system

Abstract

The system according to this embodiment aims to improve business efficiency by automating data linkage between different business applications. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an integration unit, and a provision unit. The reception unit receives instructions from the user. The analysis unit analyzes the instructions received by the reception unit. The integration unit integrates and analyzes data from different systems based on the instructions analyzed by the analysis unit. The provision unit provides necessary information based on the data integrated and analyzed by the integration unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, data linkage and task management between different business system applications are performed manually, resulting in problems such as a decrease in business efficiency and an increase in the burden on employees. <00​​​​​​​​The system according to this embodiment comprises a reception unit, an analysis unit, an integration unit, and a provision unit. The reception unit receives instructions from the user. The analysis unit analyzes the instructions received by the reception unit. The integration unit integrates and analyzes data from different systems based on the instructions analyzed by the analysis unit. The provision unit provides necessary information based on the data integrated and analyzed by the integration unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate data linkage between different business applications and improve operational efficiency. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The cross-business application AI agent system according to an embodiment of the present invention is a system that automates data linkage and task management between a wide range of business applications (email, web systems, CRM, etc.) used within a company. This system utilizes a business application linkage tool to link with various business applications and understands and executes user instructions through natural language processing. For example, it automates tasks such as automatic email replies, schedule management, and data entry. Furthermore, it utilizes the multimodal function of the business application linkage tool to integrate and analyze data from different systems and provide necessary information in real time. This allows users to operate multiple systems from a single interface. For example, the cross-business application AI agent system allows users to input instructions in natural language. For example, if a user instructs, "Check the schedule for tomorrow's meeting," the system analyzes the instruction, retrieves the necessary information from the schedule management system, and provides it. Also, if a user instructs, "Automatically reply to emails," the system links with the email system and performs automatic replies. Furthermore, if a user instructs, "Enter data," the system links with the data entry system and enters the specified data. This enables the cross-business application AI agent system to automate data linkage between different business applications, centralizing task management and information retrieval. As a result, the cross-business application AI agent system can significantly improve operational efficiency and reduce the burden on employees.

[0029] The cross-business application AI agent system according to this embodiment comprises a reception unit, an analysis unit, an integration unit, and a provision unit. The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit receives voice instructions using, for example, speech recognition technology. The reception unit can also receive text instructions using a text input interface. Furthermore, the reception unit can also receive gesture instructions using gesture recognition technology. For example, the reception unit converts the user's voice instructions into text using speech recognition technology and sends it to the analysis unit. The text input interface allows the user to input text instructions using a keyboard or touchscreen. Gesture recognition technology detects the user's hand movements or facial expressions with a camera and recognizes them as instructions. The analysis unit analyzes the instructions received by the reception unit. The analysis is performed using, for example, natural language processing, data mining, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit analyzes the user's instructions using natural language processing technology and understands the intent of the instructions. Furthermore, the analysis unit can extract data related to instructions using data mining techniques. In addition, the analysis unit can learn instruction patterns using machine learning algorithms to improve analysis accuracy. For example, the analysis unit uses natural language processing techniques to perform morphological and grammatical analysis on user instructions to understand their meaning. Data mining techniques are used to extract useful information from large amounts of data, and the analysis unit uses these to extract data related to instructions. Machine learning algorithms learn from past instruction data to improve analysis accuracy for new instructions. The integration unit integrates and analyzes data from different systems based on the instructions analyzed by the analysis unit. Integration and analysis are performed using methods such as data merging methods and analysis algorithms, but are not limited to these examples. For example, the integration unit merges data acquired from different systems and integrates it into a single dataset. The integration unit can also analyze the integrated data using analysis algorithms and extract necessary information.Furthermore, the integration unit can also perform data integrity checks to maintain data consistency. For example, the integration unit merges data obtained from different systems using key items and integrates it into a single dataset. Analysis algorithms are techniques for analyzing the integrated data and extracting necessary information, and the integration unit uses these to analyze the data. Data integrity checks are processes performed to maintain data consistency, and the integration unit uses these to verify data integrity. The provision unit provides the necessary information based on the data integrated and analyzed by the integration unit. Provision is performed, for example, in real time, but is not limited to such examples. For example, the provision unit provides the data integrated and analyzed by the integration unit to the user in real time. The provision unit can also provide information based on user requests. Furthermore, the provision unit can also provide information determined automatically by the system. For example, the provision unit displays the data integrated and analyzed by the integration unit to the user in real time. Information provision based on user requests occurs when a user requests specific information. Information provision determined automatically by the system occurs when the system assesses the user's situation and provides the necessary information. As a result, the cross-business application AI agent system according to the embodiment can efficiently receive and analyze user instructions, integrate and analyze data, and provide necessary information.

[0030] The reception desk receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception desk can, for example, receive voice instructions using speech recognition technology. Speech recognition technology converts user speech into text, and noise reduction and optimization of the speech model are performed to accurately recognize voice instructions. For example, if a user gives a voice instruction saying, "Tell me the schedule for today's meetings," the reception desk converts this speech into text and sends it to the analysis unit. The reception desk can also receive text instructions using a text input interface. The text input interface allows users to input text instructions using a keyboard or touchscreen. For example, if a user texts, "Check the meeting room reservation status," that instruction is sent to the analysis unit. Furthermore, the reception desk can also receive gesture instructions using gesture recognition technology. Gesture recognition technology detects the user's hand movements and facial expressions using a camera and recognizes them as instructions. For example, a user can give an instruction such as "Proceed to the next slide" by waving their hand. This allows the reception desk to support diverse input methods such as voice, text, and gestures, improving user convenience. Furthermore, the reception desk can combine these input methods; for example, it can combine voice and gesture instructions to accept more complex instructions. This enables the reception desk to respond to diverse user needs and receive instructions flexibly and efficiently.

[0031] The analysis unit analyzes the instructions received by the reception unit. Analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms, but is not limited to these examples. For example, the analysis unit may use natural language processing techniques to analyze user instructions and understand their intent. Natural language processing techniques are techniques that perform morphological and grammatical analysis to understand the meaning of text. For example, if a user instructs "Tell me the weather for tomorrow," the analysis unit understands that this instruction is a request for weather information. The analysis unit can also use data mining techniques to extract data related to the instructions. Data mining techniques are techniques that extract useful information from large amounts of data. For example, if a user instructs "Analyze the sales data for the past year," the analysis unit extracts sales data related to that instruction. Furthermore, the analysis unit can use machine learning algorithms to learn instruction patterns and improve analysis accuracy. Machine learning algorithms are techniques that learn from past instruction data and improve the accuracy of analysis for new instructions. For example, if a user instructs "Prepare the materials for the next meeting," the analysis unit learns that this instruction means preparing the meeting materials. This allows the analysis unit to quickly and accurately analyze instructions received by the reception unit and understand the user's intent. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and forecasting. For example, it can predict future sales trends based on past sales data and use this information to formulate business strategies. As a result, the analysis unit can handle not only real-time instruction analysis but also long-term data analysis and forecasting, improving the overall performance of the system.

[0032] The integration unit integrates and analyzes data from different systems based on instructions provided by the analysis unit. Integration and analysis are performed using methods such as data merging techniques and analysis algorithms, but are not limited to these examples. For instance, the integration unit merges data obtained from different systems into a single dataset. Data merging techniques are techniques for combining data based on key items; for example, customer information and purchase history can be integrated using customer ID as the key. The integration unit can also analyze the integrated data using analysis algorithms to extract necessary information. Analysis algorithms are techniques for analyzing integrated data and extracting patterns and trends; for example, customer purchasing behavior can be analyzed to formulate marketing strategies. Furthermore, the integration unit can perform data integrity checks to maintain data consistency. Data integrity checks are processes performed to maintain data consistency, such as removing duplicate data and verifying data accuracy. This allows the integration unit to efficiently integrate and analyze data from different systems. Additionally, the integration unit can share the data integration and analysis results with other systems and departments. For example, the integration department displays the integrated and analyzed data on a dashboard, allowing management and various departments to view the data in real time. This enables the integration department to streamline data management across the entire system and support business decision-making.

[0033] The information delivery department provides necessary information based on data integrated and analyzed by the integration department. This information is provided in real time, but is not limited to such real-time delivery. For example, the information delivery department provides users with data integrated and analyzed by the integration department in real time. Real-time information delivery allows users to instantly obtain the information they need, for example, by allowing users to view the latest sales data through a dashboard. The information delivery department can also provide information based on user requests. This occurs when a user requests specific information; for example, if a user requests "Show me this month's sales report," that report is provided. Furthermore, the information delivery department can provide information determined automatically by the system. This system-driven information delivery involves the system determining the user's situation and providing necessary information; for example, if the system detects that a user is in a meeting, it can automatically provide materials related to the meeting. This allows the information delivery department to quickly provide users with appropriate information and support improved work efficiency. Additionally, the information delivery department can manage the history of information delivery and analyze user trends based on past information delivery history. This allows the information delivery department to provide customized information tailored to user needs and improve user satisfaction.

[0034] The reception desk can receive instructions from users in natural language. For example, users can input instructions into the reception desk in natural language. For instance, if a user instructs, "Check the schedule for tomorrow's meeting," the reception desk will receive that instruction. The reception desk can also receive instructions from users such as, "Automatically reply to emails." Furthermore, the reception desk can receive instructions from users such as, "Enter data." This allows the reception desk to operate intuitively by accepting instructions from users in natural language. Natural language includes, but is not limited to, Japanese, English, and other languages. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's natural language instructions into a generating AI and have the generating AI analyze the instructions.

[0035] The analysis unit can analyze instructions received by the reception unit using natural language processing. For example, the analysis unit uses natural language processing technology to analyze user instructions and understand their intent. For instance, if a user instructs the analysis unit to "check the schedule for tomorrow's meeting," the analysis unit analyzes this instruction using natural language processing technology and retrieves the necessary information from the schedule management system. Furthermore, if a user instructs the analysis unit to "automatically reply to an email," the analysis unit can analyze this instruction using natural language processing technology and automatically reply to the email in conjunction with the email system. Additionally, if a user instructs the analysis unit to "enter data," the analysis unit can analyze this instruction using natural language processing technology and input the specified data in conjunction with the data entry system. This improves the analysis unit's ability to understand instructions by analyzing them using natural language processing. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user instructions into the generation AI and have the generation AI perform the analysis of those instructions.

[0036] The integration unit can integrate and analyze data from different systems. For example, the integration unit can merge data obtained from different systems and integrate it into a single dataset. For example, the integration unit can merge schedule data obtained from a schedule management system and email data obtained from an email system and integrate them into a single dataset. The integration unit can also analyze the integrated data using analysis algorithms and extract the necessary information. For example, the integration unit can analyze the integrated data and extract the information that the user needs. Furthermore, the integration unit can perform data integrity checks to maintain data consistency. For example, the integration unit can verify the integrity of the integrated data and provide a consistent dataset. This enables centralized data management by integrating and analyzing data from different systems. Different systems include, but are not limited to, ERP systems, CRM systems, and database systems. Some or all of the above-described processes in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input data obtained from different systems into a generating AI and have the generating AI perform data integration and analysis.

[0037] The service provider can provide necessary information in real time based on data integrated and analyzed by the integration unit. For example, the service provider can provide the user with data integrated and analyzed by the integration unit in real time. For example, if the user instructs the service provider to "check the schedule for tomorrow's meeting," the service provider can provide the user with schedule data integrated and analyzed by the integration unit in real time. The service provider can also provide the user with email data integrated and analyzed by the integration unit in real time if the user instructs to "automatically reply to emails." Furthermore, if the user instructs the service provider to "enter data," the service provider can provide the user with data integrated and analyzed by the integration unit in real time. This enables the service provider to make quick decisions by providing necessary information in real time. Real time includes, but is not limited to, seconds or milliseconds. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input data integrated and analyzed by the integration unit into a generating AI and have the generating AI perform the task of providing the necessary information.

[0038] The service department can automate tasks such as automatic email replies, schedule management, and data entry. For example, if a user instructs the service department to "automatically reply to emails," it will work in conjunction with the email system to automatically reply. For example, if a user instructs the service department to "manage the schedule," it will work in conjunction with the schedule management system to manage the schedule. Furthermore, if a user instructs the service department to "enter data," it can work in conjunction with the data entry system to enter the specified data. As a result, the service department can improve its operational efficiency through task automation. Task automation includes, but is not limited to, automatic email replies, schedule management, and data entry. Some or all of the above processes in the service department may be performed using, for example, AI, or not using AI. For example, the service department can input user instructions into a generating AI and have the generating AI execute the task automation.

[0039] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, the reception desk may prioritize suggesting instruction methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may predict and suggest instruction methods to be used during specific time periods based on the user's past instruction history. The reception desk can also suggest relevant instruction methods based on the content of instructions the user has given in the past. This improves user convenience by providing the optimal reception method based on past instruction history. Past instruction history includes, but is not limited to, the type, frequency, and success rate of past instructions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past instruction history data into a generating AI and have the generating AI select the optimal reception method.

[0040] The reception unit can filter instructions based on the user's current work status and areas of interest when receiving them. For example, the reception unit can prioritize receiving instructions related to projects the user is currently working on. For example, the reception unit can filter and receive relevant instructions based on the user's areas of interest. The reception unit can also prioritize receiving instructions of high urgency depending on the user's work status. This improves the efficiency of the reception unit by receiving instructions that are tailored to the user's work status and areas of interest. Work status includes, but is not limited to, current projects and task progress. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's work status data into a generating AI and have the generating AI perform the instruction filtering.

[0041] The reception unit can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the reception unit will prioritize receiving instructions related to that location. For example, the reception unit will prioritize receiving instructions related to locations close to the user's current location. Furthermore, if the user is on the move, the reception unit can also prioritize receiving instructions related to their destination. This improves the efficiency of the reception unit's operations by allowing it to receive instructions based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform the instruction reception.

[0042] The reception desk can analyze the user's social media activity when receiving instructions and accept relevant instructions. For example, the reception desk can prioritize instructions related to what the user has mentioned on social media. For example, the reception desk can analyze the content of the user's social media posts and accept relevant instructions. The reception desk can also accept relevant instructions based on the activity of the user's social media followers and friends. This improves the efficiency of the reception desk's operations by allowing it to accept instructions based on the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the instruction acceptance.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the instructions. For example, for instructions of high importance, the analysis unit performs a detailed analysis and provides comprehensive information. For instructions of low importance, the analysis unit performs a simplified analysis and provides only the necessary minimum information. The analysis unit can also perform a rapid analysis and provide immediate results for instructions of high urgency. In this way, the analysis unit improves the accuracy of the analysis by performing analysis according to the importance of the instructions. The importance of instructions includes, but is not limited to, the degree of impact on business operations and urgency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input instruction importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing the instruction. For example, the analysis unit can apply a natural language processing algorithm to email-related instructions. For example, the analysis unit can apply a calendar analysis algorithm to schedule management-related instructions. The analysis unit can also apply a data analysis algorithm to data entry-related instructions. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the category of the instruction. The categories of instructions include, but are not limited to, work instructions, technical instructions, and management instructions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input instruction category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the submission date of the instructions. For example, the analysis unit may prioritize the analysis of recently submitted instructions. For example, the analysis unit may postpone the analysis of older instructions. The analysis unit may also prioritize the analysis of instructions with high urgency, regardless of the submission date. This improves the accuracy of the analysis by providing a priority for analysis based on the submission date of the instructions. The submission date of an instruction includes, but is not limited to, the submission date and time, or the time elapsed since submission. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input instruction submission date data into a generating AI and have the generating AI determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the instructions when analyzing them. For example, the analysis unit may prioritize the analysis of highly relevant instructions. For example, the analysis unit may postpone the analysis of less relevant instructions. The analysis unit can also group highly relevant instructions together for analysis. This improves the accuracy of the analysis by providing an analysis order based on the relevance of the instructions. The relevance of instructions includes, but is not limited to, similarity of instruction content or related projects. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input instruction relevance data into a generating AI and have the generating AI adjust the order of analysis.

[0047] The integration unit can improve the accuracy of data integration by considering the interrelationships between data. For example, the integration unit can improve the accuracy of integration by analyzing correlations between data. For example, the integration unit can improve the accuracy of integration by considering interdependencies between data. The integration unit can also perform integration while considering interrelationships in order to maintain data consistency. As a result, the integration unit improves the accuracy of integration by considering the interrelationships between data. Interrelationships between data include, but are not limited to, relationships and dependencies between data. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input data interrelationship data into a generating AI and have the generating AI perform the integration accuracy improvement.

[0048] The integration unit can perform data integration while considering the attribute information of the data provider. For example, the integration unit can improve the accuracy of the integration by considering the reliability of the data provider. For example, the integration unit can improve the accuracy of the integration by considering the expertise of the data provider. The integration unit can also improve the accuracy of the integration by considering the past performance of the data provider. In this way, the integration unit improves the accuracy of the integration by considering the attribute information of the data provider. The attribute information of the data provider includes, but is not limited to, the provider's job title and area of ​​expertise. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the data provider's attribute information data into a generating AI and have the generating AI perform the integration accuracy improvement.

[0049] The integration unit can perform data integration while considering the geographical distribution of the data. For example, the integration unit can analyze the geographical distribution of the data to improve the accuracy of the integration. For example, the integration unit can prioritize the integration of geographically related data. The integration unit can also perform integration while considering the geographical distribution to maintain data consistency. As a result, the integration unit improves the accuracy of the integration by considering the geographical distribution of the data. The geographical distribution of the data includes, but is not limited to, data distribution by region and geographical relationships. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of the integration.

[0050] The integration unit can improve the accuracy of data integration by referring to relevant literature during data integration. For example, the integration unit can optimize the data integration method by referring to relevant literature. For example, the integration unit can improve the accuracy of data integration based on information from relevant literature. The integration unit can also analyze relevant literature and perform integration to maintain data consistency. In this way, the integration unit improves the accuracy of integration by referring to relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input relevant literature data into a generating AI and have the generating AI perform the integration accuracy improvement.

[0051] The information provider can select the optimal information delivery method by referring to the user's past information usage history when providing information. For example, the information provider may prioritize providing information delivery methods that the user has frequently used in the past. For example, the information provider may predict and propose information delivery methods that the user will use during a specific time period based on the user's past information usage history. The information provider can also propose relevant information delivery methods based on the user's past information usage history. This improves user convenience by allowing the information provider to provide the optimal delivery method based on past information usage history. Past information usage history includes, but is not limited to, past search history and browsing history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past information usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0052] The information delivery unit can customize the means of information delivery based on the user's current work situation when providing information. For example, if the user is in a meeting, the unit will prioritize providing information related to the meeting. For example, if the user is out of the office, the unit will provide an information delivery method optimized for mobile devices. The unit can also provide an information delivery method optimized for desktop devices if the user is working at a desk. This improves work efficiency by allowing the unit to provide information delivery methods tailored to the user's work situation. Work situation includes, but is not limited to, current projects and task progress. Some or all of the above processing in the information delivery unit may be performed using, for example, AI, or not using AI. For example, the unit can input user work situation data into a generating AI and have the generating AI customize the means of information delivery.

[0053] The information provider can select the optimal method of providing information by considering the user's geographical location when providing information. For example, if the user is in a specific location, the information provider can prioritize providing information related to that location. For example, the information provider can prioritize providing information related to locations close to the user's current location. Furthermore, if the user is on the move, the information provider can prioritize providing information related to their destination. This improves visibility by providing information based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select the method of providing information.

[0054] The information provider can analyze the user's social media activity and propose means of providing information when providing information. For example, the provider may prioritize providing information related to what the user has mentioned on social media. For example, the provider may analyze the content of the user's social media posts and provide relevant information. The provider may also provide relevant information based on the activity of the user's social media followers and friends. This improves visibility by providing means of information provision based on the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the provider may input the user's social media activity data into a generating AI and have the generating AI propose means of information provision.

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

[0056] The reception desk can analyze the user's past instruction history and select the optimal reception method when receiving user instructions. For example, it can prioritize suggesting instruction methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest instruction methods to be used during specific time periods based on the user's past instruction history. Furthermore, it can suggest relevant instruction methods based on the content of instructions the user has given in the past. In this way, the reception desk improves user convenience by providing the optimal reception method based on past instruction history. Past instruction history includes, but is not limited to, the type, frequency, and success rate of past instructions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past instruction history data into a generating AI and have the generating AI select the optimal reception method.

[0057] The integration unit can improve the accuracy of data integration by considering the interrelationships between data. For example, it can improve the accuracy of integration by analyzing the correlations between data. It can also improve the accuracy of integration by considering the interdependencies between data. Furthermore, integration can be performed while considering interrelationships in order to maintain data consistency. In this way, the integration unit improves the accuracy of integration by considering the interrelationships between data. Data interrelationships include, but are not limited to, relationships and dependencies between data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data interrelationship data into a generating AI and have the generating AI perform the integration accuracy improvement.

[0058] The reception unit can filter instructions based on the user's current work status and areas of interest when receiving them. For example, it can prioritize instructions related to projects the user is currently working on. It can also filter and accept relevant instructions based on the user's areas of interest. Furthermore, it can prioritize instructions of high urgency depending on the user's work status. This improves the efficiency of the reception unit by allowing it to receive instructions that match the user's work status and areas of interest. Work status includes, but is not limited to, current projects and task progress. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input user work status data into a generating AI and have the generating AI perform the instruction filtering.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the instructions. For example, for highly important instructions, a detailed analysis is performed to provide comprehensive information. For less important instructions, a simplified analysis is performed to provide only the necessary minimum information. Furthermore, for highly urgent instructions, a rapid analysis can be performed to provide results immediately. In this way, the analysis unit improves the accuracy of the analysis by performing analysis according to the importance of the instructions. The importance of instructions includes, but is not limited to, the degree of impact on business operations and urgency. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input instruction importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0060] The information delivery unit can select the optimal delivery method by referring to the user's past information usage history when providing information. For example, it can prioritize providing information delivery methods that the user has frequently used in the past. It can also predict and suggest information delivery methods that the user will use at a specific time of day based on the user's past information usage history. Furthermore, it can suggest relevant information delivery methods based on the user's past information usage history. In this way, the information delivery unit improves user convenience by providing the optimal delivery method based on past information usage history. Past information usage history includes, but is not limited to, past search history and browsing history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's past information usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0061] The information provider can select the optimal method of providing information by considering the user's geographical location when providing information. For example, if the user is in a specific location, information related to that location can be provided preferentially. Information related to locations close to the user's current location can also be provided preferentially. Furthermore, if the user is on the move, information related to their destination can be provided preferentially. This improves visibility by allowing the information provider to provide information based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select the method of providing information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives user instructions. User instructions include voice instructions, text instructions, and gesture instructions. For example, the reception desk receives voice instructions using voice recognition technology, text instructions using a text input interface, and gesture instructions using gesture recognition technology. Step 2: The analysis unit analyzes the instructions received by the reception unit. The analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms. For example, the analysis unit uses natural language processing technology to analyze the user's instructions and understand the intent behind them. Step 3: The integration unit integrates and analyzes data from different systems based on instructions provided by the analysis unit. The integration unit merges data acquired from different systems and integrates it into a single dataset. It also analyzes the integrated data using an analysis algorithm and extracts the necessary information. Step 4: The provisioning unit provides the necessary information based on the data integrated and analyzed by the integration unit. The provisioning unit can also provide the data integrated and analyzed by the integration unit to the user in real time and provide information based on the user's requests.

[0064] (Example of form 2) The cross-business application AI agent system according to an embodiment of the present invention is a system that automates data linkage and task management between a wide range of business applications (email, web systems, CRM, etc.) used within a company. This system utilizes a business application linkage tool to link with various business applications and understands and executes user instructions through natural language processing. For example, it automates tasks such as automatic email replies, schedule management, and data entry. Furthermore, it utilizes the multimodal function of the business application linkage tool to integrate and analyze data from different systems and provide necessary information in real time. This allows users to operate multiple systems from a single interface. For example, the cross-business application AI agent system allows users to input instructions in natural language. For example, if a user instructs, "Check the schedule for tomorrow's meeting," the system analyzes the instruction, retrieves the necessary information from the schedule management system, and provides it. Also, if a user instructs, "Automatically reply to emails," the system links with the email system and performs automatic replies. Furthermore, if a user instructs, "Enter data," the system links with the data entry system and enters the specified data. This enables the cross-business application AI agent system to automate data linkage between different business applications, centralizing task management and information retrieval. As a result, the cross-business application AI agent system can significantly improve operational efficiency and reduce the burden on employees.

[0065] The cross-business application AI agent system according to this embodiment comprises a reception unit, an analysis unit, an integration unit, and a provision unit. The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit receives voice instructions using, for example, speech recognition technology. The reception unit can also receive text instructions using a text input interface. Furthermore, the reception unit can also receive gesture instructions using gesture recognition technology. For example, the reception unit converts the user's voice instructions into text using speech recognition technology and sends it to the analysis unit. The text input interface allows the user to input text instructions using a keyboard or touchscreen. Gesture recognition technology detects the user's hand movements or facial expressions with a camera and recognizes them as instructions. The analysis unit analyzes the instructions received by the reception unit. The analysis is performed using, for example, natural language processing, data mining, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit analyzes the user's instructions using natural language processing technology and understands the intent of the instructions. Furthermore, the analysis unit can extract data related to instructions using data mining techniques. In addition, the analysis unit can learn instruction patterns using machine learning algorithms to improve analysis accuracy. For example, the analysis unit uses natural language processing techniques to perform morphological and grammatical analysis on user instructions to understand their meaning. Data mining techniques are used to extract useful information from large amounts of data, and the analysis unit uses these to extract data related to instructions. Machine learning algorithms learn from past instruction data to improve analysis accuracy for new instructions. The integration unit integrates and analyzes data from different systems based on the instructions analyzed by the analysis unit. Integration and analysis are performed using methods such as data merging methods and analysis algorithms, but are not limited to these examples. For example, the integration unit merges data acquired from different systems and integrates it into a single dataset. The integration unit can also analyze the integrated data using analysis algorithms and extract necessary information.Furthermore, the integration unit can also perform data integrity checks to maintain data consistency. For example, the integration unit merges data obtained from different systems using key items and integrates it into a single dataset. Analysis algorithms are techniques for analyzing the integrated data and extracting necessary information, and the integration unit uses these to analyze the data. Data integrity checks are processes performed to maintain data consistency, and the integration unit uses these to verify data integrity. The provision unit provides the necessary information based on the data integrated and analyzed by the integration unit. Provision is performed, for example, in real time, but is not limited to such examples. For example, the provision unit provides the data integrated and analyzed by the integration unit to the user in real time. The provision unit can also provide information based on user requests. Furthermore, the provision unit can also provide information determined automatically by the system. For example, the provision unit displays the data integrated and analyzed by the integration unit to the user in real time. Information provision based on user requests occurs when a user requests specific information. Information provision determined automatically by the system occurs when the system assesses the user's situation and provides the necessary information. As a result, the cross-business application AI agent system according to the embodiment can efficiently receive and analyze user instructions, integrate and analyze data, and provide necessary information.

[0066] The reception desk receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception desk can, for example, receive voice instructions using speech recognition technology. Speech recognition technology converts user speech into text, and noise reduction and optimization of the speech model are performed to accurately recognize voice instructions. For example, if a user gives a voice instruction saying, "Tell me the schedule for today's meetings," the reception desk converts this speech into text and sends it to the analysis unit. The reception desk can also receive text instructions using a text input interface. The text input interface allows users to input text instructions using a keyboard or touchscreen. For example, if a user texts, "Check the meeting room reservation status," that instruction is sent to the analysis unit. Furthermore, the reception desk can also receive gesture instructions using gesture recognition technology. Gesture recognition technology detects the user's hand movements and facial expressions using a camera and recognizes them as instructions. For example, a user can give an instruction such as "Proceed to the next slide" by waving their hand. This allows the reception desk to support diverse input methods such as voice, text, and gestures, improving user convenience. Furthermore, the reception desk can combine these input methods; for example, it can combine voice and gesture instructions to accept more complex instructions. This enables the reception desk to respond to diverse user needs and receive instructions flexibly and efficiently.

[0067] The analysis unit analyzes the instructions received by the reception unit. Analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms, but is not limited to these examples. For example, the analysis unit may use natural language processing techniques to analyze user instructions and understand their intent. Natural language processing techniques are techniques that perform morphological and grammatical analysis to understand the meaning of text. For example, if a user instructs "Tell me the weather for tomorrow," the analysis unit understands that this instruction is a request for weather information. The analysis unit can also use data mining techniques to extract data related to the instructions. Data mining techniques are techniques that extract useful information from large amounts of data. For example, if a user instructs "Analyze the sales data for the past year," the analysis unit extracts sales data related to that instruction. Furthermore, the analysis unit can use machine learning algorithms to learn instruction patterns and improve analysis accuracy. Machine learning algorithms are techniques that learn from past instruction data and improve the accuracy of analysis for new instructions. For example, if a user instructs "Prepare the materials for the next meeting," the analysis unit learns that this instruction means preparing the meeting materials. This allows the analysis unit to quickly and accurately analyze instructions received by the reception unit and understand the user's intent. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and forecasting. For example, it can predict future sales trends based on past sales data and use this information to formulate business strategies. As a result, the analysis unit can handle not only real-time instruction analysis but also long-term data analysis and forecasting, improving the overall performance of the system.

[0068] The integration unit integrates and analyzes data from different systems based on instructions provided by the analysis unit. Integration and analysis are performed using methods such as data merging techniques and analysis algorithms, but are not limited to these examples. For instance, the integration unit merges data obtained from different systems into a single dataset. Data merging techniques are techniques for combining data based on key items; for example, customer information and purchase history can be integrated using customer ID as the key. The integration unit can also analyze the integrated data using analysis algorithms to extract necessary information. Analysis algorithms are techniques for analyzing integrated data and extracting patterns and trends; for example, customer purchasing behavior can be analyzed to formulate marketing strategies. Furthermore, the integration unit can perform data integrity checks to maintain data consistency. Data integrity checks are processes performed to maintain data consistency, such as removing duplicate data and verifying data accuracy. This allows the integration unit to efficiently integrate and analyze data from different systems. Additionally, the integration unit can share the data integration and analysis results with other systems and departments. For example, the integration department displays the integrated and analyzed data on a dashboard, allowing management and various departments to view the data in real time. This enables the integration department to streamline data management across the entire system and support business decision-making.

[0069] The information delivery department provides necessary information based on data integrated and analyzed by the integration department. This information is provided in real time, but is not limited to such real-time delivery. For example, the information delivery department provides users with data integrated and analyzed by the integration department in real time. Real-time information delivery allows users to instantly obtain the information they need, for example, by allowing users to view the latest sales data through a dashboard. The information delivery department can also provide information based on user requests. This occurs when a user requests specific information; for example, if a user requests "Show me this month's sales report," that report is provided. Furthermore, the information delivery department can provide information determined automatically by the system. This system-driven information delivery involves the system determining the user's situation and providing necessary information; for example, if the system detects that a user is in a meeting, it can automatically provide materials related to the meeting. This allows the information delivery department to quickly provide users with appropriate information and support improved work efficiency. Additionally, the information delivery department can manage the history of information delivery and analyze user trends based on past information delivery history. This allows the information delivery department to provide customized information tailored to user needs and improve user satisfaction.

[0070] The reception desk can receive instructions from users in natural language. For example, users can input instructions into the reception desk in natural language. For instance, if a user instructs, "Check the schedule for tomorrow's meeting," the reception desk will receive that instruction. The reception desk can also receive instructions from users such as, "Automatically reply to emails." Furthermore, the reception desk can receive instructions from users such as, "Enter data." This allows the reception desk to operate intuitively by accepting instructions from users in natural language. Natural language includes, but is not limited to, Japanese, English, and other languages. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's natural language instructions into a generating AI and have the generating AI analyze the instructions.

[0071] The analysis unit can analyze instructions received by the reception unit using natural language processing. For example, the analysis unit uses natural language processing technology to analyze user instructions and understand their intent. For instance, if a user instructs the analysis unit to "check the schedule for tomorrow's meeting," the analysis unit analyzes this instruction using natural language processing technology and retrieves the necessary information from the schedule management system. Furthermore, if a user instructs the analysis unit to "automatically reply to an email," the analysis unit can analyze this instruction using natural language processing technology and automatically reply to the email in conjunction with the email system. Additionally, if a user instructs the analysis unit to "enter data," the analysis unit can analyze this instruction using natural language processing technology and input the specified data in conjunction with the data entry system. This improves the analysis unit's ability to understand instructions by analyzing them using natural language processing. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user instructions into the generation AI and have the generation AI perform the analysis of those instructions.

[0072] The integration unit can integrate and analyze data from different systems. For example, the integration unit can merge data obtained from different systems and integrate it into a single dataset. For example, the integration unit can merge schedule data obtained from a schedule management system and email data obtained from an email system and integrate them into a single dataset. The integration unit can also analyze the integrated data using analysis algorithms and extract the necessary information. For example, the integration unit can analyze the integrated data and extract the information that the user needs. Furthermore, the integration unit can perform data integrity checks to maintain data consistency. For example, the integration unit can verify the integrity of the integrated data and provide a consistent dataset. This enables centralized data management by integrating and analyzing data from different systems. Different systems include, but are not limited to, ERP systems, CRM systems, and database systems. Some or all of the above-described processes in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input data obtained from different systems into a generating AI and have the generating AI perform data integration and analysis.

[0073] The service provider can provide necessary information in real time based on data integrated and analyzed by the integration unit. For example, the service provider can provide the user with data integrated and analyzed by the integration unit in real time. For example, if the user instructs the service provider to "check the schedule for tomorrow's meeting," the service provider can provide the user with schedule data integrated and analyzed by the integration unit in real time. The service provider can also provide the user with email data integrated and analyzed by the integration unit in real time if the user instructs to "automatically reply to emails." Furthermore, if the user instructs the service provider to "enter data," the service provider can provide the user with data integrated and analyzed by the integration unit in real time. This enables the service provider to make quick decisions by providing necessary information in real time. Real time includes, but is not limited to, seconds or milliseconds. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input data integrated and analyzed by the integration unit into a generating AI and have the generating AI perform the task of providing the necessary information.

[0074] The service department can automate tasks such as automatic email replies, schedule management, and data entry. For example, if a user instructs the service department to "automatically reply to emails," it will work in conjunction with the email system to automatically reply. For example, if a user instructs the service department to "manage the schedule," it will work in conjunction with the schedule management system to manage the schedule. Furthermore, if a user instructs the service department to "enter data," it can work in conjunction with the data entry system to enter the specified data. As a result, the service department can improve its operational efficiency through task automation. Task automation includes, but is not limited to, automatic email replies, schedule management, and data entry. Some or all of the above processes in the service department may be performed using, for example, AI, or not using AI. For example, the service department can input user instructions into a generating AI and have the generating AI execute the task automation.

[0075] The reception desk can estimate the user's emotions and adjust how instructions are received based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input and receive instructions quickly if the user is in a hurry. This improves user satisfaction by providing an instruction receiving method that is tailored to the user's emotions. User emotions include, but are not limited to, joy, sadness, and anger. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, the reception desk may prioritize suggesting instruction methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may predict and suggest instruction methods to be used during specific time periods based on the user's past instruction history. The reception desk can also suggest relevant instruction methods based on the content of instructions the user has given in the past. This improves user convenience by providing the optimal reception method based on past instruction history. Past instruction history includes, but is not limited to, the type, frequency, and success rate of past instructions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past instruction history data into a generating AI and have the generating AI select the optimal reception method.

[0077] The reception unit can filter instructions based on the user's current work status and areas of interest when receiving them. For example, the reception unit can prioritize receiving instructions related to projects the user is currently working on. For example, the reception unit can filter and receive relevant instructions based on the user's areas of interest. The reception unit can also prioritize receiving instructions of high urgency depending on the user's work status. This improves the efficiency of the reception unit by receiving instructions that are tailored to the user's work status and areas of interest. Work status includes, but is not limited to, current projects and task progress. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's work status data into a generating AI and have the generating AI perform the instruction filtering.

[0078] The reception desk can estimate the user's emotions and determine the priority of instructions to accept based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize high-priority instructions. For example, if the user is relaxed, the reception desk will accept instructions with normal priority. Also, if the user is in a hurry, the reception desk can prioritize high-urgency instructions. This improves user satisfaction by providing instruction priorities that correspond to the user's emotions. Instruction priorities include, but are not limited to, urgency, importance, and the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of instructions.

[0079] The reception unit can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific location, the reception unit will prioritize receiving instructions related to that location. For example, the reception unit will prioritize receiving instructions related to locations close to the user's current location. Furthermore, if the user is on the move, the reception unit can also prioritize receiving instructions related to their destination. This improves the efficiency of the reception unit's operations by allowing it to receive instructions based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI perform the instruction reception.

[0080] The reception desk can analyze the user's social media activity when receiving instructions and accept relevant instructions. For example, the reception desk can prioritize instructions related to what the user has mentioned on social media. For example, the reception desk can analyze the content of the user's social media posts and accept relevant instructions. The reception desk can also accept relevant instructions based on the activity of the user's social media followers and friends. This improves the efficiency of the reception desk's operations by allowing it to accept instructions based on the user's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI perform the instruction acceptance.

[0081] The analysis unit can estimate the user's emotions and adjust the instruction analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method and provide results quickly. For example, if the user is relaxed, the analysis unit can apply a detailed analysis method and provide more information. The analysis unit can also apply an analysis method focused on key points if the user is in a hurry. This improves the accuracy of the analysis by providing an analysis method that is appropriate to the user's emotions. Adjusting the instruction analysis method based on emotions includes, but is not limited to, selecting an analysis algorithm appropriate to the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the instruction analysis method.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the instructions. For example, for instructions of high importance, the analysis unit performs a detailed analysis and provides comprehensive information. For instructions of low importance, the analysis unit performs a simplified analysis and provides only the necessary minimum information. The analysis unit can also perform a rapid analysis and provide immediate results for instructions of high urgency. In this way, the analysis unit improves the accuracy of the analysis by performing analysis according to the importance of the instructions. The importance of instructions includes, but is not limited to, the degree of impact on business operations and urgency. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input instruction importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing the instruction. For example, the analysis unit can apply a natural language processing algorithm to email-related instructions. For example, the analysis unit can apply a calendar analysis algorithm to schedule management-related instructions. The analysis unit can also apply a data analysis algorithm to data entry-related instructions. This improves the accuracy of the analysis by applying an analysis algorithm appropriate to the category of the instruction. The categories of instructions include, but are not limited to, work instructions, technical instructions, and management instructions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input instruction category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize the analysis of high-importance instructions. For example, if the user is relaxed, the analysis unit will analyze instructions with normal priority. Also, if the user is in a hurry, the analysis unit can prioritize the analysis of high-urgency instructions. In this way, the analysis unit improves the accuracy of the analysis by providing analysis priorities according to the user's emotions. Determining the priority of the analysis based on emotions includes, but is not limited to, the intensity and type of emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the determination of analysis priorities.

[0085] The analysis unit can determine the priority of the analysis based on the submission date of the instructions. For example, the analysis unit may prioritize the analysis of recently submitted instructions. For example, the analysis unit may postpone the analysis of older instructions. The analysis unit may also prioritize the analysis of instructions with high urgency, regardless of the submission date. This improves the accuracy of the analysis by providing a priority for analysis based on the submission date of the instructions. The submission date of an instruction includes, but is not limited to, the submission date and time, or the time elapsed since submission. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input instruction submission date data into a generating AI and have the generating AI determine the priority of the analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the instructions when analyzing them. For example, the analysis unit may prioritize the analysis of highly relevant instructions. For example, the analysis unit may postpone the analysis of less relevant instructions. The analysis unit can also group highly relevant instructions together for analysis. This improves the accuracy of the analysis by providing an analysis order based on the relevance of the instructions. The relevance of instructions includes, but is not limited to, similarity of instruction content or related projects. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input instruction relevance data into a generating AI and have the generating AI adjust the order of analysis.

[0087] The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated user emotions. For example, if the user is stressed, the integration unit may apply a simple integration method to provide results quickly. For example, if the user is relaxed, the integration unit may apply a detailed integration method to provide more information. The integration unit may also apply a focused integration method to key points if the user is in a hurry. This improves integration accuracy by providing a data integration method that is tailored to the user's emotions. Adjusting the data integration method based on emotions includes, but is not limited to, prioritizing data according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI adjust the data integration method.

[0088] The integration unit can improve the accuracy of data integration by considering the interrelationships between data. For example, the integration unit can improve the accuracy of integration by analyzing correlations between data. For example, the integration unit can improve the accuracy of integration by considering interdependencies between data. The integration unit can also perform integration while considering interrelationships in order to maintain data consistency. As a result, the integration unit improves the accuracy of integration by considering the interrelationships between data. Interrelationships between data include, but are not limited to, relationships and dependencies between data. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input data interrelationship data into a generating AI and have the generating AI perform the integration accuracy improvement.

[0089] The integration unit can perform data integration while considering the attribute information of the data provider. For example, the integration unit can improve the accuracy of the integration by considering the reliability of the data provider. For example, the integration unit can improve the accuracy of the integration by considering the expertise of the data provider. The integration unit can also improve the accuracy of the integration by considering the past performance of the data provider. In this way, the integration unit improves the accuracy of the integration by considering the attribute information of the data provider. The attribute information of the data provider includes, but is not limited to, the provider's job title and area of ​​expertise. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the data provider's attribute information data into a generating AI and have the generating AI perform the integration accuracy improvement.

[0090] The integration unit can estimate the user's emotions and adjust the display method of the integrated data based on the estimated user emotions. For example, if the user is stressed, the integration unit provides a simple and highly visible display method. For example, if the user is relaxed, the integration unit provides a display method that includes detailed information. The integration unit can also provide a concise display method if the user is in a hurry. In this way, the integration unit improves visibility by providing a display method that is appropriate to the user's emotions. Adjusting the display method of integrated data based on emotions includes, but is not limited to, selecting a display format appropriate to the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0091] The integration unit can perform data integration while considering the geographical distribution of the data. For example, the integration unit can analyze the geographical distribution of the data to improve the accuracy of the integration. For example, the integration unit can prioritize the integration of geographically related data. The integration unit can also perform integration while considering the geographical distribution to maintain data consistency. As a result, the integration unit improves the accuracy of the integration by considering the geographical distribution of the data. The geographical distribution of the data includes, but is not limited to, data distribution by region and geographical relationships. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of the integration.

[0092] The integration unit can improve the accuracy of data integration by referring to relevant literature during data integration. For example, the integration unit can optimize the data integration method by referring to relevant literature. For example, the integration unit can improve the accuracy of data integration based on information from relevant literature. The integration unit can also analyze relevant literature and perform integration to maintain data consistency. In this way, the integration unit improves the accuracy of integration by referring to relevant literature. Relevant literature includes, but is not limited to, academic papers and technical reports. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input relevant literature data into a generating AI and have the generating AI perform the integration accuracy improvement.

[0093] The information provider can estimate the user's emotions and adjust the way information is delivered based on those emotions. For example, if the user is stressed, the provider can provide a simple and highly visible way of delivering information. For example, if the user is relaxed, the provider can provide a way of delivering information that includes detailed information. The provider can also provide a concise way of delivering information if the user is in a hurry. This improves visibility by providing information delivered in a way that suits the user's emotions. Adjusting the way information is delivered based on emotions includes, but is not limited to, methods of presenting information according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the way information is delivered.

[0094] The information provider can select the optimal information delivery method by referring to the user's past information usage history when providing information. For example, the information provider may prioritize providing information delivery methods that the user has frequently used in the past. For example, the information provider may predict and propose information delivery methods that the user will use during a specific time period based on the user's past information usage history. The information provider can also propose relevant information delivery methods based on the user's past information usage history. This improves user convenience by allowing the information provider to provide the optimal delivery method based on past information usage history. Past information usage history includes, but is not limited to, past search history and browsing history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past information usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0095] The information delivery unit can customize the means of information delivery based on the user's current work situation when providing information. For example, if the user is in a meeting, the unit will prioritize providing information related to the meeting. For example, if the user is out of the office, the unit will provide an information delivery method optimized for mobile devices. The unit can also provide an information delivery method optimized for desktop devices if the user is working at a desk. This improves work efficiency by allowing the unit to provide information delivery methods tailored to the user's work situation. Work situation includes, but is not limited to, current projects and task progress. Some or all of the above processing in the information delivery unit may be performed using, for example, AI, or not using AI. For example, the unit can input user work situation data into a generating AI and have the generating AI customize the means of information delivery.

[0096] The information provider can estimate the user's emotions and prioritize information based on those emotions. For example, if the user is stressed, the provider will prioritize providing high-priority information. For example, if the user is relaxed, the provider will provide information with normal priority. The provider can also prioritize highly urgent information if the user is in a hurry. This improves visibility by providing information prioritization according to the user's emotions. Prioritizing information based on emotions includes, but is not limited to, the intensity and type of emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0097] The information provider can select the optimal method of providing information by considering the user's geographical location when providing information. For example, if the user is in a specific location, the information provider can prioritize providing information related to that location. For example, the information provider can prioritize providing information related to locations close to the user's current location. Furthermore, if the user is on the move, the information provider can prioritize providing information related to their destination. This improves visibility by providing information based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select the method of providing information.

[0098] The information provider can analyze the user's social media activity and propose means of providing information when providing information. For example, the provider may prioritize providing information related to what the user has mentioned on social media. For example, the provider may analyze the content of the user's social media posts and provide relevant information. The provider may also provide relevant information based on the activity of the user's social media followers and friends. This improves visibility by providing means of information provision based on the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the provider may input the user's social media activity data into a generating AI and have the generating AI propose means of information provision.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can analyze the user's past instruction history and select the optimal reception method when receiving user instructions. For example, it can prioritize suggesting instruction methods (voice, text, etc.) that the user has frequently used in the past. It can also predict and suggest instruction methods to be used during specific time periods based on the user's past instruction history. Furthermore, it can suggest relevant instruction methods based on the content of instructions the user has given in the past. In this way, the reception desk improves user convenience by providing the optimal reception method based on past instruction history. Past instruction history includes, but is not limited to, the type, frequency, and success rate of past instructions. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past instruction history data into a generating AI and have the generating AI select the optimal reception method.

[0101] The analysis unit can estimate the user's emotions and adjust the instruction analysis method based on the estimated user emotions. For example, if the user is stressed, a simple analysis method can be applied to provide results quickly. If the user is relaxed, a detailed analysis method can be applied to provide more information. If the user is in a hurry, an analysis method focused on key points can also be applied. This improves the accuracy of the analysis by providing an analysis method that is appropriate to the user's emotions. Adjusting the instruction analysis method based on emotions includes, but is not limited to, selecting an analysis algorithm appropriate to the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the instruction analysis method.

[0102] The integration unit can improve the accuracy of data integration by considering the interrelationships between data. For example, it can improve the accuracy of integration by analyzing the correlations between data. It can also improve the accuracy of integration by considering the interdependencies between data. Furthermore, integration can be performed while considering interrelationships in order to maintain data consistency. In this way, the integration unit improves the accuracy of integration by considering the interrelationships between data. Data interrelationships include, but are not limited to, relationships and dependencies between data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data interrelationship data into a generating AI and have the generating AI perform the integration accuracy improvement.

[0103] The information provider can estimate the user's emotions and adjust the way information is delivered based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible information delivery method. If the user is relaxed, it can provide a delivery method that includes detailed information. If the user is in a hurry, it can also provide a concise information delivery method. This improves visibility by providing information delivery methods that are tailored to the user's emotions. Adjusting the information delivery method based on emotions includes, but is not limited to, methods of presenting information according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust the information delivery method.

[0104] The reception unit can filter instructions based on the user's current work status and areas of interest when receiving them. For example, it can prioritize instructions related to projects the user is currently working on. It can also filter and accept relevant instructions based on the user's areas of interest. Furthermore, it can prioritize instructions of high urgency depending on the user's work status. This improves the efficiency of the reception unit by allowing it to receive instructions that match the user's work status and areas of interest. Work status includes, but is not limited to, current projects and task progress. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input user work status data into a generating AI and have the generating AI perform the instruction filtering.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the instructions. For example, for highly important instructions, a detailed analysis is performed to provide comprehensive information. For less important instructions, a simplified analysis is performed to provide only the necessary minimum information. Furthermore, for highly urgent instructions, a rapid analysis can be performed to provide results immediately. In this way, the analysis unit improves the accuracy of the analysis by performing analysis according to the importance of the instructions. The importance of instructions includes, but is not limited to, the degree of impact on business operations and urgency. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input instruction importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0106] The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated emotions. For example, if the user is stressed, a simple integration method can be applied to provide results quickly. If the user is relaxed, a detailed integration method can be applied to provide more information. If the user is in a hurry, an integration method focused on key points can also be applied. This improves integration accuracy by providing a data integration method that is tailored to the user's emotions. Adjusting the data integration method based on emotions includes, but is not limited to, prioritizing data according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input user emotion data into a generative AI and have the generative AI adjust the data integration method.

[0107] The information delivery unit can select the optimal delivery method by referring to the user's past information usage history when providing information. For example, it can prioritize providing information delivery methods that the user has frequently used in the past. It can also predict and suggest information delivery methods that the user will use at a specific time of day based on the user's past information usage history. Furthermore, it can suggest relevant information delivery methods based on the user's past information usage history. In this way, the information delivery unit improves user convenience by providing the optimal delivery method based on past information usage history. Past information usage history includes, but is not limited to, past search history and browsing history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's past information usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0108] The reception desk can estimate the user's emotions and determine the priority of instructions to accept based on the estimated emotions. For example, if the user is stressed, high-importance instructions will be given priority. If the user is relaxed, instructions will be accepted with normal priority. Also, if the user is in a hurry, instructions of high urgency can be given top priority. In this way, the reception desk can improve user satisfaction by providing instructions prioritized according to the user's emotions. The priority of instructions may include, but is not limited to, urgency, importance, and the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of instructions.

[0109] The information provider can select the optimal method of providing information by considering the user's geographical location when providing information. For example, if the user is in a specific location, information related to that location can be provided preferentially. Information related to locations close to the user's current location can also be provided preferentially. Furthermore, if the user is on the move, information related to their destination can be provided preferentially. This improves visibility by allowing the information provider to provide information based on the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location data into a generating AI and have the generating AI select the method of providing information.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives user instructions. User instructions include voice instructions, text instructions, and gesture instructions. For example, the reception desk receives voice instructions using voice recognition technology, text instructions using a text input interface, and gesture instructions using gesture recognition technology. Step 2: The analysis unit analyzes the instructions received by the reception unit. The analysis is performed using methods such as natural language processing, data mining, and machine learning algorithms. For example, the analysis unit uses natural language processing technology to analyze the user's instructions and understand the intent behind them. Step 3: The integration unit integrates and analyzes data from different systems based on instructions provided by the analysis unit. The integration unit merges data acquired from different systems and integrates it into a single dataset. It also analyzes the integrated data using an analysis algorithm and extracts the necessary information. Step 4: The provisioning unit provides the necessary information based on the data integrated and analyzed by the integration unit. The provisioning unit can also provide the data integrated and analyzed by the integration unit to the user in real time and provide information based on the user's requests.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, integration unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user instructions using the microphone 38B or touch panel 38A of the smart device 14 and transmits those instructions to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes data from different systems. The provision unit provides information to the user using, for example, the display 40A or speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 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.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, integration unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's voice instructions using the microphone 238 of the smart glasses 214 and transmits those instructions to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes data from different systems. The provision unit provides information to the user, for example, using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, integration unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives the user's voice instructions using the microphone 238 of the headset terminal 314 and transmits those instructions to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes data from different systems. The provision unit provides information to the user, for example, using the display 343 and speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, integration unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice instructions from the user using the microphone 238 of the robot 414 and transmits those instructions to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's instructions using natural language processing technology. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and integrates and analyzes data from different systems. The provision unit provides information to the user using, for example, the speaker 240 or display device of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A reception desk that takes user instructions, An analysis unit that analyzes the instructions received by the reception unit, An integration unit that integrates and analyzes data from different systems based on instructions analyzed by the aforementioned analysis unit, The system includes a providing unit that provides necessary information based on the data integrated and analyzed by the aforementioned integration unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Accepts user instructions in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The instructions received by the aforementioned reception unit are analyzed using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned integration unit is Integrating and analyzing data from different systems. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on the data integrated and analyzed by the aforementioned integration unit, the necessary information is provided in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Automate tasks such as automatic email replies, schedule management, and data entry. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how instructions are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past instruction history and select the optimal method of receiving instructions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of instructions to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving instructions, the system prioritizes accepting instructions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving instructions, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the instruction analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing instructions, adjust the level of detail in the analysis based on the importance of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing instructions, different analysis algorithms are applied depending on the category of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing instructions, the priority of the analysis is determined based on when the instructions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing instructions, adjust the order of analysis based on the relationships between the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned integration unit is We estimate user sentiment and adjust the data integration method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned integration unit is When integrating data, consider the interrelationships between data to improve the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned integration unit is When integrating data, the data provider's attribute information should be taken into consideration during the integration process. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned integration unit is It estimates the user's emotions and adjusts how the integrated data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned integration unit is When integrating data, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned integration unit is When integrating data, referencing relevant literature improves the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the system selects the most suitable method of delivery by referring to the user's past information usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, customize the method of information delivery based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the optimal method of information delivery is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we analyze users' social media activity and propose methods for providing that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that takes user instructions, An analysis unit that analyzes the instructions received by the reception unit, An integration unit that integrates and analyzes data from different systems based on instructions analyzed by the aforementioned analysis unit, The system includes a providing unit that provides necessary information based on the data integrated and analyzed by the aforementioned integration unit. A system characterized by the following features.

2. The aforementioned reception unit is Accepts user instructions in natural language. The system according to feature 1.

3. The aforementioned analysis unit, The instructions received by the aforementioned reception unit are analyzed using natural language processing. The system according to feature 1.

4. The aforementioned integration unit is Integrating and analyzing data from different systems. The system according to feature 1.

5. The aforementioned supply unit is, Based on the data integrated and analyzed by the aforementioned integration unit, the necessary information is provided in real time. The system according to feature 1.

6. The aforementioned supply unit is, Automate tasks such as automatic email replies, schedule management, and data entry. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how instructions are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past instruction history and select the optimal method of receiving instructions. The system according to feature 1.

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