system

Abstract

The system according to the embodiment aims to respond to user questions and instructions by efficiently utilizing information stored in an in-house cloud storage or database. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an information acquisition unit, and an information provision unit. The reception unit receives questions or instructions from a user. The analysis unit analyzes the questions or instructions received by the reception unit and identifies necessary information. The information acquisition unit accesses a cloud storage service or a database system to acquire the information identified by the analysis unit. The information provision unit provides the information acquired by the information acquisition unit to the user as an answer or suggestion.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently utilize information stored in in-house cloud storage or databases to respond to user questions and instructions.

[0005] The system according to the embodiment aims to respond to user questions and instructions by efficiently utilizing information stored in an in-house cloud storage or database. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an information acquisition unit, and an information provision unit. The reception unit receives questions or instructions from a user. The analysis unit analyzes the questions or instructions received by the reception unit and identifies necessary information. The information acquisition unit accesses a cloud storage service or a database system to acquire the information identified by the analysis unit. The information provision unit provides the information acquired by the information acquisition unit to the user as an answer or suggestion. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently utilize information stored in an in-house cloud storage or database to respond to user questions and instructions. [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) An information linkage system according to an embodiment of the present invention links information stored in a cloud storage service or a database system and provides answers or suggestions to questions or instructions from a user. In this information linkage system, a user inputs a question or instruction, a reception unit receives the question, an analysis unit analyzes the question or instruction, and identifies the required information. Next, an information acquisition unit accesses the cloud storage service or the database system to acquire the identified information, and an information provision unit provides the acquired information to the user as an answer or suggestion. For example, if a user wants to know specific data in a spreadsheet stored in a cloud storage service, the user inputs a question, and the reception unit accepts the question. The analysis unit analyzes the question and identifies the required information. The information acquisition unit accesses the cloud storage service and acquires the identified spreadsheet data. The information provision unit provides the acquired data to the user as an answer. This system allows users to easily acquire information stored in a cloud storage service or a database system and receive answers or suggestions to their questions or instructions. This improves business efficiency and enables rapid decision-making. The information linkage system can link information stored in a cloud storage service or a database system to provide answers or suggestions to users' questions or instructions.

[0029] The information linkage system according to the embodiment includes a reception unit, an analysis unit, an information acquisition unit, and an information provision unit. The reception unit receives questions and instructions from a user. The questions and instructions from the user include, but are not limited to, text, audio, and questions about a specific topic. The reception unit, for example, receives text input by the user. The reception unit can also receive audio input. For example, speech recognition technology can be used to convert the user's voice into text and accept the text as a question or instruction. The analysis unit analyzes the question or instruction received by the reception unit and identifies necessary information. The analysis unit, for example, uses natural language processing technology to analyze the user's question or instruction. The analysis unit can also analyze the user's question or instruction using a machine learning algorithm. For example, the analysis unit understands the intent of the user's question and identifies the necessary information. The information acquisition unit accesses a cloud storage service or a database system to acquire the information identified by the analysis unit. For example, the information acquisition unit accesses a cloud storage service to acquire specific files or data. The information acquisition unit can also access a database system to acquire specific records or data. For example, the information acquisition unit acquires information from a database using an SQL query. The information provision unit provides the information acquired by the information acquisition unit to the user as an answer or suggestion. The information provision unit provides the acquired information to the user in text format, for example. The information provision unit can also provide the acquired information to the user in report format. For example, the information provision unit visually displays the acquired data as a graph or chart. As a result, the information linkage system according to the embodiment can provide answers or suggestions in response to user questions or instructions by linking information stored in a cloud storage service or a database system.

[0030] The reception unit can analyze the user's past question history and select a reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be asked in a specific time period based on the user's past question history. This makes it possible to provide the optimal reception method based on the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past question history data into a generation AI and have the generation AI select the optimal reception method.

[0031] When receiving questions or instructions, the reception unit can filter the questions or instructions based on the user's current project or area of ​​interest. For example, the reception unit prioritizes receiving questions related to a project currently underway by the user. The reception unit can also filter related questions and instructions based on the user's area of ​​interest. The reception unit can also work with the user's project management tool to automatically extract related questions. This allows questions and instructions related to the user's current project or area of ​​interest to be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI filter out related questions.

[0032] When receiving questions or instructions, the reception unit can prioritize receiving highly relevant questions or instructions based on the user's geographical location information. For example, if the user is in a specific office, the reception unit can prioritize receiving questions or instructions related to the office. Furthermore, if the user is on a business trip, the reception unit can prioritize receiving questions or instructions related to the business trip destination. Furthermore, if the user is working remotely, the reception unit can prioritize receiving questions or instructions related to the user's home. This allows highly relevant questions and instructions to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to filter relevant questions.

[0033] When receiving a question or instruction, the reception unit can analyze the user's social media activity and receive related questions or instructions. For example, the reception unit can prioritize receiving questions or instructions related to topics mentioned by the user on social media. The reception unit can also extract topics of interest from the user's social media activity and receive related questions or instructions. The reception unit can also prioritize receiving questions or instructions related to accounts the user follows on social media. This makes it possible to prioritize receiving related questions and instructions based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to filter related questions.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question or instruction. For example, the analysis unit performs a detailed analysis for a question or instruction with a high level of importance. The analysis unit can also perform a concise analysis for a question or instruction with a low level of importance. The analysis unit can also dynamically allocate analysis resources according to the importance of the question or instruction. This makes it possible to provide analysis results with an appropriate level of detail according to the importance of the question or instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the question or instruction to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the question or instruction. For example, the analysis unit can apply a specialized analysis algorithm to technical questions. The analysis unit can also apply a business-oriented analysis algorithm to business-related questions. The analysis unit can also apply a general-purpose analysis algorithm to general questions. This makes it possible to apply an appropriate analysis algorithm depending on the category of the question or instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input category data of the question or instruction to the generation AI and have the generation AI select an analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time when the question or instruction was submitted. For example, if the question or instruction is submitted early in the morning, the analysis unit can prioritize analysis. Furthermore, if the question or instruction is submitted outside of business hours, the analysis unit can also prioritize analysis at the next business hour. Furthermore, if the question or instruction is urgent, the analysis unit can also perform analysis immediately. This allows analysis to be performed with appropriate priority based on the time when the question or instruction was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the question or instruction was submitted to the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of questions and instructions. For example, if a question or instruction is related to another question or instruction, the analysis unit performs the analysis in order of relevance. Furthermore, if the questions or instructions are independent, the analysis unit can also perform the analysis in the order of submission. Furthermore, if the questions or instructions are related to a specific project, the analysis unit can also perform the analysis based on the priority of that project. This allows the analysis to be performed in an appropriate order based on the relevance of the questions and instructions. 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 input relevance data of questions and instructions to a generation AI and have the generation AI adjust the order of analysis.

[0038] When acquiring information, the information acquisition unit can analyze the user's past information acquisition history and select the optimal information acquisition method. For example, the information acquisition unit prioritizes acquiring information that the user frequently acquired in the past. The information acquisition unit can also prioritize suggesting information acquisition methods (APIs, database queries, etc.) that the user has used in the past. The information acquisition unit can also predict and suggest information to be acquired during a specific time period based on the user's past information acquisition history. This makes it possible to provide the optimal information acquisition method based on the user's past information acquisition history. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the user's past information acquisition history data into the generation AI and cause the generation AI to select the optimal information acquisition method.

[0039] When acquiring information, the information acquisition unit can filter information based on the user's current project or area of ​​interest. For example, the information acquisition unit prioritizes acquiring information related to a project currently underway by the user. The information acquisition unit can also filter related information based on the user's area of ​​interest. The information acquisition unit can also work with the user's project management tool to automatically extract related information. This allows information related to the user's current project or area of ​​interest to be acquired preferentially. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the user's project data into a generation AI and cause the generation AI to filter related information.

[0040] When acquiring information, the information acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific office, the information acquisition unit prioritizes acquiring information related to the office. Furthermore, when the user is on a business trip, the information acquisition unit can prioritize acquiring information related to the business trip destination. Furthermore, when the user is working remotely, the information acquisition unit can prioritize acquiring information related to the user's home. This makes it possible to prioritize acquiring highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input the user's geographical location data to the generation AI and cause the generation AI to acquire related information.

[0041] The information acquisition unit can analyze the user's social media activity and acquire related information when acquiring information. For example, the information acquisition unit can prioritize acquiring information related to topics mentioned by the user on social media. The information acquisition unit can also extract topics of interest from the user's social media activity and acquire related information. The information acquisition unit can also prioritize acquiring information related to accounts the user follows on social media. This makes it possible to prioritize acquiring related information based on the user's social media activity. Some or all of the above-described processing in the information acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the information acquisition unit can input the user's social media data into a generation AI and cause the generation AI to acquire related information.

[0042] When providing information, the information providing unit can select the optimal information providing method by analyzing the user's past information providing history. For example, the information providing unit can prioritize providing information methods that the user has frequently received in the past. The information providing unit can also prioritize suggesting information providing formats (text, graphs, etc.) that the user has used in the past. The information providing unit can also predict and suggest information to be provided during a specific time period based on the user's past information providing history. This makes it possible to provide the optimal information providing method based on the user's past information providing history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past information providing history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0043] When providing information, the information providing unit can customize the information based on the user's current project or areas of interest. For example, the information providing unit can prioritize providing information related to a project currently underway by the user. The information providing unit can also customize related information based on the user's areas of interest. The information providing unit can also work with the user's project management tool to automatically extract and provide related information. This allows information related to the user's current project or areas of interest to be prioritized. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input the user's project data into a generation AI and cause the generation AI to customize the related information.

[0044] When providing information, the information providing unit can select the optimal information provision method by taking into account the user's geographical location information. For example, if the user is in a specific office, the information providing unit can prioritize providing information related to the office. Furthermore, if the user is on a business trip, the information providing unit can prioritize providing information related to the business trip destination. Furthermore, if the user is working remotely, the information providing unit can prioritize providing information related to the user's home. This makes it possible to provide the optimal information provision method based on the user's geographical location information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal information provision method.

[0045] The information providing unit can analyze the user's social media activity and provide related information when providing information. For example, the information providing unit can prioritize providing information related to topics mentioned by the user on social media. The information providing unit can also extract topics of interest from the user's social media activity and provide related information. The information providing unit can also prioritize providing information related to accounts the user follows on social media. This makes it possible to prioritize providing related information based on the user's social media activity. Some or all of the above-described processing in the information providing unit can be performed using, or without, AI. For example, the information providing unit can input the user's social media data into a generation AI and cause the generation AI to provide related information.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can analyze the user's past questions and instructions and learn the user's tendencies. For example, if a user frequently asks questions about a specific topic, the analysis unit will prioritize analyzing information related to that topic. Also, if a user tends to ask questions in a specific format, the analysis unit can perform analysis tailored to that format. Furthermore, the system can learn the patterns of questions the user has asked in the past and respond quickly to similar questions. This allows for more efficient and accurate analysis based on the user's past behavior.

[0048] The information acquisition unit monitors the user's current activity status and can acquire information at the optimal timing. For example, if the user is in a meeting, information related to the meeting is acquired first. Also, if the user is traveling, information related to the user's destination can be acquired. Furthermore, if the user is concentrating on a specific task, information related to that task can be acquired quickly. This makes it possible to acquire appropriate information according to the user's activity status.

[0049] The information provision unit can analyze the user's past information provision history and select an information provision method that matches the user's preferences. For example, it can provide information in the form of information (text, graphs, etc.) that the user has frequently received in the past with priority. Also, if the user prefers to receive information during a specific time period, it can provide information tailored to that time period. Furthermore, it can also prioritize information provision methods that the user has given high ratings to in the past. This makes it possible to provide optimal information according to the user's preferences.

[0050] The information acquisition unit can prioritize acquisition of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific office, information related to that office can be prioritized. Also, if the user is on a business trip, information related to the business trip destination can be prioritized. Furthermore, if the user is working remotely, information related to the user's home can be prioritized. This makes it possible to prioritize acquisition of highly relevant information based on the user's geographical location information.

[0051] The information providing unit can analyze the user's social media activity and provide related information. For example, it can prioritize information related to topics mentioned by the user on social media. It can also extract topics of interest from the user's social media activity and provide related information. It can also prioritize information related to accounts the user follows on social media. This makes it possible to prioritize providing related information based on the user's social media activity.

[0052] The information providing unit can customize information based on the user's current project or area of ​​interest. For example, information related to the project the user is currently working on can be provided preferentially. The information providing unit can also customize related information based on the user's area of ​​interest. Furthermore, the information providing unit can link with the user's project management tool to automatically extract and provide related information. This makes it possible to provide information related to the user's current project or area of ​​interest preferentially.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives questions and instructions from the user. Questions and instructions from the user may be in text format, voice format, or questions about a specific topic. The reception unit not only receives text entered by the user, but can also use voice recognition technology to convert voice input into text and accept it as a question or instruction. Step 2: The analysis unit analyzes the questions and instructions received by the reception unit and identifies the necessary information. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the user's questions and instructions, understand their intent, and identify the necessary information. Step 3: The information acquisition unit accesses a cloud storage service or a database system to acquire the information identified by the analysis unit. The information acquisition unit accesses the cloud storage service to acquire specific files or data, and also accesses the database system to acquire specific records or data. For example, the information is acquired from the database using an SQL query. Step 4: The information providing unit provides the information acquired by the information acquiring unit to the user as an answer or a suggestion. The information providing unit can provide the acquired information in text format or in report format. For example, the acquired data can be visually displayed as a graph or chart.

[0055] (Example 2) An information linkage system according to an embodiment of the present invention links information stored in a cloud storage service or a database system and provides answers or suggestions to questions or instructions from a user. In this information linkage system, a user inputs a question or instruction, a reception unit receives the question, an analysis unit analyzes the question or instruction, and identifies the required information. Next, an information acquisition unit accesses the cloud storage service or the database system to acquire the identified information, and an information provision unit provides the acquired information to the user as an answer or suggestion. For example, if a user wants to know specific data in a spreadsheet stored in a cloud storage service, the user inputs a question, and the reception unit accepts the question. The analysis unit analyzes the question and identifies the required information. The information acquisition unit accesses the cloud storage service and acquires the identified spreadsheet data. The information provision unit provides the acquired data to the user as an answer. This system allows users to easily acquire information stored in a cloud storage service or a database system and receive answers or suggestions to their questions or instructions. This improves business efficiency and enables rapid decision-making. The information linkage system can link information stored in a cloud storage service or a database system to provide answers or suggestions to users' questions or instructions.

[0056] The information linkage system according to the embodiment includes a reception unit, an analysis unit, an information acquisition unit, and an information provision unit. The reception unit receives questions and instructions from a user. The questions and instructions from the user include, but are not limited to, text, audio, and questions about a specific topic. The reception unit, for example, receives text input by the user. The reception unit can also receive audio input. For example, speech recognition technology can be used to convert the user's voice into text and accept the text as a question or instruction. The analysis unit analyzes the question or instruction received by the reception unit and identifies necessary information. The analysis unit, for example, uses natural language processing technology to analyze the user's question or instruction. The analysis unit can also analyze the user's question or instruction using a machine learning algorithm. For example, the analysis unit understands the intent of the user's question and identifies the necessary information. The information acquisition unit accesses a cloud storage service or a database system to acquire the information identified by the analysis unit. For example, the information acquisition unit accesses a cloud storage service to acquire specific files or data. The information acquisition unit can also access a database system to acquire specific records or data. For example, the information acquisition unit acquires information from a database using an SQL query. The information provision unit provides the information acquired by the information acquisition unit to the user as an answer or suggestion. The information provision unit provides the acquired information to the user in text format, for example. The information provision unit can also provide the acquired information to the user in report format. For example, the information provision unit visually displays the acquired data as a graph or chart. As a result, the information linkage system according to the embodiment can provide answers or suggestions in response to user questions or instructions by linking information stored in a cloud storage service or a database system.

[0057] The reception unit can estimate the user's emotions and adjust the method for receiving questions and instructions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of questions and instructions. This makes it possible to provide an appropriate reception method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0058] The reception unit can analyze the user's past question history and select a reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be asked in a specific time period based on the user's past question history. This makes it possible to provide the optimal reception method based on the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past question history data into a generation AI and have the generation AI select the optimal reception method.

[0059] When receiving questions or instructions, the reception unit can filter the questions or instructions based on the user's current project or area of ​​interest. For example, the reception unit prioritizes receiving questions related to a project currently underway by the user. The reception unit can also filter related questions and instructions based on the user's area of ​​interest. The reception unit can also work with the user's project management tool to automatically extract related questions. This allows questions and instructions related to the user's current project or area of ​​interest to be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI filter out related questions.

[0060] The reception unit can estimate the user's emotions and determine the priority of questions and instructions to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes receiving questions and instructions of high importance. Furthermore, when the user is relaxed, the reception unit can also receive questions and instructions with normal priority. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving questions and instructions of high urgency. This allows the priority of questions and instructions to be adjusted according to the user's emotions. 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0061] When receiving questions or instructions, the reception unit can prioritize receiving highly relevant questions or instructions based on the user's geographical location information. For example, if the user is in a specific office, the reception unit can prioritize receiving questions or instructions related to the office. Furthermore, if the user is on a business trip, the reception unit can prioritize receiving questions or instructions related to the business trip destination. Furthermore, if the user is working remotely, the reception unit can prioritize receiving questions or instructions related to the user's home. This allows highly relevant questions and instructions to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to filter relevant questions.

[0062] When receiving a question or instruction, the reception unit can analyze the user's social media activity and receive related questions or instructions. For example, the reception unit can prioritize receiving questions or instructions related to topics mentioned by the user on social media. The reception unit can also extract topics of interest from the user's social media activity and receive related questions or instructions. The reception unit can also prioritize receiving questions or instructions related to accounts the user follows on social media. This makes it possible to prioritize receiving related questions and instructions based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to filter related questions.

[0063] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for providing an appropriate analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without 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.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question or instruction. For example, the analysis unit performs a detailed analysis for a question or instruction with a high level of importance. The analysis unit can also perform a concise analysis for a question or instruction with a low level of importance. The analysis unit can also dynamically allocate analysis resources according to the importance of the question or instruction. This makes it possible to provide analysis results with an appropriate level of detail according to the importance of the question or instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the question or instruction to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the question or instruction. For example, the analysis unit can apply a specialized analysis algorithm to technical questions. The analysis unit can also apply a business-oriented analysis algorithm to business-related questions. The analysis unit can also apply a general-purpose analysis algorithm to general questions. This makes it possible to apply an appropriate analysis algorithm depending on the category of the question or instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input category data of the question or instruction to the generation AI and have the generation AI select an analysis algorithm.

[0066] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows an analysis result of an appropriate length to be provided depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without 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.

[0067] During analysis, the analysis unit can determine the priority of analysis based on the time when the question or instruction was submitted. For example, if the question or instruction is submitted early in the morning, the analysis unit can prioritize analysis. Furthermore, if the question or instruction is submitted outside of business hours, the analysis unit can also prioritize analysis at the next business hour. Furthermore, if the question or instruction is urgent, the analysis unit can also perform analysis immediately. This allows analysis to be performed with appropriate priority based on the time when the question or instruction was submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the question or instruction was submitted to the generation AI and have the generation AI determine the analysis priority.

[0068] During analysis, the analysis unit can adjust the order of analysis based on the relevance of questions and instructions. For example, if a question or instruction is related to another question or instruction, the analysis unit performs the analysis in order of relevance. Furthermore, if the questions or instructions are independent, the analysis unit can also perform the analysis in the order of submission. Furthermore, if the questions or instructions are related to a specific project, the analysis unit can also perform the analysis based on the priority of that project. This allows the analysis to be performed in an appropriate order based on the relevance of the questions and instructions. 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 input relevance data of questions and instructions to a generation AI and have the generation AI adjust the order of analysis.

[0069] The information acquisition unit can estimate the user's emotions and adjust the information acquisition method based on the estimated user emotions. For example, if the user is feeling stressed, the information acquisition unit can provide a simple information acquisition method. Furthermore, if the user is relaxed, the information acquisition unit can also provide a detailed information acquisition method. Furthermore, if the user is in a hurry, the information acquisition unit can also provide a method for quickly acquiring information. This makes it possible to provide an appropriate information acquisition method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0070] When acquiring information, the information acquisition unit can analyze the user's past information acquisition history and select the optimal information acquisition method. For example, the information acquisition unit prioritizes acquiring information that the user frequently acquired in the past. The information acquisition unit can also prioritize suggesting information acquisition methods (APIs, database queries, etc.) that the user has used in the past. The information acquisition unit can also predict and suggest information to be acquired during a specific time period based on the user's past information acquisition history. This makes it possible to provide the optimal information acquisition method based on the user's past information acquisition history. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the user's past information acquisition history data into the generation AI and cause the generation AI to select the optimal information acquisition method.

[0071] When acquiring information, the information acquisition unit can filter information based on the user's current project or area of ​​interest. For example, the information acquisition unit prioritizes acquiring information related to a project currently underway by the user. The information acquisition unit can also filter related information based on the user's area of ​​interest. The information acquisition unit can also work with the user's project management tool to automatically extract related information. This allows information related to the user's current project or area of ​​interest to be acquired preferentially. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the user's project data into a generation AI and cause the generation AI to filter related information.

[0072] The information acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, the information acquisition unit prioritizes acquiring information of high importance. Furthermore, when the user is relaxed, the information acquisition unit can also acquire information with normal priority. Furthermore, when the user is in a hurry, the information acquisition unit can also prioritize acquiring information of high urgency. This allows the priority of information to be adjusted according to the user's emotions. 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 information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] When acquiring information, the information acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific office, the information acquisition unit prioritizes acquiring information related to the office. Furthermore, when the user is on a business trip, the information acquisition unit can prioritize acquiring information related to the business trip destination. Furthermore, when the user is working remotely, the information acquisition unit can prioritize acquiring information related to the user's home. This makes it possible to prioritize acquiring highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the information acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the information acquisition unit can input the user's geographical location data to the generation AI and cause the generation AI to acquire related information.

[0074] The information acquisition unit can analyze the user's social media activity and acquire related information when acquiring information. For example, the information acquisition unit can prioritize acquiring information related to topics mentioned by the user on social media. The information acquisition unit can also extract topics of interest from the user's social media activity and acquire related information. The information acquisition unit can also prioritize acquiring information related to accounts the user follows on social media. This makes it possible to prioritize acquiring related information based on the user's social media activity. Some or all of the above-described processing in the information acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the information acquisition unit can input the user's social media data into a generation AI and cause the generation AI to acquire related information.

[0075] The information providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, if the user is feeling stressed, the information providing unit can provide a simple, highly visible information provision method. Furthermore, if the user is relaxed, the information providing unit can provide a detailed information provision method. Furthermore, if the user is in a hurry, the information providing unit can provide a concise information provision method that focuses on the main points. This makes it possible to provide an appropriate information provision method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using 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 information providing unit can be performed using, for example, AI, or without AI. For example, the information providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] When providing information, the information providing unit can select the optimal information providing method by analyzing the user's past information providing history. For example, the information providing unit can prioritize providing information methods that the user has frequently received in the past. The information providing unit can also prioritize suggesting information providing formats (text, graphs, etc.) that the user has used in the past. The information providing unit can also predict and suggest information to be provided during a specific time period based on the user's past information providing history. This makes it possible to provide the optimal information providing method based on the user's past information providing history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past information providing history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0077] When providing information, the information providing unit can customize the information based on the user's current project or areas of interest. For example, the information providing unit can prioritize providing information related to a project currently underway by the user. The information providing unit can also customize related information based on the user's areas of interest. The information providing unit can also work with the user's project management tool to automatically extract and provide related information. This allows information related to the user's current project or areas of interest to be prioritized. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input the user's project data into a generation AI and cause the generation AI to customize the related information.

[0078] The information providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, when the user is feeling stressed, the information providing unit can provide information with a higher priority. When the user is relaxed, the information providing unit can also provide information with a normal priority. When the user is in a hurry, the information providing unit can also provide information with a higher priority. This allows the priority of information provision to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 information providing unit can be performed using an AI, for example, or without an AI. For example, the information providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] When providing information, the information providing unit can select the optimal information provision method by taking into account the user's geographical location information. For example, if the user is in a specific office, the information providing unit can prioritize providing information related to the office. Furthermore, if the user is on a business trip, the information providing unit can prioritize providing information related to the business trip destination. Furthermore, if the user is working remotely, the information providing unit can prioritize providing information related to the user's home. This makes it possible to provide the optimal information provision method based on the user's geographical location information. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal information provision method.

[0080] The information providing unit can analyze the user's social media activity and provide related information when providing information. For example, the information providing unit can prioritize providing information related to topics mentioned by the user on social media. The information providing unit can also extract topics of interest from the user's social media activity and provide related information. The information providing unit can also prioritize providing information related to accounts the user follows on social media. This makes it possible to prioritize providing related information based on the user's social media activity. Some or all of the above-described processing in the information providing unit can be performed using, or without, AI. For example, the information providing unit can input the user's social media data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, information acquisition unit, and information provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text or voice input from a user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes questions or instructions using natural language processing technology or machine learning algorithms. The information acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accesses a cloud storage service or a database system to acquire necessary information. The information provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the acquired information to the user in text or report format. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, information acquisition unit, and information provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes questions and instructions using natural language processing techniques or machine learning algorithms. The information acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accesses a cloud storage service or a database system to acquire necessary information. The information provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the acquired information to the user audibly or visually. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, information acquisition unit, and information provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes questions and instructions using natural language processing technology or machine learning algorithms. The information acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accesses a cloud storage service or a database system to acquire necessary information. The information provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and visually provides the acquired information to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, information acquisition unit, and information provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes questions and instructions using natural language processing technology or machine learning algorithms. The information acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and accesses a cloud storage service or a database system to acquire necessary information. The information provision unit is realized, for example, by the speaker 240 or display device of the robot 414 and provides the acquired information to the user audibly or visually.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The analysis unit can analyze the user's past questions and instructions and learn the user's tendencies. For example, if a user frequently asks questions about a specific topic, the analysis unit will prioritize analyzing information related to that topic. Also, if a user tends to ask questions in a specific format, the analysis unit can perform analysis tailored to that format. Furthermore, the system can learn the patterns of questions the user has asked in the past and respond quickly to similar questions. This allows for more efficient and accurate analysis based on the user's past behavior.

[0083] The information providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the information providing unit can provide information in a simple, visually easy-to-understand format. If the user is relaxed, the information providing unit can provide detailed information to enable the user to understand more deeply. Furthermore, if the user is in a hurry, the information providing unit can provide information that is a concise summary of the main points. This makes it possible to provide optimal information according to the user's emotions.

[0084] The information acquisition unit monitors the user's current activity status and can acquire information at the optimal timing. For example, if the user is in a meeting, information related to the meeting is acquired first. Also, if the user is traveling, information related to the user's destination can be acquired. Furthermore, if the user is concentrating on a specific task, information related to that task can be acquired quickly. This makes it possible to acquire appropriate information according to the user's activity status.

[0085] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated user emotions. For example, if the user is nervous, it can provide simple, visually easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that briefly summarize the main points. This makes it possible to provide optimal analysis results according to the user's emotions.

[0086] The information provision unit can analyze the user's past information provision history and select an information provision method that matches the user's preferences. For example, it can provide information in the form of information (text, graphs, etc.) that the user has frequently received in the past with priority. Also, if the user prefers to receive information during a specific time period, it can provide information tailored to that time period. Furthermore, it can also prioritize information provision methods that the user has given high ratings to in the past. This makes it possible to provide optimal information according to the user's preferences.

[0087] The information acquisition unit can prioritize acquisition of highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific office, information related to that office can be prioritized. Also, if the user is on a business trip, information related to the business trip destination can be prioritized. Furthermore, if the user is working remotely, information related to the user's home can be prioritized. This makes it possible to prioritize acquisition of highly relevant information based on the user's geographical location information.

[0088] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize analysis of questions and instructions with high importance. If the user is relaxed, it can also perform analysis with normal priority. Furthermore, if the user is in a hurry, it can also prioritize analysis of questions and instructions with high urgency. This makes it possible to adjust the analysis priority according to the user's emotions.

[0089] The information providing unit can analyze the user's social media activity and provide related information. For example, it can prioritize information related to topics mentioned by the user on social media. It can also extract topics of interest from the user's social media activity and provide related information. It can also prioritize information related to accounts the user follows on social media. This makes it possible to prioritize providing related information based on the user's social media activity.

[0090] The information acquisition unit can estimate the user's emotions and adjust the information acquisition method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple information acquisition method can be provided. If the user is relaxed, a detailed information acquisition method can be provided. Furthermore, if the user is in a hurry, a method that allows information to be acquired quickly can be provided. In this way, an appropriate information acquisition method can be provided according to the user's emotions.

[0091] The information providing unit can customize information based on the user's current project or area of ​​interest. For example, information related to the project the user is currently working on can be provided preferentially. The information providing unit can also customize related information based on the user's area of ​​interest. Furthermore, the information providing unit can link with the user's project management tool to automatically extract and provide related information. This makes it possible to provide information related to the user's current project or area of ​​interest preferentially.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives questions and instructions from the user. Questions and instructions from the user may be in text format, voice format, or questions about a specific topic. The reception unit not only receives text entered by the user, but can also use voice recognition technology to convert voice input into text and accept it as a question or instruction. Step 2: The analysis unit analyzes the questions and instructions received by the reception unit and identifies the necessary information. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the user's questions and instructions, understand their intent, and identify the necessary information. Step 3: The information acquisition unit accesses a cloud storage service or a database system to acquire the information identified by the analysis unit. The information acquisition unit accesses the cloud storage service to acquire specific files or data, and also accesses the database system to acquire specific records or data. For example, the information is acquired from the database using an SQL query. Step 4: The information providing unit provides the information acquired by the information acquiring unit to the user as an answer or a suggestion. The information providing unit can provide the acquired information in text format or in report format. For example, the acquired data can be visually displayed as a graph or chart.

[0094] 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.

[0095] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[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] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 reception unit that receives questions or instructions from a user; an analysis unit that analyzes the question or instruction received by the reception unit and identifies necessary information; an information acquisition unit that accesses a cloud storage service or a database system to acquire the information identified by the analysis unit; an information providing unit that provides the information acquired by the information acquiring unit to a user as an answer or a suggestion; Equipped with A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the way questions and instructions are received based on the estimated user emotions. The system of claim 1 .

3. The reception unit Analyze the user's past question history and select the appropriate method for receiving inquiries. The system of claim 1 .

4. The reception unit Filtering questions and instructions based on your current projects and interests The system of claim 1 .

5. The reception unit Estimate the user's emotions and prioritize the questions and instructions to be received based on the estimated user emotions. The system of claim 1 .

6. The reception unit When accepting questions or instructions, prioritize relevant questions or instructions based on the user's geographic location. The system of claim 1 .

7. The reception unit When receiving a question or instruction, analyze the user's social media activity and receive related questions or instructions. The system of claim 1 .

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions The system of claim 1 .

Images