system

Abstract

The system according to this embodiment aims to efficiently carry out tasks by suggesting a suitable function based on the user's work content. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a display unit, and a storage unit. The reception unit receives the user's work content. The proposal unit proposes a suitable function based on the work content received by the reception unit. The display unit displays the calculation result using the function proposed by the proposal unit. The storage unit stores the data of the function proposed by the proposal unit and uses it to make proposals to other users.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response 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, as the amount of data processed in business increases and the processing content becomes more complex, sufficient support for efficiently proceeding with work has not been provided, and there is room for improvement.

[0005] The system according to the embodiment aims to propose a suitable function based on the work content of the user and efficiently proceed with work.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a display unit, and a storage unit. The reception unit receives the user's work details. The proposal unit proposes a suitable function based on the work details received by the reception unit. The display unit displays the calculation results using the function proposed by the proposal unit. The storage unit stores the data of the function proposed by the proposal unit and uses it to make suggestions to other users. [Effects of the Invention]

[0007] The system according to this embodiment can propose a suitable function based on the user's work content, enabling efficient work execution. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 AI ​​agent system according to an embodiment of the present invention is a system that proposes and supports how to efficiently carry out tasks using office software, in response to the increasing volume of data processing handled in business operations and the increasing complexity of the processing requirements. This AI agent system makes suggestions for efficiently carrying out tasks using office software and comprehensively reviews and optimizes / updates the work procedures. In this process, the AI ​​agent accumulates and utilizes the work processes it has learned as general-purpose work code patterns, enabling it to support a variety of processing tasks. For example, when a user encounters a problem or has questions, the AI ​​agent system can be called up from a plugin. A chat screen will appear on the screen, and after the user explains what they want to do, the system will search for a suitable function and display the calculation result in a file. For example, if the user inputs "I want to change 2024 / 11 / 18 to 2024 / 11 / 18", the AI ​​agent will propose a suitable function for changing the date format and display the calculation result in a file. Next, the AI ​​agent system compares the files before and after the work and constructs the work procedure. For example, when a user drops files before and after a task, the AI ​​agent identifies the file with the shorter interval between the creation date and modification date in the properties of each file as the before file and begins working to create the same data as the after file. The AI ​​agent compares the data types (names, functions, field names, etc.) in the before and after files and constructs the work procedure. Furthermore, the AI ​​agent system stores and utilizes the work processes learned by the AI ​​agent as general-purpose work code patterns, enabling the AI ​​agent to support a variety of processing tasks. For example, it stores data on functions suggested by the AI ​​agent to the user and uses this data to make suggestions to other users. This allows the AI ​​agent to support a variety of processing tasks, improving the user's work efficiency. Through this mechanism, the AI ​​agent can make suggestions to efficiently proceed with work in office software and comprehensively review and streamline work procedures.Furthermore, by accumulating and utilizing the work processes learned by the AI ​​agent as general-purpose work code patterns, the AI ​​agent can support a wide range of processing tasks. This improves user work efficiency and streamlines operations. As a result, the AI ​​agent system can efficiently suggest, display, and store user work content.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, a suggestion unit, a display unit, and a storage unit. The reception unit receives the user's work content. The user's work content includes, but is not limited to, data entry, calculation processing, and document creation. The reception unit receives, for example, the work content that the user performs using office software. The suggestion unit suggests a suitable function based on the work content received by the reception unit. The suggestion unit, for example, calls the AI ​​agent from a plugin and suggests a suitable function when the user encounters a problem or has questions. The suggestion unit, for example, suggests a suitable function for changing the date format when the user inputs "I want to change 2024 / 11 / 18 to 2024 / 11 / 18". The suggestion unit compares the file before and after the work and constructs a work procedure. The display unit displays the calculation result using the function suggested by the suggestion unit. The display unit, for example, displays the calculation result in a file using the suggested function. The display unit, for example, displays the calculation result in a spreadsheet or text file. The storage unit stores data on functions proposed by the proposal unit and uses it to make suggestions to other users. For example, the storage unit stores data on proposed functions and uses it to make suggestions to other users. For example, the storage unit stores usage history and performance data of proposed functions. As a result, the AI ​​agent system according to the embodiment can efficiently propose, display, and store the user's work content.

[0030] The reception desk receives information about the user's work. This includes, but is not limited to, data entry, calculations, and document creation. For example, the reception desk receives information about the user's work using office software. Specifically, when a user enters data into a spreadsheet, the reception desk recognizes the data type and format and collects information for appropriate processing. Similarly, when a user creates a report using word processing software, the reception desk analyzes the document's structure and content to provide necessary support. Furthermore, the reception desk monitors the user's operations and input in real time and transmits information to the proposal department at the appropriate time as needed. This allows users to work smoothly without encountering difficulties or delays. The reception desk also supports various input methods, such as voice input and handwriting input, improving user convenience. For example, it can use voice recognition technology to accurately recognize verbal instructions from users and process them appropriately. In the case of handwriting input, it uses handwriting recognition technology to convert the user's handwritten input into digital data for processing. This allows the reception desk to meet diverse user needs and provide efficient work support.

[0031] The Proposal Department suggests appropriate functions based on the work content received by the Reception Department. For example, when a user encounters a problem or has questions, the Proposal Department calls an AI agent from a plugin and suggests an appropriate function. Specifically, if a user inputs "I want to change 2024 / 11 / 18 to 2024 / 11 / 18", the Proposal Department will suggest a function suitable for changing the date format. For example, the Proposal Department compares files before and after the work and constructs a work procedure. The Proposal Department uses AI to analyze the user's input and operation history and suggests the optimal function and operation procedure. For example, it uses natural language processing technology to analyze the text entered by the user and understand its intent. Furthermore, it selects the most appropriate function and operation procedure based on past data and the operation history of other users. To maximize the efficiency of the work performed by the user, the Proposal Department presents multiple options so that the user can choose the best one. The Proposal Department also collects user feedback and continuously improves the accuracy of its suggestions. For example, it evaluates the results of users using the suggested functions and improves the suggestion algorithm based on that evaluation. This allows the proposal department to make optimal proposals tailored to user needs and improve work efficiency.

[0032] The display unit displays calculation results using the functions proposed by the proposal unit. For example, the display unit displays calculation results in a file using the proposed functions. Specifically, it displays calculation results in a spreadsheet or text file. The display unit utilizes visualization tools such as graphs and charts to display results in a format that is easy for the user to understand visually. For example, it can display calculation results as bar graphs or line graphs on a spreadsheet, allowing users to grasp data trends and patterns at a glance. In the case of a text file, it organizes the calculation results in a table format for easy viewing. The display unit can also adjust font size, color, layout, etc., to make it easier for users to check the results. Furthermore, the display unit updates calculation results in real time, ensuring users always have access to the latest information. For example, if data is changed on a spreadsheet, it immediately recalculates the results and updates the display. The display unit also provides a function that allows users to export results to other applications or systems. This allows users to utilize the calculation results in other tasks or projects. To improve user efficiency, the display unit provides an intuitive and user-friendly interface, making it easy to check and utilize calculation results.

[0033] The storage unit stores data on functions proposed by the proposal unit and uses it to make suggestions to other users. Specifically, it stores usage history and performance data of proposed functions. The storage unit meticulously records the history of functions and operation procedures used by users and builds a database based on this to make optimal suggestions to other users. For example, it records the situations in which a particular function was used and how effective it was, and makes optimal suggestions to other users who face similar situations. The storage unit also analyzes performance data such as the frequency of function use and success rate, and provides feedback to improve the accuracy of the proposed algorithm. Furthermore, the storage unit collects user feedback and evaluations and uses them to improve the content of suggestions. For example, it evaluates the results of users using proposed functions and improves the proposed algorithm based on that evaluation. In this way, the storage unit can make optimal suggestions that meet user needs and improve the overall performance of the system. To ensure data security and privacy, the storage unit implements appropriate encryption technologies and access controls to safely manage user data. This allows the data storage unit to gain user trust while achieving efficient data storage and improved accuracy of recommendations.

[0034] The suggestion function can call an AI agent from the plugin and suggest a suitable function when a user encounters a problem or has questions. For example, if a user doesn't know how to operate something, the suggestion function can call an AI agent from the plugin and suggest a suitable function. For example, if a user receives an error message, the suggestion function can call an AI agent from the plugin and suggest a suitable function. For example, if a user wants to know how to perform a specific operation, the suggestion function can call an AI agent from the plugin and suggest a suitable function. This improves work efficiency by suggesting the appropriate function when the user encounters a problem.

[0035] The display unit can display the calculation results in a file using the proposed function. For example, the display unit can display the calculation results in a spreadsheet using the proposed function. For example, the display unit can display the calculation results in a text file using the proposed function. For example, the display unit can display the calculation results as a graph using the proposed function. This makes it easier for users to review the results by displaying them using the proposed function.

[0036] The storage unit stores data on proposed functions, which can then be used to make suggestions to other users. For example, the storage unit stores the usage history of proposed functions. For example, the storage unit stores performance data of proposed functions. For example, the storage unit stores the frequency of use of proposed functions. This allows the storage unit to accumulate data on proposed functions and utilize it to make suggestions to other users.

[0037] The suggestion function can suggest a suitable function for changing the date format when a user inputs "I want to change 2024 / 11 / 18 to 2024 / 11 / 18". For example, the suggestion function will suggest a suitable function if the user wants to change the date format. For example, the suggestion function will suggest a suitable function if the user wants to change the date format. For example, the suggestion function will suggest a suitable function if the user wants to change the date display format. This improves work efficiency by suggesting appropriate functions in response to specific user input.

[0038] The proposal unit can compare files before and after the work is completed and construct a work procedure. For example, the proposal unit can compare files before and after the work is completed to construct an efficient work procedure. For example, the proposal unit can compare the data types of files before and after the work is completed to construct a work procedure. For example, the proposal unit can compare the properties of files before and after the work is completed to construct a work procedure. In this way, by comparing files before and after the work is completed, an efficient work procedure can be constructed.

[0039] The storage unit can compare the data types in the files before and after the operation and construct a work procedure. For example, the storage unit can compare the data types in the files before and after the operation to construct an efficient work procedure. For example, the storage unit can compare the names and functions in the files before and after the operation to construct a work procedure. For example, the storage unit can compare the item names in the files before and after the operation to construct a work procedure. In this way, by comparing data types, an efficient work procedure can be constructed.

[0040] The suggestion unit can store data on functions suggested to users and use it to make suggestions to other users. For example, the suggestion unit can store the usage history of functions suggested to users. For example, the suggestion unit can store performance data of functions suggested to users. For example, the suggestion unit can store the usage frequency of functions suggested to users. In this way, by accumulating data on suggested functions, it can be used to make suggestions to other users.

[0041] The reception desk can analyze the user's past work history and select the optimal reception method. For example, the reception desk prioritizes receiving requests for tasks that the user has frequently performed in the past. For example, the reception desk can predict the type of tasks to be performed during a specific time period based on the user's past work history and accept those requests. For example, the reception desk analyzes the user's past work history and proposes the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the user's past work history.

[0042] The reception desk can filter work requests based on the user's current projects and areas of interest. For example, the reception desk can prioritize receiving work requests related to the user's current projects. For example, the reception desk can filter and receive relevant work requests based on the user's areas of interest. For example, the reception desk can receive appropriate work requests according to the progress of the user's current projects. In this way, by filtering based on the user's current projects and areas of interest, highly relevant work requests can be received.

[0043] The reception desk can prioritize accepting work requests by considering the user's geographical location. For example, if the user is in the office, the reception desk will prioritize accepting office-related work. If the user is on a business trip, the reception desk will prioritize accepting work related to the business trip location. If the user is at home, the reception desk will prioritize accepting work that can be done at home. In this way, by considering the user's geographical location, the reception desk can prioritize accepting work requests that are highly relevant.

[0044] The reception department can analyze a user's social media activity when receiving work requests and accept relevant work requests. For example, the reception department can prioritize work requests related to projects mentioned by the user on social media. For example, the reception department can prioritize work requests related to topics of interest based on the user's social media activity. For example, the reception department can prioritize important work requests based on the user's social media follower count and influence. In this way, relevant work requests can be accepted by analyzing the user's social media activity.

[0045] The proposal department can adjust the level of detail in a proposal based on the importance of the task. For example, the proposal department will provide a detailed proposal for important tasks, a concise proposal for low-priority tasks, and a prompt proposal for urgent tasks. By adjusting the level of detail based on the importance of the task, the proposal department can provide more appropriate proposals.

[0046] The proposal function can apply different proposal algorithms depending on the category of work during the proposal process. For example, the proposal function can apply a statistical proposal algorithm to data analysis work, a natural language processing proposal algorithm to document creation work, and a visual proposal algorithm to presentation creation work. By applying different proposal algorithms depending on the category of work, more appropriate proposals can be made.

[0047] The proposal department can prioritize proposals based on the submission deadlines for each task. For example, the department will prioritize proposals for tasks with approaching deadlines. For example, it will postpone proposals for tasks with distant submission deadlines. For example, it will propose tasks with unknown submission deadlines based on the prioritization of other tasks. By prioritizing proposals based on the submission deadlines, the department can make more appropriate proposals.

[0048] The proposal department can adjust the order of proposals based on the relevance of the tasks. For example, the proposal department will prioritize proposals for highly relevant tasks. For example, it will postpone proposals for less relevant tasks. For example, it will propose tasks whose relevance is unclear based on the relevance of other tasks. By adjusting the order of proposals based on the relevance of the tasks, more appropriate proposals can be made.

[0049] The display unit can adjust the level of detail based on the importance of the task during display. For example, it will display detailed information for important tasks, concise information for low-priority tasks, and rapid information for urgent tasks. By adjusting the level of detail based on the importance of the task, more appropriate information can be displayed.

[0050] The display unit can apply different display algorithms depending on the category of work during display. For example, the display unit applies a statistical display algorithm for data analysis work. For example, the display unit applies a natural language processing display algorithm for document creation work. For example, the display unit applies a visual display algorithm for presentation creation work. By applying different display algorithms depending on the category of work, more appropriate displays can be achieved.

[0051] The display unit can determine the display priority based on the submission date of each task. For example, the display unit will prioritize displaying tasks with approaching deadlines. For example, the display unit will postpone displaying tasks with distant submission dates. For example, the display unit will display tasks with unknown submission dates based on the priority of other tasks. By determining the display priority based on the submission date of each task, more appropriate displays can be achieved.

[0052] The display unit can adjust the display order based on the relevance of tasks during display. For example, the display unit will prioritize displaying tasks that are highly relevant. For example, the display unit will postpone displaying tasks that are less relevant. For example, the display unit will display tasks whose relevance is unclear based on the relevance of other tasks. In this way, by adjusting the display order based on the relevance of tasks, more appropriate displays can be achieved.

[0053] The data storage unit can improve the accuracy of data storage by considering the interrelationships between tasks during the storage process. For example, the storage unit groups and stores related task content. For example, the storage unit analyzes the interrelationships between task content and prioritizes storing highly relevant data. For example, the storage unit stores data in a way that avoids duplication by considering the interrelationships between task content. As a result, the accuracy of data storage is improved by considering the interrelationships between tasks.

[0054] The data storage unit can store data while considering the attribute information of the person submitting the work. For example, the storage unit can determine data priority based on the submitter's job title and assigned duties. For example, the storage unit can prioritize storing highly relevant data by referring to the submitter's past work history. For example, the storage unit can adjust the level of detail of the data according to the submitter's skill level. This allows for more appropriate data storage by considering the attribute information of the person submitting the work.

[0055] The data storage unit can perform data storage while considering the geographical distribution of tasks. For example, the storage unit can group and store tasks that are geographically close. For example, the storage unit can prioritize the storage of highly relevant data while considering geographical distribution. For example, the storage unit can store data while considering geographical distribution to avoid duplication. In this way, more appropriate data storage can be performed by considering the geographical distribution of tasks.

[0056] The data storage unit can improve the accuracy of data storage by referring to relevant literature during the storage process. For example, the storage unit stores background information about the work content by referring to relevant literature. For example, the storage unit adjusts the level of detail of the work content based on relevant literature before storing it. For example, the storage unit stores data in a way that avoids data duplication by referring to relevant literature. In this way, the accuracy of data storage is improved by referring to relevant literature for the work.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The suggestion function can propose a suitable function based on the user's task. For example, if the user wants to filter data, the suggestion function can propose a suitable filtering function. For example, if the user wants to aggregate data, the suggestion function can propose a suitable aggregation function. For example, if the user wants to sort data, the suggestion function can propose a suitable sorting function. This improves work efficiency by suggesting the appropriate function according to the user's task.

[0059] The display unit can display the calculation results as graphs using the proposed function. For example, the display unit can display the calculation results as a bar graph using the proposed function. For example, the display unit can display the calculation results as a line graph using the proposed function. For example, the display unit can display the calculation results as a pie chart using the proposed function. This makes it easier for users to visually confirm the results by displaying the calculation results as graphs using the proposed function.

[0060] The storage unit can store data on proposed functions and use it to make suggestions to other users. For example, the storage unit can store the usage history of proposed functions and use it to make suggestions to other users. For example, the storage unit can store performance data of proposed functions and use it to make suggestions to other users. For example, the storage unit can store the frequency of use of proposed functions and use it to make suggestions to other users. In this way, by storing data on proposed functions, it can be used to make suggestions to other users.

[0061] The suggestion function can call an AI agent from a plugin and suggest a suitable function when the user wants to know how to perform a specific operation. For example, if the user wants to know how to filter data, the suggestion function can suggest a suitable filtering function. For example, if the user wants to know how to aggregate data, the suggestion function can suggest a suitable aggregation function. For example, if the user wants to know how to sort data, the suggestion function can suggest a suitable sorting function. This improves work efficiency by suggesting the appropriate function when the user wants to know how to perform a specific operation.

[0062] The display unit can display the calculation results in a spreadsheet using the proposed function. For example, the display unit can display the calculation results in a cell using the proposed function. For example, the display unit can display the calculation results as a table using the proposed function. For example, the display unit can display the calculation results as a graph using the proposed function. This makes it easier for users to review the results by displaying them in a spreadsheet using the proposed function.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives the user's work details. These details may include, for example, data entry, calculations, and document creation. The reception desk also receives the user's work details using office software. Step 2: The proposal department proposes a suitable function based on the work details received by the reception department. For example, when a user encounters a problem or has questions, the proposal department calls an AI agent from the plugin and proposes a suitable function. When a user enters "I want to change 2024 / 11 / 18 to 2024 / 11 / 18", the proposal department proposes a suitable function for changing the date format. The proposal department compares the files before and after the work and constructs the work procedure. Step 3: The display unit displays the calculation results using the function proposed by the proposal unit. The display unit displays the calculation results in a file using the proposed function. The display unit displays the calculation results in a spreadsheet or text file. Step 4: The storage unit stores data on the functions proposed by the proposal unit and uses it to make suggestions to other users. The storage unit stores the usage history and performance data of the proposed functions.

[0065] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that proposes and supports how to efficiently carry out tasks using office software, in response to the increasing volume of data processing handled in business operations and the increasing complexity of the processing requirements. This AI agent system makes suggestions for efficiently carrying out tasks using office software and comprehensively reviews and optimizes / updates the work procedures. In this process, the AI ​​agent accumulates and utilizes the work processes it has learned as general-purpose work code patterns, enabling it to support a variety of processing tasks. For example, when a user encounters a problem or has questions, the AI ​​agent system can be called up from a plugin. A chat screen will appear on the screen, and after the user explains what they want to do, the system will search for a suitable function and display the calculation result in a file. For example, if the user inputs "I want to change 2024 / 11 / 18 to 2024 / 11 / 18", the AI ​​agent will propose a suitable function for changing the date format and display the calculation result in a file. Next, the AI ​​agent system compares the files before and after the work and constructs the work procedure. For example, when a user drops files before and after a task, the AI ​​agent identifies the file with the shorter interval between the creation date and modification date in the properties of each file as the before file and begins working to create the same data as the after file. The AI ​​agent compares the data types (names, functions, field names, etc.) in the before and after files and constructs the work procedure. Furthermore, the AI ​​agent system stores and utilizes the work processes learned by the AI ​​agent as general-purpose work code patterns, enabling the AI ​​agent to support a variety of processing tasks. For example, it stores data on functions suggested by the AI ​​agent to the user and uses this data to make suggestions to other users. This allows the AI ​​agent to support a variety of processing tasks, improving the user's work efficiency. Through this mechanism, the AI ​​agent can make suggestions to efficiently proceed with work in office software and comprehensively review and streamline work procedures.Furthermore, by accumulating and utilizing the work processes learned by the AI ​​agent as general-purpose work code patterns, the AI ​​agent can support a wide range of processing tasks. This improves user work efficiency and streamlines operations. As a result, the AI ​​agent system can efficiently suggest, display, and store user work content.

[0066] The AI ​​agent system according to this embodiment comprises a reception unit, a suggestion unit, a display unit, and a storage unit. The reception unit receives the user's work content. The user's work content includes, but is not limited to, data entry, calculation processing, and document creation. The reception unit receives, for example, the work content that the user performs using office software. The suggestion unit suggests a suitable function based on the work content received by the reception unit. The suggestion unit, for example, calls the AI ​​agent from a plugin and suggests a suitable function when the user encounters a problem or has questions. The suggestion unit, for example, suggests a suitable function for changing the date format when the user inputs "I want to change 2024 / 11 / 18 to 2024 / 11 / 18". The suggestion unit compares the file before and after the work and constructs a work procedure. The display unit displays the calculation result using the function suggested by the suggestion unit. The display unit, for example, displays the calculation result in a file using the suggested function. The display unit, for example, displays the calculation result in a spreadsheet or text file. The storage unit stores data on functions proposed by the proposal unit and uses it to make suggestions to other users. For example, the storage unit stores data on proposed functions and uses it to make suggestions to other users. For example, the storage unit stores usage history and performance data of proposed functions. As a result, the AI ​​agent system according to the embodiment can efficiently propose, display, and store the user's work content.

[0067] The reception desk receives information about the user's work. This includes, but is not limited to, data entry, calculations, and document creation. For example, the reception desk receives information about the user's work using office software. Specifically, when a user enters data into a spreadsheet, the reception desk recognizes the data type and format and collects information for appropriate processing. Similarly, when a user creates a report using word processing software, the reception desk analyzes the document's structure and content to provide necessary support. Furthermore, the reception desk monitors the user's operations and input in real time and transmits information to the proposal department at the appropriate time as needed. This allows users to work smoothly without encountering difficulties or delays. The reception desk also supports various input methods, such as voice input and handwriting input, improving user convenience. For example, it can use voice recognition technology to accurately recognize verbal instructions from users and process them appropriately. In the case of handwriting input, it uses handwriting recognition technology to convert the user's handwritten input into digital data for processing. This allows the reception desk to meet diverse user needs and provide efficient work support.

[0068] The Proposal Department suggests appropriate functions based on the work content received by the Reception Department. For example, when a user encounters a problem or has questions, the Proposal Department calls an AI agent from a plugin and suggests an appropriate function. Specifically, if a user inputs "I want to change 2024 / 11 / 18 to 2024 / 11 / 18", the Proposal Department will suggest a function suitable for changing the date format. For example, the Proposal Department compares files before and after the work and constructs a work procedure. The Proposal Department uses AI to analyze the user's input and operation history and suggests the optimal function and operation procedure. For example, it uses natural language processing technology to analyze the text entered by the user and understand its intent. Furthermore, it selects the most appropriate function and operation procedure based on past data and the operation history of other users. To maximize the efficiency of the work performed by the user, the Proposal Department presents multiple options so that the user can choose the best one. The Proposal Department also collects user feedback and continuously improves the accuracy of its suggestions. For example, it evaluates the results of users using the suggested functions and improves the suggestion algorithm based on that evaluation. This allows the proposal department to make optimal proposals tailored to user needs and improve work efficiency.

[0069] The display unit displays calculation results using the functions proposed by the proposal unit. For example, the display unit displays calculation results in a file using the proposed functions. Specifically, it displays calculation results in a spreadsheet or text file. The display unit utilizes visualization tools such as graphs and charts to display results in a format that is easy for the user to understand visually. For example, it can display calculation results as bar graphs or line graphs on a spreadsheet, allowing users to grasp data trends and patterns at a glance. In the case of a text file, it organizes the calculation results in a table format for easy viewing. The display unit can also adjust font size, color, layout, etc., to make it easier for users to check the results. Furthermore, the display unit updates calculation results in real time, ensuring users always have access to the latest information. For example, if data is changed on a spreadsheet, it immediately recalculates the results and updates the display. The display unit also provides a function that allows users to export results to other applications or systems. This allows users to utilize the calculation results in other tasks or projects. To improve user efficiency, the display unit provides an intuitive and user-friendly interface, making it easy to check and utilize calculation results.

[0070] The storage unit stores data on functions proposed by the proposal unit and uses it to make suggestions to other users. Specifically, it stores usage history and performance data of proposed functions. The storage unit meticulously records the history of functions and operation procedures used by users and builds a database based on this to make optimal suggestions to other users. For example, it records the situations in which a particular function was used and how effective it was, and makes optimal suggestions to other users who face similar situations. The storage unit also analyzes performance data such as the frequency of function use and success rate, and provides feedback to improve the accuracy of the proposed algorithm. Furthermore, the storage unit collects user feedback and evaluations and uses them to improve the content of suggestions. For example, it evaluates the results of users using proposed functions and improves the proposed algorithm based on that evaluation. In this way, the storage unit can make optimal suggestions that meet user needs and improve the overall performance of the system. To ensure data security and privacy, the storage unit implements appropriate encryption technologies and access controls to safely manage user data. This allows the data storage unit to gain user trust while achieving efficient data storage and improved accuracy of recommendations.

[0071] The suggestion function can call an AI agent from the plugin and suggest a suitable function when a user encounters a problem or has questions. For example, if a user doesn't know how to operate something, the suggestion function can call an AI agent from the plugin and suggest a suitable function. For example, if a user receives an error message, the suggestion function can call an AI agent from the plugin and suggest a suitable function. For example, if a user wants to know how to perform a specific operation, the suggestion function can call an AI agent from the plugin and suggest a suitable function. This improves work efficiency by suggesting the appropriate function when the user encounters a problem.

[0072] The display unit can display the calculation results in a file using the proposed function. For example, the display unit can display the calculation results in a spreadsheet using the proposed function. For example, the display unit can display the calculation results in a text file using the proposed function. For example, the display unit can display the calculation results as a graph using the proposed function. This makes it easier for users to review the results by displaying them using the proposed function.

[0073] The storage unit stores data on proposed functions, which can then be used to make suggestions to other users. For example, the storage unit stores the usage history of proposed functions. For example, the storage unit stores performance data of proposed functions. For example, the storage unit stores the frequency of use of proposed functions. This allows the storage unit to accumulate data on proposed functions and utilize it to make suggestions to other users.

[0074] The suggestion function can suggest a suitable function for changing the date format when a user inputs "I want to change 2024 / 11 / 18 to 2024 / 11 / 18". For example, the suggestion function will suggest a suitable function if the user wants to change the date format. For example, the suggestion function will suggest a suitable function if the user wants to change the date format. For example, the suggestion function will suggest a suitable function if the user wants to change the date display format. This improves work efficiency by suggesting appropriate functions in response to specific user input.

[0075] The proposal unit can compare files before and after the work is completed and construct a work procedure. For example, the proposal unit can compare files before and after the work is completed to construct an efficient work procedure. For example, the proposal unit can compare the data types of files before and after the work is completed to construct a work procedure. For example, the proposal unit can compare the properties of files before and after the work is completed to construct a work procedure. In this way, by comparing files before and after the work is completed, an efficient work procedure can be constructed.

[0076] The storage unit can compare the data types in the files before and after the operation and construct a work procedure. For example, the storage unit can compare the data types in the files before and after the operation to construct an efficient work procedure. For example, the storage unit can compare the names and functions in the files before and after the operation to construct a work procedure. For example, the storage unit can compare the item names in the files before and after the operation to construct a work procedure. In this way, by comparing data types, an efficient work procedure can be constructed.

[0077] The suggestion unit can store data on functions suggested to users and use it to make suggestions to other users. For example, the suggestion unit can store the usage history of functions suggested to users. For example, the suggestion unit can store performance data of functions suggested to users. For example, the suggestion unit can store the usage frequency of functions suggested to users. In this way, by accumulating data on suggested functions, it can be used to make suggestions to other users.

[0078] The reception desk can estimate the user's emotions and adjust the timing of task requests based on those emotions. For example, if the user is stressed, the reception desk may delay task requests to allow time to relax. For example, if the user is focused, the reception desk may immediately accept task requests to avoid interrupting their work. For example, if the user is tired, the reception desk may temporarily stop accepting task requests to encourage a break. By adjusting the timing of task requests according to the user's emotions, tasks can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0079] The reception desk can analyze the user's past work history and select the optimal reception method. For example, the reception desk prioritizes receiving requests for tasks that the user has frequently performed in the past. For example, the reception desk can predict the type of tasks to be performed during a specific time period based on the user's past work history and accept those requests. For example, the reception desk analyzes the user's past work history and proposes the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the user's past work history.

[0080] The reception desk can filter work requests based on the user's current projects and areas of interest. For example, the reception desk can prioritize receiving work requests related to the user's current projects. For example, the reception desk can filter and receive relevant work requests based on the user's areas of interest. For example, the reception desk can receive appropriate work requests according to the progress of the user's current projects. In this way, by filtering based on the user's current projects and areas of interest, highly relevant work requests can be received.

[0081] The reception desk can estimate the user's emotions and prioritize the tasks to be accepted based on those emotions. For example, if the user is stressed, the reception desk will prioritize simple tasks. For example, if the user is focused, the reception desk will prioritize important tasks. For example, if the user is tired, the reception desk will prioritize light tasks. By prioritizing tasks according to the user's emotions, more appropriate tasks can be accepted. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The reception desk can prioritize accepting work requests by considering the user's geographical location. For example, if the user is in the office, the reception desk will prioritize accepting office-related work. If the user is on a business trip, the reception desk will prioritize accepting work related to the business trip location. If the user is at home, the reception desk will prioritize accepting work that can be done at home. In this way, by considering the user's geographical location, the reception desk can prioritize accepting work requests that are highly relevant.

[0083] The reception department can analyze a user's social media activity when receiving work requests and accept relevant work requests. For example, the reception department can prioritize work requests related to projects mentioned by the user on social media. For example, the reception department can prioritize work requests related to topics of interest based on the user's social media activity. For example, the reception department can prioritize important work requests based on the user's social media follower count and influence. In this way, relevant work requests can be accepted by analyzing the user's social media activity.

[0084] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will provide polite and detailed suggestions. If the user is in a hurry, the suggestion function will provide concise and quick suggestions. If the user is stressed, the suggestion function will provide simple and easy-to-understand suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The proposal department can adjust the level of detail in a proposal based on the importance of the task. For example, the proposal department will provide a detailed proposal for important tasks, a concise proposal for low-priority tasks, and a prompt proposal for urgent tasks. By adjusting the level of detail based on the importance of the task, the proposal department can provide more appropriate proposals.

[0086] The proposal function can apply different proposal algorithms depending on the category of work during the proposal process. For example, the proposal function can apply a statistical proposal algorithm to data analysis work, a natural language processing proposal algorithm to document creation work, and a visual proposal algorithm to presentation creation work. By applying different proposal algorithms depending on the category of work, more appropriate proposals can be made.

[0087] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion function will provide a short, to-the-point suggestion. If the user is relaxed, the suggestion function will provide a longer suggestion with detailed explanations. If the user is stressed, the suggestion function will provide a simple and easy-to-understand suggestion. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The proposal department can prioritize proposals based on the submission deadlines for each task. For example, the department will prioritize proposals for tasks with approaching deadlines. For example, it will postpone proposals for tasks with distant submission deadlines. For example, it will propose tasks with unknown submission deadlines based on the prioritization of other tasks. By prioritizing proposals based on the submission deadlines, the department can make more appropriate proposals.

[0089] The proposal department can adjust the order of proposals based on the relevance of the tasks. For example, the proposal department will prioritize proposals for highly relevant tasks. For example, it will postpone proposals for less relevant tasks. For example, it will propose tasks whose relevance is unclear based on the relevance of other tasks. By adjusting the order of proposals based on the relevance of the tasks, more appropriate proposals can be made.

[0090] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. For example, if the user is in a hurry, the display unit provides a display method that gets straight to the point. By adjusting the display method according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The display unit can adjust the level of detail based on the importance of the task during display. For example, it will display detailed information for important tasks, concise information for low-priority tasks, and rapid information for urgent tasks. By adjusting the level of detail based on the importance of the task, more appropriate information can be displayed.

[0092] The display unit can apply different display algorithms depending on the category of work during display. For example, the display unit applies a statistical display algorithm for data analysis work. For example, the display unit applies a natural language processing display algorithm for document creation work. For example, the display unit applies a visual display algorithm for presentation creation work. By applying different display algorithms depending on the category of work, more appropriate displays can be achieved.

[0093] The display unit can estimate the user's emotions and adjust the length of the display based on the estimated emotions. For example, if the user is in a hurry, the display unit will show a short, concise display. For example, if the user is relaxed, the display unit will show a longer display with detailed explanations. For example, if the user is stressed, the display unit will show a simple and easy-to-understand display. By adjusting the length of the display according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The display unit can determine the display priority based on the submission date of each task. For example, the display unit will prioritize displaying tasks with approaching deadlines. For example, the display unit will postpone displaying tasks with distant submission dates. For example, the display unit will display tasks with unknown submission dates based on the priority of other tasks. By determining the display priority based on the submission date of each task, more appropriate displays can be achieved.

[0095] The display unit can adjust the display order based on the relevance of tasks during display. For example, the display unit will prioritize displaying tasks that are highly relevant. For example, the display unit will postpone displaying tasks that are less relevant. For example, the display unit will display tasks whose relevance is unclear based on the relevance of other tasks. In this way, by adjusting the display order based on the relevance of tasks, more appropriate displays can be achieved.

[0096] The data storage unit can estimate the user's emotions and determine the priority of data to store based on the estimated emotions. For example, if the user is stressed, the storage unit will prioritize storing data on simple tasks. For example, if the user is focused, the storage unit will prioritize storing data on important tasks. For example, if the user is tired, the storage unit will prioritize storing data on light tasks. This allows for more appropriate data storage by determining the priority of data to store according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The data storage unit can improve the accuracy of data storage by considering the interrelationships between tasks during the storage process. For example, the storage unit groups and stores related task content. For example, the storage unit analyzes the interrelationships between task content and prioritizes storing highly relevant data. For example, the storage unit stores data in a way that avoids duplication by considering the interrelationships between task content. As a result, the accuracy of data storage is improved by considering the interrelationships between tasks.

[0098] The data storage unit can store data while considering the attribute information of the person submitting the work. For example, the storage unit can determine data priority based on the submitter's job title and assigned duties. For example, the storage unit can prioritize storing highly relevant data by referring to the submitter's past work history. For example, the storage unit can adjust the level of detail of the data according to the submitter's skill level. This allows for more appropriate data storage by considering the attribute information of the person submitting the work.

[0099] The data storage unit can estimate the user's emotions and adjust the display method of the stored data based on the estimated user emotions. For example, if the user is tense, the storage unit provides a simple and highly visible display method. For example, if the user is relaxed, the storage unit provides a display method that includes detailed information. For example, if the user is in a hurry, the storage unit provides a display method that gets straight to the point. In this way, by adjusting the data display method according to the user's emotions, a more appropriate display can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The data storage unit can perform data storage while considering the geographical distribution of tasks. For example, the storage unit can group and store tasks that are geographically close. For example, the storage unit can prioritize the storage of highly relevant data while considering geographical distribution. For example, the storage unit can store data while considering geographical distribution to avoid duplication. In this way, more appropriate data storage can be performed by considering the geographical distribution of tasks.

[0101] The data storage unit can improve the accuracy of data storage by referring to relevant literature during the storage process. For example, the storage unit stores background information about the work content by referring to relevant literature. For example, the storage unit adjusts the level of detail of the work content based on relevant literature before storing it. For example, the storage unit stores data in a way that avoids data duplication by referring to relevant literature. In this way, the accuracy of data storage is improved by referring to relevant literature for the work.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The suggestion function can propose a suitable function based on the user's task. For example, if the user wants to filter data, the suggestion function can propose a suitable filtering function. For example, if the user wants to aggregate data, the suggestion function can propose a suitable aggregation function. For example, if the user wants to sort data, the suggestion function can propose a suitable sorting function. This improves work efficiency by suggesting the appropriate function according to the user's task.

[0104] The display unit can display the calculation results as graphs using the proposed function. For example, the display unit can display the calculation results as a bar graph using the proposed function. For example, the display unit can display the calculation results as a line graph using the proposed function. For example, the display unit can display the calculation results as a pie chart using the proposed function. This makes it easier for users to visually confirm the results by displaying the calculation results as graphs using the proposed function.

[0105] The storage unit can store data on proposed functions and use it to make suggestions to other users. For example, the storage unit can store the usage history of proposed functions and use it to make suggestions to other users. For example, the storage unit can store performance data of proposed functions and use it to make suggestions to other users. For example, the storage unit can store the frequency of use of proposed functions and use it to make suggestions to other users. In this way, by storing data on proposed functions, it can be used to make suggestions to other users.

[0106] The suggestion function can call an AI agent from a plugin and suggest a suitable function when the user wants to know how to perform a specific operation. For example, if the user wants to know how to filter data, the suggestion function can suggest a suitable filtering function. For example, if the user wants to know how to aggregate data, the suggestion function can suggest a suitable aggregation function. For example, if the user wants to know how to sort data, the suggestion function can suggest a suitable sorting function. This improves work efficiency by suggesting the appropriate function when the user wants to know how to perform a specific operation.

[0107] The display unit can display the calculation results in a spreadsheet using the proposed function. For example, the display unit can display the calculation results in a cell using the proposed function. For example, the display unit can display the calculation results as a table using the proposed function. For example, the display unit can display the calculation results as a graph using the proposed function. This makes it easier for users to review the results by displaying them in a spreadsheet using the proposed function.

[0108] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide polite and detailed suggestions. If the user is in a hurry, the suggestion function can provide concise and quick suggestions. If the user is stressed, the suggestion function can provide simple and easy-to-understand suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided.

[0109] The reception desk can estimate the user's emotions and adjust the timing of task requests based on those emotions. For example, if the user is feeling stressed, the reception desk can delay task requests to allow time for relaxation. For example, if the user is concentrating, the reception desk can immediately accept task requests to avoid interrupting their work. For example, if the user is tired, the reception desk can temporarily suspend task requests to encourage a break. By adjusting the timing of task requests according to the user's emotions, tasks can be received at a more appropriate time.

[0110] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, if the user is in a hurry, the suggestion function can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion function can provide a longer suggestion with more detailed explanations. If the user is stressed, the suggestion function can provide a simple and easy-to-understand suggestion. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be provided.

[0111] The display unit can estimate the user's emotions and adjust the display method based on those emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display method. For example, if the user is relaxed, the display unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the display unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, a more appropriate display can be provided.

[0112] The data storage unit can estimate the user's emotions and determine the priority of data to store based on those emotions. For example, if the user is stressed, the storage unit can prioritize storing data on simple tasks. For example, if the user is focused, the storage unit can prioritize storing data on important tasks. For example, if the user is tired, the storage unit can prioritize storing data on light tasks. This allows for more appropriate data storage by determining the priority of data to store according to the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk receives the user's work details. These details may include, for example, data entry, calculations, and document creation. The reception desk also receives the user's work details using office software. Step 2: The proposal department proposes a suitable function based on the work details received by the reception department. For example, when a user encounters a problem or has questions, the proposal department calls an AI agent from the plugin and proposes a suitable function. When a user enters "I want to change 2024 / 11 / 18 to 2024 / 11 / 18", the proposal department proposes a suitable function for changing the date format. The proposal department compares the files before and after the work and constructs the work procedure. Step 3: The display unit displays the calculation results using the function proposed by the proposal unit. The display unit displays the calculation results in a file using the proposed function. The display unit displays the calculation results in a spreadsheet or text file. Step 4: The storage unit stores data on the functions proposed by the proposal unit and uses it to make suggestions to other users. The storage unit stores the usage history and performance data of the proposed functions.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the reception unit, proposal unit, display unit, and storage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's work content. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a suitable function based on the work content received by the reception unit. The display unit is implemented by the control unit 46A of the smart device 14 and displays the calculation result using the proposed function. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores the data of the proposed function, which is used to make suggestions to other users. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, proposal unit, display unit, and storage unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's work content. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a suitable function based on the work content received by the reception unit. The display unit is implemented by the control unit 46A of the smart glasses 214 and displays the calculation result using the proposed function. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores the data of the proposed function, which is used to make suggestions to other users. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the reception unit, proposal unit, display unit, and storage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's work content. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a suitable function based on the work content received by the reception unit. The display unit is implemented by the control unit 46A of the headset terminal 314 and displays the calculation result using the proposed function. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores the data of the proposed function, which is used to make suggestions to other users. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the reception unit, proposal unit, display unit, and storage unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's work content. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a suitable function based on the work content received by the reception unit. The display unit is implemented by the control unit 46A of the robot 414 and displays the calculation result using the proposed function. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores the data of the proposed function, which is used to make suggestions to other users. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A reception desk that receives user work details, Based on the work content received by the reception unit, a proposal unit proposes a suitable function, A display unit that displays the calculation results using the function proposed by the aforementioned proposal unit, The system includes a storage unit that stores data of functions proposed by the aforementioned proposal unit and utilizes it for proposals to other users. A system characterized by the following features. (Note 2) The aforementioned proposal section is, When a user encounters a problem or has an unclear point, the plugin invokes an AI agent to suggest a suitable function. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Display the calculation results to a file using the proposed function. The system described in Appendix 1, characterized by the features described herein. (Note 4) The storage unit is The data from proposed functions will be accumulated and used to make suggestions to other users. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, When a user enters "I want to change 2024 / 11 / 18 to 2024 / 11 / 18", the system will suggest a suitable function for changing the date format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Compare the files before and after the work is completed, and then create a work procedure. The system described in Appendix 1, characterized by the features described herein. (Note 7) The storage unit is Compare the data types in the files before and after the work is completed, and then construct the work procedure. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, The system accumulates data on functions suggested to users and uses it to make suggestions to other users. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of task requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past work history and select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving work requests, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the tasks to be handled based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving work requests, the system prioritizes accepting requests that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving a work request, the system analyzes the user's social media activity and accepts relevant work requests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of work. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying, different display algorithms are applied depending on the category of the work. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is It estimates the user's emotions and adjusts the display length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying the work, the display priority is determined based on the submission date of the work. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying, adjust the display order based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 27) The storage unit is It estimates the user's emotions and determines the priority of data to accumulate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The storage unit is When accumulating data, the accuracy of the accumulation process is improved by considering the interrelationships between tasks. The system described in Appendix 1, characterized by the features described herein. (Note 29) The storage unit is When accumulating data, the data is stored while taking into account the attribute information of the person who submitted the work. The system described in Appendix 1, characterized by the features described herein. (Note 30) The storage unit is It estimates the user's emotions and adjusts how the accumulated data is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The storage unit is When accumulating data, the geographical distribution of the work should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 32) The storage unit is During data accumulation, we improve the accuracy of the accumulation by referring to relevant literature for the work. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 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 receives user work details, Based on the work content received by the reception unit, a proposal unit proposes a suitable function, A display unit that displays the calculation results using the function proposed by the aforementioned proposal unit, The system includes a storage unit that stores data of functions proposed by the aforementioned proposal unit and utilizes it for proposals to other users. A system characterized by the following features.

2. The aforementioned proposal section is, When a user encounters a problem or has an unclear point, the plugin calls an AI agent and suggests a suitable function. The system according to feature 1.

3. The aforementioned display unit is Display the calculation results to a file using the proposed function. The system according to feature 1.

4. The storage unit is The data from proposed functions will be accumulated and used to make suggestions to other users. The system according to feature 1.

5. The aforementioned proposal section is, Compare the files before and after the work is completed, and then create a work procedure. The system according to feature 1.

6. The storage unit is Compare the data types in the files before and after the work is completed, and then construct the work procedure. The system according to feature 1.

7. The aforementioned proposal section is, The system accumulates data on functions suggested to users and uses it to make suggestions to other users. The system according to feature 1.

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