained in a video to obtain a visual answer. The question posting unit can also post the question content in video format to obtain a visual answer. In this way, a visual answer can be obtained.

[0034] The masking processing unit can apply different masking methods to each type of confidential information. For example, the generation AI identifies the type of confidential information, and the masking processing unit applies different masking methods to each type. For example, table names are replaced with random character strings, and column names are replaced with generic names. The masking processing unit can also select the optimal masking method depending on the type of confidential information. This makes it possible to apply the optimal masking method to each type of confidential information.

[0035] The mask processing unit can summarize the question content before performing mask processing, and perform mask processing on the summary sentence. For example, the mask processing unit has a generation AI summarize the question content and perform mask processing on the summary sentence. For example, the mask processing unit summarizes a long question content in a short form and performs mask processing on the summary sentence. Furthermore, by performing mask processing on the summary sentence, the mask processing unit can perform mask processing efficiently. In this way, by performing mask processing on the summary sentence, mask processing can be performed efficiently.

[0036] The masking processor can encrypt the masked data to further strengthen security before posting. For example, the masking processor can have a generating AI encrypt the masked data to further strengthen security before posting. For example, the data can be protected using AES encryption. The masking processor can also strengthen security by encrypting the masked data. This can strengthen security by encrypting the masked data.

[0037] When a question is posted, the generation AI automatically tags it and posts it to a related category. For example, the generation AI analyzes the question content and automatically tags it appropriately. For example, a question about an SQL query is tagged with tags such as "SQL" or "database." The generation AI also automatically tags the question and posts it to a related category. This allows questions to be automatically tagged and posted to a related category.

[0038] The question posting unit allows the generation AI to check and optimize the grammar and expression of the question before it is posted. For example, the question posting unit allows the generation AI to analyze the question and automatically correct grammatical and expression errors. For example, it converts the question into grammatically correct sentences to make it easier to read. The question posting unit also allows the generation AI to check and optimize the grammar and expression of the question. This allows the grammar and expression of the question to be optimized.

[0039] The question posting unit can translate the question content into multiple languages ​​and post it simultaneously on international Q&A sites. For example, the question posting unit translates the question content into multiple languages ​​using a generation AI and posts it simultaneously on international Q&A sites. For example, the question posting unit translates the question content into English, French, Chinese, etc. and posts it. The question posting unit can also post the question content translated into multiple languages ​​on international Q&A sites to gain a wide range of knowledge. This allows the question content to be translated into multiple languages ​​and posted simultaneously on international Q&A sites.

[0040] The question posting unit allows the generation AI to automatically analyze the content of the answer, extract and summarize the important parts. For example, the question posting unit allows the generation AI to automatically analyze the content of the answer, extract and summarize the important parts. For example, it extracts the main points of the answer and the solution and summarizes them concisely. The question posting unit also allows the generation AI to analyze the content of the answer, extract and summarize the important parts. This allows the content of the answer to be automatically analyzed, extract and summarize the important parts.

[0041] The question posting unit can automatically register the answer content in the company's internal knowledge base so that other employees can refer to it. In the question posting unit, for example, a generation AI automatically analyzes the answer content and registers it in the company's internal knowledge base. For example, the main points of the answer and the solution are added to the knowledge base. The question posting unit can also automatically register the answer content in the company's internal knowledge base so that other employees can refer to it. This automatically registers the answer content in the company's internal knowledge base so that other employees can refer to it.

[0042] The question posting unit can automatically evaluate the content of the answers and prioritize displaying highly reliable answers. The question posting unit, for example, has a generation AI automatically evaluate the content of the answers and prioritize displaying highly reliable answers. For example, the accuracy and reliability of the answers are evaluated and displayed at the top. The question posting unit can also automatically evaluate the content of the answers and prioritize displaying highly reliable answers. This allows highly reliable answers to be displayed preferentially.

[0043] The question posting unit allows the generation AI to automatically analyze the relevance of the answer content so that it can be applied to other projects within the company. The question posting unit, for example, allows the generation AI to automatically analyze the answer content and analyze the relevance so that it can be applied to other projects within the company. For example, information that is useful for other projects is extracted from the answers. The question posting unit also allows the generation AI to automatically analyze the relevance so that it can be applied to other projects within the company. This allows the relevance of the answer content to be analyzed so that it can be applied to other projects.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] When posting a question, the question posting unit can refer to the asker's past posting history to check whether a similar question has already been posted. For example, if a similar question has been posted in the past, a link to that question and answer is automatically added. The question posting unit can also evaluate the quality of the answers previously received by the asker based on the past posting history, and prioritize displaying highly reliable answerers. This allows the asker to utilize past knowledge and solve their problem efficiently.

[0046] Before a question is posted, the question posting unit can automatically classify the topic of the question using the generation AI and notify relevant experts. For example, experts in a specific technical field can be notified that a question has been posted and prompted to provide a quick response. The question posting unit can also generate a list of relevant experts based on the topic of the question, allowing the questioner to directly contact the experts. This allows the questioner to receive a quick and accurate response.

[0047] When a question is posted, the question posting unit uses a generation AI to automatically evaluate the difficulty of the question and distinguish between questions for beginners and questions for advanced users. For example, questions for beginners can be accompanied by basic explanations and reference links, while questions for advanced users can use detailed technical information and specialized terminology. The question posting unit can also select an appropriate answerer depending on the difficulty of the question, allowing questioners to receive answers that match their own level.

[0048] When a question is posted, the question posting unit uses a generation AI to automatically determine the category of the question and simultaneously post it to related forums and communities. For example, if the question is technical, it will be posted to a technical forum or developer community, and if it is a business question, it will be posted to a business forum or management community. The question posting unit can also select the most appropriate forum or community depending on the category of the question, allowing the questioner to gain a wide range of knowledge.

[0049] When a question is posted, the question posting unit uses the generation AI to automatically evaluate the relevance of the question and add links to past similar questions and answers. For example, a past answer to a similar problem can be quoted and added to the question before posting. The question posting unit can also perform keyword and similarity searches of past questions and answers to add related information to the question. By adding related information, users can post more specific questions.

[0050] When a question is posted, the question posting unit uses the generation AI to automatically analyze the topic of the question and add links to related news articles and research papers. For example, it can add information on the latest technological trends and research results to complement the question. The question posting unit can also automatically search for related news articles and research papers based on the topic of the question and add them to the question. This allows the questioner to post a question based on the latest information.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The question posting department posts a question to an external Q&A site. For example, the question posting department posts a question to a technical Q&A site such as StackOverFlow or Qiita. Questions can also be posted to business Q&A sites. Step 2: The masking unit masks confidential information contained in the question. For example, it replaces database table names and column names with random strings. It can also replace information related to specific business logic with general expressions. Furthermore, it uses generation AI (for example, text generation AI or multimodal generation AI) to identify and mask confidential information. Step 3: The posting destination selection unit selects an appropriate Q&A site based on the content of the question. For example, it evaluates the technical complexity and scope of the question and selects the most appropriate Q&A site. It can also select specialized or general Q&A sites depending on the importance of the question.

[0053] (Example 2) The Q&A site utilization system according to an embodiment of the present invention utilizes external Q&A sites to post questions about general issues unrelated to internal technology, with the generation AI masking confidential information. This allows the Q&A site utilization system to protect internal confidential information while utilizing external knowledge.

[0054] A Q&A site utilization system according to an embodiment includes a question posting unit, a mask processing unit, and a posting destination selection unit. The question posting unit posts a question to an external Q&A site. For example, the question posting unit posts a question to a technical Q&A site such as StackOverFlow or Qiita. The question posting unit can also post a question to a business Q&A site. The mask processing unit masks confidential information included in the question. For example, the mask processing unit replaces database table names and column names with random character strings. The mask processing unit can also replace information related to specific business logic with general expressions. The mask processing unit identifies confidential information and performs masking using a generation AI (e.g., a text generation AI or a multimodal generation AI). The posting destination selection unit selects an appropriate Q&A site based on the question. For example, the posting destination selection unit evaluates the technical complexity and scope of impact of the question to select the optimal Q&A site. In addition, the posting destination selection unit can select specialized Q&A sites or general Q&A sites depending on the importance of the question content. This allows the Q&A site utilization system to protect internal confidential information while utilizing external knowledge. For example, the question posting unit posts the question content masked by the masking processing unit to an external Q&A site, and the obtained answer is used internally. The masking processing unit uses a generation AI to mask confidential information contained in the question content. The posting destination selection unit evaluates the importance of the question content and selects the optimal Q&A site.

[0055] The posting destination selection unit can evaluate the importance of the question content and automatically select a Q&A site according to the importance. For example, the posting destination selection unit uses a generation AI to analyze the question content and score the importance. For example, the importance is evaluated based on the technical complexity and scope of impact, and questions with high importance are posted to specialized Q&A sites. The posting destination selection unit can also evaluate the urgency and scope of impact of the question content and select the most appropriate Q&A site. This makes it possible to select the most appropriate Q&A site according to the importance of the question content.

[0056] The question posting unit can automatically search for past questions and answers related to the question content and post the question by adding related information to the question content. For example, the question posting unit uses a generation AI to analyze the question content and automatically search for related past questions and answers. For example, it can quote past answers to a similar problem and post the question by adding it to the question content. The question posting unit can also perform keyword searches and similarity searches of past questions and answers and add related information to the question content. This allows users to post more specific questions by adding related information.

[0057] The question posting unit can use the emotion estimation function to analyze the emotional state of the questioner and generate and post an optimal question. The question posting unit, for example, uses the emotion estimation function to analyze the emotional state of the questioner in real time and generate an optimal question. For example, if the questioner is impatient, the question posting unit generates a calm question. Furthermore, the question posting unit can use the emotion estimation function to generate a question that corresponds to the emotional state of the questioner. This makes it possible to generate an optimal question that corresponds to the emotional state of the questioner.

[0058] The question posting unit can simultaneously post a question to multiple Q&A sites and integrate and analyze the answers from each site. For example, the question posting unit can simultaneously post a question to multiple Q&A sites and automatically collect answers from each site. For example, it can simultaneously post a question to StackOverFlow and Qiita, and integrate and analyze the answers obtained. The question posting unit can also integrate answers from multiple Q&A sites and select the most appropriate answer. This allows answers from multiple Q&A sites to be integrated and analyzed.

[0059] The question posting unit converts the question content into a video format and posts it on a video sharing site to obtain a visual answer. The question posting unit, for example, converts the question content into a video format and posts it on a video sharing site. For example, an SQL query problem can be explained in a video to obtain a visual answer. The question posting unit can also post the question content in video format to obtain a visual answer. In this way, a visual answer can be obtained.

[0060] The question posting unit can use the emotion estimation function to analyze the emotional state of the respondent and preferentially display positive answers. The question posting unit can, for example, use the emotion estimation function to analyze the emotional state of the respondent and preferentially display positive answers. For example, answers in which the respondent has positive emotions are displayed at the top. Furthermore, the question posting unit can use the emotion estimation function to preferentially display answers that correspond to the emotional state of the respondent. This allows positive answers to be preferentially displayed.

[0061] The masking processing unit can apply different masking methods to each type of confidential information. For example, the generation AI identifies the type of confidential information, and the masking processing unit applies different masking methods to each type. For example, table names are replaced with random character strings, and column names are replaced with generic names. The masking processing unit can also select the optimal masking method depending on the type of confidential information. This makes it possible to apply the optimal masking method to each type of confidential information.

[0062] The mask processing unit can use the emotion estimation function to evaluate the impression that the question content after mask processing gives to the user and select an optimal masking method. The mask processing unit, for example, uses the emotion estimation function to evaluate the impression that the question content after mask processing gives to the user. For example, the masking method is adjusted so that the question content does not feel unnatural. Furthermore, the mask processing unit can use the emotion estimation function to evaluate the impression that the question content gives to the user and select an optimal masking method. This makes it possible to evaluate the impression that the question content gives to the user and select an optimal masking method.

[0063] The mask processing unit can summarize the question content before performing mask processing, and perform mask processing on the summary sentence. For example, the mask processing unit has a generation AI summarize the question content and perform mask processing on the summary sentence. For example, the mask processing unit summarizes a long question content in a short form and performs mask processing on the summary sentence. Furthermore, by performing mask processing on the summary sentence, the mask processing unit can perform mask processing efficiently. In this way, by performing mask processing on the summary sentence, mask processing can be performed efficiently.

[0064] The masking processor can encrypt the masked data to further strengthen security before posting. For example, the masking processor can have a generating AI encrypt the masked data to further strengthen security before posting. For example, the data can be protected using AES encryption. The masking processor can also strengthen security by encrypting the masked data. This can strengthen security by encrypting the masked data.

[0065] The mask processing unit can use the emotion estimation function to evaluate the impression that the question content after mask processing gives to the respondent, and select the optimal masking method. The mask processing unit, for example, uses the emotion estimation function to evaluate the impression that the question content after mask processing gives to the respondent. For example, it adjusts the masking method so that the question content does not seem unnatural. Furthermore, the mask processing unit can use the emotion estimation function to evaluate the impression that the question content gives to the respondent, and select the optimal masking method. This makes it possible to evaluate the impression that the question content gives to the respondent and select the optimal masking method.

[0066] When a question is posted, the generation AI automatically tags it and posts it to a related category. For example, the generation AI analyzes the question content and automatically tags it appropriately. For example, a question about an SQL query is tagged with tags such as "SQL" or "database." The generation AI also automatically tags the question and posts it to a related category. This allows questions to be automatically tagged and posted to a related category.

[0067] The question posting unit allows the generation AI to check and optimize the grammar and expression of the question before it is posted. For example, the question posting unit allows the generation AI to analyze the question and automatically correct grammatical and expression errors. For example, it converts the question into grammatically correct sentences to make it easier to read. The question posting unit also allows the generation AI to check and optimize the grammar and expression of the question. This allows the grammar and expression of the question to be optimized.

[0068] The question posting unit can use the emotion estimation function to evaluate the impression that the question content gives to the respondent and select the optimal posting timing. The question posting unit, for example, uses the emotion estimation function to evaluate the impression that the question content gives to the respondent and select the optimal posting timing. For example, the question posting unit posts during a time period when the respondent is active. The question posting unit can also use the emotion estimation function to evaluate the impression that the question content gives to the respondent and select the optimal posting timing. This allows the optimal posting timing to be selected.

[0069] The question posting unit can translate the question content into multiple languages ​​and post it simultaneously on international Q&A sites. For example, the question posting unit translates the question content into multiple languages ​​using a generation AI and posts it simultaneously on international Q&A sites. For example, the question posting unit translates the question content into English, French, Chinese, etc. and posts it. The question posting unit can also post the question content translated into multiple languages ​​on international Q&A sites to gain a wide range of knowledge. This allows the question content to be translated into multiple languages ​​and posted simultaneously on international Q&A sites.

[0070] The question posting unit can use the emotion estimation function to evaluate the impression that the question content gives to the respondent and select the optimal posting timing. The question posting unit, for example, uses the emotion estimation function to evaluate the impression that the question content gives to the respondent and select the optimal posting timing. For example, the question posting unit posts during a time period when the respondent is active. The question posting unit can also use the emotion estimation function to evaluate the impression that the question content gives to the respondent and select the optimal posting timing. This allows the optimal posting timing to be selected.

[0071] The question posting unit allows the generation AI to automatically analyze the content of the answer, extract and summarize the important parts. For example, the question posting unit allows the generation AI to automatically analyze the content of the answer, extract and summarize the important parts. For example, it extracts the main points of the answer and the solution and summarizes them concisely. The question posting unit also allows the generation AI to analyze the content of the answer, extract and summarize the important parts. This allows the content of the answer to be automatically analyzed, extract and summarize the important parts.

[0072] The question posting unit can automatically register the answer content in the company's internal knowledge base so that other employees can refer to it. In the question posting unit, for example, a generation AI automatically analyzes the answer content and registers it in the company's internal knowledge base. For example, the main points of the answer and the solution are added to the knowledge base. The question posting unit can also automatically register the answer content in the company's internal knowledge base so that other employees can refer to it. This automatically registers the answer content in the company's internal knowledge base so that other employees can refer to it.

[0073] The question posting unit can use the emotion estimation function to evaluate the impression that the answer content gives to the questioner and provide optimal feedback. The question posting unit can, for example, use the emotion estimation function to evaluate the impression that the answer content gives to the questioner and provide optimal feedback. For example, the question posting unit can adjust the feedback so that the answer gives a positive impression. The question posting unit can also use the emotion estimation function to evaluate the impression that the answer content gives to the questioner and provide optimal feedback. This allows the impression that the answer content gives to the questioner to be evaluated and optimal feedback to be provided.

[0074] The question posting unit can automatically evaluate the content of the answers and prioritize displaying highly reliable answers. The question posting unit, for example, has a generation AI automatically evaluate the content of the answers and prioritize displaying highly reliable answers. For example, the accuracy and reliability of the answers are evaluated and displayed at the top. The question posting unit can also automatically evaluate the content of the answers and prioritize displaying highly reliable answers. This allows highly reliable answers to be displayed preferentially.

[0075] The question posting unit allows the generation AI to automatically analyze the relevance of the answer content so that it can be applied to other projects within the company. The question posting unit, for example, allows the generation AI to automatically analyze the answer content and analyze the relevance so that it can be applied to other projects within the company. For example, information that is useful for other projects is extracted from the answers. The question posting unit also allows the generation AI to automatically analyze the relevance so that it can be applied to other projects within the company. This allows the relevance of the answer content to be analyzed so that it can be applied to other projects.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] When posting a question, the question posting unit can refer to the asker's past posting history to check whether a similar question has already been posted. For example, if a similar question has been posted in the past, a link to that question and answer is automatically added. The question posting unit can also evaluate the quality of the answers previously received by the asker based on the past posting history, and prioritize displaying highly reliable answerers. This allows the asker to utilize past knowledge and solve their problem efficiently.

[0078] Before a question is posted, the question posting unit can automatically classify the topic of the question using the generation AI and notify relevant experts. For example, experts in a specific technical field can be notified that a question has been posted and prompted to provide a quick response. The question posting unit can also generate a list of relevant experts based on the topic of the question, allowing the questioner to directly contact the experts. This allows the questioner to receive a quick and accurate response.

[0079] The question posting unit can use the emotion estimation function to analyze the emotional state of the questioner and select an appropriate answerer according to the question content. For example, if the questioner is confused, an answerer who will provide a kind and polite answer will be preferentially selected. Furthermore, by using the emotion estimation function to select an answerer according to the emotional state of the questioner, the question posting unit can provide an environment where the questioner can ask a question with peace of mind. This makes it possible to select the optimal answerer according to the emotional state of the questioner.

[0080] When a question is posted, the question posting unit uses a generation AI to automatically evaluate the difficulty of the question and distinguish between questions for beginners and questions for advanced users. For example, questions for beginners can be accompanied by basic explanations and reference links, while questions for advanced users can use detailed technical information and specialized terminology. The question posting unit can also select an appropriate answerer depending on the difficulty of the question, allowing questioners to receive answers that match their own level.

[0081] The question posting unit can use the emotion estimation function to analyze the respondent's emotional state and filter out negative answers. For example, if the respondent is feeling angry or irritated, the answer will not be displayed. The question posting unit can also use the emotion estimation function to filter answers according to the respondent's emotional state, providing an environment where questioners can ask questions with peace of mind. This allows negative answers to be filtered out and positive answers to be displayed preferentially.

[0082] When a question is posted, the question posting unit uses a generation AI to automatically determine the category of the question and simultaneously post it to related forums and communities. For example, if the question is technical, it will be posted to a technical forum or developer community, and if it is a business question, it will be posted to a business forum or management community. The question posting unit can also select the most appropriate forum or community depending on the category of the question, allowing the questioner to gain a wide range of knowledge.

[0083] The question posting unit can use the emotion estimation function to analyze the emotional state of the questioner and provide appropriate feedback according to the content of the question. For example, if the questioner is feeling anxious, an encouraging message can be added. Furthermore, by using the emotion estimation function to provide feedback according to the questioner's emotional state, the question posting unit can provide an environment where the questioner can ask a question with peace of mind. This makes it possible to provide optimal feedback according to the questioner's emotional state.

[0084] When a question is posted, the question posting unit uses the generation AI to automatically evaluate the relevance of the question and add links to past similar questions and answers. For example, a past answer to a similar problem can be quoted and added to the question before posting. The question posting unit can also perform keyword and similarity searches of past questions and answers to add related information to the question. By adding related information, users can post more specific questions.

[0085] The question posting unit can use the emotion estimation function to analyze the emotional state of the questioner and recommend an appropriate answerer according to the question content. For example, if the questioner is in a hurry, an answerer who can provide a quick answer can be recommended. Furthermore, by using the emotion estimation function to recommend an answerer according to the emotional state of the questioner, the question posting unit can provide an environment where the questioner can ask a question with peace of mind. This makes it possible to recommend the most appropriate answerer according to the emotional state of the questioner.

[0086] When a question is posted, the question posting unit uses the generation AI to automatically analyze the topic of the question and add links to related news articles and research papers. For example, it can add information on the latest technological trends and research results to complement the question. The question posting unit can also automatically search for related news articles and research papers based on the topic of the question and add them to the question. This allows the questioner to post a question based on the latest information.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The question posting department posts a question to an external Q&A site. For example, the question posting department posts a question to a technical Q&A site such as StackOverFlow or Qiita. Questions can also be posted to business Q&A sites. Step 2: The masking unit masks confidential information contained in the question. For example, it replaces database table names and column names with random strings. It can also replace information related to specific business logic with general expressions. Furthermore, it uses generation AI (for example, text generation AI or multimodal generation AI) to identify and mask confidential information. Step 3: The posting destination selection unit selects an appropriate Q&A site based on the content of the question. For example, it evaluates the technical complexity and scope of the question and selects the most appropriate Q&A site. It can also select specialized or general Q&A sites depending on the importance of the question.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A question submission department that posts questions to external Q&A sites, a masking processor that masks confidential information included in the question; and a posting destination selection unit that selects an appropriate Q&A site based on the content of the question. A system characterized by:

2. The posting destination selection unit Evaluate the importance of the question content and automatically select the Q&A site according to the importance.

2. The system of claim 1.

3. The question posting unit: The question is posted to multiple Q&A sites simultaneously, and the responses from each site are integrated and analyzed.

2. The system of claim 1.

4. The mask processing unit Apply different masking methods to each type of confidential information 2. The system of claim 1.

5. The question posting unit: When posting the question, the AI ​​automatically tags it and posts it to the relevant category.

2. The system of claim 1.

6. The question posting unit: Analyze the emotional state of the questioner and generate and post the most appropriate question.

2. The system of claim 1.

7. The mask processing unit Evaluate the impression that the masked questions give to respondents and select the optimal masking method.

2. The system of claim 1.

8. The question posting unit: Evaluate the impression that the answer gives to the questioner and provide optimal feedback 2. The system of claim 1.

Images